computer vision

Practical Python and OpenCV by Adrian Rosebrock

computer vision, license plate recognition, Mars Rover

Now that we can analyze breast histology images for cancer risk factors much faster. Of course, computer vision can also be applied to other areas of the medical field. Analyzing X-Rays, MRI scans, and cellular structures all can be performed using computer vision algorithms. Perhaps the biggest success computer vision success story you may have heard of is the X-Box 360 Kinect. The Kinect can use a stereo camera to understand the depth of an image, allowing it to classify and recognize human poses, with the help of some machine learning, of course. The list doesn’t stop there. Computer vision is now prevalent in many areas of your life, whether you realize it or not.

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Facial recognition is an application of computer vision in the real-world. 1 introduction What other types of useful applications of computer vision are there? Well, we could build representations of our 3D world using public image repositories like Flickr. We could download thousands and thousands of pictures of Manhattan, taken by citizens with their smartphones and cameras, and then analyze them and organize them to construct a 3D representation of the city. We would then virtually navigate this city through our computers. Sound cool? Another popular application of computer vision is surveillance. While surveillance tends to have a negative connotation of sorts, there are many different types of surveillance.

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Did you then pickup a pair and head to the dressing room? These are all types of questions that computer vision surveillance systems can answer. 2 introduction Computer vision can also be applied to the medical field. A year ago, I consulted with the National Cancer Institute to develop methods to automatically analyze breast histology images for cancer risk factors. Normally, a task like this would require a trained pathologist with years of experience – and it would be extremely time consuming! Our research demonstrated that computer vision algorithms could be applied to these images and automatically analyze and quantify cellular structures – without human intervention!

Programming Computer Vision with Python by Jan Erik Solem

augmented reality, computer vision, database schema, en.wikipedia.org, optical character recognition, pattern recognition, text mining, Thomas Bayes, web application

Chapter 10 Shows how to use the Python interface for the commonly used OpenCV computer vision library and how to work with video and camera input. There is also a bibliography at the back of the book. Citations of bibliographic entries are made by number in square brackets, as in [20]. Introduction to Computer Vision Computer vision is the automated extraction of information from images. Information can mean anything from 3D models, camera position, object detection and recognition to grouping and searching image content. In this book, we take a wide definition of computer vision and include things like image warping, de-noising, and augmented reality.[1] Sometimes computer vision tries to mimic human vision, sometimes it uses a data and statistical approach, and sometimes geometry is the key to solving problems.

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Symbols 3D plotting, A Sample Data Set 3D reconstruction, 3D Reconstruction Example 4-neighborhood, 9.1 Graph Cuts A affine transformation, 3.1 Homographies affine warping, Affine Transformations affinity matrix, Clustering Images agglomerative clustering, 6.2 Hierarchical Clustering alpha map, Image in Image AR, 4.3 Pose Estimation from Planes and Markers array, Interactive Annotation array slicing, Array Image Representation aspect ratio, 4.1 The Pin-Hole Camera Model association, 9.2 Segmentation Using Clustering augmented reality, 4.3 Pose Estimation from Planes and Markers B bag-of-visual-words, Inspiration from Text Mining—The Vector Space Model bag-of-word representation, Searching Images baseline, Bundle adjustment Bayes classifier, Classifying Images—Hand Gesture Recognition binary image, Morphology—Counting Objects blurring, Using the Pickle Module bundle adustment, Bundle adjustment C calibration matrix, 4.1 The Pin-Hole Camera Model camera calibration, Computing the Camera Center camera center, Camera Models and Augmented Reality camera matrix, Camera Models and Augmented Reality camera model, Camera Models and Augmented Reality camera pose estimation, 4.3 Pose Estimation from Planes and Markers camera resectioning, Triangulation CBIR, Searching Images Chan-Vese segmentation, 9.3 Variational Methods characteristic functions, 9.3 Variational Methods CherryPy, 7.6 Building Demos and Web Applications, Image Search Demo class centroids, Clustering Images classifying images, Classifying Image Content clustering images, Clustering Images, Clustering Images complete linking, 6.2 Hierarchical Clustering confusion matrix, Classifying Images—Hand Gesture Recognition content-based image retrieval, Searching Images convex combination, Image in Image corner detection, Local Image Descriptors correlation, 2.1 Harris Corner Detector corresponding points, 2.1 Harris Corner Detector cpickle, PCA of Images cross-correlation, Finding Corresponding Points Between Images cumulative distribution function, Graylevel Transforms cv, OpenCV, 10.4 Tracking cv2, OpenCV D de-noising, Reading and writing .mat files Delaunay triangulation, Piecewise Affine Warping dendrogram, Clustering Images dense depth reconstruction, Bundle adjustment dense image features, A Simple 2D Example dense SIFT, A Simple 2D Example descriptor, 2.1 Harris Corner Detector difference-of-Gaussian, Finding Corresponding Points Between Images digit classification, Hand Gesture Recognition Again direct linear transformation, 3.1 Homographies directed graph, Image Segmentation distance matrix, Clustering Images E Edmonds-Karp algorithm, 9.1 Graph Cuts eight point algorithm, Plotting 3D Data with Matplotlib epipolar constraint, 5.1 Epipolar Geometry epipolar geometry, Multiple View Geometry epipolar line, 5.1 Epipolar Geometry epipole, 5.1 Epipolar Geometry essential matrix, The calibrated case—metric reconstruction F factorization, Factoring the Camera Matrix feature matches, Finding Corresponding Points Between Images feature matching, Matching Descriptors flood fill, Displaying Images and Results focal length, 4.1 The Pin-Hole Camera Model fundamental matrix, 5.1 Epipolar Geometry fundamental matrix estimation, 5.3 Multiple View Reconstruction G Gaussian blurring, Using the Pickle Module Gaussian derivative filters, Image Derivatives Gaussian distributions, 8.2 Bayes Classifier gesture recognition, Dense SIFT as Image Feature GL_MODELVIEW, PyGame and PyOpenGL GL_PROJECTION, PyGame and PyOpenGL Grab Cut dataset, Segmentation with User Input gradient angle, Blurring Images gradient magnitude, Blurring Images graph, Image Segmentation graph cut, Image Segmentation GraphViz, Matching Using Local Descriptors graylevel transforms, Array Image Representation H Harris corner detection, Local Image Descriptors Harris matrix, Local Image Descriptors hierarchical clustering, 6.2 Hierarchical Clustering hierarchical k-means, 6.3 Spectral Clustering histogram equalization, Graylevel Transforms Histogram of Oriented Gradients, A Simple 2D Example HOG, A Simple 2D Example homogeneous coordinates, Image to Image Mappings homography, Image to Image Mappings homography estimation, 3.1 Homographies Hough transform, Inpainting I Image, Basic Image Handling and Processing image contours, Plotting Images, Points, and Lines image gradient, Blurring Images image graph, 9.1 Graph Cuts image histograms, Plotting Images, Points, and Lines image patch, 2.1 Harris Corner Detector image plane, Camera Models and Augmented Reality image registration, Piecewise Affine Warping image retrieval, Searching Images image search demo, 7.6 Building Demos and Web Applications image segmentation, Visualizing the Images on Principal Components, Image Segmentation image thumbnails, Convert Images to Another Format ImageDraw, Clustering Images inliers, 3.3 Creating Panoramas inpainting, Using generators integral image, Color Spaces interest point descriptor, 2.1 Harris Corner Detector interest points, Local Image Descriptors inverse depth, 4.1 The Pin-Hole Camera Model inverse document frequency, Inspiration from Text Mining—The Vector Space Model io, Useful SciPy Modules iso-contours, Plotting Images, Points, and Lines J JSON, Downloading Geotagged Images from Panoramio K k-means, Clustering Images k-nearest neighbor classifier, Classifying Image Content kernel functions, 8.3 Support Vector Machines kNN, Classifying Image Content L Laplacian matrix, 6.3 Spectral Clustering least squares triangulation, Triangulation LibSVM, 8.3 Support Vector Machines local descriptors, Local Image Descriptors Lucas-Kanade tracking algorithm, Optical Flow M marking points, Interactive Annotation mathematical morphology, Morphology—Counting Objects Matplotlib, Create Thumbnails maximum flow (max flow), 9.1 Graph Cuts measurements, Morphology—Counting Objects, Extracting Cells and Recognizing Characters metric reconstruction, 5.1 Epipolar Geometry, Computing the Camera Matrix from a Fundamental Matrix minidom, Registering Images minimum cut (min cut), 9.1 Graph Cuts misc, Useful SciPy Modules morphology, Morphology—Counting Objects, Morphology—Counting Objects, Exercises mplot3d, A Sample Data Set, 3D Reconstruction Example multi-class SVM, Selecting Features multi-dimensional arrays, Interactive Annotation multi-dimensional histograms, Clustering Images multiple view geometry, Multiple View Geometry N naive Bayes classifier, Classifying Images—Hand Gesture Recognition ndimage, Affine Transformations ndimage.filters, Computing Disparity Maps normalized cross-correlation, Finding Corresponding Points Between Images normalized cut, 9.2 Segmentation Using Clustering NumPy, Interactive Annotation O objloader, Tying It All Together OCR, Hand Gesture Recognition Again OpenCV, Chapter Overview, OpenCV OpenGL, PyGame and PyOpenGL OpenGL projection matrix, From Camera Matrix to OpenGL Format optic flow, 10.4 Tracking optical axis, Camera Models and Augmented Reality optical center, The Camera Matrix optical character recognition, Hand Gesture Recognition Again optical flow, 10.4 Tracking optical flow equation, 10.4 Tracking outliers, 3.3 Creating Panoramas overfitting, Exercises P panograph, Exercises panorama, 3.3 Creating Panoramas PCA, PCA of Images pickle, PCA of Images, The SciPy Clustering Package, Creating a Vocabulary pickling, PCA of Images piecewise affine warping, Image in Image piecewise constant image model, 9.3 Variational Methods PIL, Basic Image Handling and Processing pin-hole camera, Camera Models and Augmented Reality plane sweeping, 5.4 Stereo Images plot formatting, Plotting Images, Points, and Lines plotting, Create Thumbnails point correspondence, 2.1 Harris Corner Detector pose estimation, 4.3 Pose Estimation from Planes and Markers Prewitt filters, Blurring Images Principal Component Analysis, PCA of Images, 8.2 Bayes Classifier principal point, The Camera Matrix projection, Camera Models and Augmented Reality projection matrix, Camera Models and Augmented Reality projective camera, Camera Models and Augmented Reality projective transformation, Image to Image Mappings pydot, Matching Using Local Descriptors pygame, PyGame and PyOpenGL pygame.image, PyGame and PyOpenGL pygame.locals, PyGame and PyOpenGL Pylab, Create Thumbnails PyOpenGL, PyGame and PyOpenGL pyplot, Exercises pysqlite, Setting Up the Database pysqlite2, Setting Up the Database Python Imaging Library, Basic Image Handling and Processing python-graph, 9.1 Graph Cuts Q quad, From Camera Matrix to OpenGL Format query with image, Querying with an Image quotient image, Exercises R radial basis functions, 8.3 Support Vector Machines ranking using homographies, 7.5 Ranking Results Using Geometry RANSAC, 3.3 Creating Panoramas, 5.3 Multiple View Reconstruction rectified image pair, Bundle adjustment rectifying images, Extracting Cells and Recognizing Characters registration, Piecewise Affine Warping rigid transformation, 3.1 Homographies robust homography estimation, RANSAC ROF, Reading and writing .mat files, 9.3 Variational Methods RQ-factorization, Factoring the Camera Matrix Rudin-Osher-Fatemi de-noising model, Reading and writing .mat files S Scale-Invariant Feature Transform, Finding Corresponding Points Between Images scikit.learn, Exercises Scipy, Using the Pickle Module scipy.cluster.vq, The SciPy Clustering Package, Clustering Images scipy.io, Useful SciPy Modules, Reading and writing .mat files scipy.misc, Reading and writing .mat files scipy.ndimage, Blurring Images, Morphology—Counting Objects, Extracting Cells and Recognizing Characters, Rectifying Images, Exercises scipy.ndimage.filters, Blurring Images, Blurring Images, 2.1 Harris Corner Detector scipy.sparse, Exercises searching images, Searching Images, Adding Images segmentation, Image Segmentation self-calibration, Bundle adjustment separating hyperplane, Using PCA to Reduce Dimensions SfM, The calibrated case—metric reconstruction SIFT, Finding Corresponding Points Between Images similarity matrix, Clustering Images similarity transformation, 3.1 Homographies similarity tree, 6.2 Hierarchical Clustering simplejson, Downloading Geotagged Images from Panoramio, Downloading Geotagged Images from Panoramio single linking, 6.2 Hierarchical Clustering slicing, Array Image Representation Sobel filters, Blurring Images spectral clustering, Clustering Images, 9.2 Segmentation Using Clustering SQLite, Setting Up the Database SSD, Finding Corresponding Points Between Images stereo imaging, Bundle adjustment stereo reconstruction, Bundle adjustment stereo rig, Bundle adjustment stereo vision, Bundle adjustment stitching images, Robust Homography Estimation stop words, Inspiration from Text Mining—The Vector Space Model structure from motion, The calibrated case—metric reconstruction structuring element, Morphology—Counting Objects Sudoku reader, Hand Gesture Recognition Again sum of squared differences, Finding Corresponding Points Between Images Support Vector Machines, Using PCA to Reduce Dimensions support vectors, 8.3 Support Vector Machines SVM, Using PCA to Reduce Dimensions T term frequency, Inspiration from Text Mining—The Vector Space Model term frequency–inverse document frequency, Inspiration from Text Mining—The Vector Space Model text mining, Searching Images tf-idf weighting, Inspiration from Text Mining—The Vector Space Model total variation, Reading and writing .mat files total within-class variance, Clustering Images tracking, 10.4 Tracking triangulation, 5.2 Computing with Cameras and 3D Structure U unpickling, PCA of Images unsharp masking, 1.5 Advanced Example: Image De-Noising urllib, Downloading Geotagged Images from Panoramio V variational methods, 9.3 Variational Methods variational problems, 9.3 Variational Methods vector quantization, The SciPy Clustering Package vector space model, Searching Images vertical field of view, From Camera Matrix to OpenGL Format video, Displaying Images and Results visual codebook, Inspiration from Text Mining—The Vector Space Model visual vocabulary, Inspiration from Text Mining—The Vector Space Model visual words, Inspiration from Text Mining—The Vector Space Model visualizing image distribution, Visualizing the Images on Principal Components VLFeat, Interest Points W warping, Affine Transformations watershed, Inpainting web applications, 7.6 Building Demos and Web Applications webcam, Optical Flow word index, Setting Up the Database X XML, Registering Images xml.dom, Registering Images About the Author Jan Erik Solem is a Python enthusiast and a computer vision researcher and entrepreneur. He is an applied mathematician and has worked as associate professor, startup CTO, and now also book author. He sometimes writes about computer vision and Python on his blog www.janeriksolem.net. He has used Python for computer vision in teaching, research and industrial applications for many years. He currently lives in San Francisco. Colophon The animal on the cover of Programming Computer Vision with Python is a bullhead. Often referred to as “bullhead catfish,” members of the genus Ameiurus come in three common types: the black bullhead (Ameiurus melas), the yellow bullhead (Ameiurus natalis), and the brown bullhead (Ameiurus nebulosus).

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Programming a computer and designing algorithms for understanding what is in these images is the field of computer vision. Computer vision powers applications like image search, robot navigation, medical image analysis, photo management, and many more. The idea behind this book is to give an easily accessible entry point to hands-on computer vision with enough understanding of the underlying theory and algorithms to be a foundation for students, researchers, and enthusiasts. The Python programming language, the language choice of this book, comes with many freely available, powerful modules for handling images, mathematical computing, and data mining. When writing this book, I have used the following principles as a guideline.

pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

FIGURE 48: Four straightforward instances of “walking a dog” While thinking about this topic, I was particularly taken by a delightful and insightful blog post written by Andrej Karpathy, the deep-learning and computer-vision expert who now directs AI efforts at Tesla. In his post, titled “The State of Computer Vision and AI: We Are Really, Really Far Away,”24 Karpathy describes his reactions, as a computer-vision researcher, to one specific photo, shown in figure 50. Karpathy notes that we humans find this image quite humorous, and asks, “What would it take for a computer to understand this image as you or I do?”

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v=v1dW7ViahEc. 14. K. He et al., “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” in Proceedings of the IEEE International Conference on Computer Vision (2015), 1026–34. 15. A. Linn, “Microsoft Researchers Win ImageNet Computer Vision Challenge,” AI Blog, Microsoft, Dec. 10, 2015, blogs.microsoft.com/ai/2015/12/10/microsoft-researchers-win-imagenet-computer-vision-challenge. 16. A. Hern, “Computers Now Better than Humans at Recognising and Sorting Images,” Guardian, May 13, 2015, www.theguardian.com/global/2015/may/13/baidu-minwa-supercomputer-better-than-humans-recognising-images; T.

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What’s more, “dog pixels” might look a lot like “cat pixels” or other animals. Under some lighting conditions, a cloud in the sky might even look very much like a dog. FIGURE 7: Object recognition: easy for humans, hard for computers Since the 1950s, the field of computer vision has struggled with these and other issues. Until recently, a major job of computer-vision researchers was to develop specialized image-processing algorithms that would identify “invariant features” of objects that could be used to recognize these objects in spite of the difficulties I sketched above. But even with sophisticated image processing, the abilities of object-recognition programs remained far below those of humans.

pages: 138 words: 27,404

OpenCV Computer Vision With Python by Joseph Howse

augmented reality, computer vision, Debian, optical character recognition, pattern recognition

In 2005, he finished his studies in IT from the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He had a final project based on this subject and published it on HCI Spanish congress. He participated in Blender source code, an open source and 3D-software project, and worked in his first commercial movie Plumiferos—Aventuras voladoras as a Computer Graphics Software Developer. David now has more than 10 years of experience in IT, with more than seven years experience in computer vision, computer graphics, and pattern recognition working on different projects and startups, applying his knowledge of computer vision, optical character recognition, and augmented reality.

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We have also practiced wrapping this functionality in a high-level, reusable, and object-oriented design. Congratulations! You now have the skill to develop computer vision applications in Python using OpenCV. Still, there is always more to learn and do! If you liked working with NumPy and OpenCV, please check out these other titles from Packt Publishing: NumPy Cookbook, Ivan Idris OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, which uses OpenCV's C++ API for desktops Mastering OpenCV with Practical Computer Vision Projects, (by multiple authors), which uses OpenCV's C++ API for multiple platforms The upcoming book, OpenCV for iOS How-to, which uses OpenCV's C++ API for iPhone and iPad OpenCV Android Application Programming, my upcoming book, which uses OpenCV's Java API for Android Here ends of our tour of OpenCV's Python bindings.

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Generating Haar Cascades for Custom Targets Gathering positive and negative training images Finding the training executables On Windows On Mac, Ubuntu, and other Unix-like systems Creating the training sets and cascade Creating <negative_description> Creating <positive_description> Creating <binary_description> by running <opencv_createsamples> Creating <cascade> by running <opencv_traincascade> Testing and improving <cascade> Summary Index OpenCV Computer Vision with Python * * * OpenCV Computer Vision with Python Copyright © 2013 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Robot Futures by Illah Reza Nourbakhsh

3D printing, autonomous vehicles, Burning Man, business logic, commoditize, computer vision, digital divide, Mars Rover, Menlo Park, phenotype, Skype, social intelligence, software as a service, stealth mode startup, strong AI, telepresence, telepresence robot, Therac-25, Turing test, Vernor Vinge

In 2008, a new company, Willow Garage, threw its hat into the ring with the Robot Operating System (ROS). This product takes some inspiration from the success that Intel Corporation had achieved in the prior decade with the Intel OpenCV library, an open-source collection of computer vision routines that greatly influenced hobby, educational, and research computer vision work (Bradski and Kaehler 2008). By selecting important computer vision capabilities, then optimizing software for performing those skills on Intel computer chips, the company delivered highly competent vision behaviors into the hands of those who wished to create end user applications (Quigley et al. 2009).

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But what about the creation and manipulation of customer desire—what are the real-world equivalents of A/B split testing and customized online pricing? Companies are already prototyping digital walls that will replace fixed advertising posters throughout physical stores (Müller et al. 2009). These digital walls will contain embedded computer vision systems that track face and eye movement, giving them direct access to knowledge about who is looking at the wall. Computer vision will not only track fine-grained human behavior, but will also be able to estimate age, sex, even fashion sense. Spoken language accents will yield clues about each customer’s socioeconomic class, ethnicity, and educational level.

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The store achieves faster throughput because you do not wait, and the store throws away far less expired food, because it did not have to make a steady stream of fries no one purchases to keep customer lines short. It made fries when it knew, almost for certain, that you would buy them. Everyone is happier in this model, and the store improves efficiency and profit margins. New Mediocracy 11 Now the kicker: this is not science fiction; it was demonstrated five years ago. Hyperactive Bob, a computer vision system tied to cameras around a store perimeter, watched the incoming cars (Shropshire 2006). After months of data mining on makes and models of cars and which orders correlate to each type of vehicle, the system reliably estimated what the short-order cooks should deliver as customers drove up.

pages: 414 words: 109,622

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

Krizhevsky, Sutskever, and Hinton went on to publish a paper describing their system (later christened AlexNet), which Krizhevsky unveiled at a computer vision conference in Florence, Italy, near the end of October. Addressing an audience of more than a hundred researchers, he described the project in his typically soft, almost apologetic tones. Then, as he finished, the room erupted with argument. Rising from his seat near the front of the room, a Berkeley professor named Alexei Efros told the room that ImageNet was not a reliable test of computer vision. “It is not like the real world,” he said. It might include hundreds of photos of T-shirts and AlexNet might have learned to identify these T-shirts, he told the room, but the T-shirts were neatly laid out on tables without a wrinkle, not worn by real people.

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The career path of one of Hinton’s key collaborators, a young researcher named Sara Sabour, exemplified the international nature of AI and how susceptible it was to political interference. In 2013, after completing a computer science degree at the Sharif University of Technology in Iran, Sabour had applied to the University of Washington, hoping to study computer vision and other forms of artificial intelligence, and she had been accepted. But then the U.S. government denied her a visa, apparently because she had grown up and studied in Iran and aimed to specialize in an area, computer vision, that potentially played into military and security technologies. The following year, she enrolled at the University of Toronto, before finding her way to Hinton and Google. Meanwhile, the Trump administration continued to focus on keeping people out of the country.

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This was his way of showing that vision was more complex than it might seem, that people understood what was in front of them in ways machines still could not. “It is a fact that is ignored by researchers in computer vision,” he said. “And that is a huge mistake.” He was pointing to the limitations of the technology he helped build over the last four decades. Researchers in computer vision now relied on deep learning, he said, and deep learning solved only part of the problem. If a neural network analyzed thousands of coffee cup photos, it could learn to recognize a coffee cup. But if those photos pictured coffee cups only from the side, it couldn’t recognize a coffee cup turned upside down.

Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

It was slow and painstaking work for humans to do manually. The sheer volume of data was unmanageable. The goal was to have computer vision help, not to replace the job of human intel analysts but to assist them. Computer vision algorithms that could recognize objects could sift through reams of video to find objects of interest. Colonel Brown explained, “Where we expected a computer vision solution to help us was, you could go back into the data, ‘Tell me every time a car left this location.’” Then, “the computer vision algorithm would timestamp when certain activities would happen in certain places.” Humans would still need to be intimately involved in intelligence analysis, but automated tools could speed up the process.

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Traditional defense contractors like Lockheed Martin or Northrop Grumman could make a stealth airplane, but the leaders in computer vision were tech companies like Google and Microsoft. DoD was largely flying in the dark with these relationships. That’s where Brendan came in. In July 2017, McCord traveled with Maven leads Lieutenant General Jack Shanahan, Colonel Drew Cukor, and Air Force Colonel Jason Brown to Honolulu to the Computer Vision and Pattern Recognition conference to meet with some of the top minds on computer vision. They also pitched Google, who would go on later that year to join Maven as one of its biggest partners.

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DoD’s traditional processes were too sluggish to adopt a rapidly maturing technology like computer vision. An acquisition timeline that took seven to ten years to achieve first fielding would be several generations behind the state of the art and would be too slow for most commercial companies which operate on faster timelines. In his tasking memo, Bob Work directed the Maven team to “integrate algorithmic-based technology” in “90-day sprints,” which was effectively light speed for the DoD bureaucracy. The second problem was that, to harness computer vision technology, DoD needed to reach beyond the traditional defense contractors and tap into technology companies.

The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

., p.16. Little did anyone suspect in the 1960s that it would take fifty years and a millionfold increase in computer power before computer vision would reach human levels of performance. The misleading intuition that it would be easy to write a computer vision program is based on activities that we find easy to do, such as seeing, hearing, and moving around—but that took evolution millions of years to get right. Much to their chagrin, early AI pioneers found the computer vision problem to be extremely hard to solve. In contrast, they found it much easier to program computers to prove mathematical theorems—a process thought to require the highest levels of intelligence—because computers turn out to be much better at logic than we are.

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A document from the archives at MIT confirms his version of the story.2 What looked like it would be an easy problem to solve proved to be quicksand that swallowed a generation of researchers in computer vision. Why Vision Is a Hard Problem We rarely have difficulty identifying what an object is despite differences in the location, size, orientation, and lighting of the object. One of the earliest ideas in computer vision was to match a template of the object with the pixels in the image, but that approach failed because the pixels of the two images of the same object in different orientations don’t match.

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It was generally thought that wider neural networks with a greater number of hidden units were more effective than deeper networks with a greater number of layers, but this was shown not to be the case for networks trained layer by layer,4 and the vanishing error gradient problem was identified, which slowed down learning near the input layer.5 When this problem was eventually overcome, however, it became possible to train deep backprop networks that performed favorably on benchmarks.6 And, as deep backprop networks began to challenge traditional approaches in computer vision, the word at the 2012 NIPS Conference was that the “Neural” was back in “Neural Information Processing Systems.” In computer vision, steady progress in recognizing objects in images over the last decade of the previous century and the first decade of the current one had improved performance on benchmarks (used to compare different methods) by a fraction of a percent per year.

pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

We thought that just from the inputs and outputs, you should be able to learn all these weights; and that was just unrealistic. You were going to have to wire in lots of knowledge to make anything work. That was the view of people in computer vision until 2012. Most people in computer vision thought this stuff was crazy, even though Yann LeCun sometimes got systems working better than the best computer vision systems, they still thought this stuff was crazy, it wasn’t the right way to do vision. They even rejected papers by Yann, even though they worked better than the best computer vision systems on particular problems, because the referees thought it was the wrong way to do things. That’s a lovely example of scientists saying, “We’ve already decided what the answer has to look like, and anything that doesn’t look like the answer we believe in is of no interest.”

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There is a very large dataset—the ImageNet dataset—which is used in computer vision, and people in that field would only believe in our deep learning methods if we could show good results on that dataset. Geoffrey Hinton’s group actually did it, following up on earlier work by Yann LeCun on convolutional networks—that is, neural networks which were specialized for images. In 2012, these new deep learning architectures with extra twists were used with huge success and showed a big improvement on existing methods. Within a couple of years, the whole computer vision community switched to these kinds of networks. MARTIN FORD: So that’s the point at which deep learning really took off?

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It was revolutionary for the fact that the same technology of deep learning could be used for both computer vision and speech recognition. It drove a lot of attention toward the field. MARTIN FORD: Thinking back to when you first started in neural networks, are you surprised at the distance things have come and the fact that they’ve become so central to what large companies, like Google and Facebook, are doing now? YOSHUA BENGIO: Of course, we didn’t expect that. We’ve had a series of important and surprising breakthroughs with deep learning. I mentioned earlier that speech recognition came around 2010, and then computer vision around 2012. A couple of years later, in 2014 and 2015, we had breakthroughs in machine translation that ended up being used in Google Translate in 2016. 2016 was also the year we saw the breakthroughs with AlphaGo.

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The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches by Marshall Brain

Amazon Web Services, basic income, clean water, cloud computing, computer vision, digital map, driverless car, en.wikipedia.org, full employment, Garrett Hardin, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Neil Armstrong, Occupy movement, ocean acidification, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, Tragedy of the Commons, working poor

LIDAR and radar are the two most essential sensor packages on a self-driving car. There is a simple reason for this difference: the computer vision systems that exist in production today (2015) are still fairly primitive. Computer scientists still have a ways to go when it comes to perfecting general vision systems. Yes, there are simple things that computer vision systems can do (for example, this video [14] shows a simple camera system to detect pancakes on a conveyor belt). But at this moment in history, there is not a computer vision system that can look at a common scene of a farm and say, “that is a barn, that is a horse, that is a man, that is the man's hat, that is grass, that is a tree, etc.”

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The truckers who become unemployed will really have nowhere in the modern economy to go for new jobs. It is going to be a horrible situation for them. But truckers are just the tip of the iceberg. Many, many other people will become unemployed by the second intelligent species in the near future... Chapter 5 - How Computer Vision Systems will Destroy Jobs If you look back at the description of self-driving cars in the previous chapter, notice that computer vision does not really play a role. Current self-driving cars do not have two eyes on the roof or the hood looking out at the road and deciding what to do based on visual input. Self-driving cars do have an optical camera, but it plays a small role.

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Computer scientists simply have not created the algorithms yet for computer vision at that level. But research in this area is occurring on many different fronts, both for the general case and specific situations. In the same way that Chess-playing computers eventually beat human players after several decades of research, and a Jeopardy-playing computer beat the best human players, there will eventually be computers running algorithms that are better than human beings at seeing the world. We simply haven't arrived there yet. The thing to understand is that we will arrive there eventually. As this computer vision research bears fruit, a surprising thing will happen.

AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

When we “see,” we are actually applying our accumulated knowledge of the world—everything we’ve learned in our lives about perspective, geometry, common sense, and what we have seen previously. These come naturally to us but are very difficult to teach a computer. Computer vision is the field of study that tries to overcome these difficulties to get computers to see and understand. COMPUTER VISION APPLICATIONS We are already using computer vision technologies every day. Computer vision can be used in real time, in areas ranging from transportation to security. Existing examples include: driver assistants installed in some cars that can detect a driver who nods off autonomous stores like Amazon Go, where cameras recognize when you’ve put a product in your shopping cart airport security (counting people, recognizing terrorists) gesture recognition (scoring your moves in an Xbox dancing game) facial recognition (using your face to unlock your mobile phone) smart cameras (your iPhone’s portrait mode recognizes and extracts people in the foreground, and then “beautifully” blurs the background to create a DSLR-like effect) military applications (separating enemy soldiers from civilians) autonomous navigation of drones and automobiles In the opening of “Gods Behind the Masks,” we saw the use of real-time facial recognition to automatically deduct payment by recognizing commuters as they pass through a turnstile.

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Existing examples include: driver assistants installed in some cars that can detect a driver who nods off autonomous stores like Amazon Go, where cameras recognize when you’ve put a product in your shopping cart airport security (counting people, recognizing terrorists) gesture recognition (scoring your moves in an Xbox dancing game) facial recognition (using your face to unlock your mobile phone) smart cameras (your iPhone’s portrait mode recognizes and extracts people in the foreground, and then “beautifully” blurs the background to create a DSLR-like effect) military applications (separating enemy soldiers from civilians) autonomous navigation of drones and automobiles In the opening of “Gods Behind the Masks,” we saw the use of real-time facial recognition to automatically deduct payment by recognizing commuters as they pass through a turnstile. We also saw pedestrians interact with cartoon animals in ads, using hand gestures. And Amaka’s smartstream used computer vision to recognize the street ahead of him and gave him directions to get to his destination. Computer vision can also be applied to images and videos—in less immediate but no less important ways. Some examples: smart editing of photos and videos (tools like Photoshop use computer vision extensively to find facial borders, remove red eyes, and beautify selfies) medical image analysis (to determine if there are malignant tumors in a lung CT) content moderation (detection of pornographic and violent content in social media) related advertising selection based on the content of a given video smart image search (that can find images from keywords or other images) and, of course, making deepfakes (replacing occurrences of one face with another in a video) In “Gods Behind the Masks,” we saw a deepfake-making tool that is essentially an automatic video-editing tool that replaces one person with another, from face, fingers, hand, and voice to body language, gait, and facial expression.

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—AFRICAN PROVERB NOTE FROM KAI-FU: This story revolves around a Nigerian video producer who is recruited to make an undetectable deepfake with dangerous consequences. A major branch of AI, computer vision teaches computers to “see,” and recent breakthroughs allow AI to do so like never before. The story imagines a future world marked by unprecedented high-tech cat-and-mouse games between the fakers and detectors, and between defenders and perpetrators. Is there any way to avoid a world in which all visual lines are blurred? I’ll explore that question in my commentary, as I describe recent and impending breakthroughs in computer vision, biometrics, and AI security, three AI technology areas enabling deepfakes and many other applications.

pages: 688 words: 107,867

Python Data Analytics: With Pandas, NumPy, and Matplotlib by Fabio Nelli

Amazon Web Services, backpropagation, centre right, computer vision, data science, Debian, deep learning, DevOps, functional programming, Google Earth, Guido van Rossum, Internet of things, optical character recognition, pattern recognition, sentiment analysis, speech recognition, statistical model, web application

Population in 2014 Conclusions Chapter 12: Recognizing Handwritten Digits Handwriting Recognition Recognizing Handwritten Digits with scikit-learn The Digits Dataset Learning and Predicting Recognizing Handwritten Digits with TensorFlow Learning and Predicting Conclusions Chapter 13: Textual Data Analysis with NLTK Text Analysis Techniques The Natural Language Toolkit (NLTK) Import the NLTK Library and the NLTK Downloader Tool Search for a Word with NLTK Analyze the Frequency of Words Selection of Words from Text Bigrams and Collocations Use Text on the Network Extract the Text from the HTML Pages Sentimental Analysis Conclusions Chapter 14: Image Analysis and Computer Vision with OpenCV Image Analysis and Computer Vision OpenCV and Python OpenCV and Deep Learning Installing OpenCV First Approaches to Image Processing and Analysis Before Starting Load and Display an Image Working with Images Save the New Image Elementary Operations on Images Image Blending Image Analysis Edge Detection and Image Gradient Analysis Edge Detection The Image Gradient Theory A Practical Example of Edge Detection with the Image Gradient Analysis A Deep Learning Example: The Face Detection Conclusions Appendix A: Writing Mathematical Expressions with LaTeX With matplotlib With IPython Notebook in a Markdown Cell With IPython Notebook in a Python 2 Cell Subscripts and Superscripts Fractions, Binomials, and Stacked Numbers Radicals Fonts Accents Appendix B: Open Data Sources Political and Government Data Health Data Social Data Miscellaneous and Public Data Sets Financial Data Climatic Data Sports Data Publications, Newspapers, and Books Musical Data Index About the Author and About the Technical Reviewer About the Author Fabio Nelliis a data scientist and Python consultant, designing and developing Python applications for data analysis and visualization.

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© Fabio Nelli 2018 Fabio NelliPython Data Analyticshttps://doi.org/10.1007/978-1-4842-3913-1_14 14. Image Analysis and Computer Vision with OpenCV Fabio Nelli1 (1)Rome, Italy In the previous chapters, the analysis of data was centered entirely on numerical and tabulated data, while in the previous one we saw how to process and analyze data in textual form. This book rightfully closes by introducing the last aspect of data analysis: image analysis. During the chapter, topics such as computer vision and face recognition will be introduced. You will see how the techniques of deep learning are at the base of this king of analysis.

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In recent years, especially because of the development of deep learning, image analysis has experienced huge development in solving problems that were previously impossible, giving rise to a new discipline called computer vision . In Chapter 9, you learned about artificial intelligence, which is the branch of calculation that deals with solving problems of pure “human relevance”. Computer vision is part of this, since its purpose is to reproduce the way the human brain perceives images. In fact, seeing is not just the acquisition of a two-dimensional image, but above all it is the interpretation of the content of that area.

pages: 413 words: 119,587

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

At the time, the open-source software movement was incredibly popular. His background was in computer vision, and so he put two and two together and decided to create a project to build a library of open-source machine vision software tools. Taking the Linux operating system as a reference, it was obvious that when programmers worldwide have access to an extraordinary common set of tools, it makes everybody’s research a lot easier. “I should give everyone that tool in vision research,” he decided. While his boss was on sabbatical he launched OpenCV, or Open Source Computer Vision, a software library that made it easier for researchers to develop vision applications using Intel hardware.

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“Better to seek forgiveness than to ask permission” was his motto. Eventually OpenCV contained a library of more than 2,500 algorithms including both computer vision and machine-learning software. OpenCV also hosted programs that could recognize faces, identify objects, classify human motion, and so on. From his initial team of just a handful of Intel researchers, a user community grew to more than 47,000 people, and more than ten million copies of the toolset have been downloaded to date. Gary Bradski created a popular computer vision software library and helped design robots. He would later leave robotics to work with a company seeking to build augmented reality glasses.

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Indeed, during the mid-sixties there was virtually boundless optimism among the small community of artificial intelligence researchers on both coasts. In 1966, when SRI and SAIL were beginning to build robots and AI programs in California, another artificial intelligence pioneer, Marvin Minsky, assigned an undergraduate to work on the problem of computer vision on the other side of the country, at MIT. He envisioned it as a summer project. The reality was disappointing. Although AI might be destined to transform the world, Duvall, who worked on several SRI projects before transferring to the Shakey project to work in the trenches as a young programmer, immediately saw that the robot was barely taking baby steps.

pages: 337 words: 103,522

The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

AARON 117–18, 119, 121, 122 Adams, Douglas: The Hitchhiker’s Guide to the Galaxy 66–7, 268 adversarial algorithms 132–42, 298, 300 AIVA 229–30; Genesis 230 Alberti bass pattern 197, 197 algebra 44, 47, 65, 158–60, 158, 171, 182, 237 Algorithmic Justice League 94 algorithms 2, 5, 11, 13, 17, 21, 24; adversarial 132–42, 298, 300; art and see art; biases and blind spots 91–5; characteristics, key 46; computer vision and see computer vision; consciousness and see consciousness; dating/matching and 57–61, 58, 59, 60; first 44–7, 45, 158–9; free will and 112–13, 300, 301; games and see individual game name; Google search 47–56, 50, 51, 52, 57; language and see language; Lovelace Test and 7–8, 102–3, 219–20; mathematics and see mathematics; music and see musical composition; neural networks and see neural networks; Nobel Prize and 57; recommender 79–80, 81–91, 85, 86; reinforcement learning and 27, 96–7; spam filters and 90–1; sports and 55–6; supervised learning and 95–6, 97, 137; tabula rasa learning and 97, 98; term 46; training 89–91; unexpected consequences of 62–5 see also individual algorithm name Al-Khwarizmi, Muhammad 46, 47, 159 AlphaGo 22, 29–43, 95–6, 97–8, 131, 145, 168, 209, 219–20, 233 AlphaZero 97–8 Al Qadiri, Fatima 224 Altamira, Cave of, Spain 104, 105 Amazon (online retailer) 62, 67, 286 Amiga Power 23 Analytical Engine 1–2, 44 Android Lloyd Webber 290 Annals of Mathematics 152, 170–1, 177, 243 Appel, Kenneth 170, 174 Apple 117 Archer, Jodie 283 Argand, Jean-Robert 237 Aristophanes 165 Aristotle: The Art of Rhetoric 166 Arnold, Malcolm 231 art: AARON and 117–18, 119, 121, 122; adversarial networks and generating new 132–42, 135, 136, 137, 140; animals and 107–9; BOB (artificial life form) and 146–8; bone carvings, ancient 104–5; cave art, ancient 103–4, 156; coding the visual world 110–12; commercial considerations and 131–2; copyright ownership and 108–9; creativity and see creativity; definition of 103–7; emotional response, AI and 106–7; fractals and 113–16, 124–5; future of AI 148–9; identifying artists and waves of creativity with AI 134–9, 135, 136; mathematics and 99–103, 106, 146, 155; origins of human 103; ‘The Painting Fool’ 119–22, 200, 291; Pollock, attempts to fake a 123–6; Rembrandt, recreating 127–32; rules and 1; sale of computer generated work, first 141; visual recognition algorithms, understanding 142–5; Wundt Curve and 139–40, 140 Art Basel 141, 142, 143, 145, 151 artificial intelligence (AI): algorithms and see algorithms; art and see art; birth of 1–2, 67; computer vision and see computer vision; consciousness and see consciousness; creativity and see creativity; data, importance of 67–8; games and see individual game name; language and see language; Lovelace Test and 7–8, 102–3, 219–20; mathematics and see mathematics; music and see musical composition; neural networks and see neural networks; systems see individual system name; term 24; transformational impact of 66–7 Ascent of Man, The (TV series) 104 Ashwood, Mary 48 Associated Press 293, 294 Atari 25–8, 92, 97, 115–16, 132 Atiyah, Michael 179, 248–9 Augustus, Ron 127 Automated Insights 293 Babbage, Charles 1, 7, 65 Babylonians, Ancient 157–60, 161, 165 Bach, Carl Philipp Emanuel 189–90, 193–4; ‘Inventions by Which Six Measures of Double Counterpoint Can be Written without Knowledge of the Rules’ 193–4 Bach, Johann Sebastian 10, 185, 186–7, 189–93, 204, 205, 207, 230, 231, 299; AIVA and 230; algorithms and method of composing music 189–94, 191; The Art of Fugue 186, 198; DeepBach and 207–12, 232; Emmy and 195–6, 197, 198, 200, 201; The Musical Offering 189–94; ‘Ricercar’ 192; St John Passion 207–8 Baroque 10, 13, 138 Barreau, Pierre 230 Barreau, Vincent 230 Barry, Robert 106 Barthes, Roland 251–2 Bartók, Béla 186–7, 197, 205 Batten, Dan 234 Beatles, the 224; ‘Yesterday’ 223 Beckett, Samuel 17 Beethoven, Ludwig van 10, 41, 127, 200, 230, 244 Belamy, Edmond 141 BellKor’s Pragmatic Chaos 87–8 Berlyne, D.

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Sure, the machine was programmed by humans, but that doesn’t really seem to make it feel better. AlphaGo has since retired from competitive play. The Go team at DeepMind has been disbanded. Hassabis proved his Cambridge lecturer wrong. DeepMind has now set its sights on other goals: health care, climate change, energy efficiency, speech recognition and generation, computer vision. It’s all getting very serious. Given that Go was always my shield against computers doing mathematics, was my own subject next in DeepMind’s cross hairs? To truly judge the potential of this new AI we are going to need to look more closely at how it works and dig around inside. The crazy thing is that the tools DeepMind is using to create the programs that might put me out of a job are precisely the ones that mathematicians have created over the centuries.

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Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioﬀ, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

From the Mechanistic to the Organic The potential power of AI to transform businesses is unprecedented, and yet there is an urgent and growing challenge. Companies are now reaching a crossroad in their use of AI, which we define as systems that extend human capability by sensing, comprehending, acting, and learning. As businesses deploy such systems—spanning from machine learning to computer vision to deep learning—some firms will continue to see modest productivity gains over the short run, but those results will eventually stall out. Other companies will be able to attain breakthrough improvements in performance, often by developing game-changing innovations. What accounts for the difference?

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For instance, Minsky, with Seymour Papert, wrote what was considered the foundational book on scope and limitations of neural networks, a kind of AI that uses biological neurons as its model. Other ideas like expert systems—wherein a computer contained deep stores of “knowledge” for specific domains like architecture or medical diagnosis—and natural language processing, computer vision, and mobile robotics can also be traced back to the event. One conference participant was Arthur Samuel, an engineer at IBM who was building a computer program to play checkers. His program would assess the current state of a checkers board and calculate the probability that a given position could lead to a win.

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In other words, it automates existing processes. But in order to reimagine processes, firms must utilize more advanced technologies—namely, AI. (See the sidebar “AI Technologies and Applications: How Does This All Fit Together?” at the end of this chapter.) Now we’re talking about systems that deploy AI techniques such as computer vision, or machine-learning tools to analyze unstructured or complex information. It might be able to read various styles of invoices, contracts, or purchase orders, for instance. It can process these documents—no matter the format—and put the correct values into forms and databases for further action.

pages: 307 words: 88,180

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

By 2017, almost every team had driven error rates below 5 percent—approximately the accuracy of humans performing the same task—with the average algorithm of that year making only one-third of the mistakes of the top algorithm of 2012. In the years since the Oxford experts made their predictions, computer vision has now surpassed human capabilities and dramatically expanded real-world use-cases for the technology. Those amped-up capabilities extend far beyond computer vision. New algorithms constantly set and surpass records in fields like speech recognition, machine reading, and machine translation. While these strengthened capabilities don’t constitute fundamental breakthroughs in AI, they do open the eyes and spark the imaginations of entrepreneurs.

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Soon, these juiced-up neural networks—now rebranded as “deep learning”—could outperform older models at a variety of tasks. But years of ingrained prejudice against the neural networks approach led many AI researchers to overlook this “fringe” group that claimed outstanding results. The turning point came in 2012, when a neural network built by Hinton’s team demolished the competition in an international computer vision contest. After decades spent on the margins of AI research, neural networks hit the mainstream overnight, this time in the form of deep learning. That breakthrough promised to thaw the ice from the latest AI winter, and for the first time truly bring AI’s power to bear on a range of real-world problems.

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After that, a smaller number of Chinese entrepreneurs and venture-capital funds like my own began to invest in this area. But the great majority of China’s technology community didn’t properly wake up to the deep-learning revolution until its Sputnik Moment in 2016, a full decade behind the field’s breakthrough academic paper and four years after it proved itself in the computer vision competition. American universities and technology companies have for decades reaped the rewards of the country’s ability to attract and absorb talent from around the globe. Progress in AI appeared to be no different. The United States looked to be out to a commanding lead, one that would only grow as these elite researchers leveraged Silicon Valley’s generous funding environment, unique culture, and powerhouse companies.

pages: 205 words: 20,452

Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke

backpropagation, call centre, computer vision, discrete time, G4S, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

Shen, P. S. P. Wang and T. Zhang) Vol. 45: Hidden Markov Models: Applications in Computer Vision (Eds. H. Bunke and T. Caelli) Vol. 46: Syntactic Pattern Recognition for Seismic Oil Exploration (K. Y. Huang) Vol. 47: Hybrid Methods in Pattern Recognition (Eds. H. Bunke and A. Kandel ) Vol. 48: Multimodal Interface for Human-Machine Communications (Eds. P. C. Yuen, Y. Y. Tang and P. S. P. Wang) Vol. 49: Neural Networks and Systolic Array Design (Eds. D. Zhang and S. K. Pal ) Vol. 50: Empirical Evaluation Methods in Computer Vision (Eds. H. I. Christensen and P. J. Phillips) Vol. 51: Automatic Diatom Identification (Eds.

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Fagin, R. and Stockmeyer, L. (1998). Relaxing the Triangle Inequality in Pattern Matching. Int. Journal on Computer Vision, 28(3), 219–231. 7. Frances, M. and Litman, A. (1997). On Covering Problems of Codes. Theory of Computing Systems, 30(2), 113–119. 8. Fred, A.L.N. and Leitão, J.M.N. (1998). A Comparative Study of String Dissimilarity Measures in Structural Clustering. Proc. of Int. Conf. on Document Analysis and Recognition, pp. 385–394. 9. Gramkow, C. (2001). On Averaging Rotations. Int. Journal on Computer Vision, 42(1/2), 7–16. 10. Gregor, J. and Thomason, M.G. (1993). Dynamic Programming Alignment of Sequences Representing Cyclic Patterns.

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., and Wang, X.S. (1998). Supporting Fast Search in Time Series for Movement Patterns in Multiple Scales. Proceedings of the Seventh International Conference on Information and Knowledge Management, pp. 251–258. 57. Sahoo, P.K., Soltani, S., Wong, A.K.C., and Chen, Y.C. (1988). A Survey of Thresholding Techniques. Computer Vision, Graphics and Image Processing, 41, 233–260. 58. Samet, H. (1990). The Design and Analysis of Spatial Data Structures. Addison-Wesley, Reading, MA. 59. Sheikholeslami, G., Chatterjee, S., and Zhang, A. (1998). WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases.

pages: 625 words: 167,349

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

., and Rob Fergus. “Visualizing and Understanding Convolutional Networks.” In European Conference on Computer Vision, 818–33. Springer, 2014. Zeiler, Matthew D., Dilip Krishnan, Graham W. Taylor, and Rob Fergus. “Deconvolutional Networks.” In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2528–35. IEEE, 2010. Zeiler, Matthew D., Graham W. Taylor, and Rob Fergus. “Adaptive Deconvolutional Networks for Mid and High Level Feature Learning.” In 2011 International Conference on Computer Vision, 2018–25. IEEE, 2011. Zeng, Jiaming, Berk Ustun, and Cynthia Rudin. “Interpretable Classification Models for Recidivism Prediction.”

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It was understood that, in principle, a big-enough neural network, with enough training examples and time, can learn almost anything.16 But no one had fast-enough computers, enough data to train on, or enough patience to make good on that theoretical potential. Many lost interest, and the field of computer vision, along with computational linguistics, largely moved on to other things. As Hinton would later summarize, “Our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow.”17 Both of these things, however, would change. With the growth of the web, if you wanted not fifty but five hundred thousand “flash cards” for your network, suddenly you had a seemingly bottomless repository of images.

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In 2007, Princeton professor Fei-Fei Li used Amazon Mechanical Turk to recruit human labor, at a scale previously unimaginable, to build a dataset that was previously impossible. It took more than two years to build, and had three million images, each labeled, by human hands, into more than five thousand categories. Li called it ImageNet, and released it in 2009. The field of computer vision suddenly had a mountain of new data to learn from, and a new grand challenge. Beginning in 2010, teams from around the world began competing to build a system that can reliably look at an image—dust mite, container ship, motor scooter, leopard—and say what it is. Meanwhile, the relatively steady progress of Moore’s law throughout the 2000s meant that computers could do in minutes what the computers of the 1980s took days to do.

pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

Tribes typically observe rules and rituals, so let’s explore the rights of initiation for AI’s tribes. It begins with a rigorous university education. In North America, the emphasis within universities has centered on hard skills—like mastery of the R and Python programming languages, competency in natural language processing and applied statistics, and exposure to computer vision, computational biology, and game theory. It’s frowned upon to take classes outside the tribe, such as a course on the philosophy of mind, Muslim women in literature, or colonialism. If we’re trying to build thinking machines capable of thinking like humans do, it would seem counterintuitive to exclude learning about the human condition.

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In my own meetings at the Pentagon with Department of Defense officials, an alternative view on the future of warfare (code vs. combat) has taken a long time to find widespread alignment. For example, in 2017, the DoD established an Algorithmic Warfare Cross-Functional Team to work on something called Project Maven—a computer vision and deep-learning system that autonomously recognizes objects from still images and videos. The team didn’t have the necessary AI capabilities, so the DoD contracted with Google for help training AI systems to analyze drone footage. But no one told the Google employees assigned to the project that they’d actually been working on a military project, and that resulted in high-profile backlash.

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Since they favor optimization over precision and are basically made up of dense linear algebra operations, it makes sense that a new neural network architecture would lead to greater efficiencies and, more importantly, speed in the design and deployment process. The faster research teams can build and test real-world models, the closer they can get to practical-use cases for AI. For example, training a complicated computer vision model currently takes weeks or months—and the end result might only prove that further adjustments need to be made, which means starting over again. Better hardware means training models in a matter of hours, or even minutes, which could lead to weekly—or even daily—breakthroughs. That’s why Google created its own custom silicon, called Tensor Processing Units (TPUs).

Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, backpropagation, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, deep learning, digital map, Donald Shoup, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, General Motors Futurama, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Hans Moravec, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, Jeff Hawkins, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, Philippa Foot, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, TED Talk, Tesla Model S, Travis Kalanick, trolley problem, Uber and Lyft, uber lyft, Unsafe at Any Speed, warehouse robotics

Source: Jakob Engel, Jorg Stuckler, and Daniel Cremers, “Large-Scale Direct Slam with Stereo Cameras,” in 2015 IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1935–1942. IEEE, 2015; Andreas Geiger, Philip Lenz, and Raquel Urtasun, “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361, IEEE, 2012. Figure 12.1 GM’s Electric Networked-Vehicle (EN-V) Concept pod, an autonomous two-seater codeveloped with Segway for short trips in cities. Source: General Motors Figure 12.2 The most common job in most U.S. states in 2014 was truck driving.

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George Hotz’s home-built Acura is evidence that today a skilled developer can build a pretty good driverless car in a fairly short period of time. However, as Musk points out, when it comes to software you will trust with your life, the leap from 99 percent to 99.9999 percent accuracy is a big one. In the past few years, mobile robots have gotten better at finding their way around their environment. It helps that the performance of computer vision software has improved dramatically, aided by the advent of big data, high resolution digital cameras, and faster processors. Another catalyst has been the successful application of machine-learning software to solve thorny problems of machine vision, sparking a mini-renaissance in the study of artificial perception.

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Both of these approaches worked some of the time but were too slow and still didn’t provide the software with a crucial skill, the ability to consistently recognize objects in unfamiliar settings. To automate the process of object recognition, it’s necessary to have software that can extract visual information from raw data in order to identify the objects depicted. Over the years, researchers have attempted to do this in several different ways. One of the earliest forms of computer vision software developed in the 1960s worked by distilling digital images into simple line drawings. A famous example of this approach was a robot named Shakey, described somewhat optimistically by his creator, Stanford researcher Charles Rosen, as “the first electronic person.” Shakey’s “body” consisted of a stack of heavy boxes containing electronic equipment stacked onto a cart.

pages: 362 words: 97,288

Ghost Road: Beyond the Driverless Car by Anthony M. Townsend

A Pattern Language, active measures, AI winter, algorithmic trading, Alvin Toffler, Amazon Robotics, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing, Blitzscaling, Boston Dynamics, business process, Captain Sullenberger Hudson, car-free, carbon footprint, carbon tax, circular economy, company town, computer vision, conceptual framework, congestion charging, congestion pricing, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deep learning, deepfake, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, Didi Chuxing, drive until you qualify, driverless car, drop ship, Edward Glaeser, Elaine Herzberg, Elon Musk, en.wikipedia.org, extreme commuting, financial engineering, financial innovation, Flash crash, food desert, Ford Model T, fulfillment center, Future Shock, General Motors Futurama, gig economy, Google bus, Greyball, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, Jevons paradox, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, Kiva Systems, Lewis Mumford, loss aversion, Lyft, Masayoshi Son, megacity, microapartment, minimum viable product, mortgage debt, New Urbanism, Nick Bostrom, North Sea oil, Ocado, openstreetmap, pattern recognition, Peter Calthorpe, random walk, Ray Kurzweil, Ray Oldenburg, rent-seeking, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, SoftBank, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, TED Talk, Tesla Model S, The Coming Technological Singularity, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Great Good Place, too big to fail, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, urban sprawl, US Airways Flight 1549, Vernor Vinge, vertical integration, Vision Fund, warehouse automation, warehouse robotics

When in-house projects failed to produce convincing results, many companies simply acquired promising startups to get hold of the needed technology instead. In a two-year period during 2016 and 2017 alone, some $80 billion surged into self-driving vehicle technologies. The biggest deal, Intel’s panicked 2017 acquisition of computer-vision pioneer Mobileye, an Israel-based maker of computer-vision systems, was valued at an eye-watering $15 billion. As this flurry of mergers and acquisitions unfolded, the web of partnerships and cross holdings linking automakers and the tech sector grew ever more tangled. Two of the world’s biggest consumer industries—computers and cars—had seen their future in each other.

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This sea of human teachers—including, for instance, the team Google kept in India to train its first AVs and the 300,000 online gig workers of Seattle-based Mighty AI—performs endless hours of mind-numbing human intelligence tasks (HITs in AI jargon), the most underappreciated role in the creation story of this technology. Some of this work is done once, early in the development of AV software, to provide a baseline for algorithmic training. But many human handlers must be kept on to decipher imagery that computer vision can’t interpret. Despite autonomists’ overreaches, progress toward full autonomy continues, as measured by the number of disengagements—incidents where human safety engineers are forced to intervene and take back control from stymied self-driving computers during test drives. For instance, in 2017, GM’s fleet of Chevy Bolts disengaged on average once every 1,254 miles during testing on the challenging terrain of San Francisco streets, a huge leap over the previous year, when the computers balked every 235 miles.

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When the space shuttle Endeavor was moved across Los Angeles in 2012, the 12-mile journey required a custom-built 160-wheel carrier and hundreds of human escorts, at a cost of more than $10 million. But could such a heavy lift become an inexpensive, routine, automated operation in cities of the future? Civic caravans wouldn’t simply be self-driving versions of prefab government trailers. Computer vision, at such low speeds, wouldn’t be used just to look for obstructions ahead. Its gaze could be turned down as well, collecting precise imagery of potholes and road conditions. Pairing such superhuman sensing with active, computer-controlled suspensions could create a mobile platform as stable as the ground below—allowing delicate, light, and airy structures of metal and glass to rise above.

pages: 296 words: 66,815

The AI-First Company by Ash Fontana

23andMe, Amazon Mechanical Turk, Amazon Web Services, autonomous vehicles, barriers to entry, blockchain, business intelligence, business process, business process outsourcing, call centre, Charles Babbage, chief data officer, Clayton Christensen, cloud computing, combinatorial explosion, computer vision, crowdsourcing, data acquisition, data science, deep learning, DevOps, en.wikipedia.org, Geoffrey Hinton, independent contractor, industrial robot, inventory management, John Conway, knowledge economy, Kubernetes, Lean Startup, machine readable, minimum viable product, natural language processing, Network effects, optical character recognition, Pareto efficiency, performance metric, price discrimination, recommendation engine, Ronald Coase, Salesforce, single source of truth, software as a service, source of truth, speech recognition, the scientific method, transaction costs, vertical integration, yield management

Integrators collate data from their customers through a set of integrations with other products, but only if that fulfills a specific customer need and goes through existing pipes between the customer’s data storage and the application to be integrated. This also means that vendors don’t have to negotiate deals with each of those external data sources. For instance, computer vision offers a promising path to helping physicians identify diseased tissues such as cancers of the skin, breast, lung, and so on. However, computer vision models tend to need many images on which to train, and there are strict controls on handling patient data such as X-rays. Companies building computer vision–based systems need that data but can’t get it without either running their own medical facilities—which would take years to build and approve—or obtaining it from an existing medical facility.

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Having a greater volume of data enables customers to effectively average across many data points when training their systems. Members can see different variations of the same category of data from other members. For example, images of a product taken in different lighting conditions used to train computer vision models to recognize the product in different environments. Members can validate data points for each other. For example, if they have a user with the same (not personally identifiable) data in some dimension—email, let’s say—but not others, such as a preference for liquid or powder detergent, they can correct those other data points by unifying on the email address and filling out the “detergent preference” column for the user with that email address.

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Sometimes the priority is avoiding missed diagnoses, in the case of particularly deadly cancers such as lung cancer, while in others it’s about cost-effectively screening lots of patients on a regular basis, in the case of particularly prevalent cancers like basal cell carcinoma of the skin. Medical facilities achieve this by using AI to augment the physicians on staff but don’t typically have the ML computer vision expertise to build AI. They have a valuable asset to leverage as well as protect, so they typically strike partnerships that give them exclusive access to the AI product for a period of time, include strict controls on data, and allow for integration into existing hardware such as X-ray machines.

pages: 569 words: 156,139

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire by Brad Stone

activist fund / activist shareholder / activist investor, air freight, Airbnb, Amazon Picking Challenge, Amazon Robotics, Amazon Web Services, autonomous vehicles, Bernie Sanders, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, business climate, call centre, carbon footprint, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, company town, computer vision, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, data science, deep learning, disinformation, disintermediation, Donald Trump, Downton Abbey, Elon Musk, fake news, fulfillment center, future of work, gentrification, George Floyd, gigafactory, global pandemic, Greta Thunberg, income inequality, independent contractor, invisible hand, Jeff Bezos, John Markoff, Kiva Systems, Larry Ellison, lockdown, Mahatma Gandhi, Mark Zuckerberg, Masayoshi Son, mass immigration, minimum viable product, move fast and break things, Neal Stephenson, NSO Group, Paris climate accords, Peter Thiel, Ponzi scheme, Potemkin village, private spaceflight, quantitative hedge fund, remote working, rent stabilization, RFID, Robert Bork, Ronald Reagan, search inside the book, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, SoftBank, SpaceX Starlink, speech recognition, Steve Ballmer, Steve Jobs, Steven Levy, tech billionaire, tech bro, techlash, TED Talk, Tim Cook: Apple, Tony Hsieh, too big to fail, Tragedy of the Commons, two-pizza team, Uber for X, union organizing, warehouse robotics, WeWork

“Just as he got really enthusiastic about computer voice recognition, he was also really excited about computer vision.” The allure of computer vision, along with his interest in pressing Amazon’s advantage in the cloud to push the frontiers of artificial intelligence, again sparked the fertile imagination of Amazon’s founder. More than 90 percent of retail transactions were conducted in physical stores, according to the U.S. Census Bureau. Perhaps there was a way to tap this vast reservoir of sales with a completely self-service physical store that harnessed emerging technologies like computer vision and robotics. In 2012, Bezos pitched this broad idea at an off-site meeting to the S-team.

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Bezos liked to say Amazon was “stubborn on vision, flexible on details,” and here was an illustration: groups working on parallel tracks would essentially compete to fulfill the “Just Walk Out” ideal and solve the problem of the cashierless store. Kumar’s group continued to develop a store with futuristic computer vision technology embedded in the ceilings and shelves. Meanwhile, Kessel asked Jeremy De Bonet, an Amazon technology director based in Boston, to form his own internal startup of engineers and computer vision scientists. They would end up flipping the problem around and integrating computer vision technology and sensors into a shopping cart, instead of blanketing them around the store. In some ways, this was a harder problem. While the Go store could partially deduce the identity of an item based on where in the store it was located, a so-called “smart cart” would have to account for the possibility of a shopper selecting, say, a bag of oranges from the produce aisle but scanning them somewhere else in the store.

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But Bezos didn’t want them to take an easy path; he wanted them to innovate in the field of computer vision, which he saw as important to Amazon’s future. So they settled on the idea of cameras in the ceiling and algorithms behind the scenes that would try to spot when customers selected products and charge them for it. Scales hidden inside the shelves would provide another reliable sensor to determine when products were being removed and corroborate who was buying what. Over the next few years, Dilip Kumar recruited experts from outside Amazon, such as the University of Southern California’s renowned computer vision scientist, Gérard Medioni, as well as engineers from inside who worked on complex technologies like Amazon’s pricing algorithms.

Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, backpropagation, behavioural economics, Big Tech, bike sharing, bitcoin, business intelligence, business logic, business process, chief data officer, circular economy, clean tech, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, congestion pricing, continuous integration, crowdsourcing, data is the new oil, data science, deep learning, digital rights, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, hydroponic farming, income inequality, information security, Infrastructure as a Service, Internet of things, Large Hadron Collider, Masdar, microservices, Minecraft, OSI model, platform as a service, pneumatic tube, ransomware, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

We have no idea how the neural net splits up the information into discrete patterns and really no way of knowing. We can only stand by and watch the output and decide whether it is accurate. It is great for situations where a lot of information has to be condensed into categorical knowledge like computer vision, where we are interested in parsing an image and extracting data about objects in the image. Use cases for cities would be in computer vision where images could be converted into counts of pedestrians, cars, and bicycles or for allowing speech interaction with city services. The resident could speak, and the input converted into text that could then be processed as questions and answers.

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Consider traffic counts: most cities need to manually count traffic to understand the flow of traffic in central corridors. This is a tedious and resource-intensive task, since you need a human to manually count each vehicle. IoT offers alternatives such as counting vehicles by pneumatic tubes on the ground, infrared light, and radar or using computer vision built in to cameras. This can be done at a much bigger scale, since humans need to go and sleep every once in a while, whereas devices never sleep and will keep counting when they are set up, and they will be doing it at a lower cost. This makes devices an attractive alternative for cities. However, devices are also liabilities in terms of security.

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This is a good case to show how simple tracking of vehicles can give insight and transparency to a concern for city residents, but also why privacy concerns can impact a solution. Exteros This New York startup has developed a device that can count and categorize people, for example, in a shopping mall. It uses computer vision and artificial intelligence to categorize and count people as they move through the field of vision. The device is based on a Raspberry Pi, a camera, and a 3D printed case. This is a great example of the flexibility and availability of components to make innovative IoT solutions today. Earlier a vendor would have had to find an adequate camera and microcontroller.

From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry by Martin Campbell-Kelly

Apple II, Apple's 1984 Super Bowl advert, barriers to entry, Bill Gates: Altair 8800, business process, card file, Charles Babbage, computer age, computer vision, continuous integration, Dennis Ritchie, deskilling, Donald Knuth, Gary Kildall, Grace Hopper, history of Unix, hockey-stick growth, independent contractor, industrial research laboratory, information asymmetry, inventory management, John Markoff, John von Neumann, Larry Ellison, linear programming, longitudinal study, machine readable, Menlo Park, Mitch Kapor, Multics, Network effects, popular electronics, proprietary trading, RAND corporation, Robert X Cringely, Ronald Reagan, seminal paper, Silicon Valley, SimCity, software patent, Steve Jobs, Steve Wozniak, Steven Levy, Thomas Kuhn: the structure of scientific revolutions, vertical integration

There were also two major cross-industry markets: office automation and computer-aided design. The market leaders were Wang Laboratories and Computer Vision, respectively. Both went beyond writing software and integrating it with hardware; they also manufactured their own hardware. The Shaping of the Software Products Industry 129 The turnkey supplier epitomizes the problem of measuring the size of the software industry. For example, Wang, the leading producer of word processors, was usually classified as a hardware supplier, whereas Computer Vision was regarded as a software vendor, yet these firms provided similar packages of hardware and software.

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Wang suffered huge losses in the late 1980s and filed for Chapter X bankruptcy in 1992. Computer Vision and CAD Systems What the word processor did for the ordinary office, the computeraided design system did for the engineering design office. But whereas the turnkey word processor displaced an earlier generation of noncomputerized word processors, the CAD system came out of nowhere. CAD was made possible by the minicomputer, the graphical display tube, and the graph-plotter, all which became affordable in the late 1960s. The first entrant into the CAD turnkey market was Computer Vision, founded in Bedford, Massachusetts, in 1969.60 Rather than develop a software package to run on a commercially available minicomputer, Computer Vision developed its own processor, which was specially designed for graphics work and was therefore more effective than a general-purpose minicomputer.61 This initial advantage was short-lived.

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The first entrant into the CAD turnkey market was Computer Vision, founded in Bedford, Massachusetts, in 1969.60 Rather than develop a software package to run on a commercially available minicomputer, Computer Vision developed its own processor, which was specially designed for graphics work and was therefore more effective than a general-purpose minicomputer.61 This initial advantage was short-lived. In the early 1970s, Computer Vision was joined in the CAD market by Intergraph, Calman, Applicon, Auto-trol, and Gerber, all of whose systems used standard minicomputers. IBM, DEC, Prime, and Data General also provided systems for the booming turnkey market. CAD systems were expensive, costing perhaps 10 times as much per user as word processing systems.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn

A Pattern Language, Airbnb, algorithmic trading, automated trading system, business intelligence, business logic, business process, combinatorial explosion, computer vision, continuous integration, COVID-19, data science, deep learning, DevOps, discrete time, en.wikipedia.org, Hacker News, industrial research laboratory, iterative process, Kubernetes, machine translation, microservices, mobile money, natural language processing, Netflix Prize, optical character recognition, pattern recognition, performance metric, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, speech recognition, statistical model, the payments system, web application

Embeddings Hashed Feature Neutral Class Multimodal Input Transfer Learning Two-Phase Predictions Cascade Windowed Inference Computer Vision Computer vision is the broad parent name for AI that trains machines to understand visual input, such as images, videos, icons, and anything where pixels might be involved. Computer vision models aim to automate any task that might rely on human vision, from using an MRI to detect lung cancer to self-driving cars. Some classical applications of computer vision are image classification, video motion analysis, image segmentation, and image denoising. Reframing Neutral Class Multimodal Input Transfer Learning Embeddings Multilabel Cascade Two-Phase Predictions Predictive Analytics Predictive modeling uses historical data to find patterns and determine the likelihood of a certain event occurring in the future.

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MLPerf name and logo are trademarks. See www.mlperf.org for more information. 3 Jia Deng et al.,“ImageNet: A Large-Scale Hierarchical Image Database,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (2009): 248–255. 4 For more information, see “CS231n Convolutional Neural Networks for Visual Recognition.” 5 Victor Campos et al., “Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster,” International Conference on Computational Science, ICCS 2017, June 12–14, 2017. 6 Ibid. 7 Jeffrey Dean et al. “Large Scale Distributed Deep Networks,” NIPS Proceedings (2012). 8 Priya Goyal et al., “Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour” (2017), arXiv:1706.02677v2 [cs.CV].

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, Data and Model Tooling, Concept, Saving predictions Cloud AI Platform Pipelines, Solution, Running the pipeline on Cloud AI Platform Cloud AI Platform Predictions, Lambda architecture Cloud AI Platform Training, Solution, Running the pipeline on Cloud AI Platform Cloud Build, Integrating CI/CD with pipelines Cloud Composer/Apache Airflow, Scheduled retraining Cloud Dataflow, Lambda architecture Cloud Functions, Triggers for retraining, Integrating CI/CD with pipelines Cloud Run, Create web endpoint, Other serverless versioning tools Cloud Spanner, Cached results of batch serving clustering, Models and Frameworks clustering models, Models and Frameworks CNN, Images as tiled structures, Why It Works-Why It Works cold start, Problem, Cold start combinatorial explosion, Grid search and combinatorial explosion completeness, Data Quality components, definition of, Solution computer vision, Computer Vision concept drift, Problem, Estimating retraining interval confidence, Inputs with overlapping labels, When human experts disagree, Saving predictions confusion matrix, Problem, Evaluating model performance consistency, Data Quality-Data Quality containers, Design Pattern 25: Workflow Pipeline, Solution, Why It Works context language models, Context language models-Context language models(see also BERT, Word2Vec) Continued Model Evaluation design pattern, Design Patterns for Resilient Serving, Design Pattern 18: Continued Model Evaluation-Estimating retraining interval, Model versioning with a managed service, Responsible AI, Automating data evaluation, Pattern Interactions Continuous Bag of Words (see CBOW) continuous evaluation, Continuous evaluation-Continuous evaluation continuous integration and continuous delivery (see CI/CD) convolutional neural network (see CNN) Coral Edge TPU, Phase 1: Building the offline model counterfactual analysis, Counterfactual analysis and example-based explanations-Counterfactual analysis and example-based explanations counterfactual reasoning, Capturing ground truth cryptographic algorithms, Cryptographic hash custom serving function, Custom serving function D DAG, Why It Works, Apache Airflow and Kubeflow Pipelines Darwin, Charles, Genetic algorithms data accuracy, Data Quality data analysts, Roles data augmentation, Data augmentation data collection bias, Before training, Before training data distribution bias, Problem data drift, Data Drift-Data Drift, Problem, Estimating retraining interval, Continuous evaluation for offline models, Problem data engineers, Roles, Scale, Solution data parallelism, Solution-Solution, Synchronous training, Why It Works, Model parallelism data preprocessing, Data and Feature Engineering(see also data transformation, feature engineering) data representation, Data Representation Design Patterns-Data Representation Design Patterns data representation bias, Before training data scientistsrole of, Roles, Multiple Objectives-Multiple Objectives, Why It Works, Problem tasks of, Problem, Problem, Solution data transformation, Data and Feature Engineering data validation, Data and Feature Engineering, Data validation with TFX data warehouses, Embeddings in a data warehouse-Embeddings in a data warehouse dataset-level transformations, Efficient transformations with tf.transform datasets, definition of, Data and Feature Engineering Datastore, Cached results of batch serving decision trees, Models and Frameworks, Data Representation Design Patterns-Data Representation Design Patterns, Decreased model interpretability, Choosing a model architecture, Typical Training Loop, Solution Deep Galerkin Method, Data-driven discretizations-Unbounded domains deep learning, Models and Frameworks-Models and Frameworks, Multimodal feature representations and model interpretability deep neural network (see DNN model) default, definition of, Model versioning with a managed service Dense layers, Solution, Using images with metadata design patterns, definition of, What Are Design Patterns?

pages: 326 words: 88,968

The Science and Technology of Growing Young: An Insider's Guide to the Breakthroughs That Will Dramatically Extend Our Lifespan . . . And What You Can Do Right Now by Sergey Young

23andMe, 3D printing, Albert Einstein, artificial general intelligence, augmented reality, basic income, Big Tech, bioinformatics, Biosphere 2, brain emulation, caloric restriction, caloric restriction, Charles Lindbergh, classic study, clean water, cloud computing, cognitive bias, computer vision, coronavirus, COVID-19, CRISPR, deep learning, digital twin, diversified portfolio, Doomsday Clock, double helix, Easter island, Elon Musk, en.wikipedia.org, epigenetics, European colonialism, game design, Gavin Belson, George Floyd, global pandemic, hockey-stick growth, impulse control, Internet of things, late capitalism, Law of Accelerating Returns, life extension, lockdown, Lyft, Mark Zuckerberg, meta-analysis, microbiome, microdosing, moral hazard, mouse model, natural language processing, personalized medicine, plant based meat, precision agriculture, radical life extension, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, self-driving car, seminal paper, Silicon Valley, stem cell, Steve Jobs, tech billionaire, TED Talk, uber lyft, ultra-processed food, universal basic income, Virgin Galactic, Vision Fund, X Prize

Your shower runs a full-body scan, before your ultrasound bathroom scale checks your organs, soft tissues, and arteries for any signs of tumors, disease, and obstructed blood flow. Your diagnostic toothbrush and microbiome-monitoring commode watch for dangerous changes in your cells and gut, while your computer-vision-equipped bedroom mirror checks your skin for potentially dangerous mole growth. As you sit down for breakfast, a tiny chip embedded at the tip of a blood vessel just beneath the surface of your skin tracks nutrients, immune cells, vitamins, minerals, foreign substances, and disease indicators.

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There are bathroom scales that measure your body fat percentage and hydration level, home blood tests that monitor your cholesterol24 and blood glucose, and even home tests that help diagnose STDs, allergies, and food intolerances. Smartphone apps like UM SkinCheck, Miiskin, and MoleMapper leverage your smartphone’s camera and computer-vision AI to offer early guidance and detection of skin cancer. DIY health diagnostic devices like these are becoming increasingly portable, wearable, implantable, ingestible, and affordable. They are also becoming vastly more sophisticated. In 2018, the FDA granted approval for some of the Apple smart watch functionality, which now includes blood oxygen level readings and an electrocardiogram (ECG) monitoring function, to help detect atrial fibrillation, the most common heart rhythm disorder.25 (In 2019, a doctor friend of mine who travels frequently was called upon five times to assist in an in-flight medical emergency, and he used his Apple Watch to take an ECG every time.)

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For these data to be used effectively, they must be cross-referenced with scores of pharmaceutical options, surgical treatments, lifestyle adjustments, and other interventions. To quote a viral YouTube video, “Ain’t nobody got time for that.” This is where artificial intelligence enters the picture. If you are familiar with terms like computer vision, deep neural networks, and machine learning, you probably already have a good sense of what happens next. I won’t clutter up the chapter with an AI primer. But AI is rapidly advancing to make precision medicine truly possible. Here are some examples of AI in action: 1.AI Case Study #1: Continuous Monitoring in the UK In the UK, more than one million people live with chronic obstructive pulmonary disease (COPD).

pages: 444 words: 117,770

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma by Mustafa Suleyman

"World Economic Forum" Davos, 23andMe, 3D printing, active measures, Ada Lovelace, additive manufacturing, agricultural Revolution, AI winter, air gap, Airbnb, Alan Greenspan, algorithmic bias, Alignment Problem, AlphaGo, Alvin Toffler, Amazon Web Services, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, ASML, autonomous vehicles, backpropagation, barriers to entry, basic income, benefit corporation, Big Tech, biodiversity loss, bioinformatics, Bletchley Park, Blitzscaling, Boston Dynamics, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, ChatGPT, choice architecture, circular economy, classic study, clean tech, cloud computing, commoditize, computer vision, coronavirus, corporate governance, correlation does not imply causation, COVID-19, creative destruction, CRISPR, critical race theory, crowdsourcing, cryptocurrency, cuban missile crisis, data science, decarbonisation, deep learning, deepfake, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, disinformation, drone strike, drop ship, dual-use technology, Easter island, Edward Snowden, effective altruism, energy transition, epigenetics, Erik Brynjolfsson, Ernest Rutherford, Extinction Rebellion, facts on the ground, failed state, Fairchild Semiconductor, fear of failure, flying shuttle, Ford Model T, future of work, general purpose technology, Geoffrey Hinton, global pandemic, GPT-3, GPT-4, hallucination problem, hive mind, hype cycle, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, Internet of things, invention of the wheel, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kickstarter, lab leak, large language model, Law of Accelerating Returns, Lewis Mumford, license plate recognition, lockdown, machine readable, Marc Andreessen, meta-analysis, microcredit, move 37, Mustafa Suleyman, mutually assured destruction, new economy, Nick Bostrom, Nikolai Kondratiev, off grid, OpenAI, paperclip maximiser, personalized medicine, Peter Thiel, planetary scale, plutocrats, precautionary principle, profit motive, prompt engineering, QAnon, quantum entanglement, ransomware, Ray Kurzweil, Recombinant DNA, Richard Feynman, Robert Gordon, Ronald Reagan, Sam Altman, Sand Hill Road, satellite internet, Silicon Valley, smart cities, South China Sea, space junk, SpaceX Starlink, stealth mode startup, stem cell, Stephen Fry, Steven Levy, strong AI, synthetic biology, tacit knowledge, tail risk, techlash, techno-determinism, technoutopianism, Ted Kaczynski, the long tail, The Rise and Fall of American Growth, Thomas Malthus, TikTok, TSMC, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, warehouse robotics, William MacAskill, working-age population, world market for maybe five computers, zero day

The resulting paper by Hinton and his colleagues became one of the most frequently cited works in the history of AI research. Thanks to deep learning, computer vision is now everywhere, working so well it can classify dynamic real-world street scenes with visual input equivalent to twenty-one full-HD screens, or about 2.5 billion pixels per second, accurately enough to weave an SUV through busy city streets. Your smartphone recognizes objects and scenes, while vision systems automatically blur the background and highlight people in your videoconference calls. Computer vision is the basis of Amazon’s checkout-less supermarkets and is present in Tesla’s cars, pushing them toward increasing autonomy.

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The challenge of managing the coming wave’s technologies means understanding them and taking them seriously, starting with the one I have spent my career working on: AI. THE AI SPRING: DEEP LEARNING COMES OF AGE AI is at the center of this coming wave. And yet, since the term “artificial intelligence” first entered the lexicon in 1955, it has often felt like a distant promise. For years progress in computer vision, for example—the challenge of building computers that can recognize objects or scenes—was slower than expected. The legendary computer science professor Marvin Minsky famously hired a summer student to work on an early vision system in 1966, thinking that significant milestones were just within reach.

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Keep doing this, modifying the weights again and again, and you gradually improve the performance of the neural network so that eventually it’s able to go all the way from taking in single pixels to learning the existence of lines, edges, shapes, and then ultimately entire objects in scenes. This, in a nutshell, is deep learning. And this remarkable technique, long derided in the field, cracked computer vision and took the AI world by storm. AlexNet was built by the legendary researcher Geoffrey Hinton and two of his students, Alex Krizhevsky and Ilya Sutskever, at the University of Toronto. They entered the ImageNet Large Scale Visual Recognition Challenge, an annual competition designed by the Stanford professor Fei-Fei Li to focus the field’s efforts around a simple goal: identifying the primary object in an image.

pages: 404 words: 95,163

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights

3D printing, Adam Neumann (WeWork), Airbnb, Amazon Robotics, Amazon Web Services, asset light, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, deep learning, DeepMind, digital divide, Donald Trump, Doomsday Clock, driverless car, electronic shelf labels (ESLs), Elon Musk, fulfillment center, gig economy, independent contractor, Internet of things, inventory management, invisible hand, Jeff Bezos, Kiva Systems, market fragmentation, new economy, Ocado, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, random stow, recommendation engine, remote working, Salesforce, sensor fusion, sharing economy, Skype, SoftBank, Steve Bannon, sunk-cost fallacy, supply-chain management, TaskRabbit, TechCrunch disrupt, TED Talk, trade route, underbanked, urban planning, vertical integration, warehouse automation, warehouse robotics, WeWork, white picket fence, work culture

The removal of any human interface from the most friction-filled process of any store-based shopping journey, ie checkout, affords the customer unprecedented speed and simplicity. Autonomous computing: AI-based computer vision, sensor fusion and deep learning technologies power Amazon Go’s Just Walk Out technology. Just Walk Out technology operates without manual intervention, eliminating the need for checkout staff or hardware. It also eliminates shrinkage as a major source of loss for traditional brick and mortar retailers. Customers are charged with whatever goods they walk out with, even if they try to hide the fact from the store’s extensive computer vision camera systems. The untapped potential of voice It’s taken a while to get here.

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Even so, robots won’t be replacing humans altogether anytime soon. From self-checkout to no checkout So, we come to Amazon Go, which first opened its doors in Seattle in 2018. The computer vision-equipped, AI-powered store uses Amazon’s patented ‘Just Walk Out’ technology to enable customers to literally walk out with their goods without having to go through any checkout process at all. Customers have to scan their Amazon Go app to gain entry and register a form of payment that is charged when they leave according to what the computer vision systems detect they have taken from the shelves. Its beauty is Amazon knows precisely who is in its store and what they do at every move, while the technology eliminates shrink.

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Addresses the perennial headache that is online returns, while driving footfall to Kohl’s. We expect this to be rolled out internationally. No 2018 Amazon Go Retail First checkout-free store. Shoppers scan their Amazon app to enter. The high-tech convenience store uses a combination of computer vision, sensor fusion and deep learning to create a frictionless customer experience. No 2019 and beyond Fashion or furniture stores would be a logical next step NOTE Amazon Go officially opened its doors to the public in 2018 SOURCE Amazon; author research as of June 2018 However, it was Amazon’s rather ironic launch of physical bookstores in 2015 that marked a genuine shift in strategy, as this was the first time Amazon mimicked digital merchandising and pricing in a physical setting.

pages: 175 words: 54,755

Robot, Take the Wheel: The Road to Autonomous Cars and the Lost Art of Driving by Jason Torchinsky

autonomous vehicles, barriers to entry, call centre, commoditize, computer vision, connected car, DARPA: Urban Challenge, data science, driverless car, Elon Musk, en.wikipedia.org, interchangeable parts, job automation, Philippa Foot, ransomware, self-driving car, sensor fusion, side project, Tesla Model S, trolley problem, urban sprawl

He was able to prove that with the delay, the rover would not be reliably controllable at speeds over 0.2 mph, which is, as you can guess, really, really slow.¹⁷ To overcome this, experiments began to attempt to give the cart its own ability to “see” its environment, detect obstacles, and take steps to avoid them. This was the birth of nearly all computer vision systems employed by autonomous vehicles (and, really, any robot that uses some manner of camera-based synthetic vision) today. By 1964 the cart had been re-outfitted with a low-power television transmitter that broadcast TV signals to a PDP-6¹⁸ computer to process the images. With this setup, which I’m dramatically simplifying here, the cart was able to visually follow a high-contrast white line on the road at about 0.8 mph. This was a big deal, as it represented real computer vision controlling a moving machine, even if it was quite crude.

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Extending from the two engine air scoops on each side of the nose of the car are probes or antennas which pick up wave impulses from the conductor strip in the center of the control lane.¹³ Of course, none of these things actually worked, but it is interesting to see how the very sticky problems of computer vision could be avoided if fully autonomous operation is limited to areas where an infrastructure has been built to guide the cars. No mention is made of obstacle avoidance or anything like that; presumably, it is the job of the singing gentlemen in the control towers to make sure everything is running smoothly and to warn drivers to stop if they’re approaching a broken-down vehicle or a coyote on the road or something else they don’t want to barrel through.

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The cart was eventually able to navigate a chair-filled room in about five hours, and while it may be tempting to laugh at that idea now, I can think of plenty of times I’ve not been able to navigate a chair-filled room without running into half the chairs and looking like an idiot. 1977: Tsukuba Mechanical Engineering Lab, Japan Arguably the first fully autonomous, computer-vision-controlled car was shown in 1977 by the Tsukuba Mechanical Engineering Lab, in Japan. The project, headed by Sadayuki Tsugawa,¹⁹ modified a full-size car to follow special white road markings and was able to drive at speeds of nearly 20 mph. While still essentially a follower of specially contrived external visual guides, the fact that this technology was implemented in a full-size car driving at a reasonable speed (compared to, say, the Stanford Cart) and using computer-interpreted visual information made this a significant milestone. 1980s: Ernst Dickmanns: The Man Who Made Cars See If the overall concept of vehicles driven via true computer “vision” can be said to have a father, that father would have a German accent and a hilarious last name: Dickmanns.

pages: 161 words: 39,526

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

The fields of pathology and radiology, both of which rely largely on trained human eyes to spot anomalies, are being revolutionized by advancements in computer vision. Pathology is especially subjective, with studies showing that two pathologists assessing the same slide of biopsied tissue will only agree about 60 percent of the time.(25) Researchers at Houston Methodist Research Institute in Texas announced an AI system for diagnosing breast cancer that utilizes computer vision techniques optimized for medical image recognition,(26) which interpreted patient records with a 99 percent accuracy rate.(27) In radiology, 12.1 million mammograms are performed annually in the United States, but half yield false positive results, which means that one in two healthy women may be wrongly diagnosed with cancer.

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Much media attention has been focused on deep learning, and an increasing number of sophisticated technology companies have successfully implemented deep learning for enterprise-scale products. Google replaced previous statistical methods for machine translation with neural networks to achieve superior performance.(4) Microsoft announced in 2017 that they had achieved human parity in conversational speech recognition.(5) Promising computer vision startups like Clarifai employ deep learning to achieve state-of-the-art results in recognizing objects in images and video for Fortune 500 brands.(6) While deep learning models outperform older machine learning approaches to many problems, they are more difficult to develop because they require robust training of data sets and specialized expertise in optimization techniques.

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We’ve probably all run into organizations that continue to insist on handwritten forms that some poor intern must then painstakingly enter into legacy databases. The need for manual entry creates a bottleneck and increases the risk of error, especially as the prevalence of keyboards has sent handwriting legibility into a steep decline. To deal with this problem, HyperScience utilizes advanced computer vision techniques to scan and process handwritten forms to eliminate the data entry bottleneck. Once a form is scanned, their software cleans the image, matches the format to the correct form, then extracts and stores the relevant information in the correct database. General Operations Most companies have tons of repetitive digital workflows.

Artificial Whiteness by Yarden Katz

affirmative action, AI winter, algorithmic bias, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, benefit corporation, Black Lives Matter, blue-collar work, Californian Ideology, Cambridge Analytica, cellular automata, Charles Babbage, cloud computing, colonial rule, computer vision, conceptual framework, Danny Hillis, data science, David Graeber, deep learning, DeepMind, desegregation, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Ferguson, Missouri, general purpose technology, gentrification, Hans Moravec, housing crisis, income inequality, information retrieval, invisible hand, Jeff Bezos, Kevin Kelly, knowledge worker, machine readable, Mark Zuckerberg, mass incarceration, Menlo Park, military-industrial complex, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, pattern recognition, phenotype, Philip Mirowski, RAND corporation, recommendation engine, rent control, Rodney Brooks, Ronald Reagan, Salesforce, Seymour Hersh, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, speech recognition, statistical model, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, surveillance capitalism, talking drums, telemarketer, The Signal and the Noise by Nate Silver, W. E. B. Du Bois, Whole Earth Catalog, WikiLeaks

Brian Wallis, “Black Bodies, White Science: Louis Agassiz’s Slave Daguerreotypes,” American Art 9, no. 2 (1995): 39–61. 33. Anh Nguyen, Jason Yosinski, and Jeff Clune, “Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 427–36. 34. Oriol Vinyals et al., “Show and Tell: A Neural Image Caption Generator,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 3156–64. 35. The incident occurred during a protest in the village of Nabi Saleh in the West Bank in the summer of 2015. See “A Perfect Picture of the Occupation,” editorial, Haaretz, August 31, 2015. 36.

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AI practitioners have steadily produced racial, gendered, and classed models of the self that both reflect and recharge these projects. This raises the question: Can something like what AI purports to be—an attempt to understand ourselves, our minds, and behavior in computational terms—be pursued differently? Might there be radical computational visions that shed AI’s epistemic forgeries and oppose the imperial and capitalist interests that have sustained that endeavor? Since AI has been contested from the start, even by insiders, can we find alternatives in these critiques? Or is the endeavor, by virtue of its framing around computation, or its place in a scientific tradition intimately connected to state power and capital, so compromised that it will merely reproduce AI’s flaws?

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., “Building Machines That Learn and Think Like People,” Behavioral and Brain Sciences 40, no. E253 (2016): 1–101. 29. Lake et al., 8. 30. DeepMind, “AlphaGo Zero.” 31. Microsoft COCO is described in Tsung-Yi Lin et al., “Microsoft COCO: Common Objects in Context,” in Proceedings of the European Conference on Computer Vision, 2014, 740–55. For a similar data set, the “Caltech” objects, see Fei-Fei Li, Rob Fergus, and Pietro Perona, “One-Shot Learning of Object Categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 4 (2006): 594–611. 32. Brian Wallis, “Black Bodies, White Science: Louis Agassiz’s Slave Daguerreotypes,” American Art 9, no. 2 (1995): 39–61. 33.

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Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Robotics, Amazon Web Services, Andy Rubin, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Cambridge Analytica, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, fake news, Firefox, fulfillment center, gigafactory, Google Chrome, growth hacking, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Nick Bostrom, off-the-grid, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, work culture , zero-sum game

Facebook, at the time, was unable to build features like tag suggestions on its own, because identifying faces in photos required expertise in machine learning that Facebook did not have. Hirsch and Shochat, meanwhile, were applying computer vision brilliantly within Zuckerberg’s own product, and he was eager to learn more about what they were doing. “Zuck was curious from the get-go,” Shochat told me. “He knew that something interesting was going on there, and he wanted to be close to such technology.” For the next ninety minutes, Zuckerberg interrogated Hirsch and Shochat about the future of computer vision and facial recognition. And as the conversation wrapped, his focus turned to acquisition. “If it makes sense, we should make this work,” he said before walking out.

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Go has no lines, no waiting, and no cashiers. It feels like the future, and it very well might be. Go is powered by some impressive technology, much of which you can see by looking up. Cameras and sensors line its ceilings, pointing every which way to capture your body and its movements as you walk its aisles. Using computer vision (a subset of machine learning), Go figures out who you are, what you’ve taken, and what you’ve put back. Then it charges you. The store is almost always accurate, as I’ve found in my various attempts to trick it. No matter the method, be it concealing products or running in and out at my top speed (sixteen seconds total visit time), Go has never missed an item.

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When a robot passes over a code, it’s instructed either to wait or to move to the next QR code, where it’s given more instructions. The system knows how fast each picker and stower works, and automatically sends more robots to the faster workers and fewer to the slower ones. At another FC I visited, in Kent, Washington, the robots stop in front of cameras that scan the racks, assess the amount of space left (using computer vision), and determine when they should be sent back for more stowing (or sent to a problem-solving team when items look askew). As pickers work, some compete voluntarily in “FC games,” which rank them on speed. The employees I met at the two FCs I visited seemed to be in good spirits and happy to work at Amazon.

Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

Let’s see what the ML community as a whole (all academics and quite a few industry practitioners) has achieved on probably the most widely used dataset in computer vision today. That Why we need to analyze the ML pipeline 127 dataset is a Modified National Institute of Standards and Technology (MNIST) dataset [102], which consists of 60,000 handwritten digits from 0 to 9.2 The MNIST dataset was often used to benchmark computer vision algorithms. The AI community has tracked the accuracy of the various computer vision algorithms on the MNIST dataset. According to LeCun et al. [102] and Benenson [104], algorithm improvements by the community between 1998 and 2013 resulted in the accuracy of digit recognition improving by only 2.19%—the error rate declined from 2.4% [102] to 0.21% [104].

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Professional interpretation is also costly and something that your hospital would save money on if you could make an alternate system that’s helpful when diagnosing eye diseases. This use case is worth further investigation. Further research from your data science team shows that there has been significant progress in the application of computer vision to medical diagnosis. You find that Google’s team created an AI capable of diagnosing cases of moderate to severe diabetic retinopathy [49]. You have enough data from past optometry exams that you can train AI on that data. To make sure the Sense/Analyze/React loop is applicable in this use case, you need to cover only the Sense part.

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According to LeCun et al. [102] and Benenson [104], algorithm improvements by the community between 1998 and 2013 resulted in the accuracy of digit recognition improving by only 2.19%—the error rate declined from 2.4% [102] to 0.21% [104]. Although the 2.19% better accuracy for digit recognition the community has achieved on MNIST is a significant improvement for computer vision algorithms, how relevant is 2.19% to us? We have to remember that, in our use case, 5% of our data is wrong. Moreover, the improvement in vision algorithms came at a significant cost. Some of the algorithms used to achieve that 2.19% improvement were the result of the best efforts of the entire ML community!

pages: 252 words: 74,167

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

What the X stood for depended on who was doing the asking. One researcher wrote a checkers program capable of beating most amateurs, including himself. Another breakthrough included a perceptive AI able to rearrange coloured, differently shaped blocks on a table using a robotic hand: an astonishing feat in computer vision. A program called SAINT proved able to solve calculus integration problems of the level found on a first-year college course. Another, called ANALOGY, did the same for the geometric questions found in IQ tests, while STUDENT cracked complex algebra story conundrums such as: ‘If the number of customers Tom gets is twice the square of 20 per cent of the number of advertisements he runs, and the number of advertisements he runs is 45, what is the number of customers Tom gets?

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The garage door had to be left open for ventilation. It must have seemed innocuous at the time, but over the next two decades, Larry Page and Sergey Brin’s company would make some of the biggest advances in AI history. These spanned fields including machine translation, pattern recognition, computer vision, autonomous robots and far more, which AI researchers had struggled with for half a century. Virtually none of it was achieved using Good Old-Fashioned AI. The company’s name, of course, was Google. CHAPTER 2 Another Way to Build AI IT IS 2014 and, in the Google-owned London offices of an AI company called DeepMind, a computer whiles away the hours by playing an old Atari 2600 video game called Breakout.

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In a strikingly prescient 1958 article, marred by the hyperbolic title, ‘Human Brains Replaced?’, a writer for Science magazine gushed: ‘Perceptrons may eventually be able to learn, make decisions, and translate languages.’ A New Yorker article meanwhile quoted Rosenblatt as saying perceptrons should prove capable of telling ‘the difference between a dog and a cat’ using computer vision. In 1960, Rosenblatt oversaw the creation of an ‘alpha-perceptron’ computer called the MARK I, for which he received sponsorship from the Information Systems Branch of the Office of Naval Research. It became one of the first computers in history to be able to acquire new skills through trial and error.

pages: 326 words: 74,433

Do More Faster: TechStars Lessons to Accelerate Your Startup by Brad Feld, David Cohen

An Inconvenient Truth, augmented reality, computer vision, corporate governance, crowdsourcing, deal flow, disintermediation, fail fast, hiring and firing, hockey-stick growth, Inbox Zero, independent contractor, Jeff Bezos, Kickstarter, knowledge worker, Lean Startup, lolcat, Ray Kurzweil, recommendation engine, risk tolerance, Silicon Valley, Skype, slashdot, social web, SoftBank, software as a service, Steve Jobs, subscription business

We didn't need anyone else's money. We already had what we needed, which was a core competency in computer vision, a technology area that we believed had incredible intrinsic value. In fact, we were borderline arrogant about it—we hypothesized that we could just hack off a tiny chunk of this technology and turn it into revenue. We tested this, stayed small, and launched ClearCam on February 3, 2009. ClearCam is a $10 iPhone application that captures high-resolution photos with the aid of computer vision. ClearCam was popular and we immediately were cash-flow positive. Near-death averted and hypothesis reinforced.

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In addition to WordPress, YouTube, Google Apps, and Skype, the TechStars companies told us that they routinely use the following free or very inexpensive products: Balsamiq for screen prototyping DimDim for web meetings DropBox for file storage and sharing Evernote for organizing tidbits of information Gist for keeping on top of your contacts GitHub for source code sharing Jing for screencasting MogoTest (TechStars 2009) for making sure your applications look great on every browser Pivotal Tracker for issue tracking SendGrid (TechStars 2009) for e-mail delivery SnapABug (TechStars 2009) for chatting with customers who visit your web site Twilio for audio conferencing and phone and SMS services Vanilla (TechStars 2009) for hosting a great forum for your community Be Tiny Until You Shouldn't Be Jeffrey Powers Jeffrey is a co-founder of Occipital, which uses state of the art computer vision in mobile applications for faster information capture and retrieval. On June 23, 2010, Occipital sold its RedLaser product line to eBay. Occipital remains an independent company. In December 2008, the situation for Occipital was dire. We had a $10,000 deferred legal bill, dried up personal bank accounts, and no revenue.

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That led to a near-merger with a group of seasoned entrepreneurs and another failed attempt at getting investors excited. The chip on our shoulder got bigger and led us to hack off a slightly larger chunk of technology than ClearCam. This turned into RedLaser, the first iPhone barcode scanner that really worked because it used computer vision to compensate for blur. The response to our new product blew us away and RedLaser claimed a position in the top five paid applications on the iPhone App Store for many months. Today, we're more confident than ever about the technology area we have focused on, we have a growing reputation with consumers, and we have the money to stop worrying about the premature death of the company.

Mastering Machine Learning With Scikit-Learn by Gavin Hackeling

backpropagation, computer vision, constrained optimization, correlation coefficient, data science, Debian, deep learning, distributed generation, iterative process, natural language processing, Occam's razor, optical character recognition, performance metric, recommendation engine

Another disadvantage of the hashing trick is that the resulting model is more difficult to inspect, as the hashing function cannot recall what input token is mapped to each element of the feature vector. [ 62 ] www.it-ebooks.info Chapter 3 Extracting features from images Computer vision is the study and design of computational artifacts that process and understand images. These artifacts sometimes employ machine learning. An overview of computer vision is far beyond the scope of this book, but in this section we will review some basic techniques used in computer vision to represent images in machine learning problems. Extracting features from pixel intensities A digital image is usually a raster, or pixmap, that maps colors to coordinates on a grid.

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A model trained on our basic feature representations might not be able to recognize the same zero if it were shifted a few pixels in any direction, enlarged, or rotated a few degrees. Furthermore, learning from pixel intensities is itself problematic, as the model can become sensitive to changes in illumination. For these reasons, this representation is ineffective for tasks that involve photographs or other natural images. Modern computer vision applications frequently use either hand-engineered feature extraction methods that are applicable to many different problems, or automatically learn features without supervision problem using techniques such as deep learning. We will focus on the former in the next section. [ 64 ] www.it-ebooks.info Chapter 3 Extracting points of interest as features The feature vector we created previously represents every pixel in the image; all of the informative attributes of the image are represented and all of the noisy attributes are represented too.

pages: 661 words: 156,009

Your Computer Is on Fire by Thomas S. Mullaney, Benjamin Peters, Mar Hicks, Kavita Philip

"Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, algorithmic bias, AlphaGo, AltaVista, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, An Inconvenient Truth, Asilomar, autonomous vehicles, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 737 MAX, book value, British Empire, business cycle, business process, Californian Ideology, call centre, Cambridge Analytica, carbon footprint, Charles Babbage, cloud computing, collective bargaining, computer age, computer vision, connected car, corporate governance, corporate social responsibility, COVID-19, creative destruction, cryptocurrency, dark matter, data science, Dennis Ritchie, deskilling, digital divide, digital map, don't be evil, Donald Davies, Donald Trump, Edward Snowden, en.wikipedia.org, European colonialism, fake news, financial innovation, Ford Model T, fulfillment center, game design, gentrification, George Floyd, glass ceiling, global pandemic, global supply chain, Grace Hopper, hiring and firing, IBM and the Holocaust, industrial robot, informal economy, Internet Archive, Internet of things, Jeff Bezos, job automation, John Perry Barlow, Julian Assange, Ken Thompson, Kevin Kelly, Kickstarter, knowledge economy, Landlord’s Game, Lewis Mumford, low-wage service sector, M-Pesa, Mark Zuckerberg, mass incarceration, Menlo Park, meta-analysis, mobile money, moral panic, move fast and break things, Multics, mutually assured destruction, natural language processing, Neal Stephenson, new economy, Norbert Wiener, off-the-grid, old-boy network, On the Economy of Machinery and Manufactures, One Laptop per Child (OLPC), packet switching, pattern recognition, Paul Graham, pink-collar, pneumatic tube, postindustrial economy, profit motive, public intellectual, QWERTY keyboard, Ray Kurzweil, Reflections on Trusting Trust, Report Card for America’s Infrastructure, Salesforce, sentiment analysis, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, smart cities, Snapchat, speech recognition, SQL injection, statistical model, Steve Jobs, Stewart Brand, tacit knowledge, tech worker, techlash, technoutopianism, telepresence, the built environment, the map is not the territory, Thomas L Friedman, TikTok, Triangle Shirtwaist Factory, undersea cable, union organizing, vertical integration, warehouse robotics, WikiLeaks, wikimedia commons, women in the workforce, Y2K

These include but are not limited to: • A variety of what I would describe as “first-order,” more rudimentary, blunt tools that are long-standing and widely adopted, such as keyword ban lists for content and user profiles, URL and content filtering, IP blocking, and other user-identifying mechanisms;13 • More sophisticated automated tools such as hashing technologies used in products like PhotoDNA (used to automate the identification and removal of child sexual exploitation content; other engines based on this same technology do the same with regard to terroristic material, the definitions of which are the province of the system’s owners);14 • Higher-order AI tools and strategies for content moderation and management at scale, examples of which might include: ◦ Sentiment analysis and forecasting tools based on natural language processing that can identify when a comment thread has gone bad or, even more impressive, when it is in danger of doing so;15 ◦ AI speech-recognition technology that provides automatic, automated captioning of video content;16 ◦ Pixel analysis (to identify, for example, when an image or a video likely contains nudity);17 ◦ Machine learning and computer vision-based tools deployed toward a variety of other predictive outcomes (such as judging potential for virality or recognizing and predicting potentially inappropriate content).18 Computer vision was in its infancy when I began my research on commercial content moderation. When I queried a computer scientist who was a researcher in a major R&D site at a prominent university about the state of that art some years ago and how it might be applied to deal with user-generated social media content at scale, he gestured at a static piece of furniture sitting inside a dark visualization chamber and remarked, “Right now, we’re working on making the computer know that that table is a table.”

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Historically, these training sets have treated the white male adult face as a default against which computer vision algorithms are trained.37 Databases of missing and abused children held by NCMEC in the US disproportionately contain images of white children, and nonwhite children are statistically less likely to be reported as missing or have extensive case files of data. In examining how image-recognition software and content reviewers “see” abuse images, I consider how skin tone, as a category for detection, is made manifest as digital racial matter.38 In the course of my fieldwork, computer vision researchers often remark that they earnestly want to address the “problem” of underdetectability, or even undetectability, of darker skin tones, Black and East Asian features, and younger ages.

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Different actors—be they law enforcement investigators, digital forensic startups, social media company reviewer teams, or the outsourced content review workers who are contracted by larger corporations to make the first reviews of potentially abusive images—learn to adapt shared ways of seeing images. Cristina Grasseni emphasizes that “one never simply looks. One learns how to look.”28 Such ways of seeing,29 distributed and honed across human and computer vision, become manifest as ways of accessing the world and managing it. Seeing Like an Image-Recognition Algorithm The image forensics software used by NCMEC searches through an in-house database of known images of child pornography to see if the new image might be similar, or even identical, to an image that has already gone through that system.

pages: 263 words: 81,527

The Mind Is Flat: The Illusion of Mental Depth and the Improvised Mind by Nick Chater

Albert Einstein, battle of ideas, behavioural economics, classic study, computer vision, Daniel Kahneman / Amos Tversky, deep learning, double helix, Geoffrey Hinton, Henri Poincaré, Jacquard loom, lateral thinking, loose coupling, machine translation, speech recognition, tacit knowledge

Yet progress seemed slower, and the challenges far greater, than had been imagined. By the 1970s, serious doubts began to set in; by the 1980s, the programme of mining and systematizing knowledge started to grind to a halt. Indeed, the project of modelling human intelligence has since been quietly abandoned, in favour of specialist projects in computer vision, speech-processing, machine translation, game-playing, robotics and self-driving vehicles. Artificial intelligence since the 1980s has been astonishingly successful in tackling these specialized problems. This success has come, though, from completely bypassing the extraction of human knowledge into common-sense theories.

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Neisser’s first intriguing finding was that, from the start, people found this apparently substantial complication of the task no problem at all – they were easily able to lock their attention onto one stream of video and ignore the other. The brain was able to monitor one video almost as if the other superimposed video was not there at all. By contrast, for current computer vision systems, ‘unscrambling’ the scenes, and attending to one and ignoring the other, would be enormously challenging. But Neisser’s second finding was the real surprise. He added a highly salient, and unexpected, event during the course of the video: a woman carrying a large umbrella strolled into view among the players, walked right across the scene, before disappearing from view.

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To the extent that the brain focuses on just a few constraints, satisfies them as well as possible, and then looks at the remaining constraints, there is a real danger of heading up a cul-de-sac – the next constraints may not fit our tentative interpretation at all, and it will then have to be abandoned. The task of simultaneously matching a huge number of clues and constraints is just what the brain’s cooperative style of computation is wonderfully good at. But these are the calculations that our imagined computer vision program would have to carry out – and which, we can conjecture, the brain must carry out in order to create Idesawa’s spiky sphere. It turns out, in fact, that the brain may be particularly well adapted to solving problems in which large numbers of constraints must be satisfied simultaneously.

pages: 288 words: 86,995

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

DEEP LEARNING AND THE FUTURE OF ARTIFICIAL INTELLIGENCE 1. Martin Ford, Interview with Geoffrey Hinton, in Architects of Intelligence: The Truth about AI from the People Building It, Packt Publishing, 2018, pp. 72–73. 2. Matt Reynolds, “New computer vision challenge wants to teach robots to see in 3D,” New Scientist, April 7, 2017, www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/. 3. Ashlee Vance, “Silicon Valley’s latest unicorn is run by a 22-year-old,” Bloomberg Businessweek, August 5, 2019, www.bloomberg.com/news/articles/2019-08-05/scale-ai-is-silicon-valley-s-latest-unicorn. 4.

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This data gusher would soon intersect with the latest machine learning algorithms to enable a revolution in artificial intelligence. One of the most consequential new data troves resulted from the efforts of a young computer science professor at Princeton University. Fei-Fei Li, whose work was focused on computer vision, realized that teaching machines to make visual sense of the real world would require a comprehensive teaching resource with properly labeled examples showing many variations of people, animals, buildings, vehicles, objects—and just about anything else one might encounter. Over a two-and-a-half-year period, she set out to give titles to more than three million images across over 5,000 categories.

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Facial recognition, in particular, is being widely deployed in the United States and other democratic countries and has already led to intense debate and accusations of bias and misuse. These issues will become only more fraught as the technology continues to become more powerful and—unless it is strictly regulated—ubiquitous. CHINA’S LEAP TO THE FOREFRONT OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT In June 2018, a major conference on computer vision was held in Salt Lake City, Utah. In the six years since the famous 2012 ImageNet competition, the field of machine vision had advanced dramatically, and researchers were now focused on solving far more difficult problems. One of the highlights of the conference was the Robust Vision Challenge.

pages: 416 words: 112,268

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

Roughly speaking, satellites image the entire world every day at an average resolution of around fifty centimeters per pixel. At this resolution, every house, ship, car, cow, and tree on Earth is visible. Well over thirty million full-time employees would be needed to examine all these images;25 so, at present, no human ever sees the vast majority of satellite data. Computer vision algorithms could process all this data to produce a searchable database of the whole world, updated daily, as well as visualizations and predictive models of economic activities, changes in vegetation, migrations of animals and people, the effects of climate change, and so on. Satellite companies such as Planet and DigitalGlobe are busy making this idea a reality.

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It is a straightforward process, using methods such as inductive logic programming,44 to create programs that propose new concepts and definitions in order to identify theories that are both accurate and concise. At present, we know how to do this for relatively simple cases, but for more complex theories the number of possible new concepts that could be introduced becomes simply enormous. This makes the recent success of deep learning methods in computer vision all the more intriguing. The deep networks usually succeed in finding useful intermediate features such as eyes, legs, stripes, and corners, even though they are using very simple learning algorithms. If we can understand better how this happens, we can apply the same approach to learning new concepts in the more expressive languages needed for science.

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It searches for up to six hours in a given geographical region for any target that meets a given criterion and then destroys it. The criterion could be “emits a radar signal resembling antiaircraft radar” or “looks like a tank.” By combining recent advances in miniature quadrotor design, miniature cameras, computer vision chips, navigation and mapping algorithms, and methods for detecting and tracking humans, it would be possible in fairly short order to field an antipersonnel weapon like the Slaughterbot13 shown in figure 7 (right). Such a weapon could be tasked with attacking anyone meeting certain visual criteria (age, gender, uniform, skin color, and so on) or even specific individuals based on face recognition.

pages: 196 words: 61,981

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside by Xiaowei Wang

4chan, AI winter, Amazon Web Services, artificial general intelligence, autonomous vehicles, back-to-the-land, basic income, Big Tech, bitcoin, blockchain, business cycle, cloud computing, Community Supported Agriculture, computer vision, COVID-19, cryptocurrency, data science, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, Donald Trump, drop ship, emotional labour, Ethereum, ethereum blockchain, Francis Fukuyama: the end of history, Garrett Hardin, gig economy, global pandemic, Great Leap Forward, high-speed rail, Huaqiangbei: the electronics market of Shenzhen, China, hype cycle, income inequality, informal economy, information asymmetry, Internet Archive, Internet of things, job automation, Kaizen: continuous improvement, Kickstarter, knowledge worker, land reform, Marc Andreessen, Mark Zuckerberg, Menlo Park, multilevel marketing, One Laptop per Child (OLPC), Pearl River Delta, peer-to-peer lending, precision agriculture, QR code, ride hailing / ride sharing, risk tolerance, Salesforce, Satoshi Nakamoto, scientific management, self-driving car, Silicon Valley, Snapchat, SoftBank, software is eating the world, surveillance capitalism, TaskRabbit, tech worker, technological solutionism, the long tail, TikTok, Tragedy of the Commons, universal basic income, vertical integration, Vision Fund, WeWork, Y Combinator, zoonotic diseases

And I am struck by her relationship to machines, and to her own body. In the same way hardware can have different enclosures, she says, she sees her own body as an enclosure. She performs body modification because she believes “you have to give the computer what it wants.” She anticipates a world of computer vision algorithms on video platforms that increase rankings based on the content of the video, with platforms placing “attractive women” first in search results. Naomi wants to show up first. In an ideal universe, she says, she would have a shop at Huaqiangbei, the famed electronics market of Shenzhen, known as “the market of the future.”

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Each house is perfectly numbered, some with hyphens like 1-1 or 684-1. When he clicks on the house, a list of residents pops up in a small window. I ask him how the numbers are so precise, in the absence of formal addresses, and how they get the information about the residents. In my mind, I imagine some sophisticated computer vision tool that looks at the aerial image, calculates the boundary of the house, and then assigns it a number. I imagine that the city has sensors and surveillance cameras to capture how many people leave the house. I also imagine that the surveillance cameras would know the face and personal ID number of each resident, perhaps tracked all the way from their tiny rural village through the numerous cameras I see everywhere—in train stations, at vending machines, on the street.

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Face recognition is a system with numerous parts, and each part is the domain of a private company—whether the one that owns the surveillance cameras used, the algorithm, or the computational power rented out on a server. The Face++ showroom has plush white carpeting and shiny white walls with inset screens. One wall features real-time camera footage from outside the showroom, in the office and outside the building. The display showcases how fast and precise Face++ computer vision algorithms are—as someone walks by the building, the algorithm detects their blue pants and umbrella. There’s also a hidden camera that you can stand in front of and the algorithm instantly classifies your age and gender. I am for some reason characterized by the Face++ algorithm as a twenty-seven-year-old male.

pages: 282 words: 63,385

Attention Factory: The Story of TikTok and China's ByteDance by Matthew Brennan

Airbnb, AltaVista, augmented reality, Benchmark Capital, Big Tech, business logic, Cambridge Analytica, computer vision, coronavirus, COVID-19, deep learning, Didi Chuxing, Donald Trump, en.wikipedia.org, fail fast, Google X / Alphabet X, growth hacking, ImageNet competition, income inequality, invisible hand, Kickstarter, Mark Zuckerberg, Menlo Park, natural language processing, Netflix Prize, Network effects, paypal mafia, Pearl River Delta, pre–internet, recommendation engine, ride hailing / ride sharing, Sheryl Sandberg, Silicon Valley, Snapchat, social graph, Steve Jobs, TikTok, Travis Kalanick, WeWork, Y Combinator

The size of each video’s audience is decided predominantly by the system’s ever-changing and mysterious algorithms, and the key to gaming the system is understanding how these algorithms work. The moment a video is uploaded to TikTok, the clip and its text description are queued up to go through an automated audit. Computer vision is used to analyze and identify elements within the clip, which are then tagged and categorized with keywords. Videos suspected of violating the platform’s content guidelines are flagged for human review. The audit cross-checks the footage against a massive archive for duplicate content. This system is designed to prevent plagiarism, as well as the practice of downloading popular videos, removing the watermark, and reuploading them to a new account.

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This core system for recommending written articles with Toutiao was later adapted and used for short videos with TikTok and Douyin. All these apps make use of the same ByteDance backend recommendation engine system. Videos are more challenging as they tend to be uploaded without keyword tagging or accurate titles and descriptions, making for an exciting computer vision challenge to work out what is actually in the video. The beauty of relying on recommendations to improve engagement is that it creates a virtuous cycle of continual improvement over time, often referred to as a “data network effect.” The more time spent using the app, the more enriched becomes the user profile, which leads to more accurate content matches and better user experience.

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In a presentation 203 discussing the rise of Douyin, Kelly Zhang drew attention to four factors—full-screen high definition, music, special effects filters, and personalized recommendations. S martphone screens had overall gotten much larger with higher definition, greatly improving the video watching experience. Face recognition and augmented reality effects had become commonplace, which allowed for more engaging, fun special effects and filters. Image recognition and computer vision had made very considerable advances, greatly reducing the need for manual audits of inappropriate content and allowing for the classification of videos that lacked meta-data. Most relevant of all were the advances made in big data and recommendation technology in which ByteDance specialized, which leads us nicely into the next reason.

pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

The other was that “at least [Deep Blue] didn’t enjoy beating me.”18 These will be important themes in our discussion of what AI can (and can’t) do for medicine. Even if Deep Blue didn’t have much of anything to do with deep learning, the technology’s day was coming. The founding of ImageNet by Fei-Fei Li in 2007 had historic significance. That massive database of 15 million labeled images would help catapult DNN into prominence as a tool for computer vision. In parallel, natural-language processing for speech recognition based on DNN at Microsoft and Google was moving into full swing. More squarely in the public eye was man versus machine in 2011, when IBM Watson beat the human Jeopardy! champions. Despite the relatively primitive AI that was used, which had nothing to do with deep learning networks and which relied on speedy access to Wikipedia’s content, IBM masterfully marketed it as a triumph of AI.

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Car perception is achieved by a combination of cameras, radar, UDAR (light pulses reflected off objects), and the AI “multi-domain controller” that handles, with DNN, the inputs and the outputs of decisions. Simulating human perceptive capabilities through software is still considered a formidable challenge. Computer vision has since reduced its error rate at identifying a pedestrian from 1 out of 30 frames to 1 in 30 million frames. There’s the power of fleet learning to help, whereby the communication and sharing among all autonomous cars with the same operating system can make them smarter. There are other challenges besides perception, however.

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It’s the combination of AI learning with key human-specific features like common sense that is alluring for medicine. All too commonly we ascribe the capability of machines to “read” scans or slides, when they really can’t read. Machines’ lack of understanding cannot be emphasized enough. Recognition is not understanding; there is zero context, exemplified by Fei-Fei Li’s TED Talk on computer vision. A great example is the machine interpretation of “a man riding a horse down the street,” which actually is a man on a horse sitting high on a statue going nowhere. That symbolizes the plateau we’re at for image recognition. When I asked Fei-Fei Li in 2018 whether anything had changed or improved, she said, “Not at all.”

pages: 49 words: 12,968

Industrial Internet by Jon Bruner

air gap, autonomous vehicles, barriers to entry, Boeing 747, commoditize, computer vision, data acquisition, demand response, electricity market, en.wikipedia.org, factory automation, Google X / Alphabet X, industrial robot, Internet of things, job automation, loose coupling, natural language processing, performance metric, Silicon Valley, slashdot, smart grid, smart meter, statistical model, the Cathedral and the Bazaar, web application

Automotive Google captured the public imagination when, in 2010, it announced that its autonomous cars had already driven 140,000 miles of winding California roads without incident. The idea of a car that drives itself was finally realized in a practical way by software that has strong links to the physical world around it: inbound, through computer vision software that takes in images and rangefinder data and builds an accurate model of the environment around the car; and outbound, through a full linkage to the car’s controls. The entire system is encompassed in a machine-learning algorithm that observes the results of its actions to become a better driver, and that draws software updates and useful data from the Internet.

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Silicon Valley and industry adapting to each other Nathan Oostendorp thought he’d chosen a good name for his new startup: “Ingenuitas,” derived from the Latin for “freely born” — appropriate, he thought, for a company that would be built on his own commitment to open-source software. But Oostendorp, earlier a co-founder of Slashdot, was aiming to bring modern computer vision systems to heavy industry, where the Latinate name didn’t resonate. At his second meeting with a salty former auto executive who would become an advisor to his company, Oostendorp says, “I told him we were going to call the company Ingenuitas, and he immediately said, ‘bronchitis, gingivitis, inginitis.

pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

Grainy SAR images of tanks, artillery, or airplanes parked on a runway often push the limits of human abilities to recognize objects, and historically ATR algorithms have fallen far short of human abilities. The poor performance of military ATR stands in stark contrast to recent advances in computer vision. Artificial intelligence has historically struggled with object recognition and perception, but the field has seen rapid gains recently due to deep learning. Deep learning uses neural networks, a type of AI approach that is analogous to biological neurons in animal brains. Artificial neural networks don’t directly mimic biology, but are inspired by it.

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Within other parts of TensorFlow, though, lie more powerful tools to use existing neural networks or design custom ones, all within reach of a reasonably competent programmer in Python or C++. TensorFlow includes extensive tutorials on convolutional neural nets, the particular type of neural network used for computer vision. In short order, I found a neural network available for download that was already trained to recognize images. The neural network Inception-v3 is trained on the ImageNet dataset, a standard database of images used by programmers. Inception-v3 can classify images into one of 1,000 categories, such as “gazelle,” “canoe,” or “volcano.”

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ROBOTS EVERYWHERE Just because the tools needed to make an autonomous weapon were widely available didn’t tell me how easy or hard it would be for someone to actually do it. What I wanted to understand was how widespread the technological know-how was to build a homemade robot that could harness state-of-the-art techniques in deep learning computer vision. Was this within reach of a DIY drone hobbyist or did these techniques require a PhD in computer science? There is a burgeoning world of robot competitions among high school students, and this seemed like a great place to get a sense of what an amateur robot enthusiast could do. The FIRST Robotics Competition is one such competition that includes 75,000 students organized in over 3,000 teams across twenty-four countries.

pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, augmented reality, autonomous vehicles, Benchmark Capital, Big Tech, bike sharing, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, clean tech, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, data science, deep learning, Deng Xiaoping, Didi Chuxing, digital map, disruptive innovation, Donald Trump, El Camino Real, electricity market, Elon Musk, fake news, family office, fear of failure, fulfillment center, glass ceiling, global supply chain, Great Leap Forward, income inequality, industrial robot, information security, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, Mary Meeker, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, SoftBank, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, stock buybacks, supply-chain management, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, Vision Fund, warehouse automation, WeWork, winner-take-all economy, Y Combinator, young professional

Determined to keep a lead in cutting-edge AI technology, Baidu budgeted $300 million for a second Silicon Valley research lab in 2017, supplementing its first in 2014, and the Beijing-based titan has set up an engineering office in Seattle to focus on autonomous driving and internet security. Baidu has pumped loads of capital into AI startups in the United States with technologies for deep learning, data analytics, and computer vision. See table 2-3. “Having missed out on the social mobile and e-commerce waves of the past few years, Baidu is trying not to repeat the same mistake by going all in on AI, on all fronts,” observes Evdemon of Sinovation Ventures, the Beijing-based venture capital firm headed by AI expert and investor Kai-Fu Lee.

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In China, Baidu, Alibaba, and Tencent are working on similar technologies and racing with the US tech giants to become world leaders in AI. The Ministry of Science and Technology in China has earmarked specialties for each of these Chinese tech titans in its master plan for AI global dominance: Baidu for autonomous driving, Alibaba for smart-city initiatives, and Tencent for computer vision in medical diagnoses. The Chinese government also has designated two startups to lead AI development: SenseTime for facial recognition and iFlytek for speech recognition. Baidu, Alibaba, and Tencent are all powering up in autonomous driving, and each has a specialty focus area in AI. Baidu has its DuerOS line of smart household goods and Apollo, an open platform for self-driving technology solutions, and detoured on the AI journey several years before Google in 2015.

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Baidu’s AI plate includes not only 95 partners in its ecosystem worldwide working on autonomous driving but also investments in AI-related startups in the US: ZestFinance in fintech underwriting, Kitt.ai in conversational language search, TigerGraph in data link analytics, Tiger Computing Solutions in big data, and xPerception in computer vision for self-driving. Tencent has a number of AI partnerships in the health-care space globally and has invested in 12 US startups in AI, including avatar creator ObEN and two in drug discovery based on deep learning, Atomwise and XtalPi. White House Weighs In China hasn’t created any world leaders in cars or semiconductors, but few pooh-pooh its growing ability of AI fundamental technology that touches our everyday lives, from e-commerce fraud detection to systems that can detect cancer; to sensors for self-driving; to robot-powered deliveries, education, and online lending.

pages: 475 words: 134,707

The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt by Sinan Aral

Airbnb, Albert Einstein, algorithmic bias, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, AOL-Time Warner, augmented reality, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Cambridge Analytica, carbon footprint, Cass Sunstein, computer vision, contact tracing, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, data science, death of newspapers, deep learning, deepfake, digital divide, digital nomad, disinformation, disintermediation, Donald Trump, Drosophila, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, experimental subject, facts on the ground, fake news, Filter Bubble, George Floyd, global pandemic, hive mind, illegal immigration, income inequality, Kickstarter, knowledge worker, lockdown, longitudinal study, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, meta-analysis, Metcalfe’s law, mobile money, move fast and break things, multi-sided market, Nate Silver, natural language processing, Neal Stephenson, Network effects, performance metric, phenotype, recommendation engine, Robert Bork, Robert Shiller, Russian election interference, Second Machine Age, seminal paper, sentiment analysis, shareholder value, Sheryl Sandberg, skunkworks, Snapchat, social contagion, social distancing, social graph, social intelligence, social software, social web, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steve Jurvetson, surveillance capitalism, Susan Wojcicki, Telecommunications Act of 1996, The Chicago School, the strength of weak ties, The Wisdom of Crowds, theory of mind, TikTok, Tim Cook: Apple, Uber and Lyft, uber lyft, WikiLeaks, work culture , Yogi Berra

Video accounts for 80 percent of all consumer Internet traffic: Mary Lister, “37 Staggering Video Marketing Statistics for 2018,” Wordstream Blog, June 9, 2019, https://www.wordstream.com/blog/ws/2017/03/08/video-marketing-statistics. Facebook’s “visual cortex”: Manohar Paluri, manager of Facebook’s Computer Vision Group, speaking at the LDV Capital “Vision Summit” in 2017, https://www.ldv.co/blog/2018/4/4/facebook-is-building-a-visual-cortex-to-better-understand-content-and-people. “We’ve pushed computer vision to the next stage”: Joaquin Quiñonero Candela, “Building Scalable Systems to Understand Content,” Facebook Engineering Blog, February 2, 2017, https://engineering.fb.com/ml-applications/building-scalable-systems-to-understand-content/.

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VidMob is a portfolio company of Manifest Capital, the venture fund I started in 2016 with my longtime friend and business partner Paul Falzone. I work directly with VidMob on developing its Agile Creative Studio (ACS), the leading platform for video optimization. The task of video optimization is challenging. It requires a complex combination of machine learning, computer vision, predictive modeling, and optimization. But the basic process is easy to understand. The main goal is to understand, second by second, what’s in a video, what it’s about, its context, feelings, and sentiment, and to compare the presence or absence of these elements to key performance indicators (KPIs) like video view-throughs, retention, drop-off rates, clicks, engagement, brand recognition, and satisfaction.

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The main goal is to understand, second by second, what’s in a video, what it’s about, its context, feelings, and sentiment, and to compare the presence or absence of these elements to key performance indicators (KPIs) like video view-throughs, retention, drop-off rates, clicks, engagement, brand recognition, and satisfaction. By closing the loop of video production, analytics, optimization, and publishing, VidMob can improve its clients’ return on marketing investment. ACS automatically extracts video metadata and performs sentiment analysis. It uses deep learning and computer vision to identify the emotions, objects, logos, people, and words in videos; it can detect facial expressions like delight, surprise, or disgust. It then analyzes how each of these elements corresponds, for instance, to moments when viewers are dropping off from watching the video, and it recommends (and automates) editing that improves retention.

pages: 472 words: 80,835

Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, Boeing 747, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, data science, deep learning, DeepMind, deskilling, disruptive innovation, Donald Shoup, driverless car, edge city, Elon Musk, en.wikipedia.org, fake news, Ford Model T, future of work, General Motors Futurama, hype cycle, invention of the wheel, Just-in-time delivery, Lewis Mumford, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technological determinism, technoutopianism, TED Talk, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, warehouse robotics, Yogi Berra, young professional, zero-sum game, Zipcar

Google announce Car: https://googleblog.blogspot.ie/2010/10/what-were-driving-at.html http://www.makeuseof.com/tag/how-self-driving-cars-work-the-nuts-and-bolts-behind-googles-autonomous-car-program/ https://techcrunch.com/2017/02/12/wtf-is-lidar/ http://velodynelidar.com/hdl-64e.html https://www.bloomberg.com/news/articles/2017-05-04/another-group-of-google-veterans-starts-a-self-driving-technology-company http://www.wsj.com/articles/google-tries-to-make-its-cars-drive-more-like-humans-1443463523 Skill Atrophy: http://cacm.acm.org/magazines/2016/5/201592-the-challenges-of-partially-automated-driving/fulltext http://news.stanford.edu/2016/12/06/taking-back-control-autonomous-car-affects-human-steering-behavior/review/ https://arxiv.org/pdf/1704.07911.pdf https://blogs.nvidia.com/blog/2017/04/27/how-nvidias-neural-net-makes-decisions/ https://www.technologyreview.com/s/601567/tesla-tests-self-driving-functions-with-secret-updates-to-its-customers-cars/ http://www.engadget.com/2016/01/11/ford-is-testing-autonomous-cars-in-the-snow/ http://www.engadget.com/2015/11/13/ford-first-self-driving-mcity-michigan/ https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/disengagement_report_2016 If you want to understand more about the technicalities of SDC Computer Vision, there’s a massively detailed review of computer vision. https://arxiv.org/pdf/1704.05519.pdf Chapter 4 - Safety Cost of crashes: http://www.rmiia.org/auto/traffic_safety/Cost_of_crashes.asp http://www.who.int/mediacentre/factsheets/fs358/en/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2610566/ http://www.nytimes.com/2016/05/23/science/its-no-accident-advocates-want-to-speak-of-car-crashes-instead.html?

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In November 2015, in widely reported comments, an electronics researcher for Volkswagen, said at the Connected Car Expo event in Los Angeles that even a tumbleweed in the road can bring a driverless car to a halt.[268] The point is valid in so far as an unknown object represents a challenge to a driverless car where normally none would exist for a human. I agree that we must plan for the unusual but we must also keep some perspective. Is it really any harm if a car stops for a tumbleweed? As long as the cars all stop, it’s better than the number of accidents caused by drivers avoiding more real obstacles today. More likely though, the computer vision technology will soon evolve to the point where it can identify tumbleweeds and deal appropriately with them. These challenges can either be presented in a sensationalist negative way or as a more matter-of-fact challenge to solve. Stop the Lights! Another often quoted challenge for driverless cars is identifying traffic lights.

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Another frequently mooted challenge is the condition of roads.[286] Between their multitude of sensors, GPS and detailed map data, driverless cars can now cope much better with obscured, weathered, or inaccurate road edges or lane markings than they could at the start of their development. But it remains preferable for them to have good quality markings, and I, for one, would certainly prefer that the computer vision abilities of the car were scanning for any danger rather than exerting effort on simply trying to figure out where the road was supposed to be. We know that trains aren’t very good at running without tracks. But if we want trains, we put tracks down and maintain them. Similarly, we know that cars need roads.

pages: 290 words: 90,057

Billion Dollar Brand Club: How Dollar Shave Club, Warby Parker, and Other Disruptors Are Remaking What We Buy by Lawrence Ingrassia

air freight, Airbnb, airport security, Amazon Robotics, augmented reality, barriers to entry, call centre, commoditize, computer vision, data science, fake news, fulfillment center, global supply chain, Hacker News, industrial robot, Jeff Bezos, Kickstarter, Kiva Systems, Lyft, Mark Zuckerberg, minimum viable product, natural language processing, Netflix Prize, rolodex, San Francisco homelessness, side project, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, supply-chain management, Uber and Lyft, uber lyft, warehouse automation, warehouse robotics, WeWork

They soon chose the name ThirdLove for the bra business, to convey the three attributes—style, feel, and fit—that they wanted women to “love” about their bras (in contrast to most brands, which offered fashion or comfort, but rarely both, and even more rarely all three). Their first hire was Ra’el Cohen, a lingerie designer who had worked for several fashion retailers and had even started her own boutique luxury bra company a few years earlier, though it hadn’t worked out. Their second hire was a NASA engineer who had expertise in computer vision technology, using cameras to collect and analyze digital images, to create a better-fitting bra. Even though Spector had come from the venture capital world, raising money wasn’t easy. After all, this was in the early days of direct-to-consumer brands. But there was another reason: Zak recalls that they pitched their idea to more than fifty VC companies, almost always conference rooms full of men who didn’t understand why women might need a better bra.

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The photo’s digital data—though not the photo itself, to avoid privacy concerns—would then be sent to the company, where an algorithm would translate the two-dimensional image’s data into three-dimensional measurements to recommend the right size. In early 2013, when a version of the bra app (whose computer-vision imaging technology has since received two patents) was ready for testing, ThirdLove placed ads on Craigslist (where most ad postings are free) and invited women to come to the company’s small office in San Francisco wearing their best bra. About one hundred showed up and used the app, and then tried on the prototype bra that Zak and Cohen had created based on standard sizes used in the industry.

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“For example, the way a certain blouse fits tightly on the shoulders and flaunts the upper-arms may provide value to some clients while being an undesirable quality to others … Machines are great at finding and applying these relationships.” As Stitch Fix has developed more sophisticated algorithms, it has incorporated the use of computer vision to help select clothing. “We have our machines look at photos of clothing that customers like (e.g., from Pinterest), and look for visually similar items,” the website explains. And while the company initially sold apparel and accessories made by others, its data scientists in 2017 started designing “Stitch Fix exclusive brand” items by combining different style characteristics from popular clothing.

pages: 339 words: 92,785

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

And Shakey wasn’t even moving about the real world, but a carefully simplified lab version of it, whose surfaces were painted and brightly lit to assist its computer modelling of the environment. If there was too much novelty—something in the wrong place, lighting not quite right—the robot was flummoxed. Researchers would say that its intelligence was ‘brittle’—unable to cope with novelty. Shakey was hugely ambitious, combining distinct research sub-fields in computer vision, language processing and robotics. And like many AI projects of the era, it was funding by the Pentagon, through its Advanced Research Projects Agency (ARPA—a D, for Defense, was added in 1972). The Pentagon was an enthusiastic sponsor of many AI research projects, forging links with centres of excellence at universities around the United States, including Shakey’s home department at Stanford, but also MIT and Carnegie Mellon: today these remain leading departments in the field.

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But now there was a new challenge—how to make use of the new technology landscape, with Google, Amazon and Facebook sponsoring cutting-edge research. In 2017, the Pentagon stood up an ‘algorithmic warfare cross functional team’, known as Project Maven. The team would consolidate ‘all initiatives that develop, employ, or field artificial intelligence, automation, machine learning, deep learning, and computer vision algorithms’.12 It was a small team, initially, but would grow rapidly. And one of its main contractors on image recognition? Google, of course. The stage was set for the arrival of deep warbots. Hype or hope? The deep learning revolution has produced a new wave of AI hype, only some of which is justified.

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Democracies and authoritarians sometimes develop exactly the same technology, see Collingridge, John, and Rob Watts, ‘Huawei buys stake in UK spy firm Vision Semantics’, The Times, 19 July 2020, https://www.thetimes.co.uk/article/huawei-buys-stake-in-uk-spy-firm-vision-semantics-65t98vdz0. 25. Thys, Simen, Wiebe Van Ranst, and Toon Goedemé. ‘Fooling automated surveillance cameras: adversarial patches to attack person detection’, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 49–55. 2019. 26. Ilyas, Andrew, Logan Engstrom, Anish Athalye, and Jessy Lin. ‘Black-box adversarial attacks with limited queries and information’, arXiv preprint arXiv:1804.08598 (2018). 27. For the British Army, see chapter 6, ‘Mission Command’, of Land Warfare Development Centre, ‘Land Operations,’ Army Doctrine Publication AC 71940, 31 March 2017, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/605298/Army_Field_Manual__AFM__A5_Master_ADP_Interactive_Gov_Web.pdf.

When Computers Can Think: The Artificial Intelligence Singularity by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann, Michelle Estes

3D printing, Abraham Maslow, AI winter, air gap, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, backpropagation, blue-collar work, Boston Dynamics, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, Computing Machinery and Intelligence, create, read, update, delete, cuban missile crisis, David Attenborough, DeepMind, disinformation, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Nick Bostrom, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, Recombinant DNA, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

But today there are several quite effective translation engines. They do not produce human quality output, but they are certainly very usable. Computer vision is another technology that is surprisingly difficult to implement. Yet today’s computers regularly review the vast quantity of recorded surveillance video. People can be recognized and tracked over time, and this data can then be stored and analyzed. The Curiosity rover on Mars uses computer vision technology to navigate over the terrain without getting stuck. None of the above involves human-level reasoning, but they address difficult problems that form a basis for that reasoning.

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Traditional AGI systems were strictly symbolic systems that communicated with the outside world via people typing on a teletype. These machines had very little access to the “real” world — they were like a brain in a vat that could only send and receive written letters to other people. Today work on computer vision and robotics has progressed enormously. As robots leave the factory, they will indeed be both able to and required to see and touch the real world. This will produce much richer symbolic and pre-symbolic models that should provide plenty of “intentionality”. This is also known as the symbol grounding problem and will be discussed in part II of this book.

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A naively applied support vector machine produced a very reasonable error rate of 1.1%. A tuned version was then built that centered the image and focused the kernels on nearby pixels. This produced an impressive 0.56% error rate. A shape-matching approach explicitly looked at edges between black and white pixels in a similar manner to computer vision systems. It then attempted to match corresponding features of each pair of images using nearest neighbour clustering. This produced an error rate of 0.63%. Decision tables have also been effective in image analysis, although their effectiveness largely depends on the tests that can be applied to the image.

pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AI winter, air gap, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, business process, call centre, Cambridge Analytica, Celtic Tiger, Charlie Hebdo massacre, chief data officer, cloud computing, computer vision, corporate social responsibility, data science, deep learning, digital divide, disinformation, Donald Trump, Eben Moglen, Edward Snowden, en.wikipedia.org, Hacker News, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Kevin Roose, Laura Poitras, machine readable, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, Nick Bostrom, off-the-grid, operational security, opioid epidemic / opioid crisis, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, Salesforce, school vouchers, self-driving car, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, surveillance capitalism, tech worker, The Rise and Fall of American Growth, Tim Cook: Apple, Wargames Reagan, WikiLeaks, women in the workforce

Paul Scharre, a former US defense official working at a think tank, brings to life increasingly pertinent questions in his book, Army of None: Autonomous Weapons and the Future of War.19 As he illustrates, a central question is not just when but how computers should be empowered to launch a weapon without additional human review. On the one hand, even though a drone with computer vision and facial recognition might exceed human accuracy in identifying a terrorist on the ground, this doesn’t mean that military officials need to or should take personnel and common sense out of the loop. On the other hand, if dozens of missiles are launched at a naval flotilla, the Aegis combat system’s antimissile defenses need to respond according to computer-based decision-making.

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The technology that recognizes Cruise in the Gap store is informed by a chip embedded inside him. But the real-world technology advances of the first two decades of the twenty-first century have outpaced even Spielberg’s imagination, as today no such chip is needed. Facial-recognition technology, utilizing AI-based computer vision with cameras and data in the cloud, can identify the faces of customers as they walk into a store based on their visit last week—or an hour ago. It is creating one of the first opportunities for the tech sector and governments to address ethical and human rights issues for artificial intelligence in a focused and concrete way, by deciding how facial recognition should be regulated.

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Given the nature and role of academic research, universities have begun to set up data depositories, where data can be shared for multiple uses. Microsoft Research is pursuing this data-sharing approach too, making available a collection of free data sets to advance research in areas such as natural language processing and computer vision, as well as in the physical and social sciences. It was this ability to share data that inspired Matthew Trunnell. He recognized that the best way to accelerate the race to cure cancer is to enable multiple research organizations to share their data in new ways. While this sounds simple in theory, its execution is complicated.

pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta-analysis, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

This was the situation when a young computer engineer named Rosalind Picard came to the MIT Media Lab in 1987 as a teaching and research assistant before joining the Vision and Modeling group as faculty in 1991. There Picard taught and worked on a range of new technologies and engineering challenges, including developing new architectures for pattern recognition, mathematical modeling, computer vision, perceptual science, and signal processing. With a degree in electrical engineering and later computer science, Picard had already made major contributions in a number of these areas. But it was Picard’s work developing image modeling and content-based retrieval systems that led her in a direction no one could have foreseen, least of all herself.

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She’d driven herself hard doing pioneering research in image pattern modeling and developing the world’s first content-based retrieval system. Additionally, as she wrote in an article for IEEE, the Institute of Electrical and Electronics Engineers: I was busy working six days and nights a week building the world’s first content-based retrieval system, creating and mixing mathematical models from image compression, computer vision, texture modeling, statistical physics, machine learning, and ideas from filmmaking, and spending all my spare cycles advising students, building and teaching new classes, publishing, reading, reviewing, and serving on non-stop conference and lab committees. I worked hard to be taken as the serious researcher I was, and I had raised over a million dollars in funding for my group’s work.

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As they gathered more and more samples of expressive responses to each ad, the system got better and better. As el Kaliouby explained in a keynote address: We capture emotions by looking at the face. The face happens to be one the most powerful channels for communicating social and emotion information. And we do that by using computer vision and machine learning algorithms that track your face, your facial features, your eyes, your mouth, your eyebrows, and we map those to emotional data points. Then we take all this information and we map it into emotional states, like confusion, interest, enjoyment. And what we’ve found over the past couple of years as we’ve started to run off all this data is that the more data we had, the more accurate our emotion classifiers were able to be.

Calling Bullshit: The Art of Scepticism in a Data-Driven World by Jevin D. West, Carl T. Bergstrom

airport security, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Andrew Wiles, Anthropocene, autism spectrum disorder, bitcoin, Charles Babbage, cloud computing, computer vision, content marketing, correlation coefficient, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, deep learning, deepfake, delayed gratification, disinformation, Dmitri Mendeleev, Donald Trump, Elon Musk, epigenetics, Estimating the Reproducibility of Psychological Science, experimental economics, fake news, Ford Model T, Goodhart's law, Helicobacter pylori, Higgs boson, invention of the printing press, John Markoff, Large Hadron Collider, longitudinal study, Lyft, machine translation, meta-analysis, new economy, nowcasting, opioid epidemic / opioid crisis, p-value, Pluto: dwarf planet, publication bias, RAND corporation, randomized controlled trial, replication crisis, ride hailing / ride sharing, Ronald Reagan, selection bias, self-driving car, Silicon Valley, Silicon Valley startup, social graph, Socratic dialogue, Stanford marshmallow experiment, statistical model, stem cell, superintelligent machines, systematic bias, tech bro, TED Talk, the long tail, the scientific method, theory of mind, Tim Cook: Apple, twin studies, Uber and Lyft, Uber for X, uber lyft, When a measure becomes a target

Essentially, they aim to determine whether advanced computer vision can reveal subtle cues and patterns that Lombroso and his followers might have missed. To test this hypothesis, the authors use machine learning algorithms to determine what features of the human face are associated with “criminality.” Wu and Zhang claim that based on a simple headshot, their programs can distinguish criminal from noncriminal faces with nearly 90 percent accuracy. Moreover, they argue that their computer algorithms are free from the myriad biases and prejudices that cloud human judgment: Unlike a human examiner/judge, a computer vision algorithm or classifier has absolutely no subjective baggages [sic], having no emotions, no biases whatsoever due to past experience, race, religion, political doctrine, gender, age, etc., no mental fatigue, no preconditioning of a bad sleep or meal.

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It is called MNIST, the Modified National Institute of Standards and Technology database for handwritten digits, and it includes seventy thousand labeled images of handwritten digits, similar to those drawn below. So how does the algorithm “see” images? If you don’t have a background in computer vision, this may seem miraculous. Let’s take a brief digression to consider how it works. A computer stores an image as a matrix. A matrix can be thought of as a table of rows and columns. Each cell in this table contains a number. For simplicity, let’s assume that our image is black and white.

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.*5 The Economist and Guardian stories described a research paper in which Stanford University researchers Yilun Wang and Michal Kosinski trained a deep neural network to predict whether someone was straight or gay by looking at their photograph. Wang and Kosinski collected a set of training images from an Internet dating website, photos of nearly eight thousand men and nearly seven thousand women, evenly split between straight and gay. The researchers used standard computer vision techniques for processing the facial images. When given pictures of two people, one straight and the other gay, the algorithm did better than chance at guessing which was which. It also did better than humans charged with the same task. There are so many questions one could ask about the training data.

pages: 245 words: 64,288

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, gamification, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, quantum entanglement, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, seminal paper, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, synthetic biology, technological singularity, TED Talk, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

Robots will eventually steal your job, but before them something else is going to jump in. In fact, it already has, in a much more pervasive way that any physical machine could ever do. I am of course talking about computer programs in general. Automated Planning and Scheduling, Machine Learning, Natural Language Processing, Machine Perception, Computer Vision, Speech Recognition, Affective Computing, Computational Creativity, these are all fields of Artificial Intelligence that do not have to face the cumbersome issues that Robotics has to. It is much easier to enhance an algorithm than it is to build a better robot. A more accurate title for the book would have been “Machine intelligence and computer algorithms are already stealing your job, and they will do so ever more in the future” – but that was not exactly a catchy title.

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What this means is a database of information (thirteen years of studies and training) connected to a visual recognition system (the radiologist’s brain) is a process that already exists today and finds many applications. Visual pattern recognition software is already highly sophisticated, one such example is Google Images. You can upload an image to the search engine, Google uses computer vision techniques to match your image to other images in the Google Images index and additional image collections. From those matches, they try to generate an accurate “best guess” text description of your image, as well as find other images that have the same content as your uploaded image. * * * Figure 6.1: Front page of Google Images.

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The approach by Andrew Ng inspired many others, who are now teaching under the umbrella of a non-profit called ‘Coursera’, with high level subjects such as Model Thinking, Natural Language Processing, Game Theory, Probabilistic Graphical Models, Cryptography, Design and Analysis of Algorithms, Software as a Service, Computer Vision, Computer Science, Machine Learning, Human-Computer Interaction, Making Green Buildings, Information Theory, Anatomy, and Computer Security. Needless to say, this is just the beginning. It is the natural evolution of education when combined with technology. Embrace change, or die. So, how does this apply to you?

pages: 371 words: 107,141

You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All by Adrian Hon

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 4chan, Adam Curtis, Adrian Hon, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Astronomia nova, augmented reality, barriers to entry, Bellingcat, Big Tech, bitcoin, bread and circuses, British Empire, buy and hold, call centre, computer vision, conceptual framework, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, cryptocurrency, David Graeber, David Sedaris, deep learning, delayed gratification, democratizing finance, deplatforming, disinformation, disintermediation, Dogecoin, electronic logging device, Elon Musk, en.wikipedia.org, Ethereum, fake news, fiat currency, Filter Bubble, Frederick Winslow Taylor, fulfillment center, Galaxy Zoo, game design, gamification, George Floyd, gig economy, GitHub removed activity streaks, Google Glasses, Hacker News, Hans Moravec, Ian Bogost, independent contractor, index fund, informal economy, Jeff Bezos, job automation, jobs below the API, Johannes Kepler, Kevin Kelly, Kevin Roose, Kickstarter, Kiva Systems, knowledge worker, Lewis Mumford, lifelogging, linked data, lockdown, longitudinal study, loss aversion, LuLaRoe, Lyft, Marshall McLuhan, megaproject, meme stock, meta-analysis, Minecraft, moral panic, multilevel marketing, non-fungible token, Ocado, Oculus Rift, One Laptop per Child (OLPC), orbital mechanics / astrodynamics, Parler "social media", passive income, payment for order flow, prisoner's dilemma, QAnon, QR code, quantitative trading / quantitative ﬁnance, r/findbostonbombers, replication crisis, ride hailing / ride sharing, Robinhood: mobile stock trading app, Ronald Coase, Rubik’s Cube, Salesforce, Satoshi Nakamoto, scientific management, shareholder value, sharing economy, short selling, short squeeze, Silicon Valley, SimCity, Skinner box, spinning jenny, Stanford marshmallow experiment, Steve Jobs, Stewart Brand, TED Talk, The Nature of the Firm, the scientific method, TikTok, Tragedy of the Commons, transaction costs, Twitter Arab Spring, Tyler Cowen, Uber and Lyft, uber lyft, urban planning, warehouse robotics, Whole Earth Catalog, why are manhole covers round?, workplace surveillance

Everywhere you find Digital Taylorism, gamification follows. Even in what might seem like unorganised environments, like a busy shop floor or a crowded cafe, any task can look repetitive and improvable if you use enough sensors and apply enough processing power. Percolata is a “machine learning–based retail staffing” tool that uses computer vision to surveil shoppers and employees.55 It combines this information with sales data, weather forecasts, and marketing calendars to predict future shopper traffic, all in order to optimise staffing levels so employers pay only the bare minimum labour costs. At the same time, it creates a “true productivity” score for workers, ranking them from most to least productive.

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Perhaps we could place sensors onto the mop handle, so the game could tell whether you were mopping or not, and with good enough processing, it might even be able to learn which surfaces players were mopping. It’s more convenient in some ways, but on its own it wouldn’t be able to assess the cleanliness of the floor. The obvious best solution is to check for dirt in the same way humans do: by looking at the floor. These days, computer vision (i.e., combining cameras and algorithms to understand the real world) is quite powerful and likely up to the task. However, it poses a new challenge: getting visual coverage of the entire floor. Asking players to buy and mount cameras all over the walls and ceiling is a stretch, and also a bit creepy.

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AR has the potential to make challenging and mundane activities entertaining, or at the very least, slightly more bearable. AR games could also provide social benefits. I occasionally go out with a bin bag and litter picker to tidy up my street, which is something of a thankless task. It wouldn’t be difficult to turn this into a game by using computer vision to identify and classify litter, awarding extra points for especially ugly or messy things, or for collecting in neglected areas. No doubt people would try to cheat, but on balance I suspect you’d end up with happier litter pickers and much cleaner neighbourhoods. Litter picking, waste recycling, safe driving, healthy cooking, fitness, mindfulness—the sky’s the limit for AR making the world a better place!

pages: 696 words: 143,736

The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil

Ada Lovelace, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alvin Toffler, Any sufficiently advanced technology is indistinguishable from magic, backpropagation, Buckminster Fuller, call centre, cellular automata, Charles Babbage, classic study, combinatorial explosion, complexity theory, computer age, computer vision, Computing Machinery and Intelligence, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, financial engineering, first square of the chessboard / second half of the chessboard, flying shuttle, fudge factor, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Gilmore, John Markoff, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, ought to be enough for anybody, pattern recognition, phenotype, punch-card reader, quantum entanglement, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Robert Metcalfe, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, social intelligence, speech recognition, Steven Pinker, Stewart Brand, stochastic process, Stuart Kauffman, technological singularity, Ted Kaczynski, telepresence, the medium is the message, The Soul of a New Machine, There's no reason for any individual to have a computer in his home - Ken Olsen, traveling salesman, Turing machine, Turing test, Whole Earth Review, world market for maybe five computers, Y2K

A link to an article in USA Today on how Ian Goldberg, the graduate student from the University of California, cracked the 40-bit encryption code: <http://www.usatoday.com/life/cyber/tech/ct718.htm> Autonomous Agents Agent Web Links: <http://www.cs.bham.ac.uk/~amw/agents/links/index.html> Computer Vision Computer Vision Research Groups: <http://www.cs.cmu.edu/~cil/v-groups.html> DNA Computing “DNA-based computers could race past supercomputers, researchers predict.” A link to an article in the Chronicle of Higher Education on DNA computing, by Vincent Kiernan: <http://chronicle.com/data/articles.dir/art-44.dir/issue-14.dir/14a02301.htm> Explanation of Molecular Computing with DNA, by Fred Hapgood, Moderator of the Nanosystems Interest Group at MIT: <http://www.mitre.org/research/nanotech/hapgood_on_dna.html> The University of Wisconsin: DNA Computing: <http://corninfo.chem.wisc.edu/writings/DNAcomputing.html> Expert Systems/Knowledge Engineering Knowledge Engineering, Engineering Management Graduate Program at Christian Brothers University: Online Resources to a Variety of Links: <http://www.cbu.edu/~pong/engm624.htm> Genetic Algorithms/Evolutionary Computation The Genetic Algorithms Archive at the Navy Center for Applied Research in Artificial Intelligence: <http://www.aic.nrl.navy.mil/galist/> The Hitchhiker’s Guide to Evolutionary Computation, Issue 6.2: A List of Frequently Asked Questions (FAQ), edited by Jörg Heitkotter and David Beasley: <ftp://ftp.cs.wayne.edu/pub/EC/FAQ/www/top.htm> The Santa Fe Institute: <http://www.santafe.edu> Knowledge Management ATM Links (Asynchronous Transfer Mode): <http://www.ee.cityu.edu.hk/~splam/html/atmlinks.html> Knowledge Management Network: <http://kmn.cibit.hvu.nl/index.html> Some Ongoing KBS/Ontology Projects and Groups: <http://www.cs.utexas.edu/users/mfkb/related.html> Nanotechnology Eric Drexler’s web site at the Foresight Institute (includes the complete text of Engines of Creation): <http://www.foresight.org/EOC/index.html> Richard Feynman’s talk, “There’s Plenty of Room at the Bottom”: <http://nano.xerox.com/nanotech/feynman.html> Nanotechnology: Ralph Merkle’s web site at the Xerox Palo Alto Research Center: <http://sandbox.xerox.com/nano> MicroElectroMechanical Systems and Fluid Dynamics Research Group Professor Chih-Ming Ho’s Laboratory, University of California at Los Angeles: <http://ho.seas.ucla.edu/new/main.htm> Nanolink: Key Nanotechnology Sites on the Web: <http://sunsite.nus.sg/MEMEX/nanolink.html> Nanothinc: <http://www.nanothinc.com/> NEC Research and Development Letter: A summary of Dr.

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New York: Times Books, 1978. Finkelstein, Joseph, ed. Windows on a New World: The Third Industrial Revolution. New York: Greenwood Press, 1989. Fischler, Martin A. and Oscar Firschein. Intelligence: The Eye, the Brain and the Computer. Reading, MA: Addison-Wesley, 1987. ————, eds., Readings in Computer Vision: Issues, Problems, Principles, and Paradigms. Los Altos, CA: Morgan Kaufmann, 1987. Fjermedal, Grant. The Tomorrow Makers: A Brave New World of Living Brain Machines. New York: Macmillan Publishing Company, 1986. Flanagan, Owen. Consciousness Reconsidered. Cambridge, MA: MIT Press, 1992. Flynn, Anita, Rodney A.

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New York: Academic Press, 1980. Miller, Eric, ed. Future Vision: The 189 Most Important Trends of the 1990s. Naperville, IL: Sourcebooks Trade, 1991. Minsky, Marvin. Computation: Finite and Infinite Machines. Englewood Cliffs, NJ: Prentice-Hall, 1967. ─. “A Framework for Representing Knowledge.” In The Psychology of Computer Vision, edited by P H. Winston. New York: McGraw-Hill, 1975. ─. The Society of Mind. New York: Simon and Schuster, 1985. ─, ed. Robotics. New York: Doubleday, 1985. ─, ed. Semantic Information Processing. Cambridge, MA: MIT Press, 1968. Minsky, Marvin and Seymour A. Papert. Perceptrons: An Introduction to Computational Geometry.

pages: 913 words: 265,787

How the Mind Works by Steven Pinker

affirmative action, agricultural Revolution, Alfred Russel Wallace, Apple Newton, backpropagation, Buckminster Fuller, cognitive dissonance, Columbine, combinatorial explosion, complexity theory, computer age, computer vision, Computing Machinery and Intelligence, Daniel Kahneman / Amos Tversky, delayed gratification, disinformation, double helix, Dr. Strangelove, experimental subject, feminist movement, four colour theorem, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, greed is good, Gregor Mendel, hedonic treadmill, Henri Poincaré, Herman Kahn, income per capita, information retrieval, invention of agriculture, invention of the wheel, Johannes Kepler, John von Neumann, lake wobegon effect, language acquisition, lateral thinking, Linda problem, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mikhail Gorbachev, Murray Gell-Mann, mutually assured destruction, Necker cube, out of africa, Parents Music Resource Center, pattern recognition, phenotype, Plato's cave, plutocrats, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, Saturday Night Live, scientific worldview, Search for Extraterrestrial Intelligence, sexual politics, social intelligence, Steven Pinker, Stuart Kauffman, tacit knowledge, theory of mind, Thorstein Veblen, Tipper Gore, Turing machine, urban decay, Yogi Berra

Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional shapes. Psychonomic Bulletin and Review, 2, 55–82. Tarr, M. J., & Black, M. J. 1994a. A computational and evolutionary perspective on the role of representation in vision. Computer Vision, Graphics, and Image Processing: Image Understanding, 60, 65–73. Tarr, M. J., & Black, M. J. 1994b. Reconstruction and purpose. Computer Vision, Graphics, and Image Processing: Image Understanding, 60, 113–118. Tarr, M. J., & Bülthoff, H. H. 1995. Is human object recognition better described by geon-structural-descriptions or by multiple views? Journal of Experimental Psychology: Human Perception and Performance, 21, 1494–1505.

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The analysis begins with a goal to be attained and a world of causes and effects in which to attain it, and goes on to specify what kinds of designs are better suited to attain it than others. Unfortunately for those who think that the departments in a university reflect meaningful divisions of knowledge, it means that psychologists have to look outside psychology if they want to explain what the parts of the mind are for. To understand sight, we have to look to optics and computer vision systems. To understand movement, we have to look to robotics. To understand sexual and familial feelings, we have to look to Mendelian genetics. To understand cooperation and conflict, we have to look to the mathematics of games and to economic modeling. Once we have a spec sheet for a well-designed mind, we can see whether Homo sapiens has that kind of mind.

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Two-dimensional surfaces can be curved in the third dimension, like a rubber mold or a blister package. Fifth, we don’t immediately see “objects,” the movable hunks of matter that we count, classify, and label with nouns. As far as vision is concerned, it’s not even clear what an object is. When David Marr considered how to design a computer vision system that finds objects, he was forced to ask: Is a nose an object? Is a head one? Is it still one if it is attached to a body? What about a man on horseback? These questions show that the difficulties in trying to formulate what should be recovered as a region from an image are so great as to amount almost to philosophical problems.

pages: 396 words: 117,149

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

Like Bayesian networks, Markov networks can be represented by graphs, but they have undirected arcs instead of arrows. Two variables are connected, meaning they depend directly on each other, if they appear together in some feature, like Ballad and By a hip-hop artist in Ballad by a hip-hop artist. Markov networks are a staple in many areas, such as computer vision. For instance, a driverless car needs to segment each image it sees into road, sky, and countryside. One option is to label each pixel as one of the three according to its color, but this is not nearly good enough. Images are very noisy and variable, and the car will hallucinate rocks strewn all over the roadway and patches of road in the sky.

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., Earth orbits the sun). It discovered concepts like planet all by itself. The version we used was more advanced than the basic one I’ve described here, but the essential ideas are the same. Various research groups have used Alchemy or their own MLN implementations to solve problems in natural language processing, computer vision, activity recognition, social network analysis, molecular biology, and many other areas. Despite its successes, Alchemy has some significant shortcomings. It does not yet scale to truly big data, and someone without a PhD in machine learning will find it hard to use. Because of these problems, it’s not yet ready for prime time.

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My paper on Naïve Bayes, with Mike Pazzani, is “On the optimality of the simple Bayesian classifier under zero-one loss”* (Machine Learning, 1997; expanded journal version of the 1996 conference paper). Judea Pearl’s book,* mentioned above, discusses Markov networks along with Bayesian networks. Markov networks in computer vision are the subject of Markov Random Fields for Vision and Image Processing,* edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother (MIT Press, 2011). Markov networks that maximize conditional likelihood were introduced in “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,”* by John Lafferty, Andrew McCallum, and Fernando Pereira (International Conference on Machine Learning, 2001).

pages: 144 words: 43,356

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, Bletchley Park, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, deep learning, DeepMind, dematerialisation, Demis Hassabis, discovery of the americas, disintermediation, don't be evil, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Geoffrey Hinton, Google Glasses, hedonic treadmill, hype cycle, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, machine translation, Mahatma Gandhi, means of production, mutually assured destruction, Neil Armstrong, Nicholas Carr, Nick Bostrom, paperclip maximiser, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Robert Solow, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, TED Talk, The future is already here, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

For instance, when training a machine to recognise faces, or images of cats, the researchers will present the machine with thousands of images and the machine will devise statistical rules for categorising images based on their common features. Then the machine is presented with another set of images to see whether the rules hold up, or need revising. Machine learning has proven to be a powerful tool, with impressive performance in applications like computer vision and search. One of the most promising approaches to computer vision at the moment is “convolutional neural nets”, in which a large number of artificial neurons are each assigned to a tiny portion of an image. It is an interesting microcosm of the whole field of machine learning in that it was first invented in 1980, but did not become really useful until the 21st century when graphics processing unit (GPU) computer chips enabled researchers to assemble very large networks.

pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, air gap, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, Bletchley Park, brain emulation, California energy crisis, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, Computing Machinery and Intelligence, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, dual-use technology, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Hacker News, Hans Moravec, Isaac Newton, Jaron Lanier, Jeff Hawkins, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, machine translation, mutually assured destruction, natural language processing, Neil Armstrong, Nicholas Carr, Nick Bostrom, optical character recognition, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, precautionary principle, prisoner's dilemma, Ray Kurzweil, Recombinant DNA, Rodney Brooks, rolling blackouts, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, strong AI, Stuxnet, subprime mortgage crisis, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

Many know that DARPA (then called ARPA) funded the research that invented the Internet (initially called ARPANET), as well as the researchers who developed the now ubiquitous GUI, or Graphical User Interface, a version of which you probably see every time you use a computer or smart phone. But the agency was also a major backer of parallel processing hardware and software, distributed computing, computer vision, and natural language processing (NLP). These contributions to the foundations of computer science are as important to AI as the results-oriented funding that characterizes DARPA today. How is DARPA spending its money? A recent annual budget allocates $61.3 million to a category called Machine Learning, and $49.3 million to Cognitive Computing.

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Consider the old joke about the drunk who loses his car keys and looks for them under a streetlight. A policeman joins the search and asks, “Exactly where did you lose your keys?” The man points down the street to a dark corner. “Over there,” he says. “But the light’s better here.” Search, voice recognition, computer vision, and affinity analysis (the kind of machine learning Amazon and Netflix use to suggest what you might like) are some of the fields of AI that have seen the most success. Though they were the products of decades of research, they are also among the easiest problems, discovered where the light’s better.

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This axiom is known as Moravec’s Paradox, because AI and robotics pioneer Hans Moravec expressed it best in his robotics classic, Mind Children: “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.” Puzzles so difficult that we can’t help but make mistakes, like playing Jeopardy! and deriving Newton’s second law of thermodynamics, fall in seconds to well-programmed AI. At the same time, no computer vision system can tell the difference between a dog and a cat—something most two-year-old humans can do. To some degree these are apples-and-oranges problems, high-level cognition versus low-level sensor motor skill. But it should be a source of humility for AGI builders, since they aspire to master the whole spectrum of human intelligence.

pages: 326 words: 84,180

Dark Matters: On the Surveillance of Blackness by Simone Browne

4chan, affirmative action, Affordable Care Act / Obamacare, airport security, autonomous vehicles, bitcoin, British Empire, cloud computing, colonial rule, computer vision, crowdsourcing, dark matter, disinformation, Edward Snowden, European colonialism, ghettoisation, Google Glasses, Internet Archive, job satisfaction, lifelogging, machine readable, mass incarceration, obamacare, Panopticon Jeremy Bentham, pattern recognition, r/findbostonbombers, Scientific racism, security theater, sexual politics, transatlantic slave trade, urban renewal, US Airways Flight 1549, W. E. B. Du Bois, Wayback Machine, Works Progress Administration

Not attending to these considerations and failing to consider social impacts diminishes their efficacy and can bring serious unintended consequences,” like the further marginalization, and in some cases the disenfranchisement, of people who because of industry-determined standard algorithms encounter difficulty in using this technology.6 When dark matter troubles algorithms in this way, it amounts to a refusal of the idea of neutrality when it comes to certain technologies. But if algorithms can be troubled, this might not necessarily be a bad thing. In other words, could there be some potential in going about unknown or unremarkable, and perhaps unbothered, where CCTV, camera-enabled devices, facial recognition, and other computer vision technologies are in use? The very thing that rendered Black Desi unseen in the “HP Computers Are Racist” video is what viewers of another YouTube video are instructed to employ in order to remain undetected by facial recognition technology. In her DIY (do-it-yourself) makeup tutorial on “how to hide from cameras,” artist Jillian Mayer demonstrates how to use black lipstick, clear tape, scissors, white cream, some glitter, and black eyeliner to distort one’s face in order to make it indiscernible to cameras and “look great.”7 Modeled in a format similar to popular makeup, hair, or other beauty tutorials on YouTube, Mayer tells her viewers that the most important thing “is to really break up your face.”

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In her DIY (do-it-yourself) makeup tutorial on “how to hide from cameras,” artist Jillian Mayer demonstrates how to use black lipstick, clear tape, scissors, white cream, some glitter, and black eyeliner to distort one’s face in order to make it indiscernible to cameras and “look great.”7 Modeled in a format similar to popular makeup, hair, or other beauty tutorials on YouTube, Mayer tells her viewers that the most important thing “is to really break up your face.” Mayer’s tutorial is based on artist Adam Harvey’s cv Dazzle project, which explores the role of camouflage in subverting face-recognition technology. Computer Vision (CV) Dazzle is a play on dazzle camouflage used during World War I, which saw warships painted with block patterns and geometric shapes in contrasting colors, so that rather than concealing a ship, dazzle camouflage was intended to make it difficult to visually assess its size and speed by way of optical illusion.

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Harvey offers up “style tips for reclaiming privacy” and suggests that to decrease the possibilities of detection you should “apply makeup that contrasts with your skin tone in unusual tones and directions: light colors on dark skin, dark colors on light skin.” Makeup could be used not only to prevent recognition but to obscure skin texture analysis as well. These tactics, however, do not explicitly challenge the proliferation of CCTV and other computer vision technologies in public and private spaces, but rather leave it up to the individual to adapt. One of the tasks of Dark Matters has been to situate the dark, blackness, and the archive of slavery and its afterlife as a way to trouble and expand understandings of surveillance. Of course, some things are still left in the dark: the open secret that is the operation of black sites for rendition, torture, detention, and disappearance of people suspected as terroristic threats, or Edward Snowden’s revelations in the summer of 2013 of the National Security Agency’s warrantless wiretapping, a program representing what he called “a dangerous normalization of governing in the dark.”8 In the beginning of this book, I named dark sousveillance as a form of critique that centers black epistemologies of contending with surveillance, and I later looked to freedom acts such as escaping from enslavement by using falsified documents and aliases, or the Totau as celebratory resistance performed right under the surveillant gazes of white audiences, and Solange’s critique of TSA searches as “Discrim-FRO-nation.”

pages: 259 words: 84,261

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

The smartest are artificial intelligence machines. But if listening, understanding and speaking is still not impressive enough, look at how our computers can see. In the late 1960s, computer vision research started. It was designed to mimic the human visual system, as a stepping stone to endowing robots with intelligent behaviour based on what they could see. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extracting edges from images, labelling lines, optical flow and motion estimation. The 1980s saw studies based on more rigorous mathematical analysis while, in the 1990s, research advanced 3D reconstructions.

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This was also the decade when, for the first time, statistical learning techniques were used to recognize faces in images. All of the above, however, were based on traditional computer programming, and while they delivered impressive results, they failed to offer the accuracy and scale today’s computer vision can offer, due to the advancement of Deep Learning artificial intelligence techniques, which have completely surpassed and replaced all prior methods. This intelligence did not learn to see by following a programmer’s list of instructions, but rather through the very act of seeing itself. With AI helping computers see, they can now do it much better than we do, specifically when it comes to individual tasks.

pages: 199 words: 47,154

Gnuplot Cookbook by Lee Phillips

bioinformatics, computer vision, functional programming, general-purpose programming language, pattern recognition, statistical model, web application

His main interests are functional programming and machine-learning algorithms. David Millán Escrivá was 8 years old when he wrote his first program on 8086 PC with Basic language. He has more than 10 years of experience in IT. He has worked on computer vision, computer graphics, and pattern recognition. Currently he is working on different projects about computer vision and AR. I would like to thank Izanskun and my daughter Eider. www.PacktPub.com Support files, eBooks, discount offers, and more You might want to visit www.PacktPub.com for support files and downloads related to your book.

pages: 328 words: 90,677

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

autonomous vehicles, barriers to entry, Bear Stearns, bitcoin, business climate, call centre, carbon footprint, Clayton Christensen, clean tech, Colonization of Mars, computer vision, crowdsourcing, disruptive innovation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, facts on the ground, fake it until you make it, family office, financial engineering, Ford Model T, gigafactory, global supply chain, Google Earth, housing crisis, hype cycle, Hyperloop, junk bonds, Kaizen: continuous improvement, Kanban, Kickstarter, Lyft, Marc Andreessen, Menlo Park, minimum viable product, new economy, off grid, off-the-grid, OpenAI, Paul Graham, peak oil, performance metric, Ponzi scheme, ride hailing / ride sharing, risk tolerance, Sand Hill Road, self-driving car, short selling, short squeeze, side project, Silicon Valley, Silicon Valley startup, Skype, smart cities, Solyndra, stealth mode startup, Steve Jobs, Steve Jurvetson, tail risk, technoutopianism, Tesla Model S, too big to fail, Toyota Production System, Uber and Lyft, uber lyft, union organizing, vertical integration, WeWork, work culture , Zipcar

Mobileye’s deeply held view is that the long-term potential for vehicle automation to reduce traffic injuries and fatalities significantly is too important to risk consumer and regulatory confusion or to create an environment of mistrust that puts in jeopardy technological advances that can save lives. Indeed, Mobileye had been working on computer vision for autonomous vehicles for more than a decade, and it supplied the same EyeQ3 chip to so many automakers that its contract with Tesla made up only 1 percent of its revenue. Though Tesla’s Autopilot initially showed how capable Mobileye’s technology could be when pushed to its limits, the Brown crash suggested that Tesla was willing to sacrifice safety for the perception of a lead in the “race to autonomy.” With its head start on computer vision and supply contracts with half the industry, Mobileye had no need for the kind of risks Tesla was taking.

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According to Musk, “Mobileye’s ability to evolve its technology is unfortunately negatively affected by having to support hundreds of models from legacy auto companies, resulting in a very high engineering drag coefficient.” The war of words continued into September, with Shashua arguing that Mobileye’s chip wasn’t designed to handle the kind of cross traffic that had caused the Brown crash and Musk contending that Mobileye was threatened by Tesla’s research into computer vision. The conflict came to a head when Musk claimed that Mobileye had “attempted to force Tesla to discontinue this development, pay them more and use their products in future hardware . . . When Tesla refused to cancel its own vision development activities and plans for deployment, Mobileye discontinued hardware support for future platforms and released public statements implying that this discontinuance was motivated by safety concerns.”

pages: 721 words: 197,134

Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić

Albert Einstein, algorithmic bias, backpropagation, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) http://computer.org/tpami/ IEEE TPAMI is a scholarly archival journal published monthly. Its editorial board strives to present most important research results in areas within TPAMI’s scope. This includes all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Areas such as machine learning, search techniques, document and handwriting analysis, medical-image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition, and relevant specialized hardware and/or software architectures are also covered. 5.

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Kumar, Similarity Measures for Categorical Data: A Comparative Evaluation, SIAM Conference, 2008, pp. 243–254. Brachman, R. J., T. Khabaza, W. Kloesgen, G. S. Shapiro, E. Simoudis, Mining Business Databases, CACM, Vol. 39, No. 11, 1996, pp. 42–48. Chen, C. H., L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition & Computer Vision, World Scientific Publ. Co., Singapore, 1993. Clark, W. A. V., M. C. Deurloo, Categorical Modeling/Automatic Interaction Detection, Encyclopedia of Social Measurement, 2005, pp. 251–258. Dwinnell, W., Data Cleansing: An Automated Approach, PC AI, March/April 2001, pp 21–23. Fayyad, U. M., G.

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Thearling, Building Data Mining Applications for CRM, McGraw-Hill, New York, 2000. Brachman, R. J., T. Khabaza, W. Kloesgen, G. S. Shapiro, E. Simoudis, Mining Business Databases, CACM, Vol. 39, No. 11, 1996, pp. 42–48. Chen, C. H., L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition and Computer Vision, World Scientific Publ. Co., Singapore, 1993. Clark, W. A. V., M. C. Deurloo, Categorical Modeling/Automatic Interaction Detection, Encyclopedia of Social Measurement, 2005, pp. 251–258. Dwinnell, W., Data Cleansing: An Automated Approach, PC AI, March/April 2001, pp. 21–23. Eddy, W. F., Large Data Sets in Statistical Computing, in International Encyclopedia of the Social & Behavioral Sciences, N.

pages: 363 words: 109,077

The Raging 2020s: Companies, Countries, People - and the Fight for Our Future by Alec Ross

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Affordable Care Act / Obamacare, air gap, air traffic controllers' union, Airbnb, Albert Einstein, An Inconvenient Truth, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, Big Tech, big-box store, British Empire, call centre, capital controls, clean water, collective bargaining, computer vision, coronavirus, corporate governance, corporate raider, COVID-19, deep learning, Deng Xiaoping, Didi Chuxing, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, dumpster diving, employer provided health coverage, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, Gini coefficient, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, high-speed rail, hiring and firing, income inequality, independent contractor, information security, intangible asset, invisible hand, Jeff Bezos, knowledge worker, late capitalism, low skilled workers, Lyft, Marc Andreessen, Marc Benioﬀ, mass immigration, megacity, military-industrial complex, minimum wage unemployment, mittelstand, mortgage tax deduction, natural language processing, Oculus Rift, off-the-grid, offshore financial centre, open economy, OpenAI, Parag Khanna, Paris climate accords, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, Robert Bork, rolodex, Ronald Reagan, Salesforce, self-driving car, shareholder value, side hustle, side project, Silicon Valley, smart cities, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, sparse data, special economic zone, Steven Levy, stock buybacks, strikebreaker, TaskRabbit, tech bro, tech worker, transcontinental railway, transfer pricing, Travis Kalanick, trickle-down economics, Uber and Lyft, uber lyft, union organizing, Upton Sinclair, vertical integration, working poor

One of the main reasons is that digital tools like AI are more difficult to categorize than traditional defense technologies. Fighter jets and warships are used for one thing: the projection and exercise of military power. But artificial intelligence is a general-purpose technology with both national security applications and completely benign commercial uses. A computer vision algorithm can be trained to spot enemy combatants on a battlefield, but it can also be used to tag friends in social media posts and power self-driving cars. AI takes on the values and intentions of its human masters. The same AI-enabled facial recognition technology that can identify known terrorism suspects can just as easily profile and track members of an ethnic minority.

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Building effective artificial intelligence requires lots and lots of training data, and you would be hard-pressed to find a government with fewer qualms about general public data collection than China’s. Companies share data with the government, which then shares data back with other companies, which then refine their algorithms and continue collecting more data. The CEO of the Chinese computer vision company SenseTime, which helped construct the Xinjiang surveillance apparatus, referred to the government as the company’s “largest data source.” More data beget better algorithms, which beget better data. The surveillance state feeds itself and becomes more effective as it goes. As fifth-generation broadband networks enable China to embed more sensors on its streets, in its vehicles, and around its offices, homes, and public spaces, the panopticon will become more total.

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As of 2019, China has deployed approximately one: Thomas Ricker, “The US, Like China, Has about One Surveillance Camera for Every Four People, Says Report,” The Verge, December 9, 2019, https://www.theverge.com/2019/12/9/21002515/surveillance-cameras-globally-us-china-amount-citizens; Charlie Campbell, “‘The Entire System Is Designed to Suppress Us.’ What the Chinese Surveillance State Means for the Rest of the World,” Time, November 21, 2019, https://time.com/5735411/china-surveillance-privacy-issues/. The CEO of the Chinese computer vision company SenseTime: Ross Andersen, “The Panopticon Is Already Here,” Atlantic, September 2020, https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/. But less than six months later: Amy Hawkins, “Beijing’s Big Brother Tech Needs More African Faces,” Foreign Policy, July 24, 2018, https://foreignpolicy.com/2018/07/24/beijings-big-brother-tech-needs-african-faces/; Kudzai Chimhangwa, “How Zimbabwe’s Biometric ID Scheme—and China’s AI Aspirations—Threw a Wrench in Elections,” GlobalVoices, January 30, 2020, https://globalvoices.org/2020/01/30/how-zimbabwes-biometric-id-scheme-and-chinas-ai-aspirations-threw-a-wrench-into-the-2018-election/.

Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei

backpropagation, bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, disinformation, distributed generation, finite state, industrial research laboratory, information retrieval, information security, iterative process, knowledge worker, linked data, machine readable, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, power law, random walk, recommendation engine, RFID, search costs, semantic web, seminal paper, sentiment analysis, sparse data, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application

Machine learning and pattern recognition research is published in the proceedings of several major machine learning, artificial intelligence, and pattern recognition conferences, including the International Conference on Machine Learning (ML), the ACM Conference on Computational Learning Theory (COLT), the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Pattern Recognition (ICPR), the International Joint Conference on Artificial Intelligence (IJCAI), and the American Association of Artificial Intelligence Conference (AAAI). Other sources of publication include major machine learning, artificial intelligence, pattern recognition, and knowledge system journals, some of which have been mentioned before.

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Wavelet transforms give good results on sparse or skewed data and on data with ordered attributes. Lossy compression by wavelets is reportedly better than JPEG compression, the current commercial standard. Wavelet transforms have many real-world applications, including the compression of fingerprint images, computer vision, analysis of time-series data, and data cleaning. 3.4.3. Principal Components Analysis In this subsection we provide an intuitive introduction to principal components analysis as a method of dimesionality reduction. A detailed theoretical explanation is beyond the scope of this book. For additional references, please see the bibliographic notes (Section 3.8) at the end of this chapter.

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., the mapping of genes onto a chromosome). By casting the gene linkage problem in terms of inference on Bayesian networks, and using state-of-the art algorithms, the scalability of such analysis has advanced considerably. Other applications that have benefited from the use of belief networks include computer vision (e.g., image restoration and stereo vision), document and text analysis, decision-support systems, and sensitivity analysis. The ease with which many applications can be reduced to Bayesian network inference is advantageous in that it curbs the need to invent specialized algorithms for each such application. 9.1.2.

pages: 501 words: 114,888

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Move: See: https://www.move.com/. Redfin: See: https://www.redfin.com/. invested millions: For an example of investment in real estate AI, see this VentureBeat article about one of Zillow’s latest computer vision tools: Kyle Wiggers, “Zillow Now Uses Computer Vision To Improve Property Value Estimates,” VentureBeat, June 26, 2019. See: https://venturebeat.com/2019/06/26/zillow-now-uses-computer-vision-to-improve-property-value-estimates/. Reinventing the City five hundred coastal cities now threatened by global warming: This World Economic forum report predicts that 570 coastal cities around the world are vulnerable to a sea-level rise of 0.5 meters by 2050: http://www3.weforum.org/docs/WEF_Global_Risks_Report_2019.pdf.

pages: 447 words: 111,991

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioﬀ, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

And the Israeli-developed drones Harpy and Harop are often cited as autonomous weapons that are already in use. Harpy uses electromagnetic sensors to search for pre-specified targets; its follow-on system, Harop, uses visual and infrared sensors to hunt those targets. The development of facial recognition and computer vision will further add to the power of such technology on the battlefield. A commercial drone not even intended for military use, the Skydio R1, uses a cutting-edge computer vision system to recognise and track its owner autonomously. Thirteen on-board cameras do real-time mapping, path planning and obstacle avoidance.59 It sells for less than $2,500. Such drones, with high degrees of autonomy, are more and more commonplace.

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Bleek, ‘Drones of Mass Destruction: Drone Swarms and the Future of Nuclear, Chemical, and Biological Weapons’, War on the Rocks, 14 February 2019 <https://warontherocks.com/2019/02/drones-of-mass-destruction-drone-swarms-and-the-future-of-nuclear-chemical-and-biological-weapons/> [accessed 26 April 2021]. 58 Missy Cummings, The Human Role in Autonomous Weapon Design and Deployment, 2014 <https://www.law.upenn.edu/live/files/3884-cummings-the-human-role-in-autonomous-weapons>. 59 Nick Statt, ‘Skydio’s AI-Powered Autonomous R1 Drone Follows You around in 4K’, The Verge, 13 February 2018 <https://www.theverge.com/2018/2/13/17006010/skydio-r1-autonomous-drone-4k-video-recording-ai-computer-vision-mapping> [accessed 2 January 2021]. 60 ‘Autonomous Weapons and the New Laws of War’, The Economist, 19 January 2019 <https://www.economist.com/briefing/2019/01/19/autonomous-weapons-and-the-new-laws-of-war> [accessed 26 March 2021]. 61 Burgess Laird, ‘The Risks of Autonomous Weapons Systems for Crisis Stability and Conflict Escalation in Future U.S.

pages: 391 words: 71,600

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

3D printing, AlphaGo, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, DeepMind, Deng Xiaoping, Donald Trump, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, growth hacking, hype cycle, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, Neal Stephenson, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Robert Solow, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, Snow Crash, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, TED Talk, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

Chris Capossela, our chief marketing officer, who grew up in a family-run Italian restaurant in the North End of Boston, and joined Microsoft right out of Harvard College the year before I joined. Kevin Turner, a former Wal-Mart executive, who was chief operating officer and led worldwide sales. Harry Shum, who leads Microsoft’s celebrated Artificial Intelligence and Research Group operation, received his PhD in robotics from Carnegie Mellon and is one of the world’s authorities on computer vision and graphics. I had been a member of the SLT myself when Steve Ballmer was CEO, and, while I admired every member of our team, I felt that we needed to deepen our understanding of one another—to delve into what really makes each of us tick—and to connect our personal philosophies to our jobs as leaders of the company.

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These are the building blocks of AI, and for many years Microsoft has invested in advancing each of these tiers—statistical machine learning tools to make sense of data and recognize patterns; computers that can see, hear, and move, and even begin to learn and understand human language. Under the leadership of our chief speech scientist, Xuedong Huang, and his team, Microsoft set the accuracy record with a computer system that can transcribe the contents of a phone call more accurately than a human professional trained in transcription. On the computer vision and learning front, in late 2015 our AI group swept first prize across five challenges even though we only trained our system for one of those challenges. In the Common Objects in Context challenge, an AI system attempts to solve several visual recognition tasks. We trained our system to accomplish just the first one, simply to look at a photograph and label what it sees.

The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns, Aaron Roth

23andMe, affirmative action, algorithmic bias, algorithmic trading, Alignment Problem, Alvin Roth, backpropagation, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, data science, deep learning, DeepMind, Dr. Strangelove, Edward Snowden, Elon Musk, fake news, Filter Bubble, general-purpose programming language, Geoffrey Hinton, Google Chrome, ImageNet competition, Lyft, medical residency, Nash equilibrium, Netflix Prize, p-value, Pareto efficiency, performance metric, personalized medicine, pre–internet, profit motive, quantitative trading / quantitative ﬁnance, RAND corporation, recommendation engine, replication crisis, ride hailing / ride sharing, Robert Bork, Ronald Coase, self-driving car, short selling, sorting algorithm, sparse data, speech recognition, statistical model, Stephen Hawking, superintelligent machines, TED Talk, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

To see a particularly egregious example, let’s go back to 2015, when the market for machine learning talent was heating up. The techniques of deep learning had recently reemerged from relative obscurity (its previous incarnation was called backpropagation in neural networks, which we discussed in the introduction), delivering impressive results in computer vision and image recognition. But there weren’t yet very many experts who were good at training these algorithms—which was still more of a black art, or perhaps an artisanal craft, than a science. The result was that deep learning experts were commanding salaries and signing bonuses once reserved for Wall Street.

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See also traffic and navigation problems competition among scientific journals, 144–45 competitive equilibrium, 105–6, 108–9, 111, 115 and equilibrium states, 98–99 image recognition competition, 145–49 medical residency matchmaking, 126–30. See also games and game theory complex algorithms, 174–75 complex datasets, 9, 151, 155 compromising information, 40–45 computation, 11–12 computational complexity, 101 computational literacy, 172. See also interpretability of outputs computer science, 5 computer vision, 145–49 confirmation bias, 92 consumer data, 6–7, 13, 33 consumer Internet, 64 Consumer Reports, 116 convex minimization problems, 110 cooperation cooperative solutions in game theory, 113–15 and equilibrium in game theory, 99–100 and navigation problems, 112–13 through correlation, 113–15 Cornell University, 159 correlations cooperation through, 113–15 and dangers of adaptive data analysis, 152–55 and forbidden inputs, 66–67 and online shopping algorithms, 120 and torturing data, 159 in traffic equilibrium problems, 113–15 and word embedding, 68 costs of ethical behaviors, 19 cost structures, 103 counterfactuals, 156, 174, 191 credit.

pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

., “Raw Data” Is an Oxymoron, Cambridge, MA: MIT Press, 2013. 3.These questions are explored in greater depth in the excellent Critical Algorithm Studies reading list maintained by Tarleton Gillespie and Nick Seaver of Microsoft Research’s Social Media Collective: socialmediacollective.org/reading-lists/critical-algorithm-studies. 4.Nick Bostrom, Superintelligence: Paths, Dangers, Strategies, Oxford, UK: Oxford University Press, 2014. 5.For those inclined to dig deeper into such subjects, Andrey Kurenkov’s history of neural networks is fantastic: andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning. 6.Alistair Barr, “Google Mistakenly Tags Black People as ‘Gorillas,’ Showing Limits of Algorithms,” Wall Street Journal, July 1, 2015. 7.Aditya Khosla et al., “Novel dataset for Fine-Grained Image Categorization,” First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, 2011, vision.stanford.edu/aditya86/ImageNetDogs; ImageNet, “Large Scale Visual Recognition Challenge 2012,” image-net.org/challenges/LSVRC/2012. 8.David M. Stavens, “Learning to Drive: Perception for Autonomous Cars,” Ph.D dissertation, Stanford University Department of Computer Science, May 2011, cs.stanford.edu/people/dstavens/thesis/David_Stavens_PhD_Dissertation.pdf. 9.Tesla Motors, Inc.

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., January 1975. 47.Karl Ricanek Jr. and Chris Boehnen, “Facial Analytics: From Big Data to Law Enforcement,” Computer, Volume 45, Number 9, September 2012. 48.Charles Arthur, “Quividi Defends Tesco Face Scanners after Claims over Customers’ Privacy,” Guardian, November 4, 2013. 49.Amrutha Sethuram et al., “Facial Landmarking: Comparing Automatic Landmarking Methods with Applications in Soft Biometrics,” Computer Vision—ECCV 2012, October 7, 2012. 50.Judith Butler, Gender Trouble: Feminism and the Subversion of Identity, New York and London: Routledge, 1990. 51.Vladimir Khryashchev et al., “Gender Recognition via Face Area Analysis,” Proceedings of the World Congress on Engineering and Computer Science 2012, Volume 1, October 24, 2012. 52.Shaun Walker, “Face Recognition App Taking Russia by Storm May Bring End to Public Anonymity,” Guardian, May 17, 2016. 53.Kevin Rothrock, “The Russian Art of Meta-Stalking,” Global Voices Advox, April 7, 2016. 54.Mary-Ann Russon, “Russian Trolls Outing Porn Stars and Prostitutes with Neural Network Facial Recognition App,” International Business Times, April 27, 2016. 55.Weiyao Lin et al., “Group Event Detection for Video Surveillance,” 2009 IEEE International Symposium on Circuits and Systems, May 24, 2009, ee.washington.edu/research/nsl/papers/ISCAS-09.pdf. 56.Paul Torrens, personal conversation, 2007; see also geosimulation.org/riots.html.

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Here’s What He Said,” Recode, April 13, 2016. f 3.Brad Stone and Jack Clark, “Google Puts Boston Dynamics Up for Sale in Robotics Retreat,” Bloomberg Technology, March 17, 2016. 4.John Markoff, “Latest to Quit Google’s Self-Driving Car Unit: Top Roboticist,” New York Times, August 5, 2016. 5.Mark Harris, “Secretive Alphabet Division Funded by Google Aims to Fix Public Transit in US,” Guardian, June 27, 2016. 6.Siimon Reynolds, “Why Google Glass Failed: A Marketing Lesson,” Forbes, February 5, 2015. 7.Rajat Agrawal, “Why India Rejected Facebook’s ‘Free’ Version of the Internet,” Mashable, February 9, 2016. 8.Mark Zuckerberg, “The technology behind Aquila,” Facebook, July 21, 2016, facebook.com/notes/mark-zuckerberg/the-technology-behind-aquila/10153916136506634/. 9.Mari Saito, “Exclusive: Amazon Expanding Deliveries by Its ‘On-Demand’ Drivers,” Reuters, February 8, 2016. 10.Alan Boyle, “First Amazon Prime Airplane Debuts in Seattle After Secret Night Flight,” GeekWire, August 4, 2016. 11.Farhad Manjoo, “Think Amazon’s Drone Delivery Idea Is a Gimmick? Think Again,” New York Times, August 10, 2016. 12.CBS News, “Amazon Unveils Futuristic Plan: Delivery by Drone,” 60 Minutes, December 1, 2013. 13.Ben Popper, “Amazon’s drone program acquires a team of Europe’s top computer vision experts,” The Verge, May 10, 2016. 14.Danielle Kucera, “Amazon Acquires Kiva Systems in Second-Biggest Takeover,” Bloomberg, March 19, 2012. 15.Mike Rogoway, “Amazon Reports Price of Elemental Acquisition: $296 Million,” Oregonian, October 23, 2015. 16.Caleb Pershan, “Startup Doze Monetizes Nap Time for Tired Techies,” SFist, September 28, 2015; Kate Taylor, “Food-Tech Startup Soylent Snags $20 Million in Funding,” Entrepreneur, January 15, 2015; Michelle Starr, “Brain-to-Brain Verbal Communication in Humans Achieved for the First Time,” CNet, September 3, 2014; Frank Tobe, “When Will Sex Robots Hit the Marketplace?

pages: 494 words: 116,739

Geek Heresy: Rescuing Social Change From the Cult of Technology by Kentaro Toyama

Abraham Maslow, Albert Einstein, Apollo 11, behavioural economics, Berlin Wall, Bernie Madoff, blood diamond, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, commoditize, computer vision, conceptual framework, delayed gratification, digital divide, do well by doing good, Edward Glaeser, Edward Jenner, en.wikipedia.org, end world poverty, epigenetics, Erik Brynjolfsson, Evgeny Morozov, Francis Fukuyama: the end of history, fundamental attribution error, gamification, germ theory of disease, global village, Hans Rosling, happiness index / gross national happiness, income inequality, invention of the printing press, invisible hand, Isaac Newton, Khan Academy, Kibera, knowledge worker, Larry Ellison, Lewis Mumford, liberation theology, libertarian paternalism, longitudinal study, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, microcredit, mobile money, Neil Armstrong, Nelson Mandela, Nicholas Carr, North Sea oil, One Laptop per Child (OLPC), Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, post-industrial society, Powell Memorandum, randomized controlled trial, rent-seeking, RFID, Richard Florida, Richard Thaler, school vouchers, self-driving car, Sheryl Sandberg, Silicon Valley, Simon Kuznets, Stanford marshmallow experiment, Steve Jobs, Steven Pinker, technological determinism, technological solutionism, technoutopianism, TED Talk, The Fortune at the Bottom of the Pyramid, the long tail, Twitter Arab Spring, Upton Sinclair, Walter Mischel, War on Poverty, winner-take-all economy, World Values Survey, Y2K

So in college I majored in physics, but, as often happens, one thing led to another, and I changed fields. I did a PhD in computer science, and after that, I took a job at Microsoft Research – one of the world’s largest computer science laboratories. What didn’t change was my search for technological solutions. At first I worked in an area called computer vision, which tries to give machines a skill that one-year-olds take for granted but that science still toils to explain: converting an array of color into meaning – a crib, a mother’s smile, a looming bottle. Computers still can’t recognize these objects reliably, but the field has made progress. For example, these days we don’t think twice about the little squares that track a person’s face on our mobile-phone cameras.

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How Internet censorship actually works in China. The Atlantic, Oct. 2, 2013, www.theatlantic.com/china/archive/2013/10/how-internet-censorship-actually-works-in-china/280188/. Toyama, Kentaro, and Andrew Blake. (2001). Probabilistic tracking in a metric space. In Proceedings of the Eighth International Conference on Computer Vision 2:50–57, http://dx.doi.org/10.1109/ICCV.2001.937599. Tripathi, Salil. (2006). Microcredit won’t make poverty history. The Guardian, Oct. 17, 2006, www.theguardian.com/business/2006/oct/17/businesscomment.internationalaidanddevelopment. Tsotsis, Alexia. (2011). To celebrate the #Jan25 Revolution, Egyptian names his firstborn “Facebook.”

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Information Technologies and International Development 5(1):81–95, http://itidjournal.org/itid/article/view/327/150. Venkatesh, Sudhir. (2008). Gang Leader for a Day: A Rogue Sociologist Crosses the Line. Allen Lane. Viola, Paul, and Michael Jones. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the Conference on Computer Vision and Pattern Recognition, http://dx.doi.org/10.1109/CVPR.2001.990517. Vornovytskyy, Marina, Alfred Gottschalck, and Adam Smith. (2011). Household debt in the U.S.: 2000 to 2011. US Census Bureau, www.census.gov/people/wealth/files/Debt%20Highlights%202011.pdf. Wahba, Mahmoud A., and Lawrence G.

pages: 458 words: 135,206

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

Amazon Web Services, Andy Carvin, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, do what you love, domain-specific language, functional programming, glass ceiling, Hacker News, hype cycle, Neil Armstrong, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, Salesforce, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, systems thinking, thinkpad, web application, zero day, zero-sum game

Donaldson: So you moved away from the financial services world and established the company that you currently run. Can you tell us a little about that transition and what your company does? Bloore: Sure. So what our company does is make images searchable by looking at the patterns within the pixels themselves. We are entirely focused on using computer vision and pattern recognition algorithms to make large image sets searchable. It's very different from using keywords to search images. G. Donaldson: That's a big shift from developing risk management software to pixel imagery and software. Where did that shift come from? What was your interest in going in that direction?

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It is worthless to capture all the visual information around you if you can't make any sense of it, so that's something that I see will be an area for us to focus on, making sense of the visual world around us. S. Donaldson: With respect to the technical people you need to create these products and explore new areas, where do you look to recruit people and what do you look for in employees when you hire them? Bloore: When it comes to algorithms, computer vision algorithm work, of course we're looking at people who have advanced degrees in that field. Despite my own lack of a degree in that field, people who are able to write robust algorithms are usually coming out of an academic background. And we are very often recruiting through our network of other technology people, who are telling us about very talented people that they know.

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Donaldson: In terms of keeping current and where the future is going, are you aligned at all with any universities or research labs? Bloore: Yes, we certainly are. We keep in touch with academics at both the University of Toronto and the University of Waterloo. We've done joint projects with the University of Toronto in the past, and we're likely to in the future as well, especially in the computer vision department. G. Donaldson: Do you see your role evolving over the next several years? Bloore: Well, as the team grows, at a certain point there will be less direct contact day-to-day with all the development staff. I'll see that as an unfortunate day, but it's probably a necessary step. G. Donaldson: You have a lead engineer for your technology staff?

pages: 486 words: 132,784

Inventors at Work: The Minds and Motivation Behind Modern Inventions by Brett Stern

Apple II, augmented reality, autonomous vehicles, bioinformatics, Build a better mousetrap, business process, cloud computing, computer vision, cyber-physical system, distributed generation, driverless car, game design, Grace Hopper, human-factors engineering, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, value engineering, Yogi Berra

But many people, who have been here fifteen years or more, should be taking on somewhat of a mentorship role. In a research lab, even in periods when you are laying off, you’d better be hiring people who have the latest new skills—especially skills that I don’t have. A lot of new hires are much better at computer vision and video processing, for example, than I am. But I can help them formulate their ideas into solutions for problems, rather than just staying in their heads or on their desks. A lot of the mentoring I do is also showing them how to communicate their work and to get their solutions out to the community.

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You want to give the real technical information that the person needs to know, not all of your grief in getting there. Stern: I realize that your work is proprietary, but can you give us a general sense of where your technology and research interests are going? Loce: Most of it is in video processing and computer vision, with key application fields of transportation, health care, and education. Transportation is probably the easiest one for me to explain without stepping into specifics. Half of the car fuel used in San Francisco is burned looking for a parking space. Thirty to fifty percent of the cars driving around the streets in Brooklyn are looking for parking spaces.

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Some of the transportation problems that we are going after are to save fuel, reduce emissions, reduce congestion, improve highway safety, and reduce the cost of law enforcement. Solutions have worldwide application. Every country in the world is looking to make their transportation systems more intelligent. Computer vision and video processing are going to be key tools in making highways and transportation systems more intelligent. Look at the cameras that are out there already: there are a million CCTV cameras in London—forty thousand in the London subway system alone. Some of our buses here in Rochester have nine cameras on them.

pages: 291 words: 81,703

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

Amazon Mechanical Turk, behavioural economics, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, Charles Babbage, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, driverless car, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, Higgs boson, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Ken Thompson, Khan Academy, labor-force participation, Loebner Prize, low interest rates, low skilled workers, machine readable, manufacturing employment, Mark Zuckerberg, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, off-the-grid, P = NP, P vs NP, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

Self-scrutiny doesn’t have to be restricted to matters of the heart. Which products do we really like or really notice? How are we responding to advertisements when we see them? Consult your pocket device. There is currently a DARPA (Defense Advanced Research Projects Agency, part of the Department of Defense) project called “Cortically Coupled Computer Vision.” The initial applications help analysts scan satellite photos or help a soldier-driver navigate dangerous terrain in a Jeep. Basically, the individual wears some headgear and the device measures neural signals whenever the individual experiences a particular kind of subconscious alert (Danger!

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See Freestyle chess conscientiousness, 29–40, 201–2 conservatism, 74, 98, 235, 254–56 consulting, 41, 42–43 consumer empowerment, 122–23 consumer quality quotients, 125 contempt aversion, 98 convergence, 150–51, 155–58 cooperative research, 207 Cortically Coupled Computer Vision, 14 cosmology, 211, 212–13, 218–19, 226 cost of living, 236, 244–46, 248 counterintuitiveness, 205 coupons, 24 Cox cable company, 111, 119 Cramton, Steven, 78 credentials, 40 credit markets, 55 credit ratings, 124–25 crime, 52, 253 Cuba, 171 cultural economy, 67 Danailov, Silvio, 149–50 data collection, 95–96, 219–20, 227 data quality, 224 dating market, 73, 125.

pages: 305 words: 89,103

Scarcity: The True Cost of Not Having Enough by Sendhil Mullainathan

American Society of Civil Engineers: Report Card, Andrei Shleifer, behavioural economics, Cass Sunstein, clean water, cognitive load, computer vision, delayed gratification, double entry bookkeeping, Exxon Valdez, fault tolerance, happiness index / gross national happiness, impulse control, indoor plumbing, inventory management, knowledge worker, late fees, linear programming, mental accounting, microcredit, p-value, payday loans, purchasing power parity, randomized controlled trial, Report Card for America’s Infrastructure, Richard Thaler, Saturday Night Live, Walter Mischel, Yogi Berra

In the pilot study, they found that 106 employees at twelve banks showed increased performance on several metrics. Perhaps this sounds far-fetched. But how different is this from how we manage our bodies? To prevent repetitive strain injury, frequent computer users take mandated breaks. To help with computer vision syndrome, people are advised to look away from the screen every twenty minutes or so for about twenty seconds to rest the eyes. Why is it counterintuitive that our cognitive system should be so different from our physical one? The deeper lesson is the need to focus on managing and cultivating bandwidth, despite pressures to the contrary brought on by scarcity.

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arousal, and performance artificial scarcity Asia Atkins diet attention bottom-up processing capture of performance and top-down processing attentional blink Australia automatic bill pay automatic impulse bandwidth building cognitive capacity and comes at a price economizing on executive control and tax terminology timeline Banerjee, Abhijit Bangladesh Bank of America bankruptcy banks bargaining basketball beer bees behavioral economics Benihana restaurants Berra, Yogi Bertrand, Marianne bills automatic payment late payment of Bjorkegren, Dan Bohn, Roger Bolivia borrowing Family Feud and payday loans traps tunneling and See also borrowing; debt Boston bottom-up processing Bowen, Bruce brain development lateralization perception See also mind bridges Bryan, Chris buffer stock cabinet castaways cancer carbohydrates carbon dioxide Carlin, George cars accidents cell phone use and eating in impulse purchases insurance registration repairs repossession shopping for traffic cash transfer programs castaways cell phones Center for Responsible Lending Chapanis, Alphonse checker-shadow illusion chemistry Chennai, India Chevys restaurant child care China choices burden of one-off choking Christmas Churchill, Winston cigarettes taxes clothing packing professional purchase mistakes cockpit errors cocktails cognitive capacity cognitive science Cohen, Amanda college deadlines exams financial aid programs loans tuition communal tables commuters computers shopping for software computer vision syndrome conditional cash transfers consistency Consumer Reports contextual cues control impulses cortisol Covey, Stephen creativity credit cards crop insurance crop yields culture customer service dating, online daycare deadlines benefits of focus dividend and debt in India leveraged buyout payday loans rolled-over traps tunneling and See also borrowing; loans decisions, linking and the timing of declarative memory Dempsey, Christy diabetes dichotic listening task Dickinson, Charlie dieting diminishing marginal utility discretion, lack of disease divorce Dominican Republic DOTS (directly observed therapy) DVD players economics behavioral expertise and in India scarcity and 2008 recession edema education college financial literacy noise and Eisenhower, Dwight Eliot, T.

pages: 339 words: 88,732

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

Our digital machines have escaped their narrow confines and started to demonstrate broad abilities in pattern recognition, complex communication, and other domains that used to be exclusively human. We’ve also recently seen great progress in natural language processing, machine learning (the ability of a computer to automatically refine its methods and improve its results as it gets more data), computer vision, simultaneous localization and mapping, and many of the other fundamental challenges of the discipline. We’re going to see artificial intelligence do more and more, and as this happens costs will go down, outcomes will improve, and our lives will get better. Soon countless pieces of AI will be working on our behalf, often in the background.

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As futurist Kevin Kelly put it “You’ll be paid in the future based on how well you work with robots.”7 Sensing Our Advantage So computers are extraordinarily good at pattern recognition within their frames, and terrible outside them. This is good news for human workers because thanks to our multiple senses, our frames are inherently broader than those of digital technologies. Computer vision, hearing, and even touch are getting exponentially better all the time, but there are still tasks where our eyes, ears, and skin, to say nothing of our noses and tongues, surpass their digital equivalents. At present and for some time to come, the sensory package and its tight connection to the pattern-recognition engine of the brain gives us a broader frame.

pages: 340 words: 90,674

The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future by Geoffrey Cain

airport security, Alan Greenspan, AlphaGo, anti-communist, Bellingcat, Berlin Wall, Black Lives Matter, Citizen Lab, cloud computing, commoditize, computer vision, coronavirus, COVID-19, deep learning, DeepMind, Deng Xiaoping, Edward Snowden, European colonialism, fake news, Geoffrey Hinton, George Floyd, ghettoisation, global supply chain, Kickstarter, land reform, lockdown, mass immigration, military-industrial complex, Nelson Mandela, Panopticon Jeremy Bentham, pattern recognition, phenotype, pirate software, post-truth, purchasing power parity, QR code, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Right to Buy, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, speech recognition, TikTok, Tim Cook: Apple, trade liberalization, trade route, undersea cable, WikiLeaks

Dave Gershgorn, “The Inside Story of How AI Got Good Enough to Dominate Silicon Valley,” Business Insider, June 28, 2018, https://qz.com/1307091/the-inside-story-of-how-ai-got-good-enough-to-dominate-silicon-valley/. 10. I am grateful to a former Google AI developer for illustrating the techniques and commercial applications of GPU technologies in an interview. 11. Allison Linn, “Microsoft Researchers Win ImageNet Computer Vision Challenge,” AI Blog, Microsoft, December 10, 2015, https://blogs.microsoft.com/ai/microsoft-researchers-win-imagenet-computer-vision-challenge/. 12. Crunchbase, “Series A—MEGVII,” announcement of Series A funding round, July 18, 2013, https://www.crunchbase.com/funding_round/megvii-technology-series-a--927a6b8b. 13. Shu-Ching Jean Chen, “SenseTime: The Faces behind China’s Artificial Intelligence Unicorn,” Forbes Asia, March 7, 2018, https://www.forbes.com/sites/shuchingjeanchen/2018/03/07/the-faces-behind-chinas-omniscient-video-surveillance-technology/?

pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Perhaps Buterin, who launched Bitcoin Magazine while working as research assistant to the cryptographer Ian Goldberg, is the truest legatee of Shannon’s vision. Like Shannon he can move seamlessly between the light and dark sides of information, between communication and cryptography. Shannon’s information theory, like Turing’s computational vision, began with an understanding of codes. His first major paper, “A Mathematical Theory of Cryptography” (1945) proved that a perfect randomized one-time pad constitutes an unbreakable code, a singularity. The theory of information deals with a continuum between white noise (purely random) and perfect order (predictable and information-free).

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He ended up with a cool hat, better Mandarin, and a sharper sales pitch but no manufacturer or market for the product. “The technology was not mature,” Stephen decided. Despite the disappointment, he still did not want to work on “something that wasn’t mine.” In early 2015, Gary Bradski—the robotics pioneer who developed computer vision at Intel, founded the Willow Garage robotics incubator, which convinced Wired’s Kevin Kelly that “robots have wants,” and started Industrial Perception, which made “stevedore robots” that could, as Stephen Balaban described it, “pick up and chuck a box so elegantly” that Google bought them—that Gary Bradski—invited Balaban to join his deep-learning team at Magic Leap.

pages: 359 words: 96,019

How to Turn Down a Billion Dollars: The Snapchat Story by Billy Gallagher

Airbnb, Albert Einstein, Amazon Web Services, AOL-Time Warner, Apple's 1984 Super Bowl advert, augmented reality, Bernie Sanders, Big Tech, Black Swan, citizen journalism, Clayton Christensen, computer vision, data science, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, fail fast, Fairchild Semiconductor, Frank Gehry, gamification, gentrification, Google Glasses, Hyperloop, information asymmetry, Jeff Bezos, Justin.tv, Kevin Roose, Lean Startup, Long Term Capital Management, Mark Zuckerberg, Menlo Park, minimum viable product, Nelson Mandela, Oculus Rift, paypal mafia, Peter Thiel, power law, QR code, Robinhood: mobile stock trading app, Salesforce, Sand Hill Road, Saturday Night Live, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, Snapchat, social graph, SoftBank, sorting algorithm, speech recognition, stealth mode startup, Steve Jobs, TechCrunch disrupt, too big to fail, value engineering, Y Combinator, young professional

The video did not do much to clear up the perplexity surrounding the app. But Evan has also embraced opaqueness at Snapchat. The app is designed for existing users rather than new ones; this helped early growth at Snapchat as users showed friends how to use all of the app’s features in person. Confusion also lets Snapchat work in private, building hardware, computer-vision software capable of analyzing Snapchat pictures, and other moonshot projects that are key to the company’s future. This attitude, combined with Snapchat’s youth-focused design, has led outsiders to question how serious the company is. As Snapchat set out on its IPO roadshow, Evan, Bobby, and the team found themselves pitching the company to potential investors far outside Snapchat’s core demographic.

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When they eventually sold the company to Snapchat, it was a reunion of sorts, as Rodriguez had lived with Evan (and Reggie) in the Donner dorm at Stanford back in their freshman year. Evan set up a new division of the company, dubbed Snap Lab, and filled it with the ex-Vergence team and engineers with experience working on computer vision, gaze tracking, and speech recognition. Over the next year, Snapchat recruited a dozen wearable technology experts, industrial designers, and people with experience in the fashion industry. Members of the Snap Lab team took frequent trips to Shenzhen, China, to prepare a potential supply chain for a Snapchat hardware product.

pages: 407 words: 103,501

The Digital Divide: Arguments for and Against Facebook, Google, Texting, and the Age of Social Netwo Rking by Mark Bauerlein

Alvin Toffler, Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, digital divide, disintermediation, folksonomy, Frederick Winslow Taylor, Future Shock, Hacker News, Herbert Marcuse, Howard Rheingold, invention of movable type, invention of the steam engine, invention of the telephone, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, late fees, Lewis Mumford, Mark Zuckerberg, Marshall McLuhan, means of production, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, PalmPilot, peer-to-peer, pets.com, radical decentralization, Results Only Work Environment, Saturday Night Live, scientific management, search engine result page, semantic web, Silicon Valley, slashdot, social graph, social web, software as a service, speech recognition, Steve Jobs, Stewart Brand, technology bubble, Ted Nelson, the long tail, the strength of weak ties, The Wisdom of Crowds, Thorstein Veblen, web application, Yochai Benkler

. >>> how the web learns: explicit vs. implicit meaning But how does the Web learn? Some people imagine that for computer programs to understand and react to meaning, meaning needs to be encoded in some special taxonomy. What we see in practice is that meaning is learned “inferentially” from a body of data. Speech recognition and computer vision are both excellent examples of this kind of machine learning. But it’s important to realize that machine learning techniques apply to far more than just sensor data. For example, Google’s ad auction is a learning system, in which optimal ad placement and pricing are generated in real time by machine learning algorithms.

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Carr, David Carr, Nicholas CDDB Cell phones cameras in in Kenya task switching and Centralization Centre for Addiction and Mental Health Chevy.com Chevy Tahoe Chua, Amy Cicierega, Neil Citizendium Citizen journalism Citizen media Civic causes, Net Geners and Civic disengagement Civic engagement Classmates.com Click Health Clinton, Hillary Clocks Cloudmark Club Penguin CNN CNN Pipeline Coates, Tom Cognition Digital Native differences in Internet use and multitasking and Cognitive psychology Cognitive science Cognitive surplus Cohan, Peter Cohen, Leonard Col, Cynthia Collaboration Collective intelligence collegeabc.com Comcast Company of Friends Complex adaptive networks Comprehension Compressed workweeks Computer-aided design (CAD) Computer games. See Video games Computer vision Computing Power and Human Reason: From Judgement to Calculation (Weizenbaum) Conceptual models Conspicuous consumption Continuous partial attention Cooperating data subsystems Copernicus Counterculture craigslist Craik, Fergus Crary, Jonathan Crest Critical thinking Crowdsourcing Cunningham, Ward CUSeeMe Customization Cyberculture Cyberpunk Czerwinski, Mary Daily Kos Dallas (television series) Darwin, Charles Dateline Dean, Howard Deductions Deep reading Deep thinking del.icio.us Dell Dennett, Daniel The Departed (film) Department of Defense, U.S., learning games and The Diagnosis (Lightman) The Diffusion Group Digg Digital Immigrants Digital Media and Learning Initiative Digital Natives.

pages: 346 words: 97,330

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AlphaGo, Amazon Mechanical Turk, Apollo 13, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive load, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, deindustrialization, deskilling, digital divide, do well by doing good, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, fake news, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, machine translation, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, power law, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, TED Talk, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, work culture , Works Progress Administration, Y Combinator, Yochai Benkler

Forbes, December 18, 2017. https://www.forbes.com/sites/frederickdaso/2017/12/18/bill-gates-elon-musk-are-worried-about-automation-but-this-robotics-company-founder-embraces-it/. Dayton, Eldorous. Walter Reuther: The Autocrat of the Bargaining Table. New York: Devin-Adain, 1958. Deng, J., W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. “ImageNet: A Large-Scale Hierarchical Image Database.” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–55. Piscataway, NJ: IEEE. https://doi.org/10.1109/CVPR.2009.5206848. Denyer, Simon. Rogue Elephant: Harnessing the Power of India’s Unruly Democracy. New York: Bloomsbury Press, 2014. DePillis, Lydia. “The Next Labor Fight Is Over When You Work, Not How Much You Make.”

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Murphy, Machine Learning: A Probabilistic Perspective (Cambridge, MA: MIT Press, 2012). [back] 9. Fei-Fei Li, “ImageNet: Where Have We Been? Where Are We Going?,” ACM Learning Webinar, https://learning.am.org/, accessed September 21, 2017; Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (Piscataway, NJ: IEEE), 248–55. [back] 10. Accuracy went from 72 percent to 97 percent between 2010 and 2016. [back] 11. In fact, Alexander Wissner-Gross said, “Data sets—not algorithms—might be the limiting factor to development of human-level artificial intelligence.”

pages: 122 words: 29,286

Learning Scikit-Learn: Machine Learning in Python by Raúl Garreta, Guillermo Moncecchi

computer vision, Debian, Everything should be made as simple as possible, Higgs boson, Large Hadron Collider, natural language processing, Occam's razor, Silicon Valley

Thanks to all the people of the Natural Language Group and the Instituto de Computación at the Universidad de la República. I am proud of the great job we do every day building the uruguayan NLP and ML community. About the Reviewers Andreas Hjortgaard Danielsen holds a Master's degree in Computer Science from the University of Copenhagen, where he specialized in Machine Learning and Computer Vision. While writing his Master's thesis, he was an intern research student in the Lampert Group at the Institute of Science and Technology (IST), Austria in Vienna. The topic of his thesis was object localization using conditional random fields with special focus on efficient parameter learning. He now works as a software developer in the information services industry where he has used scikit-learn for topic classification of text documents.

pages: 214 words: 31,751

Software Engineering at Google: Lessons Learned From Programming Over Time by Titus Winters, Tom Manshreck, Hyrum Wright

anti-pattern, computer vision, continuous integration, defense in depth, en.wikipedia.org, functional programming, Jevons paradox, job automation, loss aversion, microservices, reproducible builds, supply-chain attack, transaction costs, Turing complete

It’s All About Dependencies In looking through the above problems, one theme repeats over and over: managing your own code is fairly straightforward, but managing its dependencies is much harder (see “Dependency Management”) is devoted to covering this problem in detail). There are all sorts of dependencies: sometimes there’s a dependency on a task (e.g. “push the documentation before I mark a release as complete”), and sometimes there’s a dependency on an artifact (e.g. “I need to have the latest version of the computer vision library to build my code”). Sometimes you have internal dependencies on another part of your codebase, and sometimes you have external dependencies on code or data owned by another team (either in your organization or a third party). But in any case, the idea of “I need that before I can have this” is something that recurs repeatedly in the design of build systems, and managing dependencies is perhaps the most fundamental job of a build system.

pages: 394 words: 108,215

What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry by John Markoff

Any sufficiently advanced technology is indistinguishable from magic, Apple II, back-to-the-land, beat the dealer, Bill Duvall, Bill Gates: Altair 8800, Buckminster Fuller, California gold rush, card file, computer age, Computer Lib, computer vision, conceptual framework, cuban missile crisis, different worldview, digital divide, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, Electric Kool-Aid Acid Test, Fairchild Semiconductor, General Magic , general-purpose programming language, Golden Gate Park, Hacker Ethic, Hans Moravec, hypertext link, informal economy, information retrieval, invention of the printing press, Ivan Sutherland, Jeff Rulifson, John Markoff, John Nash: game theory, John von Neumann, Kevin Kelly, knowledge worker, Lewis Mumford, Mahatma Gandhi, Menlo Park, military-industrial complex, Mother of all demos, Norbert Wiener, packet switching, Paul Terrell, popular electronics, punch-card reader, QWERTY keyboard, RAND corporation, RFC: Request For Comment, Richard Stallman, Robert X Cringely, Sand Hill Road, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, speech recognition, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, technological determinism, Ted Nelson, The Hackers Conference, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing test, union organizing, Vannevar Bush, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight

McCarthy had previously gotten Licklider interested in time-sharing, and years later McCarthy said that if he had known that Licklider was going to underwrite the MIT work, he would never have come to Stanford. Initially, McCarthy had been successful in getting a small amount of funding for AI research from Licklider, and the Digital Equipment Corporation had donated the PDP-1 to the young professor. McCarthy had meanwhile become interested in some vexing issues in computer vision that would need to be solved if robots were to recognize and manipulate blocks successfully. In 1964, he had applied for a larger grant, which he received, and he even had the audacity to ask ARPA to allow him to hire an executive officer. By that time, Ivan Sutherland, the designer of the brilliant Sketchpad drawing system, had succeeded Licklider.

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It was an instant success, but then the legend grew over time as the world came to realize what Engelbart and his research team had wrought. One reason the presentation worked as well as it did was because at the other end of the hall, standing on a raised platform, was Bill English, Engelbart’s lead engineer. It was easy for Engelbart to wave his hands and conceptualize his computing vision, but someone had to build the demonstration from scratch. And that someone was English. An absolute pragmatist, he had an uncanny knack for making things work. English was the one who had tracked down the remarkable Eidaphor video projector for the demonstration. On loan from NASA, and with the blessing of Bob Taylor at ARPA, the Eidaphor was the only technology that could create the kind of effect that Engelbart had in mind.

pages: 419 words: 109,241

A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, AlphaGo, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, financial innovation, flying shuttle, Ford Model T, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kevin Roose, Khan Academy, Kickstarter, Larry Ellison, low skilled workers, lump of labour, machine translation, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, technological solutionism, TED Talk, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

The Electronic Frontier Foundation lists the winning systems in a similar chart, and also plots the human error rate; see https://www.eff.org/ai/metrics#Vision (accessed July 2018). For an overview of the challenge, see Olga Russakovsky, Jia Deng, Hao Su, et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision 115, no. 3 (2015): 211–52. 25. Quoted in Susskind and Susskind, Future of the Professions, p. 161. 26. Not all researchers changed direction of travel in this way, though. Marvin Minsky in fact made the opposite move, moving from bottom-up approaches to AI, to top-down ones instead; see https://www.youtube.com/watch?

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“The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking.” Columbia Law Review 104, no. 4 (2004): 1150–1210. Russakovsky, Olga, Jia Deng, Hao Su, et al. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115, no. 3 (2015): 211–52. Russell, Bertrand. In Praise of Idleness and Other Essays. New York: Routledge, 2004. Saez, Emmanuel. “Striking It Richer: The Evolution of Top Incomes in the United States.” Published online at https://eml.berkeley.edu/~saez/ (2016). Saez, Emmanuel, and Thomas Piketty.

The Smart Wife: Why Siri, Alexa, and Other Smart Home Devices Need a Feminist Reboot by Yolande Strengers, Jenny Kennedy

active measures, Amazon Robotics, Anthropocene, autonomous vehicles, Big Tech, Boston Dynamics, cloud computing, cognitive load, computer vision, Computing Machinery and Intelligence, crowdsourcing, cyber-physical system, data science, deepfake, Donald Trump, emotional labour, en.wikipedia.org, Evgeny Morozov, fake news, feminist movement, game design, gender pay gap, Grace Hopper, hive mind, Ian Bogost, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, John Markoff, Kitchen Debate, knowledge economy, Masayoshi Son, Milgram experiment, Minecraft, natural language processing, Network effects, new economy, pattern recognition, planned obsolescence, precautionary principle, robot derives from the Czech word robota Czech, meaning slave, self-driving car, Shoshana Zuboff, side hustle, side project, Silicon Valley, smart grid, smart meter, social intelligence, SoftBank, Steve Jobs, surveillance capitalism, systems thinking, technological solutionism, technoutopianism, TED Talk, Turing test, Wall-E, Wayback Machine, women in the workforce

The Look Book records what you wear and when, so that you can “keep track of your favorites and take your closet with you.”25 Amazon’s Echo Look also includes all the other usual Alexa accessories, and its features are likely to continue expanding. In 2018, Look users could crowdsource votes on their outfit; they will eventually be able to make use of a “mirror” that dresses them in virtual clothes.26 Using computer vision, pattern recognition, neural networks, and machine learning, the device is part of a system that will one day be able to design clothes by analyzing the Look’s database of images, identifying emerging trends, and then applying the learning to generate new items from scratch. Such possibilities raise a whole host of other consumption and sustainability concerns associated with fast fashion that support our arguments from chapter 4.

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From 2013 on, Jenny simultaneously worked on another ARC project, titled “An Investigation of the Early Adoption and Appropriation of High-Speed Broadband in the Domestic Environment.” Together and with our colleagues, in 2018 we received a gift from the Intel Corporation to reanalyze data from Yolande’s ARC Automating the Smart Home project around Intel’s ambient computing vision for the smart home: protection, productivity, and pleasure—or the 3Ps. In 2017, we came up with the idea for this book, as a collective product of the gendered concerns that we’d both been raising in relation to our respective projects. We unofficially started a smart wife side project—and hired a fabulous research assistant, Paula Arcari, to help us fill in the gaps from our research thus far.

pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

"World Economic Forum" Davos, algorithmic bias, Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, behavioural economics, Berlin Wall, Big Tech, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, Citizen Lab, classic study, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, context collapse, corporate governance, corporate personhood, creative destruction, cryptocurrency, data science, deep learning, digital capitalism, disinformation, dogs of the Dow, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Easter island, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, facts on the ground, fake news, Ford Model T, Ford paid five dollars a day, future of work, game design, gamification, Google Earth, Google Glasses, Google X / Alphabet X, Herman Kahn, hive mind, Ian Bogost, impulse control, income inequality, information security, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kevin Roose, knowledge economy, Lewis Mumford, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, off-the-grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, public intellectual, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Salesforce, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social contagion, social distancing, social graph, social web, software as a service, speech recognition, statistical model, Steve Bannon, Steve Jobs, Steven Levy, structural adjustment programs, surveillance capitalism, technological determinism, TED Talk, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, vertical integration, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck, work culture , Yochai Benkler, you are the product

Adding insult to injury, data rendered by this wave of things are notoriously insecure and easily subject to breaches. Moreover, manufacturers have no legal responsibility to notify device owners when data are stolen or hacked. There are other, even more grandiose ambitions for the rendition of all solitary things. Companies such as Qualcomm, Intel, and ARM are developing tiny, always-on, low-power computer vision modules that can be added to any device, such as your phone or refrigerator, or any surface. A Qualcomm executive says that appliances and toys can know what’s going on around them: “A doll could detect when a child’s face turns toward it.”17 Consider “smart skin,” developed by brilliant university scientists and now poised for commercial elaboration.

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The idea here is that readily available devices “can be used to transmit information to only wireless receivers that are in contact with the body,” thus creating the basis for secure and private communications independent of normal Wi-Fi transmissions, which can easily be detected.37 Take a casual stroll through the shop at the New Museum for Contemporary Art in Manhattan, and you pass a display of its bestseller: table-top mirrors whose reflecting surface is covered with the bright-orange message “Today’s Selfie Is Tomorrow’s Biometric Profile.” This “Think Privacy Selfie Mirror” is a project of the young Berlin-based artist Adam Harvey, whose work is aimed at the problem of surveillance and foiling the power of those who surveil. Harvey’s art begins with “reverse engineering… computer vision algorithms” in order to detect and exploit their vulnerabilities through camouflage and other forms of hiding. He is perhaps best known for his “Stealth Wear,” a series of wearable fashion pieces intended to overwhelm, confuse, and evade drone surveillance and, more broadly, facial-recognition software.

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Now he redirects that meaning to create garments that separate human experience from the powers that surveil.38 Another Harvey project created an aesthetic of makeup and hairstyling—blue feathers suspended from thick black bangs, dreadlocks that dangle below the nose, cheekbones covered in thick wedges of black and white paint, tresses that snake around the face and neck like octopus tentacles—all designed to thwart facial-recognition software and other forms of computer vision. Harvey is one among a growing number of artists, often young artists, who direct their work to the themes of surveillance and resistance. Artist Benjamin Grosser’s Facebook and Twitter “demetricators” are software interfaces that present each site’s pages with their metrics deleted: “The numbers of ‘likes,’ ‘friends,’ followers, retweets… all disappear.”

pages: 197 words: 35,256

NumPy Cookbook by Ivan Idris

business intelligence, cloud computing, computer vision, data science, Debian, en.wikipedia.org, Eratosthenes, mandelbrot fractal, p-value, power law, sorting algorithm, statistical model, transaction costs, web application

Another option is to obtain the latest development version by cloning the Git repository, or downloading the repository as a zip file from Github. Then, you will need to run the following command: python setup.py install Detecting corners Corner detection (http://en.wikipedia.org/wiki/Corner_detection ) is a standard technique in Computer Vision. scikits-image offers a Harris Corner Detector, which is great, because corner detection is pretty complicated. Obviously, we could do it ourselves from scratch, but that would violate the cardinal rule of not reinventing the wheel. Getting ready You might need to install jpeglib on your system to be able to load the scikits-learn image, which is a JPEG file.

pages: 370 words: 112,809

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

The company’s lead product, Traffic Jam, helps sift through data online to search for victims and trafficking rings. Local, state, and federal law enforcement, including the FBI, have used Traffic Jam to identify thousands of victims of sex trafficking, and it has also been adopted in Canada and the United Kingdom. AI can perform tasks exponentially faster than humans, saving massive amounts of time. Computer vision can identify multiple victims advertised and sold from the same hotel bedroom, identifying the bedding or wallpaper pattern, for example. Traffic Jam also sorts the kinds of language that is coded in online human trafficking advertisements. In 2017, Marinus Analytics also released Face Search, the first facial recognition tool to fight sex trafficking.

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.… It doesn’t matter what your disease is; today, A.I. is not yet part of clinical treatment.”14 Barzilay developed an algorithm that analyzes mammogram images differently, assessing risk before cancer develops, which is something that is not attempted by human radiologists. In terms of detection of existing cancer, Barzilay believes that today the best radiologists are still better than machines, though the gap is narrowing fast. The year after she was diagnosed, she created a system that uses computer vision technology to independently learn about the patterns of diagnosing breast cancer. She partnered with Dr. Constance Lehman, chief of breast imaging at Boston’s Massachusetts General Hospital. Lehman herself serves on several key national committees and was eager to apply deep learning to all aspects of breast cancer care, from prevention to detection to treatment.

pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, Alvin Toffler, Apollo 11, Apollo 13, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Boston Dynamics, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, Citizen Lab, cloud computing, Cody Wilson, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, data science, Dean Kamen, deep learning, DeepMind, digital rights, disinformation, disintermediation, Dogecoin, don't be evil, double helix, Downton Abbey, driverless car, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Firefox, Flash crash, Free Software Foundation, future of work, game design, gamification, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, Hacker News, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, information security, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Joi Ito, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, Kuwabatake Sanjuro: assassination market, Large Hadron Collider, Larry Ellison, Laura Poitras, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, machine translation, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, offshore financial centre, operational security, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, printed gun, RAND corporation, ransomware, Ray Kurzweil, Recombinant DNA, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Russell Brand, Salesforce, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, SimCity, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, SoftBank, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, subscription business, supply-chain management, synthetic biology, tech worker, technological singularity, TED Talk, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, the long tail, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Virgin Galactic, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, you are the product, zero day

Baxter can learn to do simple tasks, such as “pick and place” objects on an assembly line, in just five minutes. It has an adorable face on its head-mounted display screen and two highly dexterous arms, which can move in any direction required to get a task done. Baxter requires no special programming and learns by using its computer vision to watch an employee perform a task, which the bot can repeat ad infinitum. As costs drop even further, these robots will be competitively priced compared with cheap overseas labor, and many hope a rise in domestic robotics use may lead to a renaissance in American manufacturing. Today robots are showing up everywhere from restaurants to hospitals.

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But now another deeply disruptive player has entered the world of robotics: Google. The search giant is on a robo-buying binge and purchased or acquired eight separate robotics companies in a six-month period through 2014, including companies that specialize in humanoid walking robots, robotic arms, robotics software, and computer vision. Its largest and most surprising robotics acquisition, however, was the military robotics company Boston Dynamics, the same folks who make BigDog, Cheetah, Sand Flea, RiSE, and PETMAN (a biped humanoid robot that might well be the soldier of the future). Google also bested Facebook’s offer to buy Titan Aerospace, a maker of jet-sized solar-powered drones that can remain aloft for three years without landing.

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Today we have the following: • algorithmic trading on Wall Street (bots carry out stock buys and sells) • algorithmic criminal justice (red-light and speeding cameras determine infractions of the law) • algorithmic border control (an AI can flag you and your luggage for screening) • algorithmic credit scoring (your FICO score determines your creditworthiness) • algorithmic surveillance (CCTV cameras can identify unusual activity by computer vision analysis, and voice recognition can scan your phone calls for troublesome keywords) • algorithmic health care (whether or not your request to see a specialist or your insurance claim is approved) • algorithmic warfare (drones and other robots have the technical capacity to find, target, and kill without human intervention) • algorithmic dating (eHarmony and others promise to use math to find your soul mate and the perfect match) Though the inventors of these algorithmic formulas might wish to suggest they are perfectly neutral, nothing could be further from the truth.

Scikit-Learn Cookbook by Trent Hauck

bioinformatics, book value, computer vision, data science, information retrieval, p-value

He is the author of the book, Code Explorer's Guide to the Open Source Jungle, available online at https://leanpub.com/opensourcebook. To my beloved. Xingzhong is a PhD candidate in Electrical Engineering at Stevens Institute of Technology, Hoboken, New Jersey, where he works as a research assistant, designing and implementing machine-learning models in computer vision and signal processing applications. Although Python is his primary programming language, occasionally, for fun and curiosity, his works might be written on golang, Scala, JavaScript, and so on. As a self-confessed technology geek, he is passionate about exploring new software and hardware. www.it-ebooks.info www.PacktPub.com Support files, eBooks, discount offers, and more For support files and downloads related to your book, please visit www.PacktPub.com.

pages: 159 words: 42,401

Snowden's Box: Trust in the Age of Surveillance by Jessica Bruder, Dale Maharidge

air gap, anti-communist, Bay Area Rapid Transit, Berlin Wall, Black Lives Matter, blockchain, Broken windows theory, Burning Man, Cambridge Analytica, cashless society, Chelsea Manning, citizen journalism, computer vision, crowdsourcing, deep learning, digital rights, disinformation, Donald Trump, Edward Snowden, Elon Musk, end-to-end encryption, Evgeny Morozov, Ferguson, Missouri, Filter Bubble, Firefox, information security, Internet of things, Jeff Bezos, Jessica Bruder, John Perry Barlow, Julian Assange, Laura Poitras, license plate recognition, Mark Zuckerberg, mass incarceration, medical malpractice, messenger bag, Neil Armstrong, Nomadland, Occupy movement, off grid, off-the-grid, pattern recognition, Peter Thiel, Robert Bork, Seymour Hersh, Shoshana Zuboff, Silicon Valley, Skype, social graph, Steven Levy, surveillance capitalism, tech bro, Tim Cook: Apple, web of trust, WikiLeaks

Beyond those we’ve already discussed, the algorithms are strongly biased towards white men and are much more likely to misidentify women and people of color — amplifying preexisting racial and gender biases. Enter Hyphen-Labs, an international collective of women technologists of color. The group is working with Berlin artist and privacy advocate Adam Harvey on the HyperFace Project: a purple camouflage scarf packed with ghost faces, designed to scramble computer-vision algorithms. In the meantime, Harvey has also been developing CV Dazzle: a free toolkit of fashion-based strategies that use hair and makeup to thwart facial-recognition software. The name is an homage to Dazzle camouflage — also known as Razzle Dazzle — a series of striking, black-and-white patterns used by the Allies during World War I to conceal their battleships’ size and orientation.

pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

Adam Curtis, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Anthropocene, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, basic income, behavioural economics, bitcoin, blockchain, bread and circuses, Charles Babbage, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, data science, deep learning, DeepMind, Demis Hassabis, digital capitalism, digital divide, digital rights, discrete time, Douglas Engelbart, driverless car, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, financial engineering, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, Great Leap Forward, Hans Moravec, hive mind, Ian Bogost, income inequality, information trail, Internet of things, invention of writing, iterative process, James Webb Space Telescope, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, Large Hadron Collider, lolcat, loose coupling, machine translation, microbiome, mirror neurons, Moneyball by Michael Lewis explains big data, Mustafa Suleyman, natural language processing, Network effects, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, Recombinant DNA, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, synthetic biology, systems thinking, tacit knowledge, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, We are as Gods, Y2K

What was needed was not only much more computer power but also a lot more data to train the network. After thirty years of research, a million-times improvement in computer power, and vast data sets from the Internet, we now know the answer to this question: Neural networks scaled up to twelve layers deep, with billions of connections, are outperforming the best algorithms in computer vision for object recognition and have revolutionized speech recognition. It’s rare for any algorithm to scale this well, which suggests that they may soon be able to solve even more difficult problems. Recent breakthroughs have been made that allow the application of deep learning to natural-language processing.

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More flexibility means a greater ability to capture the patterns appearing in data but a greater risk of finding patterns that aren’t there. In artificial intelligence research, this tension between structure and flexibility manifests in different kinds of systems that can be used to solve challenging problems like speech recognition, computer vision, and machine translation. For decades, the systems that performed best on those problems came down on the side of structure: They were the result of careful planning, design, and tweaking by generations of engineers who thought about the characteristics of speech, images, and syntax and tried to build into the system their best guesses about how to interpret those particular kinds of data.

pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

Fenech (2014). 111 Micklethwait and Wooldridge, God is Back, 268. 112 <http://www.askmoses.com>. 113 <http://www.christianmingle.com>, <http://jdate.com>, <http://www.muslima.com>. 114 Emily Greenhouse, ‘Treasures in the Wall’, New Yorker, 1 Mar. 2013. 115 Lior Wolf et al., ‘Identifying Join Candidates in the Cairo Genizah’, International Journal of Computer Vision, 94: 1 (2011), 118–35. 116 Lior Wolf and Nachum Dershowitz, ‘Automatic Scribal Analysis of Tibetan Writing’, abstract for panel at the International Association for Tibetan Studies 2013 <http://www.cs.tau.ac.il/~wolf/papers/genizahijcv.pdf> (accessed 7 March 2015). 117 Adiel Ben-Shalom et al., ‘Where is my Other Half?’

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Wolf, Lior, and Nachum Dershowitz, ‘Automatic Scribal Analysis of Tibetan Writing’, abstract for panel at the International Association for Tibetan Studies 2013 <http://www.cs.tau.ac.il/~wolf/papers/genizahijcv.pdf> (accessed 7 March 2015). Wolf, Lior, Rotem Littman, Naama Mayer, Tanya German, Nachum Dershowitz, Roni Shweka, and Yaacov Choueka, ‘Identifying Join Candidates in the Cairo Genizah’, International Journal of Computer Vision, 94: 1 (2011), 118–35. Wootton, Richard, John Craig, and Victor Patterson (eds.), Introduction to Telemedicine, 2nd edn. (London: Hodder Arnold, 2011). Zittrain, Jonathan, The Future of the Internet—And How to Stop It (New Haven: Yale University Press, 2009). Zittrain, Jonathan, and Benjamin Edelman, ‘Documentation of Internet Filtering in Saudi-Arabia’, 12 Sept. 2002 <http://cyber.law.harvard.edu/filtering/saudiarabia/> (accessed 7 March 2015).

pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett

Ada Lovelace, Airbnb, AlphaGo, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, blockchain, Boris Johnson, Californian Ideology, Cambridge Analytica, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, disinformation, Dominic Cummings, Donald Trump, driverless car, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, Filter Bubble, future of work, general purpose technology, gig economy, global village, Google bus, Hans Moravec, hive mind, Howard Rheingold, information retrieval, initial coin offering, Internet of things, Jeff Bezos, Jeremy Corbyn, job automation, John Gilmore, John Maynard Keynes: technological unemployment, John Perry Barlow, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta-analysis, mittelstand, move fast and break things, Network effects, Nicholas Carr, Nick Bostrom, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, post-truth, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Bannon, Steve Jobs, Steven Levy, strong AI, surveillance capitalism, TaskRabbit, tech worker, technological singularity, technoutopianism, Ted Kaczynski, TED Talk, the long tail, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator, you are the product

Underneath Tony’s feet and behind his oversized wheel were work-in-progress wires, pumps, shiny levers and many cogs. They were connected to computers in the back of the cab, which were under Kartik’s command. The software controlled the pedals and steering wheel, which constantly adjust to real-time data collected by mounted radars and computer vision sensors that covered the vehicle: position, speed, road markings, other cars’ positions and speed and so on. We left the narrow residential roads, and joined Freeway 95. Tony turned to Stefan: ‘I could kick the system on if you guys are ready.’ ‘Rosebud on,’ Stefan shouted into his walkie-talkie to other crew members, who were following in a car.

Speaking Code: Coding as Aesthetic and Political Expression by Geoff Cox, Alex McLean

4chan, Amazon Mechanical Turk, augmented reality, bash_history, bitcoin, Charles Babbage, cloud computing, commons-based peer production, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, dematerialisation, Donald Knuth, Douglas Hofstadter, en.wikipedia.org, Everything should be made as simple as possible, finite state, Free Software Foundation, Gabriella Coleman, Gödel, Escher, Bach, Hacker Conference 1984, Ian Bogost, Jacques de Vaucanson, language acquisition, Larry Wall, late capitalism, means of production, natural language processing, Neal Stephenson, new economy, Norbert Wiener, Occupy movement, packet switching, peer-to-peer, power law, Richard Stallman, Ronald Coase, Slavoj Žižek, social software, social web, software studies, speech recognition, SQL injection, stem cell, Stewart Brand, systems thinking, The Nature of the Firm, Turing machine, Turing test, Vilfredo Pareto, We are Anonymous. We are Legion, We are the 99%, WikiLeaks, Yochai Benkler

“Hello world!” is expressed thus: >+++++++++[<++++++++>-]<.>+++++++[<++++>-]<+.+++++++..+++.>>>++++++++[<++++>]<.>>>++++++++++[<+++++++++>-]<---.<<<<.+++.------.--------.>>+. Taking this indeterminacy further still, Brainfuck exceeds the world of computation in Bodyfuck, an interpreter using computer vision techniques to map bodily gestures to the Brainfuck instruction set.7 But as with all signifying systems, interpretation still takes place at all levels, even when they are as esoteric as the examples mentioned above. The reader, whether human or machine, is also cast as one of the objects of the software and operating system.

pages: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

23andMe, 3D printing, access to a mobile phone, Albert Einstein, Alvin Toffler, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, carbon credits, Charles Babbage, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, Dennis Tito, digital Maoism, digital map, digital nomad, driverless car, Elon Musk, energy security, Eyjafjallajökull, failed state, Ford Model T, future of work, Future Shock, gamification, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, Higgs boson, high-speed rail, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Neil Armstrong, Network effects, new economy, ocean acidification, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, private spaceflight, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, space junk, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, synthetic biology, tech billionaire, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Virgin Galactic, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

Second, if something artificial were to develop consciousness, why would it automatically let us know? Perhaps it would keep this to itself and refuse to participate in childish intelligence tests. The 60s and 70s saw a great deal of progress in AI, but breakthroughs failed to come. Instead scientists and developers focused on specific problems, such as speech and text recognition and computer vision. However, we may now be less than a decade away from seeing the AI vision become a reality. The Chinese room experiment In 1980, John Searle, an American philosopher, argued in a paper that a computer, or perhaps more accurately a bit of software, could pass the Turing test and behave much like a human being at a distance without being truly intelligent—that words, symbols or instructions could be interpreted or reacted to without any true understanding.

Natural Language Processing with Python and spaCy by Yuli Vasiliev

Bayesian statistics, computer vision, data science, database schema, Easter island, en.wikipedia.org, loose coupling, natural language processing, Skype, statistical model

You can also connect the statistical models trained by other popular machine learning (ML) libraries, such as TensorFlow, Keras, scikit-learn, and PyTorch. In addition, spaCy can operate seamlessly with other libraries in Python’s AI ecosystem, allowing you to, for example, take advantage of computer vision in your chatbot application, as you’ll do in Chapter 12. Who Should Read This Book? This book is for those interested in learning how to use NLP in practice. In particular, it might be interesting to people who want to develop chatbots for businesses or just for fun. Regardless of your background or experience with NLP or programming, you’ll be able to follow the code examples provided in this book because they all include detailed explanations of the process involved.

pages: 561 words: 163,916

The History of the Future: Oculus, Facebook, and the Revolution That Swept Virtual Reality by Blake J. Harris

"World Economic Forum" Davos, 4chan, airport security, Anne Wojcicki, Apollo 11, Asian financial crisis, augmented reality, barriers to entry, Benchmark Capital, Bernie Sanders, bitcoin, call centre, Carl Icahn, company town, computer vision, cryptocurrency, data science, disruptive innovation, Donald Trump, drone strike, Elon Musk, fake news, financial independence, game design, Grace Hopper, hype cycle, illegal immigration, invisible hand, it's over 9,000, Ivan Sutherland, Jaron Lanier, Jony Ive, Kickstarter, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, Neal Stephenson, Network effects, Oculus Rift, off-the-grid, Peter Thiel, QR code, sensor fusion, Sheryl Sandberg, side project, Silicon Valley, SimCity, skunkworks, Skype, slashdot, Snapchat, Snow Crash, software patent, stealth mode startup, Steve Jobs, unpaid internship, white picket fence

I wanted to stay, but they made me get moved.” Iribe nodded. “Where are you headed?” “I’m going up to the bank. The SECU bank.” “That’s pretty far. Why don’t you get in and let’s talk? I’ll give you a ride.” As the two caught up, and Antonov talked about a job he’d recently taken—which involved UI (user interface) design and computer vision for handwriting recognition—Iribe realized that Antonov must actually be pretty great with computers. “I’m still working for that company Quatrefoil,” Iribe explained. “It’s a tech museum project and I could really use some help.” Antonov remained relatively neutral on the idea until Iribe started talking about graphics.

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Luckey and Dycus were at the office trying to determine the ideal eye-relief settings—the ideal distance between lens and eyeball—that ought to be implemented into the eyecups of DK1 before manufacturing began in China. At a normal company, this would all be calibrated with some sort of fancy computer vision system. But this being a start-up, always short on time and money, they needed a faster and cheaper solution. “Chris! I got it!” Luckey had shouted minutes earlier. “We’re going to drill a hole in the center of the lens and then run a flat-top screw through the opening so it’s jutting out, you know?

pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

‘Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation’, arXiv, 8 October 2016 <https://arxiv.org/abs/1609.08144> (accessed 6 December 2017); Yaniv Taigman et al.,‘DeepFace: Closing the Gap to Human-Level Performance in Face Verification’, 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014 <https://www.cs.toronto.edu/~ranzato/publications/taigman_ cvpr14.pdf> (accessed 11 December 2017); Aäron van den Oord et al., ‘WaveNet: A Generative Model for Raw Audio’, arXiv, 19 September 2016 <https://arxiv.org/abs/1609.03499> (accessed 6 December 2017). 4.

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Swift, Adam. Political Philosophy: A Beginners’ Guide for Students and Politicians (Second Edition). Cambridge: Polity Press, 2007. Taigman, Yaniv, Ming Yang, Marc’ Aurelio Ranzato, and Lior Wolf. ‘DeepFace: Closing the Gap to Human-Level Performance in Face Verification’. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 <https://www.cs.toronto.edu/~ranzato/ publications/taigman_cvpr14.pdf> (accessed 11 Dec. 2017). Takahashi, Dean. ‘Magic Leap Sheds Light on its Retina-based Augmented Reality 3D Displays’. VentureBeat, 20 Feb. 2015 <http://venturebeat.com/ 2015/02/20/magic-leap-sheds-light-on-its-retina-based-augmentedreality-3d-displays/> (accessed 30 Nov. 2017).

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

They also provide important insight into the concept of causality.28 One advantage of relating learning problems from specific domains to the general problem of Bayesian inference is that new algorithms that make Bayesian inference more efficient will then yield immediate improvements across many different areas. Advances in Monte Carlo approximation techniques, for example, are directly applied in computer vision, robotics, and computational genetics. Another advantage is that it lets researchers from different disciplines more easily pool their findings. Graphical models and Bayesian statistics have become a shared focus of research in many fields, including machine learning, statistical physics, bioinformatics, combinatorial optimization, and communication theory.35 A fair amount of the recent progress in machine learning has resulted from incorporating formal results originally derived in other academic fields.

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INDEX A Afghan Taliban 215 Agricultural Revolution 2, 80, 261 AI-complete problem 14, 47, 71, 93, 145, 186 AI-OUM, see optimality notions AI-RL, see optimality notions AI-VL, see optimality notions algorithmic soup 172 algorithmic trading 16–17 anthropics 27–28, 126, 134–135, 174, 222–225 definition 225 Arendt, Hannah 105 Armstrong, Stuart 280, 291, 294, 302 artificial agent 10, 88, 105–109, 172–176, 185–206; see also Bayesian agent artificial intelligence arms race 64, 88, 247 future of 19, 292 greater-than-human, see superintelligence history of 5–18 overprediction of 4 pioneers 4–5, 18 Asimov, Isaac 139 augmentation 142–143, 201–203 autism 57 automata theory 5 automatic circuit breaker 17 automation 17, 98, 117, 160–176 B backgammon 12 backpropagation algorithm 8 bargaining costs 182 Bayesian agent 9–11, 123, 130; see also artificial agent and optimality notions Bayesian networks 9 Berliner, Hans 12 biological cognition 22, 36–48, 50–51, 232 biological enhancement 36–48, 50–51, 142–143, 232; see also cognitive enhancement boxing 129–131, 143, 156–157 informational 130 physical 129–130 brain implant, see cyborg brain plasticity 48 brain–computer interfaces 44–48, 51, 83, 142–143; see also cyborg Brown, Louise 43 C C. elegans34–35, 266, 267 capability control 129–144, 156–157 capital 39, 48, 68, 84–88, 99, 113–114, 159–184, 251, 287, 288, 289 causal validity semantics 197 CEV, see coherent extrapolated volition Chalmers, David 24, 265, 283, 295, 302 character recognition 15 checkers 12 chess 11–22, 52, 93, 134, 263, 264 child machine 23, 29; see also seed AI CHINOOK 12 Christiano, Paul 198, 207 civilization baseline 63 cloning 42 cognitive enhancement 42–51, 67, 94, 111–112, 193, 204, 232–238, 244, 259 coherent extrapolated volition (CEV) 198, 211–227, 296, 298, 303 definition 211 collaboration (benefits of) 249 collective intelligence 48–51, 52–57, 67, 72, 142, 163, 203, 259, 271, 273, 279 collective superintelligence 39, 48–49, 52–59, 83, 93, 99, 285 definition 54 combinatorial explosion 6, 9, 10, 47, 155 Common Good Principle 254–259 common sense 14 computer vision 9 computing power 7–9, 24, 25–35, 47, 53–60, 68–77, 101, 134, 155, 198, 240–244, 251, 286, 288; see also computronium and hardware overhang computronium 101, 123–124, 140, 193, 219; see also computing power connectionism 8 consciousness 22, 106, 126, 139, 173–176, 216, 226, 271, 282, 288, 292, 303; see also mind crime control methods 127–144, 145–158, 202, 236–238, 286; see also capability control and motivation selection Copernicus, Nicolaus 14 cosmic endowment 101–104, 115, 134, 209, 214–217, 227, 250, 260, 283, 296 crosswords (solving) 12 cryptographic reward tokens 134, 276 cryptography 80 cyborg 44–48, 67, 270 D DARPA, see Defense Advanced Research Projects Agency DART (tool) 15 Dartmouth Summer Project 5 data mining 15–16, 232, 301 decision support systems 15, 98; see also tool-AI decision theory 10–11, 88, 185–186, 221–227, 280, 298; see also optimality notions decisive strategic advantage 78–89, 95, 104–112, 115–126, 129–138, 148–149, 156–159, 177, 190, 209–214, 225, 252 Deep Blue 12 Deep Fritz 22 Defense Advanced Research Projects Agency (DARPA) 15 design effort, see optimization power Dewey, Daniel 291 Differential Technological Development (Principle of) 230–237 Diffie–Hellman key exchange protocol 80 diminishing returns 37–38, 66, 88, 114, 273, 303 direct reach 58 direct specification 139–143 DNA synthesis 39, 98 Do What I Mean (DWIM) 220–221 domesticity 140–143, 146–156, 187, 191, 207, 222 Drexler, Eric 239, 270, 276, 278, 300 drones 15, 98 Dutch book 111 Dyson, Freeman 101, 278 E economic growth 3, 160–166, 179, 261, 274, 299 Einstein, Albert 56, 70, 85 ELIZA (program) 6 embryo selection 36–44, 67, 268 emulation modulation 207 Enigma code 87 environment of evolutionary adaptedness 164, 171 epistemology 222–224 equation solvers 15 eugenics 36–44, 268, 279 Eurisko 12 evolution 8–9, 23–27, 44, 154, 173–176, 187, 198, 207, 265, 266, 267, 273 evolutionary selection 187, 207, 290 evolvable hardware 154 exhaustive search 6 existential risk 4, 21, 55, 100–104, 115–126, 175, 183, 230–236, 239–254, 256–259, 286, 301–302 state risks 233–234 step risks 233 expert system 7 explicit representation 207 exponential growth, see growth external reference semantics 197 F face recognition 15 failure modes 117–120 Faraday cage 130 Fields Medal 255–256, 272 Fifth-Generation Computer Systems Project 7 fitness function 25; see also evolution Flash Crash (2010) 16–17 formal language 7, 145 FreeCell (game) 13 G game theory 87, 159 game-playing AI 12–14 General Problem Solver 6 genetic algorithms 7–13, 24–27, 237–240; see also evolution genetic selection 37–50, 61, 232–238; see also evolution genie AI 148–158, 285 definition 148 genotyping 37 germline interventions 37–44, 67, 273; see also embryo selection Ginsberg, Matt 12 Go (game) 13 goal-content 109–110, 146, 207, 222–227 Good Old-Fashioned Artificial Intelligence (GOFAI) 7–15, 23 Good, I.

pages: 237 words: 64,411

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, Computing Machinery and Intelligence, corporate governance, crowdsourcing, driverless car, drop ship, Easter island, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kiva Systems, Larry Ellison, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Nick Bostrom, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, short squeeze, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Vitalik Buterin, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

A Japanese competitor claims that its technology can reduce strawberry picking time by 40 percent.23 Blue River Technologies, a Silicon Valley venture-funded startup headed by a Stanford graduate, is developing robots that can weed. To quote from their marketing materials: “We are creating systems that can distinguish crops from weeds in order to kill the weeds without harming the crops or the environment. Our systems use cameras, computer vision, and machine learning algorithms.”24 Note that the coming army of mechanical farmworkers doesn’t have to be faster than the workers they replace because, like autonomous vehicles, they can work in the dark and so aren’t limited to operating in daylight. Warehouse workers. Beyond the picking and packing of orders, as I’ve described above, there’s the loading and unloading of packages.

pages: 271 words: 62,538

The Best Interface Is No Interface: The Simple Path to Brilliant Technology (Voices That Matter) by Golden Krishna

Airbnb, Bear Stearns, computer vision, crossover SUV, data science, en.wikipedia.org, fear of failure, impulse control, Inbox Zero, Internet Archive, Internet of things, Jeff Bezos, Jony Ive, Kickstarter, lock screen, Mark Zuckerberg, microdosing, new economy, Oculus Rift, off-the-grid, Paradox of Choice, pattern recognition, QR code, RFID, self-driving car, Silicon Valley, skeuomorphism, Skype, Snapchat, Steve Jobs, tech worker, technoutopianism, TED Talk, Tim Cook: Apple, Y Combinator, Y2K

Among many things, they monitored defensive impact, the speed and distance of each player, and his number of passes.19 The players wouldn’t have to wear any extra gadgets to enable the system, and they didn’t have to download any apps to empower it; the cameras would work seamlessly and invisibly while the players did their typical thing during each game. To put it technically: There are six computer vision cameras set up along the catwalk of the arena—three per half court. These cameras are synched with complex algorithms extracting x, y, and z positioning data for all objects on the court, capturing 25 pictures per second.20 For fans, the data from the cameras gave them new ways to admire their favorite players.

pages: 225 words: 70,241

Silicon City: San Francisco in the Long Shadow of the Valley by Cary McClelland

affirmative action, Airbnb, algorithmic bias, Apple II, autonomous vehicles, barriers to entry, Black Lives Matter, Burning Man, clean water, cloud computing, cognitive dissonance, Columbine, computer vision, creative destruction, driverless car, El Camino Real, Elon Musk, Fairchild Semiconductor, full employment, gamification, gentrification, gig economy, Golden Gate Park, Google bus, Google Glasses, high net worth, housing crisis, housing justice, income inequality, John Gilmore, John Perry Barlow, Joseph Schumpeter, Loma Prieta earthquake, Lyft, mass immigration, means of production, Menlo Park, Mitch Kapor, open immigration, PalmPilot, rent control, Salesforce, San Francisco homelessness, self-driving car, sharing economy, Silicon Valley, Skype, Social Justice Warrior, Steve Jobs, Steve Wozniak, TaskRabbit, tech bro, tech worker, transcontinental railway, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, vertical integration, William Shockley: the traitorous eight, young professional

I arrived here in September of 2004 to get my PhD, and by October, I was in the Mojave Desert. The research group I joined had started a really cool project, building a car that drives itself. There was a race called the DARPA Grand Challenge, 150 miles, autonomous cars racing through the desert.† I built the computer vision: using a combination of lasers and cameras to figure out what the road looks like and to decide when we could drive faster and slower. In the desert, you just had to drive straight ahead, but on real roads, you had to look all around you for other cars, for lane markers and so on. So we decided to just play with it.

pages: 234 words: 67,589

Internet for the People: The Fight for Our Digital Future by Ben Tarnoff

4chan, A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic management, AltaVista, Amazon Web Services, barriers to entry, Bernie Sanders, Big Tech, Black Lives Matter, blue-collar work, business logic, call centre, Charles Babbage, cloud computing, computer vision, coronavirus, COVID-19, decentralized internet, deep learning, defund the police, deindustrialization, desegregation, digital divide, disinformation, Edward Snowden, electricity market, fake news, Filter Bubble, financial intermediation, future of work, gamification, General Magic , gig economy, God and Mammon, green new deal, independent contractor, information asymmetry, Internet of things, Jeff Bezos, Jessica Bruder, John Markoff, John Perry Barlow, Kevin Roose, Kickstarter, Leo Hollis, lockdown, lone genius, low interest rates, Lyft, Mark Zuckerberg, means of production, Menlo Park, natural language processing, Network effects, Nicholas Carr, packet switching, PageRank, pattern recognition, pets.com, profit maximization, profit motive, QAnon, recommendation engine, rent-seeking, ride hailing / ride sharing, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, single-payer health, smart grid, social distancing, Steven Levy, stock buybacks, supply-chain management, surveillance capitalism, techlash, Telecommunications Act of 1996, TikTok, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, UUNET, vertical integration, Victor Gruen, web application, working poor, Yochai Benkler

This revival was made possible by a number of factors, foremost among them advances in computing power and the abundance of training data that could be sourced from the internet. Deep learning is the paradigm that underlies much of what is currently known as “artificial intelligence,” and has centrally contributed to significant breakthroughs in computer vision and natural language processing. See Andrey Kurenkov, “A Brief History of Neural Nets and Deep Learning,” Skynet Today, September 27, 2020, and Alex Hanna et al., “Lines of Sight,” Logic, December 20, 2020. 109, The sophistication of these systems … “Data imperative”: Marion Fourcade and Kieran Healy, “Seeing Like a Market,” Socio-Economic Review 15, no. 1 (2017): 9–29. 110, The same individual … Smartphone usage: “Mobile Fact Sheet,” April 7, 2021, Pew Research Center.

pages: 834 words: 180,700

The Architecture of Open Source Applications by Amy Brown, Greg Wilson

8-hour work day, anti-pattern, bioinformatics, business logic, c2.com, cloud computing, cognitive load, collaborative editing, combinatorial explosion, computer vision, continuous integration, Conway's law, create, read, update, delete, David Heinemeier Hansson, Debian, domain-specific language, Donald Knuth, en.wikipedia.org, fault tolerance, finite state, Firefox, Free Software Foundation, friendly fire, functional programming, Guido van Rossum, Ken Thompson, linked data, load shedding, locality of reference, loose coupling, Mars Rover, MITM: man-in-the-middle, MVC pattern, One Laptop per Child (OLPC), peer-to-peer, Perl 6, premature optimization, recommendation engine, revision control, Ruby on Rails, side project, Skype, slashdot, social web, speech recognition, the scientific method, The Wisdom of Crowds, web application, WebSocket

He co-founded Kitware in 1998 and since then has helped grow the company to its current position as a leading R&D provider with clients across many government and commercial sectors. Aaron Mavrinac (Thousand Parsec): Aaron is a Ph.D. candidate in electrical and computer engineering at the University of Windsor, researching camera networks, computer vision, and robotics. When there is free time, he fills some of it working on Thousand Parsec and other free software, coding in Python and C, and doing too many other things to get good at any of them. His web site is http://www.mavrinac.com. Kim Moir (Eclipse): Kim works at the IBM Rational Software lab in Ottawa as the Release Engineering lead for the Eclipse and Runtime Equinox projects and is a member of the Eclipse Architecture Council.

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Several common cases, like adding a new library, required changing that source and checking in a new binary. Although this was a unified system in some sense, it had many shortcomings. The other approach the developers had experience with was a gmake based build system for TargetJr. TargetJr was a C++ computer vision environment originally developed on Sun workstations. Originally TargetJr used the imake system to create Makefiles. However, at some point, when a Windows port was needed, the gmake system was created. Both Unix compilers and Windows compilers could be used with this gmake-based system. The system required several environment variables to be set prior to running gmake.

pages: 296 words: 78,631

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael Reiter, ‘Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition’, paper presented at ACM SIGSAC Conference, 2016, https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf. See also: https://commons.wikimedia.org/wiki/File:Milla_Jovovich_Cannes_2011.jpg. 64. Ira Kemelmacher-Shlizerman, Steven M. Seitz, Daniel Miller and Evan Brossard, The MegaFace Benchmark: 1 Million Faces for Recognition at Scale, Computer Vision Foundation, 2015, https://arxiv.org/abs/1512.00596 65. ‘Half of all American adults are in a police face recognition database, new report finds’, press release, Georgetown Law, 18 Oct. 2016, https://www.law.georgetown.edu/news/press-releases/half-of-all-american-adults-are-in-a-police-face-recognition-database-new-report-finds.cfm. 66.

pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Peter Beck" AND "Rocket Lab", Alan Greenspan, Anthropocene, autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Boston Dynamics, Bretton Woods, Brexit referendum, capital controls, capitalist realism, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, CRISPR, David Ricardo: comparative advantage, decarbonisation, deep learning, dematerialisation, DIY culture, Donald Trump, double helix, driverless car, electricity market, Elon Musk, energy transition, Erik Brynjolfsson, fake news, financial independence, Francis Fukuyama: the end of history, future of work, Future Shock, G4S, general purpose technology, Geoffrey Hinton, Gregor Mendel, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, Jevons paradox, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, Leo Hollis, liberal capitalism, low earth orbit, low interest rates, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private spaceflight, Productivity paradox, profit motive, race to the bottom, rewilding, RFID, rising living standards, Robert Solow, scientific management, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, SoftBank, stem cell, Stewart Brand, synthetic biology, technological determinism, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, working-age population

While the world economy may be much bigger now than it was in 1900, employing more people and enjoying far higher output per person, the lines of work nearly everyone performs – drivers, nurses, teachers and cashiers – aren’t particularly new. Actually Existing Automation In March 2017 Amazon launched its Amazon GO store in downtown Seattle. Using computer vision, deep learning algorithms, and sensor fusion to identify selected items the company looked to build a near fully automated store without cashiers. Here Amazon customers would be able to buy items simply by swiping in with a phone, choosing the things they wanted and swiping out to leave, their purchases automatically debited to their Amazon account.

pages: 685 words: 203,949

The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

Abraham Maslow, airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, autism spectrum disorder, Bayesian statistics, behavioural economics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, cognitive load, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, deep learning, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, GPS: selective availability, haute cuisine, How many piano tuners are there in Chicago?, human-factors engineering, if you see hoof prints, think horses—not zebras, impulse control, index card, indoor plumbing, information retrieval, information security, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, language acquisition, Lewis Mumford, life extension, longitudinal study, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, Pareto efficiency, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Rubik’s Cube, Salesforce, shared worldview, Sheryl Sandberg, Skype, Snapchat, social intelligence, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, traumatic brain injury, Turing test, Twitter Arab Spring, ultimatum game, Wayback Machine, zero-sum game

How would they divide up the United States into surveillable sections with a high-enough resolution to spot the balloons, but still be able to navigate the enormous number of photographs quickly? Would the satellite images be analyzed by rooms full of humans, or would the winning team perfect a computer-vision algorithm for distinguishing the red balloons from other balloons and from other round, red objects that were not the target? (Effectively solving the Where’s Waldo? problem, something that computer programs couldn’t do until 2011.) Further speculation revolved around the use of reconnaissance planes, telescopes, sonar, and radar.

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Automatic object recognition applied to Where’s Waldo? Aerospace and Electronics Conference (NAECON), 2012 IEEE National, 117–120. and, Garg, R., Seitz, S. M., Ramanan, D., & Snavely, N. (2011, June). Where’s Waldo: Matching people in images of crowds. Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 1793–1800. Wikipedia is an example of crowdsourcing Ayers, P., Matthews, C., & Yates, B. (2008). How Wikipedia works: And how you can be a part of it. San Francisco, CA: No Starch Press, p. 514. More than 4.5 million people Kickstarter, Inc. (2014). Seven things to know about Kickstarter.

pages: 337 words: 86,320

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

affirmative action, AltaVista, Amazon Mechanical Turk, Asian financial crisis, Bernie Sanders, big data - Walmart - Pop Tarts, Black Lives Matter, Cass Sunstein, computer vision, content marketing, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, desegregation, Donald Trump, Edward Glaeser, Filter Bubble, game design, happiness index / gross national happiness, income inequality, Jeff Bezos, Jeff Seder, John Snow's cholera map, longitudinal study, Mark Zuckerberg, Nate Silver, Nick Bostrom, peer-to-peer lending, Peter Thiel, price discrimination, quantitative hedge fund, Ronald Reagan, Rosa Parks, sentiment analysis, Silicon Valley, statistical model, Steve Jobs, Steven Levy, Steven Pinker, TaskRabbit, The Signal and the Noise by Nate Silver, working poor

Politico, August 30, 2013, http://www.politico.com/media/story/2013/08/how-much-does-the-new-york-post-actually-lose-001176. 97 Shapiro told me: I interviewed Matt Gentzkow and Jesse Shapiro on August 16, 2015, at the Royal Sonesta Boston. 98 scanned yearbooks from American high schools: Kate Rakelly, Sarah Sachs, Brian Yin, and Alexei A. Efros, “A Century of Portraits: A Visual Historical Record of American High School Yearbooks,” paper presented at International Conference on Computer Vision, 2015. The photos are reprinted with permission from the authors. 99 subjects in photos copied subjects in paintings: See, for example, Christina Kotchemidova, “Why We Say ‘Cheese’: Producing the Smile in Snapshot Photography,” Critical Studies in Media Communication 22, no. 1 (2005). 100 measure GDP based on how much light there is in these countries at night: J.

pages: 301 words: 85,263

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, security theater, self-driving car, Seymour Hersh, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, warehouse robotics, WikiLeaks

Instead, they only find the faces in the bottom row appearing somewhat more relaxed than those in the top row. Perhaps, the different perceptions here are due to cultural differences.’10 What was left untouched in the original paper was the assumption that any such system could ever be free of encoded, embedded bias. At the outset of their study, the authors write, Unlike a human examiner/judge, a computer vision algorithm or classifier has absolutely no subjective baggages, having no emotions, no biases whatsoever due to past experience, race, religion, political doctrine, gender, age, etc., no mental fatigue, no preconditioning of a bad sleep or meal. The automated inference on criminality eliminates the variable of meta-accuracy (the competence of the human judge/examiner) all together.11 In their response, they double down on this assertion: ‘Like most technologies, machine learning is neutral.’

pages: 330 words: 83,319

The New Rules of War: Victory in the Age of Durable Disorder by Sean McFate

Able Archer 83, active measures, anti-communist, barriers to entry, Berlin Wall, blood diamond, Boeing 747, Brexit referendum, cognitive dissonance, commoditize, computer vision, corporate governance, corporate raider, cuban missile crisis, disinformation, Donald Trump, double helix, drone strike, escalation ladder, European colonialism, failed state, fake news, false flag, hive mind, index fund, invisible hand, John Markoff, joint-stock company, military-industrial complex, moral hazard, mutually assured destruction, Nash equilibrium, nuclear taboo, offshore financial centre, pattern recognition, Peace of Westphalia, plutocrats, private military company, profit motive, RAND corporation, ransomware, Ronald Reagan, Silicon Valley, South China Sea, Steve Bannon, Stuxnet, Suez crisis 1956, technoutopianism, vertical integration, Washington Consensus, Westphalian system, yellow journalism, Yom Kippur War, zero day, zero-sum game

Rise of the robots: Matthew Rosenberg and John Markoff, “The Pentagon’s ‘Terminator Conundrum’: Robots That Could Kill on Their Own,” New York Times, 25 October 2016, www.nytimes.com/2016/10/26/us/pentagon-artificial-intelligence-terminator.html; Kevin Warwick, “Back to the Future,” Leviathan, BBC News, 1 January 2000, http://news.bbc.co.uk/hi/english/static/special_report/1999/12/99/back_to_the_future/kevin_warwick.stm. 5. Robots are stupid: Andrej Karpathy and Li Fei-Fei, “Deep Visual-Semantic Alignments for Generating Image Descriptions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015): 3128–37, http://cs.stanford.edu/people/karpathy/cvpr2015.pdf. 6. What is “cyber”?: Cyber is a prefix used to describe anything having to do with computers, which doesn’t explain much. The term “cyber” was coined by the science fiction writer William Gibson in the 1980s but has advanced little as a concept since then.

pages: 301 words: 89,076

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AlphaGo, AltaVista, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, basic income, Big Tech, bread and circuses, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deep learning, DeepMind, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, hype cycle, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Kevin Roose, knowledge worker, laissez-faire capitalism, Les Trente Glorieuses, low skilled workers, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, mirror neurons, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, systems thinking, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

Some of the more innovative uses of white-collar robots are in psychology. Ellie is an on-screen white-collar robot (some call it an avatar but that is focusing on the image and underplaying the technology driving the image). She looks and acts human enough to make people comfortable talking to her. Computer vision and a Kinect sensor allow her to record body language and subtle facial clues that she then codifies for a human psychologist to evaluate. Research shows that she is better at such data gathering than humans—in part because people feel freer to open up to a robot. University of Southern California researchers created Ellie as part of a program financed by the US Defense Advanced Research Projects Agency.

pages: 301 words: 85,126

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

The Stanford researchers compiled 19 databases containing 129,450 images, each of them classified according to a taxonomy of 2,032 different skin lesions. More data means a wider range of experience and thus better pattern recognition, like a veteran dermatologist who’s been looking at skin lesions for decades and who’s seen it all. The second choice was their approach to computer vision, which involved the deep neural networks we met in chapter 2. These networks can extract subtle visual features, and they can combine those features into high-level visual concepts—like circles, edges, stripes, texture, or nuances of variegation—that can be used to distinguish 2,000 different types of skin lesion.

pages: 361 words: 83,886

Inside the Robot Kingdom: Japan, Mechatronics and the Coming Robotopia by Frederik L. Schodt

carbon-based life, computer age, Computer Numeric Control, computer vision, deindustrialization, Deng Xiaoping, deskilling, factory automation, game design, guest worker program, industrial robot, Jacques de Vaucanson, Norbert Wiener, post-industrial society, robot derives from the Czech word robota Czech, meaning slave, Ronald Reagan, Silicon Valley, telepresence, The Wealth of Nations by Adam Smith, V2 rocket, warehouse automation, Whole Earth Review, women in the workforce

In an attempt to bolster its competitiveness, in 1985 it once more turned to a U S. firm and took out a license for a new generation of robots made by Adept Technology, a small California firm founded by former Unimation employees. The current star of the U.S. robotics industry, Adept Technology was the first to manufacture a commercial "direct drive" robot, which uses special electric motors that eliminate the need for almost all gears and is exceedingly fast and accurate. Employing the latest in computer vision and American software, the Adept robot shows how American firms can still have an advantage over Japan in state-of-the-art technologies, and also how thoroughly intertwined the Japanese and American robotics industries have become. The first experimental direct-drive robot arm was developed in 1981 at Carnegie-Mellon University in the U.S., mainly by two Japanese scientists, Haruhiko Asada and Takeo Kanade.

pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

“Today, all of the high-end cars have features like adaptive cruise control or parking assistance. It’s getting more and more automated,” explained Dan Lee, associate professor of engineering and the team’s adviser. “Now, to do it fully, the car has to have a complete awareness of the surrounding world. These are the hard problems of robotics: computer vision, having computers ‘hear’ sounds, having computers understand what’s happening in the world around them. This is a good environment to test these things.” For Little Ben to “see” an obstacle and drive around it, the automated driving and GPS navigation had to work properly, and the laser sensors on the roof rack had to observe the object.

Know Thyself by Stephen M Fleming

Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, autism spectrum disorder, autonomous vehicles, availability heuristic, backpropagation, citation needed, computer vision, confounding variable, data science, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, Dunning–Kruger effect, Elon Musk, Estimating the Reproducibility of Psychological Science, fake news, global pandemic, higher-order functions, index card, Jeff Bezos, l'esprit de l'escalier, Lao Tzu, lifelogging, longitudinal study, meta-analysis, mutually assured destruction, Network effects, patient HM, Pierre-Simon Laplace, power law, prediction markets, QWERTY keyboard, recommendation engine, replication crisis, self-driving car, side project, Skype, Stanislav Petrov, statistical model, theory of mind, Thomas Bayes, traumatic brain injury

International Journal of Law and Psychiatry 62 (2019): 56–76. Kelley, W. M., C. N. Macrae, C. L. Wyland, S. Caglar, S. Inati, and T. F. Heatherton. “Finding the Self? An Event-Related fMRI Study.” Journal of Cognitive Neuroscience 14, no. 5 (2002): 785–794. Kendall, Alex, and Yarin Gal. “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv.org, October 5, 2017. Kentridge, R. W., and C. A. Heywood. “Metacognition and Awareness.” Consciousness and Cognition 9, no. 2 (2000): 308–312. Kepecs, Adam, Naoshige Uchida, Hatim A. Zariwala, and Zachary F. Mainen. “Neural Correlates, Computation and Behavioural Impact of Decision Confidence.”

pages: 295 words: 81,861

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation by Paris Marx

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, Airbnb, An Inconvenient Truth, autonomous vehicles, back-to-the-land, Berlin Wall, Bernie Sanders, bike sharing, Californian Ideology, car-free, carbon credits, carbon footprint, cashless society, clean tech, cloud computing, colonial exploitation, computer vision, congestion pricing, corporate governance, correlation does not imply causation, COVID-19, DARPA: Urban Challenge, David Graeber, deep learning, degrowth, deindustrialization, deskilling, Didi Chuxing, digital map, digital rights, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, Elon Musk, energy transition, Evgeny Morozov, Extinction Rebellion, extractivism, Fairchild Semiconductor, Ford Model T, frictionless, future of work, General Motors Futurama, gentrification, George Gilder, gig economy, gigafactory, global pandemic, global supply chain, Google Glasses, Google X / Alphabet X, green new deal, Greyball, high-speed rail, Hyperloop, independent contractor, Induced demand, intermodal, Jane Jacobs, Jeff Bezos, jitney, John Perry Barlow, Kevin Kelly, knowledge worker, late capitalism, Leo Hollis, lockdown, low interest rates, Lyft, Marc Benioﬀ, market fundamentalism, minimum viable product, Mother of all demos, move fast and break things, Murray Bookchin, new economy, oil shock, packet switching, Pacto Ecosocial del Sur, Peter Thiel, pre–internet, price mechanism, private spaceflight, quantitative easing, QWERTY keyboard, Ralph Nader, Richard Florida, ride hailing / ride sharing, Ronald Reagan, safety bicycle, Salesforce, School Strike for Climate, self-driving car, Sidewalk Labs, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, social distancing, Southern State Parkway, Steve Jobs, Stewart Brand, Stop de Kindermoord, streetcar suburb, tech billionaire, tech worker, techlash, technological determinism, technological solutionism, technoutopianism, the built environment, The Death and Life of Great American Cities, TikTok, transit-oriented development, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban renewal, VTOL, walkable city, We are as Gods, We wanted flying cars, instead we got 140 characters, WeWork, Whole Earth Catalog, Whole Earth Review, work culture , Yom Kippur War, young professional

The US government was also interested in the prospect of autonomous driving, both for military and civilian uses. In the 1980s, the DARPA Strategic Computing Initiative funded the Autonomous Land Vehicle project as part of its efforts to “bring new technologies to the battlefield.”9 The project made significant advances in the use of laser imaging and computer vision for autonomous navigation. One of the beneficiaries of that funding was Carnegie Mellon University, which used the money from DARPA to create its first Navlab autonomous vehicle. The experiment served as a foundation for future research aimed at civilian uses. To that end, the Intermodal Surface Transportation Efficiency Act of 1991 mandated the Department of Transportation (DOT) to “develop an automated highway and vehicle prototype from which future fully automated intelligent vehicle-highway systems can be developed.”10 To achieve its aims by 1997, it handed out nearly $100 million to partners in the private sector and university research centers, including the same team at Carnegie Mellon.

pages: 300 words: 81,293

Supertall: How the World's Tallest Buildings Are Reshaping Our Cities and Our Lives by Stefan Al

3D printing, autonomous vehicles, biodiversity loss, British Empire, Buckminster Fuller, carbon footprint, Cesare Marchetti: Marchetti’s constant, colonial rule, computer vision, coronavirus, COVID-19, Deng Xiaoping, digital twin, Disneyland with the Death Penalty, Donald Trump, Easter island, Elisha Otis, energy transition, food miles, Ford Model T, gentrification, high net worth, Hyperloop, invention of air conditioning, Kickstarter, Lewis Mumford, Marchetti’s constant, megaproject, megastructure, Mercator projection, New Urbanism, plutocrats, plyscraper, pneumatic tube, ride hailing / ride sharing, Salesforce, self-driving car, Sidewalk Labs, SimCity, smart cities, smart grid, smart meter, social distancing, Steve Jobs, streetcar suburb, synthetic biology, Tacoma Narrows Bridge, the built environment, the High Line, transit-oriented development, Triangle Shirtwaist Factory, tulip mania, urban planning, urban sprawl, value engineering, Victor Gruen, VTOL, white flight, zoonotic diseases

The computer model can run virtual experiments and test policies before they are actually implemented. For instance, it can explore the impact of a new building or park on the shadows and wind flows. Systems like this may soon be able to calculate and evaluate the many opportunities for buildings to generate resources. This Big Brother may also be watching you, though, with computer vision monitoring your every garbage and sewage output. With skyscrapers producing and sharing energy, food, species, and more, our world may look quite different. Imagine a day in Singapore a few years from now. You throw your recyclable trash into a chute, where a system of pneumatic tubes sucks it to the recycling plant, avoiding the need for a polluting garbage truck.

High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Bear Stearns, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, Large Hadron Collider, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, National best bid and offer, natural language processing, offshore financial centre, pattern recognition, power law, price discovery process, price discrimination, price stability, proprietary trading, quantitative trading / quantitative ﬁnance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

What interpretation can be given for a single order placement in a massive stream of microstructure data, or to a snapshot of an intraday order book, especially considering the fact that any outstanding order can be cancelled by the submitting party any time prior to execution?2 95 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 96 — #116 i i HIGH-FREQUENCY TRADING To offer an analogy, consider the now common application of machine learning to problems in natural language processing (NLP) and computer vision. Both of them remain very challenging domains. But, in NLP, it is at least clear that the basic unit of meaning in the data is the word, which is how digital documents are represented and processed. In contrast, digital images are represented at the pixel level, but this is certainly not the meaningful unit of information in vision applications – objects are – but algorithmically extracting objects from images remains a difficult problem.

pages: 302 words: 90,215

Experience on Demand: What Virtual Reality Is, How It Works, and What It Can Do by Jeremy Bailenson

Apollo 11, Apple II, augmented reality, computer vision, deliberate practice, experimental subject, fake news, game design, Google Glasses, income inequality, Intergovernmental Panel on Climate Change (IPCC), iterative process, Ivan Sutherland, Jaron Lanier, low earth orbit, Mark Zuckerberg, Marshall McLuhan, meta-analysis, Milgram experiment, Neal Stephenson, nuclear winter, ocean acidification, Oculus Rift, opioid epidemic / opioid crisis, overview effect, pill mill, randomized controlled trial, Silicon Valley, SimCity, Skinner box, Skype, Snapchat, Steve Jobs, Steve Wozniak, Steven Pinker, TED Talk, telepresence, too big to fail, traumatic brain injury

Bailenson, Nicole Kramer, and Benjamin Li, “Let the Avatar Brighten Your Smile: Effects of Enhancing Facial Expressions in Virtual Environments,” PLoS One (2016). 8. STORIES IN THE ROUND 1. Susan Sontag, Regarding the Pain of Others (New York: Farrar, Straus and Giroux, 2003), 54. 2. Jon Peddie, Kurt Akeley, Paul Debevec, Erik Fonseka, Maichael Mangan, and Michael Raphael, “A Vision for Computer Vision: Emerging Technologies,” July 2016 SIGGRAPH Panel, http://dl.acm.org/citation.cfm?id=2933233. 3. Zeke Miller, “Romney Campaign Exaggerates Size of Nevada Event with Altered Image,” Buzzfeed, October 26, 2012, https://www.buzzfeed.com/zekejmiller/romney-campaign-appears-to-exaggerate-size-of-neva. 4.

pages: 442 words: 94,734

The Art of Statistics: Learning From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Anthropocene, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

‘Narrow’ AI refers to systems that can carry out closely prescribed tasks, and there have been some extraordinarily successful examples based on machine learning, which involves developing algorithms through statistical analysis of large sets of historical examples. Notable successes include speech recognition systems built into phones, tablets and computers; programs such as Google Translate which know little grammar but have learned to translate text from an immense published archive; and computer vision software that uses past images to ‘learn’ to identify, say, faces in photographs or other cars in the view of self-driving vehicles. There has also been spectacular progress in systems playing games, such as the DeepMind software learning the rules of computer games and becoming an expert player, beating world-champions at chess and Go, while IBM’s Watson has beaten competing humans in general knowledge quizzes.

pages: 287 words: 95,152

The Dawn of Eurasia: On the Trail of the New World Order by Bruno Macaes

active measures, Berlin Wall, Brexit referendum, British Empire, computer vision, deep learning, Deng Xiaoping, different worldview, digital map, Donald Trump, energy security, European colonialism, eurozone crisis, failed state, Francis Fukuyama: the end of history, gentrification, geopolitical risk, global value chain, illegal immigration, intermodal, iterative process, land reform, liberal world order, Malacca Straits, mass immigration, megacity, middle-income trap, open borders, Parag Khanna, savings glut, scientific worldview, Silicon Valley, South China Sea, speech recognition, Suez canal 1869, The Brussels Effect, trade liberalization, trade route, Transnistria, young professional, zero-sum game, éminence grise

One university lecturer in Chengdu is using face recognition technology, not only to register attendance but also to help determine boredom levels among his students. Translation apps make it easy for locals and tourists to have long conversations speaking in their own languages. An app developed by Baidu uses computer vision to help blind people by telling them what is in front of them, from simple but important information like the denomination of bank notes to trickier facts like the age of an interlocutor. Baidu has also partnered with a global food chain to open a new smart restaurant in Beijing, which employs facial recognition to make recommendations about what customers might order, based on factors like their age, gender and facial expression.

pages: 336 words: 93,672

The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman

23andMe, Albert Einstein, backpropagation, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, data science, deep learning, Drosophila, epigenetics, Geoffrey Hinton, global pandemic, Google Glasses, ITER tokamak, iterative process, language acquisition, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, traumatic brain injury, Turing machine, twin studies, web application

Despite the terrific progress that cognitive neuroscience of language has made in the last twenty years, mechanistic neurobiological explanations are lacking. Some Promising Directions: Correlational Examples, with Explanatory Ambitions Syntactic Primitives The goals of syntactic research over the last twenty years align well with the goals of cognitive and systems neuroscience (for example, in work on computational vision, see chapter by Carandini): to identify fundamental neuronal computations that (i) underlie a large number of (linguistic) phenomena, and (ii) rely as little as possible on domain-specific properties. As a concrete example, the syntactic theory known as minimalism, developed by Chomsky and others, has formulated a two-step syntactic function called “Merge” (see above re concatenation) that separates into a domain-general computation that combines elements (somewhat akin to binding, in the context of systems neuroscience), and a probably more domain-specific computation that labels the output of the binding computation: (1) Bind: Given an expression A and an expression B, bind A,B → {A,B} (2) Label: Given a combined {A,B}, label the complex A or B; → {A A,B} or {B A,B} Recent work in linguistics suggests that many of the complex properties of natural languages can be modeled as repeated applications of these Bind and Label computations.

pages: 404 words: 92,713

The Art of Statistics: How to Learn From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy by Matthew Hindman

A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, AltaVista, Amazon Web Services, barriers to entry, Benjamin Mako Hill, bounce rate, business logic, Cambridge Analytica, cloud computing, computer vision, creative destruction, crowdsourcing, David Ricardo: comparative advantage, death of newspapers, deep learning, DeepMind, digital divide, discovery of DNA, disinformation, Donald Trump, fake news, fault tolerance, Filter Bubble, Firefox, future of journalism, Ida Tarbell, incognito mode, informal economy, information retrieval, invention of the telescope, Jeff Bezos, John Perry Barlow, John von Neumann, Joseph Schumpeter, lake wobegon effect, large denomination, longitudinal study, loose coupling, machine translation, Marc Andreessen, Mark Zuckerberg, Metcalfe’s law, natural language processing, Netflix Prize, Network effects, New Economic Geography, New Journalism, pattern recognition, peer-to-peer, Pepsi Challenge, performance metric, power law, price discrimination, recommendation engine, Robert Metcalfe, search costs, selection bias, Silicon Valley, Skype, sparse data, speech recognition, Stewart Brand, surveillance capitalism, technoutopianism, Ted Nelson, The Chicago School, the long tail, The Soul of a New Machine, Thomas Malthus, web application, Whole Earth Catalog, Yochai Benkler

Google has even built new globally distributed database systems called Spanner and F1, in which operations across different data centers are synced using atomic clocks.22 The latest iteration of Borg, Google’s cluster management system, coordinates “hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.”23 In recent years Google’s data centers have expanded their capabilities in other ways, too. As Google has increasingly focused on problems like computer vision, speech recognition, and natural language processing, it has worked to deploy deep learning, a variant of neural network methods. Google’s investments in deep learning have been massive and multifaceted, including (among other things) major corporate acquisitions and the development of the TensorFlow high-level programming toolkit.24 But one critical component has been the development of a custom computer chip built specially for machine learning.

pages: 406 words: 88,977

How to Prevent the Next Pandemic by Bill Gates

augmented reality, call centre, computer vision, contact tracing, coronavirus, COVID-19, data science, demographic dividend, digital divide, digital map, disinformation, Edward Jenner, global pandemic, global supply chain, Hans Rosling, lockdown, Neal Stephenson, Picturephone, profit motive, QR code, remote working, social distancing, statistical model, TED Talk, women in the workforce, zero-sum game

The change won’t come right away, since most people don’t own tools to enable this kind of capture yet, in contrast to the way the switch to video meetings was enabled by the fact that many people already had PCs or phones with cameras. Right now, you can use virtual reality goggles and gloves to control your avatar, but more sophisticated and less obtrusive tools—like lightweight glasses and contact lenses—will come along over the next few years. Improvements in computer vision, display technology, audio, and sensors will capture your facial expressions, eyeline, and body language with very little delay. Think about any time you’ve tried to jump in with a thought during a spirited video meeting, and how hard that was to do when you couldn’t see the way people’s body language shifts as they’re wrapping up a thought.

pages: 277 words: 91,698

SAM: One Robot, a Dozen Engineers, and the Race to Revolutionize the Way We Build by Jonathan Waldman

Burning Man, computer vision, Ford paid five dollars a day, glass ceiling, helicopter parent, Hyperloop, industrial robot, information security, James Webb Space Telescope, job automation, Lean Startup, minimum viable product, off grid, Ralph Nader, Ralph Waldo Emerson, Ronald Reagan, self-driving car, Silicon Valley, stealth mode startup, Steve Jobs, Strategic Defense Initiative, strikebreaker, union organizing, Yogi Berra

In his notes, he wrote, “How many brick buildings in NY?” Engineers from RPI finally showed Scott the sensing system they’d devised, and it was so complicated that it made the simple, level string line used by masons everywhere seem worthy of the Nobel Prize. RPI’s system used triangulation and computer vision and spinning lasers. The spinning lasers, fixed on one side of the building, defined two flat, parallel planes at the height of the course to be laid. On the robot’s gripper, two photo sensors detected the lasers—both the time the beams hit and the angle at which they hit. In this way, it was possible to calculate position and to figure out roll, but to get yaw and pitch, the engineers said they’d need another photo sensor and an accelerometer.

Future Files: A Brief History of the Next 50 Years by Richard Watson

Abraham Maslow, Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon credits, carbon footprint, carbon tax, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, digital nomad, disintermediation, driverless car, epigenetics, failed state, financial innovation, Firefox, food miles, Ford Model T, future of work, Future Shock, global pandemic, global supply chain, global village, hive mind, hobby farmer, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, lateral thinking, linked data, low cost airline, low skilled workers, M-Pesa, mass immigration, Northern Rock, Paradox of Choice, peak oil, pensions crisis, precautionary principle, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, synthetic biology, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, Virgin Galactic, white flight, women in the workforce, work culture , Zipcar

The true test for artificial intelligence dates to 1950, when the British mathematician Alan Turing suggested the criterion of humans submitting statements through a machine and then not being able to tell whether the responses had come from another person or the machine. The 1960s and 1970s saw a great deal of progress in AI, but real breakthroughs failed to materialize. Instead, scientists and developers focused on specific problems such as speech recognition, text recognition and computer vision. However, we may be less than ten years away from seeing Turing’s AI vision become a reality. For instance, a company in Austin, Texas has developed a product called Cyc. It is much like a “chatbot” except that, if it answers Science and Technology 45 a question incorrectly, you can correct it and Cyc will learn from its mistakes.

Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

"Hurricane Katrina" Superdome, 23andMe, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, American Legislative Exchange Council, An Inconvenient Truth, Anne Wojcicki, Anthropocene, Apollo 11, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, biodiversity loss, Burning Man, call centre, Cambridge Analytica, carbon footprint, carbon tax, Charles Lindbergh, clean water, Colonization of Mars, computer vision, CRISPR, David Attenborough, deep learning, DeepMind, degrowth, disinformation, Donald Trump, double helix, driverless car, Easter island, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Extinction Rebellion, Flynn Effect, gigafactory, Google Earth, Great Leap Forward, green new deal, Greta Thunberg, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), James Bridle, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, Kim Stanley Robinson, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Neil Armstrong, Nelson Mandela, Nick Bostrom, obamacare, ocean acidification, off grid, oil shale / tar sands, paperclip maximiser, Paris climate accords, pattern recognition, Peter Thiel, plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, San Francisco homelessness, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, tech baron, tech billionaire, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, traffic fines, Tragedy of the Commons, Travis Kalanick, Tyler Cowen, urban sprawl, Virgin Galactic, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

., p. vi. 8. Ibid., p. 215. 9. Ayn Rand, Fountainhead, p. 11. PART THREE: THE NAME OF THE GAME CHAPTER 13 1. Personal conversation, November 22, 2017. 2. James Bridle, “Known Unknowns,” Harper’s, July 2018. 3. “Rise of the Machines,” The Economist, May 22, 2017. 4. “On Welsh Corgis, Computer Vision, and the Power of Deep Learning,” microsoft.com, July 14, 2014. 5. Andrew Roberts, “Elon Musk Says to Forget North Korea Because Artificial Intelligence Is the Real Threat to Humanity,” uproxx.com, August 12, 2017. 6. Tom Simonite, “What Is Ray Kurzweil Up to at Google? Writing Your Emails,” Wired, August 2, 2017. 7.

Mindf*ck: Cambridge Analytica and the Plot to Break America by Christopher Wylie

4chan, affirmative action, Affordable Care Act / Obamacare, air gap, availability heuristic, Berlin Wall, Bernie Sanders, Big Tech, big-box store, Boris Johnson, Brexit referendum, British Empire, call centre, Cambridge Analytica, Chelsea Manning, chief data officer, cognitive bias, cognitive dissonance, colonial rule, computer vision, conceptual framework, cryptocurrency, Daniel Kahneman / Amos Tversky, dark pattern, dark triade / dark tetrad, data science, deep learning, desegregation, disinformation, Dominic Cummings, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, emotional labour, Etonian, fake news, first-past-the-post, gamification, gentleman farmer, Google Earth, growth hacking, housing crisis, income inequality, indoor plumbing, information asymmetry, Internet of things, Julian Assange, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, move fast and break things, Network effects, new economy, obamacare, Peter Thiel, Potemkin village, recommendation engine, Renaissance Technologies, Robert Mercer, Ronald Reagan, Rosa Parks, Sand Hill Road, Scientific racism, Shoshana Zuboff, side project, Silicon Valley, Skype, Stephen Fry, Steve Bannon, surveillance capitalism, tech bro, uber lyft, unpaid internship, Valery Gerasimov, web application, WikiLeaks, zero-sum game

I started as a student at UAL and ended up working under the supervision of Carolyn Mair, who had a background in cognitive psychology and machine learning. Dr. Mair wasn’t a typical fashion professor, but the match made sense, as I wasn’t a typical fashion student. After I explained to her that I wanted to start researching fashion “models” of another kind—neural networks, computer vision, and autoencoders—she convinced the university’s postgraduate research committee to allow me to commence a Ph.D. in machine learning rather than in design. It was around this time that I also began my new job at SCL Group, so my days fluctuated between fashion models and cyberwarfare. I was keen to dive into my academic research on cultural trends, so I told Nix that I did not want to work for SCL full-time, and that if SCL wanted me, they would have to accept that I would be continuing my Ph.D. in parallel to their projects.

pages: 420 words: 100,811

We Are Data: Algorithms and the Making of Our Digital Selves by John Cheney-Lippold

algorithmic bias, bioinformatics, business logic, Cass Sunstein, centre right, computer vision, critical race theory, dark matter, data science, digital capitalism, drone strike, Edward Snowden, Evgeny Morozov, Filter Bubble, Google Chrome, Google Earth, Hans Moravec, Ian Bogost, informal economy, iterative process, James Bridle, Jaron Lanier, Julian Assange, Kevin Kelly, late capitalism, Laura Poitras, lifelogging, Lyft, machine readable, machine translation, Mark Zuckerberg, Marshall McLuhan, mass incarceration, Mercator projection, meta-analysis, Nick Bostrom, Norbert Wiener, offshore financial centre, pattern recognition, price discrimination, RAND corporation, Ray Kurzweil, Richard Thaler, ride hailing / ride sharing, Rosa Parks, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software studies, statistical model, Steven Levy, technological singularity, technoutopianism, the scientific method, Thomas Bayes, Toyota Production System, Turing machine, uber lyft, web application, WikiLeaks, Zimmermann PGP

Simone Browne, “Digital Epidermalization: Race, Identity and Biometrics,” Critical Sociology 36, no. 1 (2010): 135. 27. Lev Manovich, Language of New Media (Cambridge, MA: MIT Press, 2001), 63. 28. Ibid., 63–64. 29. Ian Fasel, Bret Fortenberry, and Javier Movellan, “A Generative Framework for Real Time Object Detection and Classification,” Computer Vision and Image Understanding 98, no. 1 (2005): 182–210. 30. Jacob Whitehill, Gwen Littlewort, Ian Fasel, Marian Bartlett, and Javier Movellan, “Toward Practical Smile Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 11 (2009): 2107. 31. Ms Smith, “Face Detection Technology Tool Now Detects Your Moods Too,” Network World, July 14, 2011, www.networkworld.com; Yaniv Taigman and Lior Wolf, “Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition,” arXiv:1108.1122, 2011. 32.

pages: 418 words: 102,597

Being You: A New Science of Consciousness by Anil Seth

AlphaGo, artificial general intelligence, augmented reality, backpropagation, carbon-based life, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Drosophila, en.wikipedia.org, Filter Bubble, GPT-3, GPT-4, John Markoff, longitudinal study, Louis Pasteur, mirror neurons, Neil Armstrong, Nick Bostrom, Norbert Wiener, OpenAI, paperclip maximiser, pattern recognition, Paul Graham, Pierre-Simon Laplace, planetary scale, Plato's cave, precautionary principle, Ray Kurzweil, self-driving car, speech recognition, stem cell, systems thinking, technological singularity, TED Talk, telepresence, the scientific method, theory of mind, Thomas Bayes, TikTok, Turing test

Neuroscience of Consciousness. Haun, A. M., & Tononi, G. (2019). ‘Why does space feel the way it does? Towards a principled account of spatial experience’. Entropy, 21(12), 1160. He, K., Zhang, X., Ren, S., et al. (2016). ‘Deep residual learning for image recognition’. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Heilbron, M., Richter, D., Ekman, M., et al. (2020). ‘Word contexts enhance the neural representation of individual letters in early visual cortex’. Nature Communications, 11(1), 321. Herculano-Houzel, S. (2009). ‘The human brain in numbers: a linearly scaled-up primate brain’.

pages: 346 words: 97,890

The Road to Conscious Machines by Michael Wooldridge

Ada Lovelace, AI winter, algorithmic bias, AlphaGo, Andrew Wiles, Anthropocene, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Bletchley Park, Boeing 747, British Empire, call centre, Charles Babbage, combinatorial explosion, computer vision, Computing Machinery and Intelligence, DARPA: Urban Challenge, deep learning, deepfake, DeepMind, Demis Hassabis, don't be evil, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, Eratosthenes, factory automation, fake news, future of work, gamification, general purpose technology, Geoffrey Hinton, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Mustafa Suleyman, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, P = NP, P vs NP, paperclip maximiser, pattern recognition, Philippa Foot, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trolley problem, Turing machine, Turing test, universal basic income, Von Neumann architecture, warehouse robotics

In April 2019, you may recall seeing the first ever pictures of a black hole.1 In a mind-boggling experiment, astronomers used data collected from eight radio telescopes across the world to construct an image of a black hole which is 40 billion miles across and 55 million light years away. The image represents one of the most dramatic scientific achievements this century. But what you might not know is that it was only made possible through AI: advanced computer vision algorithms were used to reconstruct the image, ‘predicting’ missing elements of the picture. In 2018, researchers from the computer processor company Nvidia demonstrated the ability of AI software to create completely convincing but completely fake pictures of people.2 The pictures were developed by a new type of neural network, one called a generative adversarial network.

pages: 383 words: 108,266

Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions by Dan Ariely

air freight, Al Roth, Alan Greenspan, Bear Stearns, behavioural economics, Bernie Madoff, Burning Man, butterfly effect, Cass Sunstein, collateralized debt obligation, compensation consultant, computer vision, corporate governance, credit crunch, Daniel Kahneman / Amos Tversky, David Brooks, delayed gratification, endowment effect, financial innovation, fudge factor, Gordon Gekko, greed is good, housing crisis, IKEA effect, invisible hand, John Perry Barlow, lake wobegon effect, late fees, loss aversion, market bubble, Murray Gell-Mann, payday loans, Pepsi Challenge, placebo effect, price anchoring, Richard Thaler, second-price auction, Silicon Valley, Skinner box, Skype, subprime mortgage crisis, The Wealth of Nations by Adam Smith, Upton Sinclair

BY THE WAY, a funny thing happened when we ran the experiment in the Carolina Brewery: Dressed in my waiter’s outfit, I approached one of the tables and began to read the menu to the couple there. Suddenly, I realized that the man was Rich, a graduate student in computer science, someone with whom I had worked on a project related to computational vision three or four years earlier. Because the experiment had to be conducted in the same way each time, this was not a good time for me to chat with him, so I put on a poker face and launched into a matter-of-fact description of the beers. After I finished, I nodded to Rich and asked, “What can I get you?”

pages: 502 words: 107,510

Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs

Amazon Mechanical Turk, bioinformatics, cloud computing, computer vision, crowdsourcing, easy for humans, difficult for computers, finite state, Free Software Foundation, game design, information retrieval, iterative process, language acquisition, machine readable, machine translation, natural language processing, pattern recognition, performance metric, power law, sentiment analysis, social web, sparse data, speech recognition, statistical model, text mining

Snow, Rion, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. “Cheap and Fast—But Is It Good? Evaluating Non-Expert Annotations for Natural Language Tasks.” In Proceedings of EMNLP-08. Sorokin, Alexander, and David Forsyth. 2008. “Utility data annotation with Amazon Mechanical Turk.” In Proceedings of the Computer Vision and Pattern Recognition Workshops. Index A note on the digital index A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section.

Smart Mobs: The Next Social Revolution by Howard Rheingold

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", A Pattern Language, Alvin Toffler, AOL-Time Warner, augmented reality, barriers to entry, battle of ideas, Brewster Kahle, Burning Man, business climate, citizen journalism, computer vision, conceptual framework, creative destruction, Dennis Ritchie, digital divide, disinformation, Douglas Engelbart, Douglas Engelbart, experimental economics, experimental subject, Extropian, Free Software Foundation, Garrett Hardin, Hacker Ethic, Hedy Lamarr / George Antheil, Herman Kahn, history of Unix, hockey-stick growth, Howard Rheingold, invention of the telephone, inventory management, Ivan Sutherland, John Markoff, John von Neumann, Joi Ito, Joseph Schumpeter, Ken Thompson, Kevin Kelly, Lewis Mumford, Metcalfe's law, Metcalfe’s law, more computing power than Apollo, move 37, Multics, New Urbanism, Norbert Wiener, packet switching, PalmPilot, Panopticon Jeremy Bentham, pattern recognition, peer-to-peer, peer-to-peer model, pez dispenser, planetary scale, pre–internet, prisoner's dilemma, radical decentralization, RAND corporation, recommendation engine, Renaissance Technologies, RFID, Richard Stallman, Robert Metcalfe, Robert X Cringely, Ronald Coase, Search for Extraterrestrial Intelligence, seminal paper, SETI@home, sharing economy, Silicon Valley, skunkworks, slashdot, social intelligence, spectrum auction, Steven Levy, Stewart Brand, the Cathedral and the Bazaar, the scientific method, Tragedy of the Commons, transaction costs, ultimatum game, urban planning, web of trust, Whole Earth Review, Yochai Benkler, zero-sum game

I picked one in Chinese, which I don’t read. Following his directions, I pointed the lens of the device in my hand at the sign on the wall, clicked the shutter, pressed some buttons on the telephone, and in a few seconds, the English words “reservation desk” appeared on the screen of the Info-Scope. “We use computer-vision techniques to extract the text from the sign,” Haritaoglu explained. “That requires processor power.” The telephone sent the picture to a computer on IBM’s network, which crunched the numbers to parse the characters out of the image, crunched the numbers to translate the text, and sent it back to the device in my hand.

Reset by Ronald J. Deibert

23andMe, active measures, air gap, Airbnb, Amazon Web Services, Anthropocene, augmented reality, availability heuristic, behavioural economics, Bellingcat, Big Tech, bitcoin, blockchain, blood diamond, Brexit referendum, Buckminster Fuller, business intelligence, Cal Newport, call centre, Cambridge Analytica, carbon footprint, cashless society, Citizen Lab, clean water, cloud computing, computer vision, confounding variable, contact tracing, contact tracing app, content marketing, coronavirus, corporate social responsibility, COVID-19, crowdsourcing, data acquisition, data is the new oil, decarbonisation, deep learning, deepfake, Deng Xiaoping, disinformation, Donald Trump, Doomsday Clock, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Evgeny Morozov, failed state, fake news, Future Shock, game design, gig economy, global pandemic, global supply chain, global village, Google Hangouts, Great Leap Forward, high-speed rail, income inequality, information retrieval, information security, Internet of things, Jaron Lanier, Jeff Bezos, John Markoff, Lewis Mumford, liberal capitalism, license plate recognition, lockdown, longitudinal study, Mark Zuckerberg, Marshall McLuhan, mass immigration, megastructure, meta-analysis, military-industrial complex, move fast and break things, Naomi Klein, natural language processing, New Journalism, NSO Group, off-the-grid, Peter Thiel, planetary scale, planned obsolescence, post-truth, proprietary trading, QAnon, ransomware, Robert Mercer, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, single source of truth, Skype, Snapchat, social distancing, sorting algorithm, source of truth, sovereign wealth fund, sparse data, speech recognition, Steve Bannon, Steve Jobs, Stuxnet, surveillance capitalism, techlash, technological solutionism, the long tail, the medium is the message, The Structural Transformation of the Public Sphere, TikTok, TSMC, undersea cable, unit 8200, Vannevar Bush, WikiLeaks, zero day, zero-sum game

Central Asian countries like Uzbekistan and Kazakhstan have even gone so far as to advertise for Bitcoin mining operations to be hosted in their jurisdictions because of cheap and plentiful coal and other fossil-fuelled energy sources.349 Some estimates put electric energy consumption associated with Bitcoin mining at around 83.67 terawatt-hours per year, more than that of the entire country of Finland, with carbon emissions estimated at 33.82 megatons, roughly equivalent to those of Denmark.350 To put it another way, the Cambridge Centre for Alternative Finance says that the electricity consumed by the Bitcoin network in one year could power all the teakettles used to boil water in the entire United Kingdom for nineteen years.351 A similar energy-sucking dynamic underlies other cutting-edge technologies, like “deep learning.” The latter refers to the complex artificial intelligence systems used to undertake the fine-grained, real-time calculations associated with the range of social media experiences, such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, and so on. Research undertaken at the University of Massachusetts, Amherst, in which the researchers performed a life-cycle assessment for training several common large AI models, found that training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent — or nearly five times the lifetime emissions of the average American car (including its manufacturing).352 It’s become common to hear that “data is the new oil,” usually meaning that it is a valuable resource.

pages: 338 words: 104,815

Nobody's Fool: Why We Get Taken in and What We Can Do About It by Daniel Simons, Christopher Chabris

Abraham Wald, Airbnb, artificial general intelligence, Bernie Madoff, bitcoin, Bitcoin "FTX", blockchain, Boston Dynamics, butterfly effect, call centre, Carmen Reinhart, Cass Sunstein, ChatGPT, Checklist Manifesto, choice architecture, computer vision, contact tracing, coronavirus, COVID-19, cryptocurrency, DALL-E, data science, disinformation, Donald Trump, Elon Musk, en.wikipedia.org, fake news, false flag, financial thriller, forensic accounting, framing effect, George Akerlof, global pandemic, index fund, information asymmetry, information security, Internet Archive, Jeffrey Epstein, Jim Simons, John von Neumann, Keith Raniere, Kenneth Rogoff, London Whale, lone genius, longitudinal study, loss aversion, Mark Zuckerberg, meta-analysis, moral panic, multilevel marketing, Nelson Mandela, pattern recognition, Pershing Square Capital Management, pets.com, placebo effect, Ponzi scheme, power law, publication bias, randomized controlled trial, replication crisis, risk tolerance, Robert Shiller, Ronald Reagan, Rubik’s Cube, Sam Bankman-Fried, Satoshi Nakamoto, Saturday Night Live, Sharpe ratio, short selling, side hustle, Silicon Valley, Silicon Valley startup, Skype, smart transportation, sovereign wealth fund, statistical model, stem cell, Steve Jobs, sunk-cost fallacy, survivorship bias, systematic bias, TED Talk, transcontinental railway, WikiLeaks, Y2K

Maybe it would, but in the face of a compelling demo, we tend to assume that the performance we’re seeing is generalizable to similar settings even when we have no direct evidence, at least from the demo, that it does.6 The practice of developing computer systems capable of performing with apparent intelligence in highly constrained situations and either claiming or implying that they would work just as well in a broad range of contexts goes back at least fifty years. Sometimes the developers are not deliberately deceptive—they’re just overly optimistic about how easy it will be to improve their own technology so that it works in more situations. For decades, computer vision and robotics experts assumed that if a robot could understand a scene containing regular geometric solids (cubes, pyramids, cylinders, etc.), then the hard work would be done, and it would take just a small step to generalize that capability to natural scenes. But time after time, artificial intelligence (AI) systems fall short when making the jump from an optimized “microworld” to the real world, much as potential medicines can perform well in laboratory experiments with animals but fail in human trials.

pages: 629 words: 109,663

Docker in Action by Jeff Nickoloff, Stephen Kuenzli

air gap, Amazon Web Services, cloud computing, computer vision, continuous integration, database schema, Debian, end-to-end encryption, exponential backoff, fail fast, failed state, information security, Kubernetes, microservices, MITM: man-in-the-middle, peer-to-peer, software as a service, web application

Containers have access to some of the host’s devices by default, and Docker creates other devices specifically for each container. This works similarly to how a virtual terminal provides dedicated input and output devices to the user. On occasion, it may be important to share other devices between a host and a specific container. Say you’re running computer vision software that requires access to a webcam, for example. In that case, you’ll need to grant access to the container running your software to the webcam device attached to the system; you can use the --device flag to specify a set of devices to mount into the new container. The following example would map your webcam at /dev/video0 to the same location within a new container.

pages: 489 words: 106,008

Risk: A User's Guide by Stanley McChrystal, Anna Butrico

"Hurricane Katrina" Superdome, Abraham Maslow, activist fund / activist shareholder / activist investor, airport security, Albert Einstein, Apollo 13, banking crisis, Bernie Madoff, Boeing 737 MAX, business process, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, computer vision, coronavirus, corporate governance, cotton gin, COVID-19, cuban missile crisis, deep learning, disinformation, don't be evil, Dr. Strangelove, fake news, fear of failure, George Floyd, Glass-Steagall Act, global pandemic, Googley, Greta Thunberg, hindsight bias, inflight wifi, invisible hand, iterative process, late fees, lockdown, Paul Buchheit, Ponzi scheme, QWERTY keyboard, ride hailing / ride sharing, Ronald Reagan, San Francisco homelessness, School Strike for Climate, Scientific racism, Silicon Valley, Silicon Valley startup, Skype, social distancing, source of truth, Stanislav Petrov, Steve Jobs, Thomas L Friedman, too big to fail, Travis Kalanick, wikimedia commons, work culture

The DoD knew that its competitors were changing and that it was entering an “AI arms race”—and it believed Google could provide the capabilities it needed to compete. Google eagerly signed the contract. Now identifying as an AI company (not a data company, as it had been formerly known) Google would create a “customized AI surveillance engine” to scour the DoD’s massive amount of footage. Google’s computer vision, which incorporated both machine learning and deep learning, would analyze the data to track the movements of vehicles and other objects. As they quietly engaged with Project Maven, Google’s AI services showed initial progress—Google’s software had greater success than humans in detecting important footage.

pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

"World Economic Forum" Davos, 1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, behavioural economics, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carbon-based life, Charlie Hebdo massacre, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, DeepMind, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, fake news, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-truth, post-work, purchasing power parity, race to the bottom, RAND corporation, restrictive zoning, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, TED Talk, transatlantic slave trade, trolley problem, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

., ‘Communication Mediated through Natural Language Generation in Big Data Environments: The Case of Nomao’, Journal of Computer and Communication 5 (2017), 125–48; facial recognition: Florian Schroff, Dmitry Kalenichenko and James Philbin, ‘FaceNet: A Unified Embedding for Face Recognition and Clustering’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), 815–23; and driving: Cristiano Premebida, ‘A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking’, 2007 IEEE Intelligent Transportation Systems Conference (2007). 3 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus & Giroux, 2011); Dan Ariely, Predictably Irrational (New York: Harper, 2009); Brian D.

pages: 1,172 words: 114,305

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

Ideally, machines learn to spot “evil digital twins”—tissue that proved in the past to be dangerous, which is menacingly similar to your own.9 This machine vision—spotting danger where even experienced specialists might miss it—is far different from our own sense of sight. To understand machine learning—which will come up repeatedly in this book—it is helpful to compare contemporary computer vision to its prior successes in facial or number recognition. When a facial recognition program successfully identifies a picture as an image of a given person, it is matching patterns in the image to those in a preexisting database, perhaps on a 1,000-by-1,000-pixel grid. Each box in the grid can be identified as either skin or not skin, smooth or not smooth, along hundreds or even thousands of binaries, many of which would never be noticeable by the human eye.

pages: 523 words: 112,185

Doing Data Science: Straight Talk From the Frontline by Cathy O'Neil, Rachel Schutt

Amazon Mechanical Turk, augmented reality, Augustin-Louis Cauchy, barriers to entry, Bayesian statistics, bike sharing, bioinformatics, computer vision, confounding variable, correlation does not imply causation, crowdsourcing, data science, distributed generation, Dunning–Kruger effect, Edward Snowden, Emanuel Derman, fault tolerance, Filter Bubble, finite state, Firefox, game design, Google Glasses, index card, information retrieval, iterative process, John Harrison: Longitude, Khan Academy, Kickstarter, machine translation, Mars Rover, Nate Silver, natural language processing, Netflix Prize, p-value, pattern recognition, performance metric, personalized medicine, pull request, recommendation engine, rent-seeking, selection bias, Silicon Valley, speech recognition, statistical model, stochastic process, tacit knowledge, text mining, the scientific method, The Wisdom of Crowds, Watson beat the top human players on Jeopardy!, X Prize

There are convergence issues—the solution can fail to exist, if the algorithm falls into a loop, for example, and keeps going back and forth between two possible solutions, or in other words, there isn’t a single unique solution. Interpretability can be a problem—sometimes the answer isn’t at all useful. Indeed that’s often the biggest problem. In spite of these issues, it’s pretty fast (compared to other clustering algorithms), and there are broad applications in marketing, computer vision (partitioning an image), or as a starting point for other models. In practice, this is just one line of code in R: kmeans(x, centers, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen")) Your dataset needs to be a matrix, x, each column of which is one of your features.

pages: 458 words: 116,832

The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism by Nick Couldry, Ulises A. Mejias

"World Economic Forum" Davos, 23andMe, Airbnb, Amazon Mechanical Turk, Amazon Web Services, behavioural economics, Big Tech, British Empire, call centre, Cambridge Analytica, Cass Sunstein, choice architecture, cloud computing, colonial rule, computer vision, corporate governance, dark matter, data acquisition, data is the new oil, data science, deep learning, different worldview, digital capitalism, digital divide, discovery of the americas, disinformation, diversification, driverless car, Edward Snowden, emotional labour, en.wikipedia.org, European colonialism, Evgeny Morozov, extractivism, fake news, Gabriella Coleman, gamification, gig economy, global supply chain, Google Chrome, Google Earth, hiring and firing, income inequality, independent contractor, information asymmetry, Infrastructure as a Service, intangible asset, Internet of things, Jaron Lanier, job automation, Kevin Kelly, late capitalism, lifelogging, linked data, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, military-industrial complex, move fast and break things, multi-sided market, Naomi Klein, Network effects, new economy, New Urbanism, PageRank, pattern recognition, payday loans, Philip Mirowski, profit maximization, Ray Kurzweil, RFID, Richard Stallman, Richard Thaler, Salesforce, scientific management, Scientific racism, Second Machine Age, sharing economy, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Slavoj Žižek, smart cities, Snapchat, social graph, social intelligence, software studies, sovereign wealth fund, surveillance capitalism, techlash, The Future of Employment, the scientific method, Thomas Davenport, Tim Cook: Apple, trade liberalization, trade route, undersea cable, urban planning, W. E. B. Du Bois, wages for housework, work culture , workplace surveillance

The Volokh Conspiracy (blog), January 23, 2012. http://volokh.com/2012/01/23/whats-the-status-of-the-mosaic-theory-after-jones/. Khatchadourian, Raffi. “We Know How You Feel.” The New Yorker, January 12, 2015. Khosla, Aditya, Byoungkwon An, Joseph J. Lim, and Antonio Torralba. “Looking Beyond the Visible Scene.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, 3710–17. Kirkpatrick, David. The Facebook Effect. New York: Simon and Schuster, 2010. Kitchin, Rob. The Data Revolution. London: Sage, 2014. Kitchin, Rob, and Martin Dodge. Code/Space. Cambridge, MA: MIT Press, 2011. Kitchin, Rob, and Gavin McArdle.

pages: 409 words: 112,055

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, air gap, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Lives Matter, Black Swan, blockchain, Boeing 737 MAX, borderless world, Boston Dynamics, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, data science, deep learning, DevOps, disinformation, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Exxon Valdez, false flag, geopolitical risk, global village, immigration reform, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, John Perry Barlow, Julian Assange, Kubernetes, machine readable, Marc Benioﬀ, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, Morris worm, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, quantum cryptography, ransomware, Richard Thaler, Salesforce, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, The future is already here, Tim Cook: Apple, undersea cable, unit 8200, WikiLeaks, Y2K, zero day

Many of the current uses of AI are still, in fact, attempts to have machines do things that humans do. What is important for us, however, is that AI has moved on to do things that no individual human could do, indeed what even groups of highly trained humans could not reliably do in any reasonable amount of time. Using AI, machines can now have meaningful visual capacity, so-called computer vision. They can see things by translating images into code and classifying or identifying what appears in the image or video. Cars can now see other cars, view and understand certain traffic signs, and use the knowledge they gain from their visual capacity to make and implement decisions such as braking to avoid an accident.

System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

Will AGI put humanity: Edward Feigenbaum et al., Advanced Software Applications in Japan (Park Ridge, NJ: Noyes Data Corporation, 1995). problems in reasoning have the potential: Yaniv Taigman et al., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) (New York: IEEE, 2014), 1701–8, https://doi.org/10.1109/CVPR.2014.220. “nine-layer deep neural network”: Ibid. Anyone using Zoom videoconferencing: “Language Interpretation in Meetings and Webinars,” Zoom Help Center, https://support.zoom.us/hc/en-us/articles/360034919791-Language-interpretation-in-meetings-and-webinars.

Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions by Temple Grandin, Ph.D.

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, a long time ago in a galaxy far, far away, air gap, Albert Einstein, American Society of Civil Engineers: Report Card, Apollo 11, Apple II, ASML, Asperger Syndrome, autism spectrum disorder, autonomous vehicles, Black Lives Matter, Boeing 737 MAX, Captain Sullenberger Hudson, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, coronavirus, cotton gin, COVID-19, defense in depth, Drosophila, Elon Musk, en.wikipedia.org, GPT-3, Gregor Mendel, Greta Thunberg, hallucination problem, helicopter parent, income inequality, industrial robot, invention of movable type, Isaac Newton, James Webb Space Telescope, John Nash: game theory, John von Neumann, Jony Ive, language acquisition, longitudinal study, Mark Zuckerberg, Mars Rover, meta-analysis, Neil Armstrong, neurotypical, pattern recognition, Peter Thiel, phenotype, ransomware, replication crisis, Report Card for America’s Infrastructure, Robert X Cringely, Saturday Night Live, self-driving car, seminal paper, Silicon Valley, Skinner box, space junk, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Tacoma Narrows Bridge, TaskRabbit, theory of mind, TikTok, twin studies, unpaid internship, upwardly mobile, US Airways Flight 1549, warehouse automation, warehouse robotics, web application, William Langewiesche, Y Combinator

A lion attacking you in the Hilton is a hallucination. Dreams have a hallucinatory component. Everything I see in my imagination is real. And one of the things my imagination works overtime visualizing is what happens when systems controlled by artificial intelligence (AI) are hacked. In 2015, Google introduced DeepDream, a computer vision program that used AI algorithms to generate and enhance images by detecting patterns. An example of a normal use for such a program would be to find pictures of dogs on the internet. When used for their intended purpose, the programs resemble visual thinking. When forced to look for things that are not there, however, they hallucinate similarly to a person with schizophrenia.

pages: 412 words: 116,685

The Metaverse: And How It Will Revolutionize Everything by Matthew Ball

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 3D printing, Airbnb, Albert Einstein, Amazon Web Services, Apple Newton, augmented reality, Big Tech, bitcoin, blockchain, business process, call centre, cloud computing, commoditize, computer vision, COVID-19, cryptocurrency, deepfake, digital divide, digital twin, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, gig economy, Google Chrome, Google Earth, Google Glasses, hype cycle, intermodal, Internet Archive, Internet of things, iterative process, Jeff Bezos, John Gruber, Kevin Roose, Kickstarter, lockdown, Mark Zuckerberg, Metcalfe’s law, Minecraft, minimum viable product, Neal Stephenson, Network effects, new economy, non-fungible token, open economy, openstreetmap, pattern recognition, peer-to-peer, peer-to-peer model, Planet Labs, pre–internet, QR code, recommendation engine, rent control, rent-seeking, ride hailing / ride sharing, Robinhood: mobile stock trading app, satellite internet, self-driving car, SETI@home, Silicon Valley, skeuomorphism, Skype, smart contracts, Snapchat, Snow Crash, social graph, social web, SpaceX Starlink, Steve Ballmer, Steve Jobs, thinkpad, TikTok, Tim Cook: Apple, TSMC, undersea cable, Vannevar Bush, vertical integration, Vitalik Buterin, Wayback Machine, Y2K

Sony Pictures, meanwhile, is the largest movie studio by revenue, as well as the largest independent TV/film studio overall. Sony’s semiconductor division is also the world leader in image sensors, with nearly 50% market share (Apple is a top customer), while its Imageworks division is a top visual effects and computer animation studio. Sony’s Hawk-Eye is a computer vision system used by numerous professional sports leagues globally to aid officiating through 3D simulations and playblack (the football club Manchester City is also deploying the technology to create a live digital twin of its stadium, players, and fans during a match). Sony Music is the second-largest music label by revenue (Travis Scott is a Sony Music artist), while Crunchyroll and Funimation provide Sony with the world’s largest anime streaming service.

The Future of Technology by Tom Standage

air freight, Alan Greenspan, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, creative destruction, disintermediation, disruptive innovation, distributed generation, double helix, experimental economics, financial engineering, Ford Model T, full employment, hydrogen economy, hype cycle, industrial robot, informal economy, information asymmetry, information security, interchangeable parts, job satisfaction, labour market flexibility, Larry Ellison, Marc Andreessen, Marc Benioﬀ, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, PalmPilot, railway mania, rent-seeking, RFID, Salesforce, seminal paper, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, six sigma, Skype, smart grid, software as a service, spectrum auction, speech recognition, stem cell, Steve Ballmer, Steve Jurvetson, technological determinism, technology bubble, telemarketer, transcontinental railway, vertical integration, Y2K

“The processing power is so much better than before that some of the seemingly simple things we humans do, like recognising faces, can begin to be done,” says Dr Kanade. While prices drop and hardware improves, research into robotic vision, control systems and communications have jumped ahead as well. America’s military and its space agency, nasa, have poured billions into robotic research and related fields such as computer vision. The Spirit and Opportunity rovers exploring Mars can pick their way across the surface to reach a specific destination. Their human masters do not specify the route; instead, the robots are programmed to identify and avoid obstacles themselves. “Robots in the first generation helped to generate economies of scale,” says Navi Radjou, an analyst at Forrester, a consultancy.

Stock Market Wizards: Interviews With America's Top Stock Traders by Jack D. Schwager

Asian financial crisis, banking crisis, barriers to entry, Bear Stearns, beat the dealer, Black-Scholes formula, book value, commodity trading advisor, computer vision, East Village, Edward Thorp, financial engineering, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John Meriwether, John von Neumann, junk bonds, locking in a profit, Long Term Capital Management, managed futures, margin call, Market Wizards by Jack D. Schwager, money market fund, Myron Scholes, paper trading, passive investing, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk-adjusted returns, short selling, short squeeze, Silicon Valley, statistical arbitrage, Teledyne, the scientific method, transaction costs, Y2K

Still, for certain types of problems, theoretically, you could get speeds that were a thousand times faster than the fastest supercomputer. To be fair, there were a few other researchers who were interested in these sorts of "fine-grained" parallel machines at the time—for example, certain scientists working in the field of computer vision—but it was definitely not the dominant theme within the field. You said that you were trying to design a computer that worked more like the brain. Could you elaborate? At the time, one of the main constraints on computer speed was a limitation often referred to as the "von Neumann bottleneck."

pages: 402 words: 126,835

The Job: The Future of Work in the Modern Era by Ellen Ruppel Shell

"Friedman doctrine" OR "shareholder theory", 3D printing, Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, AlphaGo, Amazon Mechanical Turk, basic income, Baxter: Rethink Robotics, big-box store, blue-collar work, Buckminster Fuller, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, company town, computer vision, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, data science, deskilling, digital divide, disruptive innovation, do what you love, Donald Trump, Downton Abbey, Elon Musk, emotional labour, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, gamification, gentrification, glass ceiling, Glass-Steagall Act, hiring and firing, human-factors engineering, immigration reform, income inequality, independent contractor, industrial research laboratory, industrial robot, invisible hand, It's morning again in America, Jeff Bezos, Jessica Bruder, job automation, job satisfaction, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, means of production, move fast and break things, new economy, Norbert Wiener, obamacare, offshore financial centre, Paul Samuelson, precariat, Quicken Loans, Ralph Waldo Emerson, risk tolerance, Robert Gordon, Robert Shiller, Rodney Brooks, Ronald Reagan, scientific management, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, stock buybacks, TED Talk, The Chicago School, The Theory of the Leisure Class by Thorstein Veblen, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, Wayback Machine, WeWork, white picket fence, working poor, workplace surveillance , Y Combinator, young professional, zero-sum game

For as we’ve seen, they seem to have the most difficulty completing tasks that humans find simplest, like picking delicate items off a shelf. Computer scientist Gary Bradski, a Silicon Valley entrepreneur, is cofounder of Industrial Perception, a start-up acquired years ago by Google that developed computer vision systems and robotic arms for loading and unloading trucks. “For Amazon and all Internet retailers, moving things from one place to another is just about their entire cost,” he told me. “Basically people in that industry are used as an extension of a forklift. Human forklift extenders are pretty expensive.

pages: 448 words: 117,325

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World by Bruce Schneier

23andMe, 3D printing, air gap, algorithmic bias, autonomous vehicles, barriers to entry, Big Tech, bitcoin, blockchain, Brian Krebs, business process, Citizen Lab, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, disinformation, Donald Trump, driverless car, drone strike, Edward Snowden, Elon Musk, end-to-end encryption, fault tolerance, Firefox, Flash crash, George Akerlof, incognito mode, industrial robot, information asymmetry, information security, Internet of things, invention of radio, job automation, job satisfaction, John Gilmore, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, national security letter, Network effects, Nick Bostrom, NSO Group, pattern recognition, precautionary principle, printed gun, profit maximization, Ralph Nader, RAND corporation, ransomware, real-name policy, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Seymour Hersh, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, sparse data, Stanislav Petrov, Stephen Hawking, Stuxnet, supply-chain attack, surveillance capitalism, The Market for Lemons, Timothy McVeigh, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, Wayback Machine, web application, WikiLeaks, Yochai Benkler, zero day

Dudley (17 May 2016), “Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records,” Scientific Reports 6, no. 26094, https://www.nature.com/articles/srep26094. 83But although the system works: Will Knight (11 Apr 2017), “The dark secret at the heart of AI,” MIT Technology Review, https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai. 83A 2014 book, Autonomous Technologies: William Messner, ed. (2014), Autonomous Technologies: Applications That Matter, SAE International, http://books.sae.org/jpf-auv-004. 84One research project focused on: Anh Nguyen, Jason Yosinski, and Jeff Clune (2 Apr 2015), “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’15), https://arxiv.org/abs/1412.1897. 84A related research project was able: Christian Szegedy et al. (19 Feb 2014), “Intriguing properties of neural networks,” in Conference Proceedings: International Conference on Learning Representations (ICLR) 2014, https://arxiv.org/abs/1312.6199. 84Yet another project tricked an algorithm: Andrew Ilyas et al. (20 Dec 2017), “Partial information attacks on real-world AI,” LabSix, http://www.labsix.org/partial-information-adversarial-examples. 85Like the Microsoft chatbot Tay: James Vincent (24 Mar 2016), “Twitter taught Microsoft’s AI chatbot to be a racist asshole in less than a day,” Verge, https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist. 85In 2017, Dow Jones accidentally: Timothy B.

Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler

Airbnb, Airbus A320, algorithmic bias, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, clean tech, computer vision, conceptual framework, congestion pricing, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, deep learning, demand response, digital map, disruptive innovation, driverless car, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, trolley problem, uber lyft, upwardly mobile, urban planning, Zipcar

., Autonomous Driving, Berlin, 213 232. [57] Hoff, K. A., Bashir, M., 2015: Trust in Automation: Integrating Empirical Evidence on Factors that Inﬂuence Trust, in: Human Factors, 407 434. Bibliography 418 [58] Hong, T., Abrams, M., Chang, T., Shneier, M., 2008: An Intelligent World Model for Autonomous Off-Road Driving, in: Computer Vision and Image Understanding, 1 16. [59] Huang, P., Pruckner, A., 2016: Steer byWire, in: Harrer, M., Pfeffer, P., Steering Handbook, Cham, 513 526. [60] Hyve Science Lab, 2015: Autonomous Driving The User Perspective, Munich. [61] IBM, 2011: Global Parking Survey Drivers Share Worldwide Parking Woes. [62] IHS Automotive, 2014: Emerging Technologies. [63] IHS Markit, 2016: Autonomous Industry Analysis. [64] Institute for Mobility Research, 2016: Autonomous Driving The Impact of Vehicle Automation on Mobility Behaviour. [65] Isaac, L., 2016: How Local Governments Can Plan for Autonomous Vehicles, in: Meyer, G., Beiker, S., Road Vehicles Automation 3, Berlin, 59 71. [66] Kaufmann, S., Moss, M.

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data by Dipanjan Sarkar

bioinformatics, business intelligence, business logic, computer vision, continuous integration, data science, deep learning, Dr. Strangelove, en.wikipedia.org, functional programming, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, language acquisition, machine readable, machine translation, natural language processing, out of africa, performance metric, premature optimization, recommendation engine, self-driving car, semantic web, sentiment analysis, speech recognition, statistical model, text mining, Turing test, web application

We will be exploring several of these libraries in this book. Even though the preceding list may seem a bit overwhelming, this is just scratching the surface of what is possible with Python. It is widely used in several other domains including artificial intelligence (AI) , game development, robotics, Internet of Things (IoT), computer vision, media processing, and network and system monitoring, just to name a few. To read some of the widespread success stories achieved with Python in different diverse domains like arts, science, computer science, education, and others, enthusiastic programmers and researchers can check out www.python.org/about/success/ .

pages: 993 words: 318,161

Fall; Or, Dodge in Hell by Neal Stephenson

Ada Lovelace, augmented reality, autonomous vehicles, back-to-the-land, bitcoin, blockchain, cloud computing, coherent worldview, computer vision, crisis actor, crossover SUV, cryptocurrency, defense in depth, demographic transition, distributed ledger, drone strike, easy for humans, difficult for computers, fake news, false flag, game design, gamification, index fund, Jaron Lanier, life extension, messenger bag, microaggression, microbiome, Neal Stephenson, Network effects, no-fly zone, off grid, off-the-grid, offshore financial centre, pattern recognition, planetary scale, ride hailing / ride sharing, sensible shoes, short selling, Silicon Valley, Snow Crash, tech bro, telepresence, telepresence robot, telerobotics, The Hackers Conference, Turing test, Works Progress Administration

The VEIL had been engineered as a double-edged weapon. Yes, it jammed the facial-recognition algorithms that would enable any camera, anywhere, to know your true name. But the pattern of lights that a VEIL projected on the user’s face wasn’t mere noise. It was a signal designed to convey data to any computer vision system smart enough to read it. The protocol had been published by ENSU and was formidable in its sophistication, but the upshot was that a VEIL could, if the user so chose, project the equivalent of a barcode: a number linking the user to a PURDAH. It was all completely optional and unnecessary, which was part of the point; most people didn’t know or care about any of this and simply did things openly under their own names.

…

But it was easy, and free, and recommended, that when you were starting out in life you establish at least one PURDAH, so that you could begin compiling a record of things that you had done. You could link it to your actual legal name and face if you wanted, or not. And either way you could punch it into your VEIL system so that as you walked down the street, computer vision systems, even though they couldn’t recognize your actual face, could look up your PURDAH and from there see any activity blockchained to it. All three of the laughing, coffee-toting, VEIL-wearing schoolgirls had done this as a matter of course. It was probably a free-and-mandatory service offered by their prep school.

pages: 416 words: 129,308

The One Device: The Secret History of the iPhone by Brian Merchant

Airbnb, animal electricity, Apollo Guidance Computer, Apple II, Apple's 1984 Super Bowl advert, Black Lives Matter, Charles Babbage, citizen journalism, Citizen Lab, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, conceptual framework, cotton gin, deep learning, DeepMind, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, Ford paid five dollars a day, Frank Gehry, gigafactory, global supply chain, Google Earth, Google Hangouts, Higgs boson, Huaqiangbei: the electronics market of Shenzhen, China, information security, Internet of things, Jacquard loom, John Gruber, John Markoff, Jony Ive, Large Hadron Collider, Lyft, M-Pesa, MITM: man-in-the-middle, more computing power than Apollo, Mother of all demos, natural language processing, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shock, pattern recognition, peak oil, pirate software, profit motive, QWERTY keyboard, reality distortion field, ride hailing / ride sharing, rolodex, Shenzhen special economic zone , Silicon Valley, Silicon Valley startup, skeuomorphism, skunkworks, Skype, Snapchat, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, TED Talk, Tim Cook: Apple, Tony Fadell, TSMC, Turing test, uber lyft, Upton Sinclair, Vannevar Bush, zero day

The difference between this data-driven approach and the logic-driven approach is that this computer doesn’t know anything about Van Gogh or what an artist is. It is only imitating patterns—often very well—that it has seen before. “The thing that’s good for is perception,” Gruber says. “The computer vision, computer speech, understanding, pattern recognition, and these things did not do well with knowledge representations. They did better with data- and signal-processing techniques. So that’s what’s happened. The machine learning has just gotten really good at making generalizations over training examples.”

pages: 459 words: 140,010

Fire in the Valley: The Birth and Death of the Personal Computer by Michael Swaine, Paul Freiberger

1960s counterculture, Amazon Web Services, Andy Rubin, Apple II, barriers to entry, Bill Atkinson, Bill Gates: Altair 8800, Byte Shop, Charles Babbage, cloud computing, commoditize, Computer Lib, computer vision, Dennis Ritchie, Do you want to sell sugared water for the rest of your life?, Douglas Engelbart, Douglas Engelbart, Dynabook, Fairchild Semiconductor, Gary Kildall, gentleman farmer, Google Chrome, I think there is a world market for maybe five computers, Internet of things, Isaac Newton, Jaron Lanier, Jeff Hawkins, job automation, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Jony Ive, Ken Thompson, Larry Ellison, Loma Prieta earthquake, Marc Andreessen, Menlo Park, Mitch Kapor, Mother of all demos, Paul Terrell, popular electronics, Richard Stallman, Robert Metcalfe, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, stealth mode startup, Steve Ballmer, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, Tim Cook: Apple, urban sprawl, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, world market for maybe five computers

This computer could be a winner, or some other machine might be better. If Jobs and Wozniak really had something, Terrell figured they’d keep in touch with him. The next day, Jobs appeared, barefoot, at Byte Shop. “I’m keeping in touch,” he said. Terrell, impressed by his confidence and perseverance, ordered 50 Apple I computers. Visions of instant wealth flashed before Jobs’s eyes. But Terrell added a condition: he wanted the computers fully assembled. Woz and Jobs were back to their 60-hour work weeks. The two Steves had no parts and no money to buy them, but with a purchase order from Terrell for 50 Apple I computers, they were able to obtain net 30 credit from suppliers.

pages: 476 words: 132,042

What Technology Wants by Kevin Kelly

Albert Einstein, Alfred Russel Wallace, Apollo 13, Boeing 747, Buckminster Fuller, c2.com, carbon-based life, Cass Sunstein, charter city, classic study, Clayton Christensen, cloud computing, computer vision, cotton gin, Danny Hillis, dematerialisation, demographic transition, digital divide, double entry bookkeeping, Douglas Engelbart, Edward Jenner, en.wikipedia.org, Exxon Valdez, Fairchild Semiconductor, Ford Model T, George Gilder, gravity well, Great Leap Forward, Gregor Mendel, hive mind, Howard Rheingold, interchangeable parts, invention of air conditioning, invention of writing, Isaac Newton, Jaron Lanier, Joan Didion, John Conway, John Markoff, John von Neumann, Kevin Kelly, knowledge economy, Lao Tzu, life extension, Louis Daguerre, Marshall McLuhan, megacity, meta-analysis, new economy, off grid, off-the-grid, out of africa, Paradox of Choice, performance metric, personalized medicine, phenotype, Picturephone, planetary scale, precautionary principle, quantum entanglement, RAND corporation, random walk, Ray Kurzweil, recommendation engine, refrigerator car, rewilding, Richard Florida, Rubik’s Cube, Silicon Valley, silicon-based life, skeuomorphism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Stewart Brand, Stuart Kauffman, technological determinism, Ted Kaczynski, the built environment, the long tail, the scientific method, Thomas Malthus, Vernor Vinge, wealth creators, Whole Earth Catalog, Y2K, yottabyte

Danny Hillis, another polymath and serial inventor, is cofounder of an innovative prototype shop called Applied Minds, which is another idea factory. As you might guess from the name, they use smart people to invent stuff. Their corporate tagline is “the little Big Idea company.” Like Myhrvold’s Intellectual Ventures, they generate tons of ideas in interdisciplinary areas: bioengineering, toys, computer vision, amusement rides, military control rooms, cancer diagnostics, and mapping tools. Some ideas they sell as unadorned patents; others they complete as physical machines or operational software. I asked Hillis, “What percentage of your ideas do you find out later someone else had before you, or at the same time as you, or maybe even after you?”

pages: 496 words: 131,938

The Future Is Asian by Parag Khanna

3D printing, Admiral Zheng, affirmative action, Airbnb, Amazon Web Services, anti-communist, Asian financial crisis, asset-backed security, augmented reality, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Basel III, bike sharing, birth tourism , blockchain, Boycotts of Israel, Branko Milanovic, British Empire, call centre, capital controls, carbon footprint, cashless society, clean tech, clean water, cloud computing, colonial rule, commodity super cycle, computer vision, connected car, corporate governance, CRISPR, crony capitalism, cross-border payments, currency peg, death from overwork, deindustrialization, Deng Xiaoping, Didi Chuxing, Dissolution of the Soviet Union, Donald Trump, driverless car, dual-use technology, energy security, European colonialism, factory automation, failed state, fake news, falling living standards, family office, financial engineering, fixed income, flex fuel, gig economy, global reserve currency, global supply chain, Great Leap Forward, green transition, haute couture, haute cuisine, illegal immigration, impact investing, income inequality, industrial robot, informal economy, initial coin offering, Internet of things, karōshi / gwarosa / guolaosi, Kevin Kelly, Kickstarter, knowledge worker, light touch regulation, low cost airline, low skilled workers, Lyft, machine translation, Malacca Straits, Marc Benioﬀ, Mark Zuckerberg, Masayoshi Son, megacity, megaproject, middle-income trap, Mikhail Gorbachev, money market fund, Monroe Doctrine, mortgage debt, natural language processing, Netflix Prize, new economy, off grid, oil shale / tar sands, open economy, Parag Khanna, payday loans, Pearl River Delta, prediction markets, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, reserve currency, ride hailing / ride sharing, Ronald Reagan, Salesforce, Scramble for Africa, self-driving car, Shenzhen special economic zone , Silicon Valley, smart cities, SoftBank, South China Sea, sovereign wealth fund, special economic zone, stem cell, Steve Jobs, Steven Pinker, supply-chain management, sustainable-tourism, synthetic biology, systems thinking, tech billionaire, tech worker, trade liberalization, trade route, transaction costs, Travis Kalanick, uber lyft, upwardly mobile, urban planning, Vision Fund, warehouse robotics, Washington Consensus, working-age population, Yom Kippur War

Japanese companies are applying AI to semiconductor manufacturing, helping them retain their edge in a critical components sector. India has dozens of promising AI companies. Its Fractal Analytics has a “consumer genomics” methodology that supports many of the world’s largest retail companies. Indian AI companies will dominate the Indian market and compete globally in areas such as computer vision, medical diagnostics, legal contract analysis, and customer satisfaction surveys. Google is deploying ever more capital to fund and buy Indian AI companies. Pakistan also has one of Asia’s leading AI outsourcing companies, Afiniti, which has more than three thousand employees and a valuation of $2 billion.

The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

affirmative action, Albert Einstein, AlphaGo, Asilomar, Bayesian statistics, computer age, computer vision, Computing Machinery and Intelligence, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Great Leap Forward, Gregor Mendel, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, Monty Hall problem, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, Plato's cave, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, Recombinant DNA, selection bias, self-driving car, seminal paper, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

The one game it lost to Sedol is the only one it will ever lose to a human. All of this is exciting, and the results leave no doubt: deep learning works for certain tasks. But it is the antithesis of transparency. Even AlphaGo’s programmers cannot tell you why the program plays so well. They knew from experience that deep networks have been successful at tasks in computer vision and speech recognition. Nevertheless, our understanding of deep learning is completely empirical and comes with no guarantees. The AlphaGo team could not have predicted at the outset that the program would beat the best human in a year, or two, or five. They simply experimented, and it did. Some people will argue that transparency is not really needed.

pages: 303 words: 67,891

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006 by Ben Goertzel, Pei Wang

AI winter, artificial general intelligence, backpropagation, bioinformatics, brain emulation, classic study, combinatorial explosion, complexity theory, computer vision, Computing Machinery and Intelligence, conceptual framework, correlation coefficient, epigenetics, friendly AI, functional programming, G4S, higher-order functions, information retrieval, Isaac Newton, Jeff Hawkins, John Conway, Loebner Prize, Menlo Park, natural language processing, Nick Bostrom, Occam's razor, p-value, pattern recognition, performance metric, precautionary principle, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K

Since NARS is designed according to an experience-grounded semantics, it is situated and embodied. However, because the system is not designed to duplicate concrete human behaviors and capabilities, it is not equipped with human sensors and effecters. For example, there is no doubt that vision plays a central role in human cognition, and that computer vision has great practical value, but it does not mean that vision is needed in every intelligent system. Because of these considerations, sensors and effecters are treated as optional parts of NARS. 84 P. Wang / From NARS to a Thinking Machine 3.2 Natural languages As mentioned previously, NARS uses Narsese, a formally defined language, to communicate with its environment.

pages: 588 words: 131,025

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, Big Tech, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital divide, disintermediation, disruptive innovation, don't be evil, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, gamification, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, it's over 9,000, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, quantum cryptography, RAND corporation, randomized controlled trial, Salesforce, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, synthetic biology, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, traumatic brain injury, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

., “Giving Office-Based Physicians Electronic Access to Patients’ Prior Imaging and Lab Results Did Not Deter Ordering of Tests,” Health Affairs 31, no. 3 (2012): 488–496. 110. T. McMahon, “The Smartphone Will See You Now: How Apps and Social Media Are Revolutionizing Medicine,” Macleans, March 4, 2013, http://www2.macleans.ca/2013/03/04/the-smartphone-will-see-you-now. 111. “Turning Mobile Phones into 3D Scanners,” Computer Vision and Geometry Group, accessed August 13, 2014, http://cvg.ethz.ch/mobile/. 112. J. Lademann, “Optical Methods of Imaging in the Skin,” Journal of Biomedical Optics 18, no. 6 (2013): 061 201-1. 113. W. Sohn et al., “Endockscope: Using Mobile Technology to Create Global Point of Service Endoscopy,” Journal of Endourology 27, no. 9 (2013): 1154–1160. 114.

pages: 573 words: 142,376

Whole Earth: The Many Lives of Stewart Brand by John Markoff

A Pattern Language, air freight, Anthropocene, Apple II, back-to-the-land, Benoit Mandelbrot, Bernie Madoff, Beryl Markham, Big Tech, Bill Atkinson, Biosphere 2, Brewster Kahle, Buckminster Fuller, Burning Man, butterfly effect, Claude Shannon: information theory, cloud computing, complexity theory, computer age, Computer Lib, computer vision, Danny Hillis, decarbonisation, demographic transition, disinformation, Douglas Engelbart, Douglas Engelbart, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, experimental subject, feminist movement, Fillmore Auditorium, San Francisco, Filter Bubble, game design, gentrification, global village, Golden Gate Park, Hacker Conference 1984, Hacker Ethic, Haight Ashbury, Herman Kahn, housing crisis, Howard Rheingold, HyperCard, intentional community, Internet Archive, Internet of things, Jane Jacobs, Jaron Lanier, Jeff Bezos, John Gilmore, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, knowledge worker, Lao Tzu, Lewis Mumford, Loma Prieta earthquake, Marshall McLuhan, megacity, Menlo Park, Michael Shellenberger, microdosing, Mitch Kapor, Morris worm, Mother of all demos, move fast and break things, New Urbanism, Norbert Wiener, Norman Mailer, North Sea oil, off grid, off-the-grid, paypal mafia, Peter Calthorpe, Ponzi scheme, profit motive, public intellectual, Ralph Nader, RAND corporation, Ray Kurzweil, Richard Stallman, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, South of Market, San Francisco, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Ted Nelson, Ted Nordhaus, TED Talk, The Death and Life of Great American Cities, The Hackers Conference, Thorstein Veblen, traveling salesman, Turing test, upwardly mobile, Vernor Vinge, We are as Gods, Whole Earth Catalog, Whole Earth Review, young professional

Brand asked Shel Kaphan, one of the young computer hackers who worked at the Truck Store, to put him in touch with the researchers at computer scientist John McCarthy’s Stanford Artificial Intelligence Laboratory. SAIL had been established to build a working artificial intelligence and he had collected an eclectic group of young researchers exploring technologies like robotics, computer vision, natural language understanding, and speech recognition. Simultaneously, Bill English opened the doors to the Palo Alto Research Center. Xerox had created PARC to compete directly with IBM, and Robert Taylor, a young psychologist who had funded the development of the ARPANET while at the Pentagon, had been given the charter of rethinking the future of the office based upon computers and networks.

pages: 642 words: 141,888

Like, Comment, Subscribe: Inside YouTube's Chaotic Rise to World Domination by Mark Bergen

23andMe, 4chan, An Inconvenient Truth, Andy Rubin, Anne Wojcicki, Big Tech, Black Lives Matter, book scanning, Burning Man, business logic, call centre, Cambridge Analytica, citizen journalism, cloud computing, Columbine, company town, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, cryptocurrency, data science, David Graeber, DeepMind, digital map, disinformation, don't be evil, Donald Trump, Edward Snowden, Elon Musk, fake news, false flag, game design, gender pay gap, George Floyd, gig economy, global pandemic, Golden age of television, Google Glasses, Google X / Alphabet X, Googley, growth hacking, Haight Ashbury, immigration reform, James Bridle, John Perry Barlow, Justin.tv, Kevin Roose, Khan Academy, Kinder Surprise, Marc Andreessen, Marc Benioﬀ, Mark Zuckerberg, mass immigration, Max Levchin, Menlo Park, Minecraft, mirror neurons, moral panic, move fast and break things, non-fungible token, PalmPilot, paypal mafia, Peter Thiel, Ponzi scheme, QAnon, race to the bottom, recommendation engine, Rubik’s Cube, Salesforce, Saturday Night Live, self-driving car, Sheryl Sandberg, side hustle, side project, Silicon Valley, slashdot, Snapchat, social distancing, Social Justice Warrior, speech recognition, Stanford marshmallow experiment, Steve Bannon, Steve Jobs, Steven Levy, surveillance capitalism, Susan Wojcicki, systems thinking, tech bro, the long tail, The Wisdom of Crowds, TikTok, Walter Mischel, WikiLeaks, work culture

Well, until the summer of 2018. When Pichai took over Google, he determined that its future lay primarily in two fields: business software sales, via cloud computing, and emerging market internet consumers, which he called the “next billion users.” Google had signed a contract with the Pentagon to provide drones with computer vision, paving the way for lucrative government cloud deals. In June, after weeks of raucous internal protests over the Pentagon deal, Google caved and pledged not to renew its contract. Then, that summer, employees discovered a shocking part of the “next billion users” plan: Google was building a search engine for mainland China with censored results.

pages: 514 words: 152,903

The Best Business Writing 2013 by Dean Starkman

Alvin Toffler, Asperger Syndrome, bank run, Basel III, Bear Stearns, call centre, carbon tax, clean water, cloud computing, collateralized debt obligation, Columbine, computer vision, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Erik Brynjolfsson, eurozone crisis, Evgeny Morozov, Exxon Valdez, Eyjafjallajökull, factory automation, fixed income, fulfillment center, full employment, Future Shock, gamification, Goldman Sachs: Vampire Squid, hiring and firing, hydraulic fracturing, Ida Tarbell, income inequality, jimmy wales, job automation, John Markoff, junk bonds, Kickstarter, late fees, London Whale, low interest rates, low skilled workers, Mahatma Gandhi, market clearing, Maui Hawaii, Menlo Park, Occupy movement, oil shale / tar sands, One Laptop per Child (OLPC), Parag Khanna, Pareto efficiency, price stability, proprietary trading, Ray Kurzweil, San Francisco homelessness, Silicon Valley, Skype, sovereign wealth fund, stakhanovite, Stanford prison experiment, Steve Jobs, Stuxnet, synthetic biology, tail risk, technological determinism, the payments system, too big to fail, Vanguard fund, wage slave, warehouse automation, warehouse robotics, Y2K, zero-sum game

Upgrading Distribution Inside a spartan garage in an industrial neighborhood in Palo Alto, Calif., a robot armed with electronic “eyes” and a small scoop and suction cups repeatedly picks up boxes and drops them onto a conveyor belt. It is doing what low-wage workers do every day around the world. Older robots cannot do such work because computer vision systems were costly and limited to carefully controlled environments where the lighting was just right. But thanks to an inexpensive stereo camera and software that lets the system see shapes with the same ease as humans, this robot can quickly discern the irregular dimensions of randomly placed objects.

Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, data science, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Gregor Mendel, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, Large Hadron Collider, longitudinal study, machine readable, machine translation, Mars Rover, natural language processing, openstreetmap, Paradox of Choice, power law, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social bookmarking, social graph, SPARQL, sparse data, speech recognition, statistical model, supply-chain management, systematic bias, TED Talk, text mining, the long tail, Vernor Vinge, web application

Document Unshredding and DNA Sequencing In Vernor Vinge’s science fiction novel Rainbows End (Tor Books), the Librareome project digitizes an entire library by tossing the books into a tree shredder, photographing the pieces, and using computer algorithms to reassemble the images. In real life, the German government’s E-Puzzler project is reconstructing 45 million pages of documents shredded by the former East German secret police, the Stasi. Both these projects rely on sophisticated computer vision techniques. But once the images have been converted to characters, language models and hill-climbing search can be used to reassemble the pieces. Similar techniques can be used to read the language of life: the Human Genome Project used a technique called shotgun sequencing to reassemble shreds of DNA.

pages: 573 words: 157,767

From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

Ada Lovelace, adjacent possible, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Andrew Wiles, Bayesian statistics, bioinformatics, bitcoin, Bletchley Park, Build a better mousetrap, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, CRISPR, deep learning, disinformation, double entry bookkeeping, double helix, Douglas Hofstadter, Elon Musk, epigenetics, experimental subject, Fermat's Last Theorem, Gödel, Escher, Bach, Higgs boson, information asymmetry, information retrieval, invention of writing, Isaac Newton, iterative process, John von Neumann, language acquisition, megaproject, Menlo Park, Murray Gell-Mann, Necker cube, Norbert Wiener, pattern recognition, phenotype, Richard Feynman, Rodney Brooks, self-driving car, social intelligence, sorting algorithm, speech recognition, Stephen Hawking, Steven Pinker, strong AI, Stuart Kauffman, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, Thomas Bayes, trickle-down economics, Turing machine, Turing test, Watson beat the top human players on Jeopardy!, Y2K

Yes, but at a huge cost, says Deacon: by taking these concerns off their hands, system designers create architectures that are brittle (they can’t repair themselves, for instance), vulnerable (locked into whatever set of contingencies their designers have anticipated), and utterly dependent on their handlers.40 This makes a big difference, Deacon insists. Does it? In some ways, I think, it does. At the height of GOFAI back in the 1970s, I observed that AI programs were typically disembodied, “bedridden” aspirants to genius that could only communicate through reading and writing typed messages. (Even work on computer vision was often accomplished with a single immovable video camera eye or by simply loading still images into the system the way you load pictures into your laptop, a vision system without any kind of eyes.) An embodied mobile robot using sense “organs” to situate itself in its world would find some problems harder and other problems easier.

pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, scientific management, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

“human-level performance,” as they noted: Steven Levy, “Inside Facebook’s AI Machine,” Wired, February 23, 2017, accessed August 19, 2018, https://www.wired.com/2017/02/inside-facebooks-ai-machine/; Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” Conference on Computer Vision and Pattern Recognition (CVPR), June 24, 2014, accessed August 19, 2018, https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification. to navigate roads: Andrew J. Hawkins, “Inside Waymo’s Strategy to Grow the Best Brains for Self-driving Cars,” The Verge, May 9, 2018, accessed August 19, 2018, https://www.theverge.com/2018/5/9/17307156/google-waymo-driverless-cars-deep-learning-neural-net-interview.

pages: 660 words: 141,595

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, Apollo 13, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, data science, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, Helicobacter pylori, independent contractor, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, SoftBank, speech recognition, Steve Jobs, supply-chain management, systems thinking, Teledyne, text mining, the long tail, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

Techniques and algorithms are shared between the two; indeed, the areas are so closely related that researchers commonly participate in both communities and transition between them seamlessly. Nevertheless, it is worth pointing out some of the differences to give perspective. Speaking generally, because Machine Learning is concerned with many types of performance improvement, it includes subfields such as robotics and computer vision that are not part of KDD. It also is concerned with issues of agency and cognition—how will an intelligent agent use learned knowledge to reason and act in its environment—which are not concerns of Data Mining. Historically, KDD spun off from Machine Learning as a research field focused on concerns raised by examining real-world applications, and a decade and a half later the KDD community remains more concerned with applications than Machine Learning is.

pages: 565 words: 151,129

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, benefit corporation, big-box store, bike sharing, bioinformatics, bitcoin, business logic, business process, Chris Urmson, circular economy, clean tech, clean water, cloud computing, collaborative consumption, collaborative economy, commons-based peer production, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, DIY culture, driverless car, Eben Moglen, electricity market, en.wikipedia.org, Frederick Winslow Taylor, Free Software Foundation, Garrett Hardin, general purpose technology, global supply chain, global village, Hacker Conference 1984, Hacker Ethic, industrial robot, informal economy, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, low interest rates, machine translation, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta-analysis, Michael Milken, mirror neurons, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off grid, off-the-grid, oil shale / tar sands, pattern recognition, peer-to-peer, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, QR code, RAND corporation, randomized controlled trial, Ray Kurzweil, rewilding, RFID, Richard Stallman, risk/return, Robert Solow, Rochdale Principles, Ronald Coase, scientific management, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, the Cathedral and the Bazaar, the long tail, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, Tragedy of the Commons, transaction costs, urban planning, vertical integration, warehouse automation, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, Yochai Benkler, zero-sum game, Zipcar

Sensors are being attached to vegetable and fruit cartons in transit to both track their whereabouts and sniff the produce to warn of imminent spoilage so shipments can be rerouted to closer vendors.23 Physicians are even attaching or implanting sensors inside human bodies to monitor bodily functions including heart rate, pulse, body temperature, and skin coloration to notify doctors of vital changes that might require proactive attention. General Electric (GE) is working with computer vision software that “can analyze facial expressions for signs of severe pain, the onset of delirium or other hints of distress” to alert nurses.24 In the near future, body sensors will be linked to one’s electronic health records, allowing the IoT to quickly diagnose the patient’s likely physical state to assist emergency medical personnel and expedite treatment.

pages: 513 words: 152,381

The Precipice: Existential Risk and the Future of Humanity by Toby Ord

3D printing, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, biodiversity loss, Columbian Exchange, computer vision, cosmological constant, CRISPR, cuban missile crisis, decarbonisation, deep learning, DeepMind, defense in depth, delayed gratification, Demis Hassabis, demographic transition, Doomsday Clock, Dr. Strangelove, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Goodhart's law, Hans Moravec, Herman Kahn, Higgs boson, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Large Hadron Collider, launch on warning, Mark Zuckerberg, Mars Society, mass immigration, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Nick Bostrom, Norbert Wiener, nuclear winter, ocean acidification, OpenAI, p-value, Peter Singer: altruism, planetary scale, power law, public intellectual, race to the bottom, RAND corporation, Recombinant DNA, Ronald Reagan, self-driving car, seminal paper, social discount rate, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, synthetic biology, tacit knowledge, the scientific method, Tragedy of the Commons, uranium enrichment, William MacAskill

., and Kaneda, T. (2018). How Many People Have Ever Lived on Earth? https://www.prb.org/howmanypeoplehaveeverlivedon earth/. He, K., Zhang, X., Ren, S., and Sun, J. (2015). “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.” 2015 IEEE International Conference on Computer Vision (ICCV), 1,026–34. IEEE. Helfand, I. (2013). “Nuclear Famine: Two Billion People at Risk?” Physicians for Social Responsibility. Henrich, J. (2015). The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter. Princeton University Press.

pages: 524 words: 154,652

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

We might marvel at the progress of, say, the self-driving car, but its autonomous navigation requires the labor of numerous invisible workers who do the thankless, drudgery-filled toil, often for very low wages, of labeling image after image to make the datasets the algorithm needs in order to operate. From Amazon’s Mechanical Turk to refugee camps in Europe, workers are paid pennies to sort endless reams of data, the raw materials for computer vision programs and self-driving vehicles. The researchers Mary L. Gray and Siddharth Suri call this “ghost work”—and it’s still ascendent today. The autonomous delivery robots now common on American college campuses and downtown areas may replace delivery people—but they are digitally overseen by other workers who can control them remotely, from places like Colombia, for $2 an hour.

pages: 1,331 words: 163,200

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

AlphaGo, Amazon Mechanical Turk, Anton Chekhov, backpropagation, combinatorial explosion, computer vision, constrained optimization, correlation coefficient, crowdsourcing, data science, deep learning, DeepMind, don't repeat yourself, duck typing, Elon Musk, en.wikipedia.org, friendly AI, Geoffrey Hinton, ImageNet competition, information retrieval, iterative process, John von Neumann, Kickstarter, machine translation, natural language processing, Netflix Prize, NP-complete, OpenAI, optical character recognition, P = NP, p-value, pattern recognition, pull request, recommendation engine, self-driving car, sentiment analysis, SpamAssassin, speech recognition, stochastic process

In particular, it contains most of the state-of-the-art image classification nets such as VGG, Inception, and ResNet (see Chapter 13, and check out the models/slim directory), including the code, the pretrained models, and tools to download popular image datasets. Another popular model zoo is Caffe’s Model Zoo. It also contains many computer vision models (e.g., LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet, inception) trained on various datasets (e.g., ImageNet, Places Database, CIFAR10, etc.). Saumitro Dasgupta wrote a converter, which is available at https://github.com/ethereon/caffe-tensorflow. Unsupervised Pretraining Suppose you want to tackle a complex task for which you don’t have much labeled training data, but unfortunately you cannot find a model trained on a similar task.

pages: 561 words: 157,589

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

A participating merchant could recognize you as a customer, pulling up your stored payment credentials. As for the products you wanted to buy, I was thinking about the possibility of bar code readers in the cart, or possibly sensors that knew the exact location of each product in the store, or identified it by weight when you put it in the cart. Computer vision wasn’t yet at the point where it could reliably work the kind of magic Amazon is now practicing. Sometimes ideas are in the air, but the technology to make them a reality hasn’t yet arrived. I’ve had numerous other experiences like that. One of my earliest business ideas, back in 1981, was for an interactive hotel brochure using the new RCA LaserDisc player.

pages: 512 words: 165,704

Traffic: Why We Drive the Way We Do (And What It Says About Us) by Tom Vanderbilt

Albert Einstein, autonomous vehicles, availability heuristic, Berlin Wall, Boeing 747, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, congestion pricing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, Donald Shoup, endowment effect, extreme commuting, fundamental attribution error, Garrett Hardin, Google Earth, hedonic treadmill, Herman Kahn, hindsight bias, hive mind, human-factors engineering, if you build it, they will come, impulse control, income inequality, Induced demand, invisible hand, Isaac Newton, Jane Jacobs, John Nash: game theory, Kenneth Arrow, lake wobegon effect, loss aversion, megacity, Milgram experiment, Nash equilibrium, PalmPilot, power law, Sam Peltzman, Silicon Valley, SimCity, statistical model, the built environment, The Death and Life of Great American Cities, Timothy McVeigh, traffic fines, Tragedy of the Commons, traumatic brain injury, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor

A sophisticated analysis of motion, using several frames of motion at once, allows Richman’s system to distinguish the motion of cars from that of trees and shadows…. [Richman’s] system can trackcars through shadows, a feat that is trivial for our visual intelligence but, heretofore, quite difficult for computer vision systems. It’s easy to underestimate our sophistication at constructing visual motion. That is, until we try to duplicate that sophistication on a computer. Then it seems impossible to overestimate it.” From Donald D. Hoffman, Visual Intelligence (New York: W. W. Nortion, 1998), p. 170. “caution for the caution”: See, for example, Don Leavitt, “Insights at the Intersection,” Traffic Management and Engineering, October 2003.

pages: 596 words: 163,682

The Third Pillar: How Markets and the State Leave the Community Behind by Raghuram Rajan

"Friedman doctrine" OR "shareholder theory", activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, airline deregulation, Albert Einstein, Andrei Shleifer, banking crisis, barriers to entry, basic income, battle of ideas, Bernie Sanders, blockchain, borderless world, Bretton Woods, British Empire, Build a better mousetrap, business cycle, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, central bank independence, computer vision, conceptual framework, corporate governance, corporate raider, corporate social responsibility, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, data acquisition, David Brooks, Deng Xiaoping, desegregation, deskilling, disinformation, disruptive innovation, Donald Trump, driverless car, Edward Glaeser, facts on the ground, financial innovation, financial repression, full employment, future of work, Glass-Steagall Act, global supply chain, Great Leap Forward, high net worth, household responsibility system, housing crisis, Ida Tarbell, illegal immigration, income inequality, industrial cluster, intangible asset, invention of the steam engine, invisible hand, Jaron Lanier, job automation, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, labor-force participation, Les Trente Glorieuses, low interest rates, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, means of production, Money creation, moral hazard, Network effects, new economy, Nicholas Carr, obamacare, opioid epidemic / opioid crisis, Productivity paradox, profit maximization, race to the bottom, Richard Thaler, Robert Bork, Robert Gordon, Ronald Reagan, Sam Peltzman, shareholder value, Silicon Valley, social distancing, Social Responsibility of Business Is to Increase Its Profits, SoftBank, South China Sea, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, superstar cities, The Future of Employment, The Wealth of Nations by Adam Smith, trade liberalization, trade route, transaction costs, transfer pricing, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, Upton Sinclair, Walter Mischel, War on Poverty, women in the workforce, working-age population, World Values Survey, Yom Kippur War, zero-sum game

THE DIRECT EFFECTS ON JOBS As a number of researchers have pointed out, in recent years new technologies have eliminated jobs that involved well-specified routines or simple, predictable tasks.1 For example, the Amazon Go store (opened first in Seattle) tries to create a shopping experience with no lines and no checkout counters.2 As you walk in, you use the app on your phone to register your presence, pick up what you need, and walk out. Later, your Amazon account is billed. Computer vision and machine-learning algorithms, similar to the ones used in driverless cars, help identify what you pick up and tote up your bill. Not only does this do away with checkout clerks, the underlying software has also reduced the need for someone to monitor stock levels, order new inventory, or reconcile the store’s books at the end of the day.

pages: 626 words: 167,836

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

3D printing, AlphaGo, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, Cornelius Vanderbilt, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford Model T, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, general purpose technology, Gini coefficient, Great Leap Forward, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeremy Corbyn, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Lewis Mumford, Loebner Prize, low skilled workers, machine translation, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Nick Bostrom, Norbert Wiener, nowcasting, oil shock, On the Economy of Machinery and Manufactures, OpenAI, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, Robert Solow, robot derives from the Czech word robota Czech, meaning slave, safety bicycle, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Simon Kuznets, social intelligence, sparse data, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

Today, some 3.5 million Americans work as cashiers across the country. But if you go to an Amazon Go store, you will not see a single cashier or even a self-service checkout stand. Customers walk in, scan their phones, and walk out with what they need. To achieve this, Amazon is leveraging recent advances in computer vision, deep learning, and sensors that track customers, the items they reach for, and take with them. Amazon then bills the credit card passed through the turnstile when the customer leaves the store and sends the receipt to the Go app. While the rollout of the first Seattle, Washington, prototype store was delayed because of issues with tracking multiple users and objects, Amazon now runs three Go stores in Seattle and another in Chicago, Illinois, and plans to launch another three thousand by 2021.

pages: 598 words: 183,531

Hackers: Heroes of the Computer Revolution - 25th Anniversary Edition by Steven Levy

"Margaret Hamilton" Apollo, air freight, Apple II, Bill Gates: Altair 8800, Buckminster Fuller, Byte Shop, Compatible Time-Sharing System, computer age, Computer Lib, computer vision, corporate governance, Donald Knuth, El Camino Real, Fairchild Semiconductor, Free Software Foundation, game design, Gary Kildall, Hacker Ethic, hacker house, Haight Ashbury, John Conway, John Markoff, Mark Zuckerberg, Menlo Park, Mondo 2000, Multics, non-fiction novel, Norman Mailer, Paul Graham, popular electronics, RAND corporation, reversible computing, Richard Stallman, Silicon Valley, software patent, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Ted Nelson, The Hackers Conference, value engineering, Whole Earth Catalog, Y Combinator

They were always demanding that hackers get off the machine so they could work on their “Officially Sanctioned Programs,” and they were appalled at the seemingly frivolous uses to which the hackers put the computer. The grad students were all in the midst of scholarly and scientific theses and dissertations which pontificated on the difficulty of doing the kind of thing that David Silver was attempting. They would not consider any sort of computer-vision experiment without much more planning, complete review of previous experiments, careful architecture, and a setup which included pure white cubes on black velvet in a pristine, dustless room. They were furious that the valuable time of the PDP-6 was being taken up for this . . . toy! By a callow teenager, playing with the PDP-6 as if it were his personal go-cart.

pages: 706 words: 202,591

Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, AOL-Time Warner, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, Benchmark Capital, Big Tech, Black Lives Matter, Blitzscaling, blockchain, Burning Man, business intelligence, Cambridge Analytica, cloud computing, company town, computer vision, crowdsourcing, cryptocurrency, data science, deep learning, disinformation, don't be evil, Donald Trump, Dunbar number, East Village, Edward Snowden, El Camino Real, Elon Musk, end-to-end encryption, fake news, Firefox, Frank Gehry, Geoffrey Hinton, glass ceiling, GPS: selective availability, growth hacking, imposter syndrome, indoor plumbing, information security, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, lock screen, Lyft, machine translation, Mahatma Gandhi, Marc Andreessen, Marc Benioﬀ, Mark Zuckerberg, Max Levchin, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, natural language processing, Network effects, Oculus Rift, operational security, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Russian election interference, Salesforce, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, slashdot, Snapchat, social contagion, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, surveillance capitalism, tech billionaire, techlash, Tim Cook: Apple, Tragedy of the Commons, web application, WeWork, WikiLeaks, women in the workforce, Y Combinator, Y2K, you are the product

It is the horizon-exploring partner to the company’s Applied Machine Learning team, which directs its AI work to products. LeCun says that the integration worked superbly. The applied group imbued the product with machine learning, and the research group worked on general advances in natural-language understanding and computer vision. It often worked out that those advances helped Facebook. “If you ask Schrep or Mark, like, how much of an impact FAIR has had on product, they will say it’s much larger than they expected,” says LeCun. “They told us, Your mission is to really push the state of the art, the research. When things come out of it for a product impact, that’s great, but be ambitious.”

pages: 1,409 words: 205,237

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale by Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George

Amazon Web Services, barriers to entry, bitcoin, business intelligence, business logic, business process, cloud computing, commoditize, computer vision, continuous integration, create, read, update, delete, data science, database schema, Debian, deep learning, DevOps, domain-specific language, fault tolerance, Firefox, FOSDEM, functional programming, Google Chrome, Induced demand, information security, Infrastructure as a Service, Internet of things, job automation, Kickstarter, Kubernetes, level 1 cache, loose coupling, microservices, natural language processing, Network effects, platform as a service, single source of truth, source of truth, statistical model, vertical integration, web application

While certainly a hyped term, machine learning goes beyond classic statistics, with more advanced algorithms that predict an outcome by learning from the data—often without explicitly being programmed. The most advanced methods in machine learning, referred to as deep learning, are able to automatically discover the relevant data features for learning, which essentially enables use cases like computer vision, natural language processing, or fraud detection for any corporation. Many machine learning algorithms (even fairly simple ones) benefit from big data in an unproportional, even unreasonable way, an effect which was described as early as 2001.2 As big data becomes readily available in more and more organizations, machine learning becomes a defining movement in the overall IT industry to take advantage of this effect.

pages: 935 words: 197,338

The Power Law: Venture Capital and the Making of the New Future by Sebastian Mallaby

"Susan Fowler" uber, 23andMe, 90 percent rule, Adam Neumann (WeWork), adjacent possible, Airbnb, Apple II, barriers to entry, Ben Horowitz, Benchmark Capital, Big Tech, bike sharing, Black Lives Matter, Blitzscaling, Bob Noyce, book value, business process, charter city, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, cloud computing, cognitive bias, collapse of Lehman Brothers, Colonization of Mars, computer vision, coronavirus, corporate governance, COVID-19, cryptocurrency, deal flow, Didi Chuxing, digital map, discounted cash flows, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, Dutch auction, Dynabook, Elon Musk, Fairchild Semiconductor, fake news, family office, financial engineering, future of work, game design, George Gilder, Greyball, guns versus butter model, Hacker Ethic, Henry Singleton, hiring and firing, Hyperloop, income inequality, industrial cluster, intangible asset, iterative process, Jeff Bezos, John Markoff, junk bonds, Kickstarter, knowledge economy, lateral thinking, liberal capitalism, Louis Pasteur, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Marshall McLuhan, Mary Meeker, Masayoshi Son, Max Levchin, Metcalfe’s law, Michael Milken, microdosing, military-industrial complex, Mitch Kapor, mortgage debt, move fast and break things, Network effects, oil shock, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, plant based meat, plutocrats, power law, pre–internet, price mechanism, price stability, proprietary trading, prudent man rule, quantitative easing, radical decentralization, Recombinant DNA, remote working, ride hailing / ride sharing, risk tolerance, risk/return, Robert Metcalfe, ROLM, rolodex, Ronald Coase, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, smart grid, SoftBank, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, Steven Levy, super pumped, superconnector, survivorship bias, tech worker, Teledyne, the long tail, the new new thing, the strength of weak ties, TikTok, Travis Kalanick, two and twenty, Uber and Lyft, Uber for X, uber lyft, urban decay, UUNET, vertical integration, Vilfredo Pareto, Vision Fund, wealth creators, WeWork, William Shockley: the traitorous eight, Y Combinator, Zenefits

But in 2017, unwilling to rest on its laurels, Founders Fund designated a partner named Trae Stephens to identify a third defense startup that might break into the major league. When Stephens scoured the Valley and came up with nothing, his comrades responded with a simple prompt. If no such company exists, start one.[64] Four years later, the resulting unicorn, Anduril, is building a suite of next-generation defense systems. Its Lattice platform combines computer vision, machine learning, and mesh networking to create a picture of a battlefield. Its Ghost 4 sUAS is a military reconnaissance drone. Its solar-powered Sentry Towers have been deployed on the U.S.-Mexico border. In an age when artificial intelligence will overwhelm the war machines of yesteryear, Anduril’s aspiration is to combine the coding virtuosity of a Google with the national-security focus of a Lockheed Martin.

pages: 562 words: 201,502

Elon Musk by Walter Isaacson

4chan, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AltaVista, Apollo 11, Apple II, Apple's 1984 Super Bowl advert, artificial general intelligence, autism spectrum disorder, autonomous vehicles, basic income, Big Tech, blockchain, Boston Dynamics, Burning Man, carbon footprint, ChatGPT, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, Colonization of Mars, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, crowdsourcing, cryptocurrency, deep learning, DeepMind, Demis Hassabis, disinformation, Dogecoin, Donald Trump, Douglas Engelbart, drone strike, effective altruism, Elon Musk, estate planning, fail fast, fake news, game design, gigafactory, GPT-4, high-speed rail, hiring and firing, hive mind, Hyperloop, impulse control, industrial robot, information security, Jeff Bezos, Jeffrey Epstein, John Markoff, John von Neumann, Jony Ive, Kwajalein Atoll, lab leak, large language model, Larry Ellison, lockdown, low earth orbit, Marc Andreessen, Marc Benioﬀ, Mars Society, Max Levchin, Michael Shellenberger, multiplanetary species, Neil Armstrong, Network effects, OpenAI, packet switching, Parler "social media", paypal mafia, peer-to-peer, Peter Thiel, QAnon, Ray Kurzweil, reality distortion field, remote working, rent control, risk tolerance, Rubik’s Cube, Salesforce, Sam Altman, Sam Bankman-Fried, San Francisco homelessness, Sand Hill Road, Saturday Night Live, self-driving car, seminal paper, short selling, Silicon Valley, Skype, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Streisand effect, supply-chain management, tech bro, TED Talk, Tesla Model S, the payments system, Tim Cook: Apple, universal basic income, Vernor Vinge, vertical integration, Virgin Galactic, wikimedia commons, William MacAskill, work culture , Y Combinator

The OpenAI team rejected that idea, and Altman stepped in as president of the lab, starting a for-profit arm that was able to raise equity funding. So Musk decided to forge ahead with building a rival AI team to work on Tesla Autopilot. Even as he was struggling with the production hell surges in Nevada and Fremont, he recruited Andrej Karpathy, a specialist in deep learning and computer vision, away from OpenAI. “We realized that Tesla was going to become an AI company and would be competing for the same talent as OpenAI,” Altman says. “It pissed some of our team off, but I fully understood what was happening.” Altman would turn the tables in 2023 by hiring Karpathy back after he became exhausted working for Musk. 41 The Launch of Autopilot Tesla, 2014–2016 Franz von Holzhausen with an early “Robotaxi” Radar Musk had discussed with Larry Page the possibility of Tesla and Google working together to build an autopilot system that would allow cars to be self-driving.

pages: 915 words: 232,883

Steve Jobs by Walter Isaacson

"World Economic Forum" Davos, air freight, Albert Einstein, Andy Rubin, AOL-Time Warner, Apollo 13, Apple II, Apple's 1984 Super Bowl advert, big-box store, Bill Atkinson, Bob Noyce, Buckminster Fuller, Byte Shop, centre right, Clayton Christensen, cloud computing, commoditize, computer age, computer vision, corporate governance, death of newspapers, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, fixed income, game design, General Magic , Golden Gate Park, Hacker Ethic, hiring and firing, It's morning again in America, Jeff Bezos, Johannes Kepler, John Markoff, Jony Ive, Kanban, Larry Ellison, lateral thinking, Lewis Mumford, Mark Zuckerberg, Menlo Park, Mitch Kapor, Mother of all demos, Paul Terrell, Pepsi Challenge, profit maximization, publish or perish, reality distortion field, Recombinant DNA, Richard Feynman, Robert Metcalfe, Robert X Cringely, Ronald Reagan, Silicon Valley, skunkworks, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, The Home Computer Revolution, thinkpad, Tim Cook: Apple, Tony Fadell, vertical integration, Wall-E, Whole Earth Catalog

(TV show), 458 “Why I Won’t Buy an iPad” (Doctorow), 563 Wigginton, Randy, 81–82, 92–93, 104, 161 Wikipedia, 386 Wilkes Bashford (store), 91 Williams, Robert, 329–30 Wired, 276, 295, 311–12, 317, 408, 466 Wolf, Gary, 295 Wolfe, Tom, 58 Wolff, Michael, 523 Wonder Boys (film), 412 Woodside Design, 196 Woolard, Ed, 310, 313, 314, 318–20, 336, 338, 359, 371 options compensation issue and, 364–66, 448 “Wooly Bully” (song), 413 Wordsworth, William, 69 “Working for/with Steve Jobs” (Raskin), 112 Worldwide Developers Conference, 532–33, 536 Wozniak, Francis, 22 Wozniak, Jerry, 77 Wozniak, Stephen, xvi, 21, 29, 32, 33, 59, 62, 69, 79, 93, 94, 102, 110, 124, 132, 163, 168, 170, 217, 305, 308, 317, 319, 334–35, 354, 363, 379, 393, 412, 464, 474, 524, 560, 565 in air crash, 115 Apple I design and, 61, 67–68, 534 Apple II design and, 72–75, 80–81, 84–85, 92, 534, 562 Apple left by, 192–93 Apple partnership and, 63–65 Apple’s IPO and, 103–4 background of, 21–22 Blue Box designed by, 27–30, 81 music passion of, 25–26 personal computer vision of, 60–61 Pong design and, 52–54 as prankster, 23–29 remote control device of, 193–94, 218, 221 SJ contrasted with, 21–22, 40, 64 on SJ’s distortion of reality, 118–19 SJ’s first meeting with, 25 SJ’s friendship with, 21–23 at SJ’s 30th birthday party, 189 in White House visit, 192–93 Wright, Frank Lloyd, 7, 330 Xerox, 95–96, 98, 119, 169, 195, 565, 566 Alto GUI of, 177 Star computer of, 99, 175–76 Xerox PARC, 94–96, 98–99, 100, 111, 114, 120, 177, 179, 474 Yahoo, 502, 545 Yeah Yeah Yeah (music group), 500 Yocam, Del, 4–5, 198, 202 Yogananda, Paramahansa, 35 York, Jerry, 321, 450, 482 “You Say You Want a Revolution” (song), 526 Zaltair hoax, 81, 189 Zander, Ed, 333, 465 Zap, 53 ZDNet, 137 Zen Buddhism, 15, 34–35, 41, 57 Zen Mind, Beginner’s Mind (Suzuki), 35, 49 Ziegler, Bart, 293 Zittrain, Jonathan, 563 Zuckerberg, Mark, 275, 545–46, 552 ILLUSTRATION CREDITS Numbers in roman type refer to illustrations in the Photos section; numbers in italics refer to book pages.

pages: 898 words: 266,274

The Irrational Bundle by Dan Ariely

accounting loophole / creative accounting, air freight, Albert Einstein, Alvin Roth, An Inconvenient Truth, assortative mating, banking crisis, Bear Stearns, behavioural economics, Bernie Madoff, Black Swan, Broken windows theory, Burning Man, business process, cashless society, Cass Sunstein, clean water, cognitive dissonance, cognitive load, compensation consultant, computer vision, Cornelius Vanderbilt, corporate governance, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, delayed gratification, Demis Hassabis, Donald Trump, end world poverty, endowment effect, Exxon Valdez, fake it until you make it, financial engineering, first-price auction, Ford Model T, Frederick Winslow Taylor, fudge factor, Garrett Hardin, George Akerlof, Gordon Gekko, greed is good, happiness index / gross national happiness, hedonic treadmill, IKEA effect, Jean Tirole, job satisfaction, John Perry Barlow, Kenneth Arrow, knowledge economy, knowledge worker, lake wobegon effect, late fees, loss aversion, Murray Gell-Mann, name-letter effect, new economy, operational security, Pepsi Challenge, Peter Singer: altruism, placebo effect, price anchoring, Richard Feynman, Richard Thaler, Saturday Night Live, Schrödinger's Cat, search costs, second-price auction, Shai Danziger, shareholder value, Silicon Valley, Skinner box, Skype, social contagion, software as a service, Steve Jobs, subprime mortgage crisis, sunk-cost fallacy, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, ultimatum game, Upton Sinclair, Walter Mischel, young professional

pages: 1,079 words: 321,718

Surfaces and Essences by Douglas Hofstadter, Emmanuel Sander

Abraham Maslow, affirmative action, Albert Einstein, Arthur Eddington, Benoit Mandelbrot, Brownian motion, Charles Babbage, cognitive dissonance, computer age, computer vision, dematerialisation, Donald Trump, Douglas Hofstadter, Eddington experiment, Ernest Rutherford, experimental subject, Flynn Effect, gentrification, Georg Cantor, Gerolamo Cardano, Golden Gate Park, haute couture, haute cuisine, Henri Poincaré, Isaac Newton, l'esprit de l'escalier, Louis Pasteur, machine translation, Mahatma Gandhi, mandelbrot fractal, Menlo Park, Norbert Wiener, place-making, Sapir-Whorf hypothesis, Silicon Valley, statistical model, Steve Jobs, Steve Wozniak, theory of mind, time dilation, upwardly mobile, urban sprawl, yellow journalism, zero-sum game

If one were to draw up a table of numerical specifications, as is standardly done in comparing one computer with another, Homo sapiens sapiens would wind up in the recycling bin. Given all this, how can we explain the fact that, in terms of serious thought, machines lag woefully behind us? Why is machine translation so often inept and awkward? Why are robots so primitive? Why is computer vision restricted to the simplest kinds of tasks? Why is it that today’s search engines can instantly search billions of Web sites for passages containing the phrase “in good faith”, yet are incapable of spotting Web sites in which the idea of good faith (as opposed to the string of alphanumeric characters) is the central theme?