computer vision

133 results back to index


Practical Python and OpenCV by Adrian Rosebrock

computer vision, license plate recognition, Mars Rover

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. We apply computer vision algorithms to analyze movies, football games, hand gesture recognition (for sign language), license plates (just in case you were driving too fast), medicine, surgery, military, and retail. 3 introduction We even use computer visions in space! NASA’s Mars Rover includes capabilities to model the terrain of the planet, detect obstacles in it’s path, and stitch together panorama images.

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! 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.

NASA’s Mars Rover includes capabilities to model the terrain of the planet, detect obstacles in it’s path, and stitch together panorama images. This list will continue to grow in the coming years. Certainly, computer vision is an exciting field with endless possibilities. With this in mind, ask yourself, what does your imagination want to build? Let it run wild. And let the computer vision techniques introduced in this book help you build it. 4 2 P Y T H O N A N D R E Q U I R E D PA C K A G E S In order to explore the world of computer vision, we’ll first need to install some packages. As a first timer in computer vision, installing some of these packages (especially OpenCV) can be quite tedious, depending on what operating system you are using. I’ve tried to consolidate the installation instructions into a short how-to guide, but as you know, projects change, websites change, and installation instructions change!


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 9 Introduces different techniques for dividing an image into meaningful regions using clustering, user interactions, or image models. 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.

Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008. [37] Deepak Verma and Marina Meila. A comparison of spectral clustering algorithms. Technical report, 2003. [38] Luminita A. Vese and Tony F. Chan. A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision, 50:271–293, December 2002. [39] Paul Viola and Michael Jones. Robust real-time object detection. In International Journal of Computer Vision, 2001. [40] Marco Zuliani. Ransac for dummies. Technical report, Vision Research Lab, UCSB, 2011. Appendix E. 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.

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).


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, 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, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, 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, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

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?” FIGURE 49: Four atypical instances of “walking a dog” Karpathy lists many of the things we humans easily understand but that remain beyond the abilities of today’s best computer-vision programs. For example, we recognize that there are people in the scene, but also that there are mirrors, so some of the people are reflections in those mirrors.

While this story is merely an interesting footnote to the larger history of deep learning in computer vision, I tell it to illustrate the extent to which the ImageNet competition came to be seen as the key symbol of progress in computer vision, and AI in general. Cheating aside, progress on ImageNet continued. The final competition was held in 2017, with a winning top-5 accuracy of 98 percent. As one journalist commented, “Today, many consider ImageNet solved,”11 at least for the classification task. The community is moving on to new benchmark data sets and new problems, especially ones that integrate vision and language. What was it that enabled ConvNets, which seemed to be at a dead end in the 1990s, to suddenly dominate the ImageNet competition, and subsequently most of computer vision in the last half a decade? It turns out that the recent success of deep learning is due less to new breakthroughs in AI than to the availability of huge amounts of data (thank you, internet!)

The caveats I described above aren’t meant to diminish the amazing recent progress in computer vision. There is no question that convolutional neural networks have been stunningly successful in this and other areas, and these successes have not only produced commercial products but also resulted in a real sense of optimism in the AI community. My discussion is meant to illustrate how challenging vision turns out to be and to add some perspective on the progress made so far. Object recognition is not yet close to being “solved” by artificial intelligence. Beyond Object Recognition I have focused on object recognition in this chapter because this has been the area in which computer vision has recently seen the most progress. However, there’s obviously a lot more to vision than just recognizing objects. If the goal of computer vision is to “get a machine to describe what it sees,” then machines will need to recognize not only objects but also their relationships to one another and how they interact with the world.


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. He is the author of the DamilesBlog (http://blog.damiles.com), where he publishes research articles and tutorials about OpenCV, computer vision in general, and Optical Character Recognition algorithms.

By developing this application, we have gained practice in leveraging the functionality of OpenCV, NumPy, and other libraries. 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.

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. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented.


Robot Futures by Illah Reza Nourbakhsh

3D printing, autonomous vehicles, Burning Man, commoditize, computer vision, 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

It seems the year 2005 was still too early for a standard to be born. 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). From ping-pong-playing robots to sketching arms that would photograph your face and make a line drawing, the 40 Chapter 2 OpenCV library led to a blossoming of engaging demonstration programs throughout computer labs and science museums.

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. Since the digital wall can change content in an instant, this means that every advertisement will start to observe and individually experiment on consumer behavior.

A recent news article in the Washington Post reports on drone maneuvers at Fort Benning, Georgia, in 2010, where roboticists have programmed an unmanned aerial vehicle to fly autonomously, use on-board cameras, and computer vision algorithms to search for an object on the ground, match it to a desired Brainspotting 103 target, then automatically fire on it (Finn 2011). The article goes on to explain that the eventual goal is for drones to have a database of enemy images and fly above a battlefield, looking for the desired targets with face recognition software. Following an image match, the drones would then make the autonomous decision to kill on their own. This is a poignant example because, from some distance, it may appear reasonable to a nonroboticist that drones can do face matching and then make lethal decisions. But to any roboticist aware of state-of-the-art computer vision and face recognition, the concept is absurdly out of touch with the reality in which face recognition can easily find false matches using anything from the rear end of a cow to a poster with the target’s picture pasted on it.


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

AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, 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, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, 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, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, 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!, X Prize, Yogi Berra

The arrows point toward the location of where to find wing bars that are especially important for telling apart families of warblers: Some are conspicuous, some obscure, some double, some long, some short. From Peterson, Mountfort, and Hollom, Field Guide to the Birds of Britain and Europe, 5th ed., 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.

I once asked Marvin Minsky whether the story was true. He snapped back that I had it wrong: “We assigned the problem to undergraduate students.” 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. For example, consider the two birds in figure 2.2. If you shift the image of one bird over the other, you can get a part to match, but the rest is out of register; but you can get a fairly good match to an image of another bird species in the same pose.

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. Methods improved slowly because each new category of objects requires a domain expert to identify the pose-invariant features needed to distinguish them from other objects.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, 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, 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.”

FEI-FEI LI: During the first decade of the 21st century, object recognition was the holy grail that the field of computer vision was working on. Object recognition is a building block for all vision. As humans, if we open our eyes and look around our environment, we recognize almost every object we look at. Recognition is critically important for us to be able to navigate the world, understand the world, communicate about the world, and do things in the world. Object recognition was a very lofty holy grail in computer vision, and we were using tools such as machine learning at that time. Then in the mid-2000s, as I transitioned from a PhD student to become a professor, it became obvious that computer vision as a field was stuck, and that the machine learning models were not making huge progress. Back then, the whole international community was benchmarking autorecognition tasks with around 20 different objects.

YOSHUA BENGIO: We had previously used a sigmoid function to train neural nets, but it turned out that by using ReLUs we could suddenly train very deep nets much more easily. That was another big change that occurred around 2010 or 2011. 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? YOSHUA BENGIO: It was a bit later.


pages: 215 words: 56,215

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, en.wikipedia.org, full employment, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, working poor

Instead of cameras, a self-driving car uses different sensors to detect the world around it. 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.” A five-year-old human child can do that easily, but computers are not there yet except in special situations.

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. For example, it helps the car decide if a traffic light at an intersection is red or green.

In the same way, it is not currently possible to put a camera on the front of a car and have a computer use the pictures from the camera to identify other traffic, lane markings, bicyclists, pedestrians, dogs wandering into the street, etc. 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. It turns out that there are many sectors of the economy that will come under new pressure from robots and automation once robots can see the world.


pages: 688 words: 107,867

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

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

© 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. Furthermore, another library will be introduced, called openCV, which has always been the reference point for image analysis. Image Analysis and Computer Vision Throughout the book, you have seen how the purpose of the analysis is to extract new information, to draw new concepts and characteristics from a system under investigation.

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. The captured image is decomposed and elaborated into levels of representation that are gradually more abstract (contours, figures, objects, and words) and therefore recognizable by the human mind. In the same way, computer vision intends to process a two-dimensional image and extract the same levels of representation from it.

This is done through various operations that can be classified as follows:Detection: Detect shapes, objects, or other subjects of investigation in an image (for example finding cars) Recognition: The identified subjects are then led back to generic classes (for example, subdividing cars by brands and types) Identification: An instance of the previous class is identified (for example, find my car) OpenCV and Python OpenCV (Open Source Computer Vision) is a library written in C ++ that is specialized for computer vision and image analysis ( https://opencv.org/ ). This powerful library, designed by Gary Bradsky, was born as an Intel project and in 2000 the first version was released. Then with the passage of time, it was released under an open source license, and since then has gradually becoming more widespread, reaching the version 3.3 (2017). At this time, OpenCV supports many algorithms related to computer vision and machine learning and is expanding day by day. Its usefulness and spread is due precisely to its antagonist: MATLAB. In fact, those who need to work with image analysis can follow only two ways: purchase MATLAB packages or compile and install the open source version of OpenCV.


pages: 413 words: 119,587

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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, 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, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

In his first year at Intel he met some superstar Russian software designers who worked under contract for the chipmaker, and he realized that they could be an important resource for him. 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. Bradski was a believer in an iconoclastic operating style that was sometimes attributed to Admiral Grace Hopper and was shared by many who liked getting things done inside large organizations.

Bradski was a believer in an iconoclastic operating style that was sometimes attributed to Admiral Grace Hopper and was shared by many who liked getting things done inside large organizations. “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. (Photo © 2015 by Gary Bradski) Realizing that he would one day leave Intel and would need a powerful toolset for his next project, Bradski developed a second agenda.

To this group, Shakey was a striking portent of the future, and they believed that the scientific breakthrough to enable machines to act like humans would come in just a few short years. 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. Shakey lived in a large open room with linoleum floors and a couple of racks of electronics.


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, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, 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, 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.

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.

Professional Go players have tried to put a brave face on it, talking about the extra creativity that it has unleashed in their own play, but there is something quite soul-destroying about knowing that we are now second best to the machine. 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.


pages: 523 words: 61,179

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

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

The shelves are carried by squat, rolling robots destined to bring the goods to the worker, who plucks the items off the shelves and puts them in a box to ship. Computer vision helps the robots know where they are in the warehouse, sensors keep them from running into each other, and machine-learning algorithms help them determine the best paths and right-of-ways on a warehouse floor full of other robots. The human worker no longer needs to walk miles a day to retrieve goods for packaging. In another example of embodiment, drones are being tested to deliver health care, on demand, to remote parts of Rwanda, out of reach of traditional medical options. A company called Zipline is pioneering the technology, targeting one of the leading causes of death—postpartum hemorrhage—with a delivery of blood for transfusion.12 Drones have become a particularly interesting application of AI: computer vision and smart algorithms process video in real time, thereby allowing people to extend their vision and delivery capabilities up into the air and over miles of potentially impassable terrain.

The designer chooses one that will distinguish her drone from the rest and further tweaks the design to fit her aesthetic and engineering goals. 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? It has to do with understanding the true nature of AI’s impact. In the past, executives focused on using machines to automate specific workflow processes.

It also inspired entirely new areas of research in the decades that followed. 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. In 1959, Samuel coined the term “machine learning”: the field of study that gives computers the ability to learn without being explicitly programmed.


pages: 307 words: 88,180

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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, 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, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, 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, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

That was enough to wake up much of the AI community to this thing called deep learning, but it was just a taste of what was to come. 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. Taken together, these technical advances and emerging uses cause me to land on the higher end of task-based estimates, namely, PwC’s prediction that 38 percent of U.S. jobs will be at high risk of automatability by the early 2030s.

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. Researchers, futurists, and tech CEOs all began buzzing about the massive potential of the field to decipher human speech, translate documents, recognize images, predict consumer behavior, identify fraud, make lending decisions, help robots “see,” and even drive a car.

The truth is, the story of the birth of deep learning took place almost entirely in the United States, Canada, and the United Kingdom. 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

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

Tang and Patrick S. P. Wang) Vol. 44: Multispectral Image Processing and Pattern Recognition (Eds. J. 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. H. du Buf and M. M. Bayer) Vol. 52: Advances in Image Processing and Understanding A Festschrift for Thomas S.

Proc. of National Symposium on Pattern Recognition and Image Analysis, pp. 193–198, Barcelona, Spain. 5. Crochemore, M. and Rytter, W. (1994). Text Algorithms, Oxford University Press. 6. 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. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(2), 129–135. 11.

-P. (2000). Average Brain Models: A Convergence Study. Computer Vision and Image Understanding, 77(2), 192–210. 13. Gusfield, D. (1997). Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press. Median Strings: A Review 191 14. Guyon, Schomaker, L., Plamondon, R., Liberman, M. and Janet, S. (1994). UNIPEN Project on On-Line Exchange and Recognizer Benchmarks. Proc. of 12th Int. Conf. on Pattern Recognition, pp. 29–33. 15. de la Higuera, C. and Casacuberta, F. (2000). Topology of Strings: Median String is NP-Complete. Theoretical Computer Science, 230(1/2), 39–48. 16. Jiang, X., Schiffmann, L., and Bunke, H. (2000). Computation of Median Shapes. Proc. of 4th. Asian Conf. on Computer Vision, pp. 300–305, Taipei. 17. Jiang, X., Abegglen, K., and Bunke, H. (2001).


pages: 340 words: 97,723

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

Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, 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, technological singularity, The Coming Technological Singularity, 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

These universities are home to active academic research groups with strong industry ties. 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. Right now, courses like these are intentionally left off the curriculum, and it’s difficult to make room for them as electives outside the major.

From that perspective, and given China’s unified march advancing artificial intelligence, China is dangerously far ahead of the West. In my view, we’ve come to this realization too late. 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.

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). Those chips can handle its deep-learning AI framework, TensorFlow.


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

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

Source: Jason Yosinski, Cornell University Figure 11.1 A 3-D map built using a process of simultaneous localization and mapping (SLAM). 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. Source: National Public Radio Figure 12.3 Passengers relax with electronics in this driverless concept mockup.

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. The last mile for driverless-car technology remains the development of software to oversee the car’s perception and response.

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. On top of the boxes perched Shakey’s “head,” a tall, thin, swiveling mast of cameras and cables.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, 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

This is why it is considered a black box. 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. Naïve Bayes – Based on Bayes’ theorem, the naïve Bayes algorithm aims to make probabilistic classification based on prior knowledge.

These solutions allow us to minimize the amount of human labor in a number of tedious tasks, but also allow us to do things at a greater scale with greater intelligence. 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. There are rarely any enforced basic standards on devices, and security is frequently up to the discretion of the manufacturer.

In order to mitigate this, the PlowNYC service only updates every 30 minutes even though data is available to do it real time. 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. Then the logic would have had to be developed.


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, computer age, computer vision, continuous integration, deskilling, Donald Knuth, Grace Hopper, information asymmetry, inventory management, John Markoff, John von Neumann, linear programming, longitudinal study, Menlo Park, Mitch Kapor, Network effects, popular electronics, RAND corporation, Robert X Cringely, Ronald Reagan, Silicon Valley, software patent, Steve Jobs, Steve Wozniak, Steven Levy, Thomas Kuhn: the structure of scientific revolutions

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.

By the end of 1983, Triad had . . . 75 percent of the auto parts computer market.12 Turnkey suppliers flourished in vertical markets such as retail and professional practices. 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. Software Brokers In the 1970s, software brokers sprang up to mediate between software developers and software users.

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. A medium-size system with four workstations cost at least $300,000, a large systems at least $1 million. By 1980, CAD systems were a significant sector of the computer industry in their own right, with a total market exceeding $1 billion. Computer Vision’s annual sales were nearly $200 million. The Shaping of the Software Products Industry 161 Although originally intended for the manufacturing and engineering industries, CAD came into use in architecture, cartography, aerospace, and other industries and professions to which drawing was central.


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, Airbnb, Amazon Web Services, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, Donald Trump, Doomsday Clock, Elon Musk, gig economy, Internet of things, inventory management, invisible hand, Jeff Bezos, market fragmentation, new economy, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, recommendation engine, remote working, sensor fusion, sharing economy, Skype, supply-chain management, TaskRabbit, trade route, underbanked, urban planning, white picket fence

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. But now, within the context of Amazon’s track record of capitalizing on technology drivers of change, it is clear to see quite how important a bet it has made on voice – especially when you consider that a consensus of industry estimates predicts a 40 per cent adoption rate of voice-enabled devices in the US and 30 per cent internationally by 2020.

The German retailer is also among those around the world testing autonomous robotic delivery vehicles developed by Starship, a start-up owned by Skype founders Ahti Heinla and Janus Friis. 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. The success of the checkout-free model, particularly in attracting repeat custom, according to Amazon Go vice president Gianna Puerini,22 means more stores are likely planned for San Francisco, Chicago and London.

No 2017 Amazon Returns Fulfilment Unique agreement with Kohl’s department stores where Amazon shoppers can return unwanted online orders to their local Kohl’s. 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: 161 words: 39,526

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

Airbnb, Amazon Web Services, artificial general intelligence, autonomous vehicles, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework, en.wikipedia.org, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, technological singularity

Medical Diagnosis AI can dramatically streamline and improve medical care and our overall health and wellbeing. 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.

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. Operationalizing and productizing models for enterprise-scale usage also requires different but equally difficult-to-acquire technical expertise.

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. These workflows can be tedious to complete. Employees responsible for these tasks can easily become bored and inattentive, allowing errors to creep into your operations and your data.


pages: 260 words: 67,823

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

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, self-driving car, Silicon Valley, Skype, Snapchat, Steve Ballmer, Steve Jobs, Steve Wozniak, Tim Cook: Apple, uber lyft, wealth creators, 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. Six months later, Facebook bought Face.com for at least $55 million.

Moments later, Amazon pushes a receipt to your phone, accounting for the items you took. 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. The story behind Go extends beyond hardware and code, though.

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. But that’s not the case everywhere. James Bloodworth, a British journalist who went undercover at an Amazon FC while conducting research for his 2018 book, Hired: Six Months Undercover in Low-Wage Britain, said he once found a bottle of urine on the floor, apparently left there by an associate so afraid they’d miss productivity targets they didn’t want to take a bathroom break.


pages: 252 words: 74,167

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

Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, 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, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, 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, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

For more than a decade, researchers attacked an astonishingly broad series of challenges, often designed to disprove a hypothesis like: ‘No machine will ever be capable of doing X.’ 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?

To make it the ‘Worldwide Headquarters’ they thought it should be, they kitted it out with a few tables, three chairs, a turquoise shag rug, a folding ping-pong table and a few other items. 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.

The ensuing demonstration, combined with Rosenblatt’s extravagant claims about the possibilities of perceptrons, was enough to get people excited, however. 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. The New York Times hailed it as the ‘New Navy Device [Which] Learns By Doing’. The Problem with Perceptrons Sadly, not long after this, work with perceptrons suffered two serious setbacks.


pages: 326 words: 74,433

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

augmented reality, computer vision, corporate governance, crowdsourcing, disintermediation, hiring and firing, Inbox Zero, Jeff Bezos, Kickstarter, knowledge worker, Lean Startup, Ray Kurzweil, recommendation engine, risk tolerance, Silicon Valley, Skype, slashdot, social web, software as a service, Steve Jobs

There was a lot of buzz in our favor after we demonstrated an early prototype in September, but we failed to close funding for a number of reasons. This failure gave us one major asset: a big chip on our shoulder. 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. We got excited about going big again. But this time we wanted to become even bigger, which translated into technology that was an order of magnitude harder.

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. Seven months earlier we had flown out to Boulder to join TechStars with little more than a prototype piece of software that could recognize the logos on paper receipts.

But this time we wanted to become even bigger, which translated into technology that was an order of magnitude harder. 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. By staying tiny and taking incrementally harder technology steps, we saw Occipital's value increase dramatically.


Mastering Machine Learning With Scikit-Learn by Gavin Hackeling

computer vision, constrained optimization, correlation coefficient, Debian, 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. An image can be viewed as a matrix in which each element represents a color.

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: 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, computer vision, Daniel Kahneman / Amos Tversky, double helix, Henri Poincaré, Jacquard loom, lateral thinking, loose coupling, speech recognition

Over successive decades, leading researchers forecast that human-level intelligence would be achieved within twenty to thirty years. 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. Instead, over recent decades, AI researchers have made advances by building machines that learn not from people but from direct confrontation with the problem to be solved: much of AI has mutated into a distinct but related field: machine-learning.

So now there were two teams of people (distinguished by having two different coloured shirts), and hence two types of ball-passing, one of which was to be monitored with the button pressing, the other of which was to be ignored. 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. To a casual viewer of the video (i.e. to someone not counting the passes of one team or the other), the woman and her umbrella were all too obvious – indeed, her sudden appearance jumped out as both striking and bizarre.

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. One influential account suggests that different aspects of the sensory input (and their possible interpretations) are associated with different brain cells, and the constraints between sensory fragments and interpretations can be captured by a network of connections between these brain cells.


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, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, 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, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, 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, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

Another “superpower” that is available to machines is to see the entire world at once. 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. With the possibility of sensing on a global scale comes the possibility of decision making on a global scale.

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. This by itself would be a huge boon to humanity as well as a significant step towards general-purpose AI.

Probably the clearest example is Israel’s Harop (figure 7, left), a loitering munition with a ten-foot wingspan and a fifty-pound warhead. 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: 424 words: 114,905

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

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, 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.

The integrated, multitasking deep learning tracks other cars, pedestrians, and lane markings. 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. Even though Level 4 allows for human intervention, cars operating at that level would face catastrophic failure if they experienced the equivalent of a laptop freeze or a web browser crash.

Certainly a neural network is like a blank slate, but many researchers, such as Gary Marcus, argue that until artificial intelligence researchers account for human innateness and prewiring, computers will be incapable of feats such as becoming conversant at the same speed as children.5 So although computers can become unmatched experts at narrow tasks, as Chollet put it, “There’s no practical path from superhuman performance in thousands of narrow vertical tasks to the general intelligence and common sense of a toddler.” 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.” There are even problems with basic object recognition, exemplified by two studies.


pages: 49 words: 12,968

Industrial Internet by Jon Bruner

autonomous vehicles, barriers to entry, commoditize, computer vision, data acquisition, demand response, 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, web application

By analyzing performance metrics from existing wind installations, planners can recommend new layouts that take into account common wind patterns and minimize interference. 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. The autonomous car is a full expression of the industrial internet: software connects a machine to a network, links its components together, ingests context, and uses learned intelligence to control a complicated machine in real-time.

Collaboration between machine makers and control makers is crucial, and the quality with which machines accommodate and respond to intelligent controls will become a key differentiator. 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. Your company is a disease.’” And so Sight Machine[42] got its name — one so natural to Michigan’s manufacturers that, says CEO and co-founder Jon Sobel, visitors often say “I spent the afternoon down at Sight” in the same way they might say “down at Anderson” to refer to a tool-and-die shop called Anderson Machine.


pages: 590 words: 152,595

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

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, 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, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

While SAR images are generally not as sharp as images from electro-optical or infrared cameras, SAR is a powerful tool because radar can penetrate through clouds, allowing all-weather surveillance. Building algorithms that can automatically identify SAR images is extremely difficult, however. 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. Rather than follow a script of if-then steps for how to perform a task, neural networks work based on the strength of connections within a network.

It’s an illustration of what neural nets can do to help users see their potential. 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.” As it turns out, none of the categories Inception-v3 is trained on are those that could be used to identify people, such as “human,” “person,” “man,” or “woman.”

Trying to contain software would be pointless. Pandora’s box has already been opened. 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

Airbnb, augmented reality, autonomous vehicles, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, Deng Xiaoping, digital map, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, family office, fear of failure, glass ceiling, global supply chain, income inequality, industrial robot, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, 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, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, supply-chain management, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, 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. Table 2-3 Sampling of BAT Investments in US Technologies—2018 Company Inv. Type Inv.

Google self-driving cars are being tested on California’s Highway 101; Facebook spins out posts based on deep learning of content preferences; Amazon’s Alexa powers lights, TVs, and speakers by voice activation; and Microsoft’s Azure relies on cognitive computing for speech and language applications, while IBM Watson’s AI-based computer system increases productivity and improves customer service for call centers, production lines, and warehouses. 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.

In the United States, Tencent has made the most deals but Baidu has the most diversified AI portfolio, spanning to health care, advertising, and media startups. 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, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, Bernie Sanders, bitcoin, carbon footprint, Cass Sunstein, computer vision, coronavirus, correlation does not imply causation, COVID-19, Covid-19, crowdsourcing, cryptocurrency, death of newspapers, disintermediation, Donald Trump, Drosophila, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental subject, facts on the ground, Filter Bubble, global pandemic, hive mind, illegal immigration, income inequality, Kickstarter, knowledge worker, longitudinal study, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, mobile money, move fast and break things, move fast and break things, multi-sided market, Nate Silver, natural language processing, Network effects, performance metric, phenotype, recommendation engine, Robert Bork, Robert Shiller, Robert Shiller, Second Machine Age, sentiment analysis, shareholder value, skunkworks, Snapchat, social graph, social intelligence, social software, social web, statistical model, stem cell, Stephen Hawking, Steve Jobs, Telecommunications Act of 1996, The Chicago School, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Uber and Lyft, uber lyft, WikiLeaks, Yogi Berra

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. 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.

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/. Facebook filed a patent for making friend suggestions: Elise Thomas, “A Creepy Facebook Idea Suggests Friends by Sensing Other People’s Phones,” Wired UK, November 4, 2018, https://www.wired.co.uk/​article/​facebook-phone-tracking-patent.

Language processing enables VidMob to transcribe and analyze the text in videos and analyze how the timing and sizing of text or logos influence video performance. As Alex says, “These types of insights illustrate why we truly believe the role of AI is to empower and enhance human creativity.” Facebook has developed a similar video-understanding platform called Lumos, which Manohar Paluri, head of the company’s Computer Vision, calls Facebook’s “visual cortex.” The visual cortex is the part of our brain that processes sensory nerve impulses from our eyes. Lumos processes what we see in Facebook videos in much the same way VidMob’s ACS does for its marketing clients. The system uses deep residual learning networks, a form of machine learning that stacks multilayered neural networks, to classify images by connecting layers at multiple depths simultaneously.


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, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, deskilling, disruptive innovation, edge city, Elon Musk, en.wikipedia.org, future of work, invention of the wheel, Just-in-time delivery, 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, technoutopianism, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, 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?

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. Uber garnered headlines when their short-lived driverless car testing in San Francisco in December 2016, resulted in footage being shared of their car failing to stop at a red light.[269] Traffic light detection poses quite a challenge for a driverless car.

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. Just as humans find it more tiring to drive on poor quality roads, we should fix or replace defective infrastructure to ensure the safe running of cars - and this should be true for human and robot drivers alike.


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

3D printing, AI winter, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, blue-collar work, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, create, read, update, delete, cuban missile crisis, David Attenborough, 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, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, 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

This turns out to be a very difficult problem, as the machine needs to have some understanding of the text that is being translated in order to resolve the many ambiguities present in natural language. 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. In particular, good vision enables computers to interact with their environment — they are no longer just brains in a vat.

After all, what do we as humans know about the world apart from the properties and behaviours of objects within it? Brain in a vat Public Wikipedia There is a bit more to Searle’s argument. 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. Understanding the brain It has also been argued that building an AGI cannot happen in the foreseeable future because the human brain is incredibly complex and it will be a long time before we can understand it, and therefore build an intelligent machine.

It also used three layers of hidden units which were highly constrained so that they tended to recognize discrete features such as line segments or curves. LeNet produced a 0.9% error rate. 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. The best results can be achieved by using multiple decision trees and then averaging the results.


pages: 482 words: 121,173

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

Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden, en.wikipedia.org, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, WikiLeaks, women in the workforce

This is where distinctions enter the picture. 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. But even then the scenarios are varied and the use of the weapons system is customizable.20 A human being should typically make the initial launch decision, but there isn’t time for humans to approve each individual target.

In Minority Report, Spielberg asked theatergoers to think about how technology could be both used and abused—to eliminate crimes before they could be committed but also to abuse people’s rights when things go wrong. 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. What started for most people as a simple scenario, such as cataloging and searching photos, has rapidly become much more sophisticated.

Perhaps not surprisingly, the academic research community has been a leader in using data in this way. 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. To begin, even in a single organization data is often stashed in silos that must be connected, a challenge made even greater when the silos sit in different institutions.


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, artificial general intelligence, Asperger Syndrome, augmented reality, Berlin Wall, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, crowdsourcing, Elon Musk, en.wikipedia.org, epigenetics, friendly AI, 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, meta-analysis, Metcalfe’s law, neurotypical, Oculus Rift, old age dependency ratio, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, software as a service, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

Each had its successes and applications, but over time it became apparent that no single one of these approaches was going to lead to anything close to human-level artificial intelligence. 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. These systems use an array of mathematical models to approximate biological vision systems, emulating the way we pull objects, content, and meaning out of a scene, whether it’s a picture, a movie, or real life.

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. The last thing I wanted was to wreck it all and be associated with emotion.

Subtle enough that even a good actor might introduce characteristics that differed from those of someone truly experiencing the emotion. 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.


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, 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, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, 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, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, women in the workforce

When I chose the title of this book, Robots will steal your job, I was not completely honest with you. 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.

It is a closed system, with a number of variables well known and pretty much already defined, and the process is very repetitive. 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. You can see the camera icon on the right of the bar, click that and you can upload your image

Sebastian Thrun was so excited that he decided to leave his Professorship at Stanford and dedicate his time to teach to millions of students worldwide, for free (http://udacity.com). Sounds familiar? 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? How does this help you? In case you have not noticed, this is your winning ticket.


pages: 696 words: 143,736

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

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, 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, 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, technological singularity, Ted Kaczynski, telepresence, the medium is the message, 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, Y2K

A link to Lambertus Hesselink’s research on crystal computing: <http://www.aip.org/enews/physnews/1995/split/pnu219-2.htm> Student cracks encryption code. 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.

The Feynman Lectures in Physics. Reading, MA: Addison-Wesley, 1965. Findlay, J. N. Plato and Platonism: An Introduction. 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. Brooks, and Lee S. Tavrow. “Twilight Zones and Cornerstones: A Gnat Robot Double Feature.”

Howlett, and Gian-Carlo Rota, eds. A History of Computing in the Twentieth Century. 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. Cambridge, MA: MIT Press, 1969 (revised edition, 1988). Mitchell, Melanie.


pages: 913 words: 265,787

How the Mind Works by Steven Pinker

affirmative action, agricultural Revolution, Alfred Russel Wallace, Buckminster Fuller, cognitive dissonance, Columbine, combinatorial explosion, complexity theory, computer age, computer vision, Daniel Kahneman / Amos Tversky, delayed gratification, double helix, experimental subject, feminist movement, four colour theorem, Gordon Gekko, greed is good, hedonic treadmill, Henri Poincaré, income per capita, information retrieval, invention of agriculture, invention of the wheel, Johannes Kepler, John von Neumann, lake wobegon effect, lateral thinking, 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, pattern recognition, phenotype, plutocrats, 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, theory of mind, Thorstein Veblen, Turing machine, urban decay, Yogi Berra

.: Erlbaum. Tarr, M. J. 1995. 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. Tarr, M. J., & Pinker, S. 1989.

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. We do the experiments or surveys to get the facts down about a mental faculty, and then see whether the faculty meets the specs: whether it shows signs of precision, complexity, efficiency, reliability, and specialization in solving its assigned problem, especially in comparison with the vast number of alternative designs that are biologically growable.

Incidentally, this does not mean that the visual field is flat. 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. There is really no answer to them—all these things can be an object if you want to think of them that way, or they can be part of a larger object.


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, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, 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 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, zero-sum game

Either way, gradient descent is a good way to learn the weights. 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. We know, however, that nearby pixels in an image are usually part of the same object, and we can introduce a corresponding set of features: for each pair of neighboring pixels, the feature is true if they belong to the same object, and false otherwise.

Alchemy learned over a million such patterns from facts extracted from the web (e.g., 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. But let’s see what we can do about them. Planetary-scale machine learning In computer science, a problem isn’t really solved until it’s solved efficiently.

The need for weighting the word probabilities in speech recognition is discussed in Section 9.6 of Speech and Language Processing,* by Dan Jurafsky and James Martin (2nd ed., Prentice Hall, 2009). 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

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, 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, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, Ray Kurzweil, 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, 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, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, 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 Wozniak, strong AI, Stuxnet, 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. But AI projects are also funded under Information and Communication Technology, $400.5 million, and Classified Programs, $107.2 million.

Plus, there are functions of the human mind that current software techniques seem ill-equipped to address, including general learning, explanation, introspection, and controlling attention. So what’s really been accomplished in AI? 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. Researchers call them “low hanging fruit.” But if your goal is AGI, then all the narrow AI applications and tools may seem like low hanging fruit, and are only getting you marginally closer to your human-equivalent goal.

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. Apple cofounder Steve Wozniak has proposed an “easy” alternative to the Turing test that shows the complexity of simple tasks.


pages: 199 words: 47,154

Gnuplot Cookbook by Lee Phillips

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

He has been the reviewer for numerous scientific articles, research proposals, and books, and has been a judge in the German Federal Competition in Computer Science on several occasions. 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. Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available?


pages: 721 words: 197,134

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

Albert Einstein, 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

In addition to archival papers, the journal also publishes significant ongoing research in the form of short papers and very short papers on “visions and directions.” 4. 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.

Alexander, Data Warehouse: Practical Advice from the Experts, Prentice Hall, Inc., Upper Saddle River, NJ, 1997. Boriah, S., V. Chandola, V. 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. Piatetsky-Shapiro, P. Smith, R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Cambridge, 1996a.

, D. Zantinge, Data Mining, Addison-Wesley Publ. Co., New York, 1996. Berson, A., S. Smith, K. 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.


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

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

MCM83, MCM86, KM90 and MT94 and Readings in Machine Learning by Shavlik and Dietterich [SD90]. 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.

The computational complexity involved is linear with respect to the number of cells in the cube. 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.

Belief networks have been used to model a number of well-known problems. One example is genetic linkage analysis (e.g., 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. Training Bayesian Belief Networks “How does a Bayesian belief network learn?”


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, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, 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, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

You can learn more about Zillow’s AI strategy in this interview with Zillow’s chief analytics officer, Stan Humphries: Michael Krigsman, “Zillow: Machine Learning and Data Disrupt Real Estate,” ZDNet, July 30, 2017, https://www.zdnet.com/article/zillow-machine-learning-and-data-in-real-estate/. Trulia: See: https://www.trulia.com/. 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: 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

"Robert Solow", 3D printing, 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, Deng Xiaoping, Donald Trump, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, telepresence, telerobotics, The Rise and Fall of American Growth, 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. I knew that if we dropped those proverbial guns and channeled that collective IQ and energy into a refreshed mission, we could get back to the dream that first inspired Bill and Paul—democratizing leading-edge computer technology.

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. Yet, through early forms of transfer learning, the neural network we built managed to learn and then accomplish the other tasks on its own.


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

23andMe, affirmative action, algorithmic trading, Alvin Roth, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, Edward Snowden, Elon Musk, Filter Bubble, general-purpose programming language, 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, speech recognition, statistical model, Stephen Hawking, superintelligent machines, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

Sandy Pentland, a professor at MIT, was quoted in The Economist as saying that “according to some estimates, three-quarters of published scientific papers in the field of machine learning are bunk.” 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. But money alone wasn’t enough to recruit talent—top researchers want to work where other top researchers are—so it was important for AI labs that wanted to recruit premium talent to be viewed as places that were already on the cutting edge.

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: 494 words: 116,739

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

Albert Einstein, Berlin Wall, Bernie Madoff, blood diamonds, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, commoditize, computer vision, conceptual framework, delayed gratification, Edward Glaeser, en.wikipedia.org, end world poverty, epigenetics, Erik Brynjolfsson, Francis Fukuyama: the end of history, fundamental attribution error, 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, liberation theology, libertarian paternalism, longitudinal study, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, microcredit, mobile money, Nelson Mandela, Nicholas Carr, North Sea oil, 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, Silicon Valley, Simon Kuznets, Stanford marshmallow experiment, Steve Jobs, Steven Pinker, technoutopianism, The Fortune at the Bottom of the Pyramid, Upton Sinclair, Walter Mischel, War on Poverty, winner-take-all economy, World Values Survey, Y2K

Nuclear fusion – as a source of unlimited energy – seemed like it could put an end to these problems once and for all. I thought I could help make it work. 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.

The Atlantic, May 2, 2013, www.theatlantic.com/technology/archive/2013/05/our-future-might-be-bright-the-tentative-rosy-predictions-of-googles-eric-schmidt/275360/. ———. (2013b). 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.” Tech Crunch, Feb. 19, 2011, http://techcrunch.co/2011/02/19/facebook-egypt-newborn/.

Warana unwired: Mobile phones replacing PCs in a rural sugarcane cooperative. 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. Bridwell. (1976). Maslow reconsidered: A review of research on the need hierarchy theory. Organizational Behavior and Human Performance 15:212–240, www.sciencedirect.com/science/article/pii/0030507376900386.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, 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, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, 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, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, 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.

., 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.

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: 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, game design, Grace Hopper, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, Yogi Berra

So the attorneys here and the technical community both know that I’m kind of an official mentor in this regard. 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. Stern: Could you talk about intellectual property? Loce: There are a lot of approaches to getting intellectual property.

You don’t want tout your product in a biased, over-the-top way, but you don’t want to talk the way engineers normally talk. 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. By some estimates, you could cut eighteen percent off fuel consumption by reoptimizing traffic lights more intelligently.

By some estimates, you could cut eighteen percent off fuel consumption by reoptimizing traffic lights more intelligently. 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. Stern: Do you have a particular favorite invention? Loce: It’s probably the next one.


pages: 458 words: 135,206

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

Amazon Web Services, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, domain-specific language, glass ceiling, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day, zero-sum game

It's something that still fascinates me, but I've never really used physics in an applied approach in my work. G. 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? Bloore: I would say that it's entirely personal. I have a very visual memory, and that's what ultimately drives what direction the company took.

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. Word of mouth ends up still being a very important consideration, and the recommendation of somebody who we trust is always a crucial factor in deciding who we would consider.

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? Is that how the chain of command works?


pages: 291 words: 81,703

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

Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, P = 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: Great Stagnation, upwardly mobile, Yogi Berra

The machine could register her pulse, breathing, tone of voice, the level of detail in her narrative, or whichever biological features prove to have predictive power. 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! or, Salt! or, Familiar!). Someday we will be able to dispense with the headgear or otherwise make the device less burdensome and less conspicuous.

See also income inequality; wealth inequality Cleverbot, 140–41, 143–44 coaching, 202 cognitive biases, 99–100, 110 cognitive improvement, 106–8 Cold War, 252 college education, 37, 63, 195 Coming Apart (Murray), 249 commodities pricing, 99–100 communication technology, 162 competition and computer chess, 191–92 and education, 180, 182, 191, 192–94, 201–2 and labor markets, 163–71 and rating systems, 121 complementarity in scientific research, 218 complexity complex environments, 116 complexity economics, 222 and decision making, 98–99 and machine vs. machine chess, 74–75 and scientific advance, 205–6 computational economics, 222 computer chess. 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: 339 words: 88,732

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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, 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. They’ll help us in areas ranging from trivial to substantive to life changing.

Watson and a human doctor will be far more creative and robust than either of them working alone. 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. The Spanish clothing company Zara exploits this advantage and uses humans instead of computers to decide which clothes to make.


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, Cass Sunstein, clean water, 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

These same researchers tried a pilot “energy management” program. This included breaks for walks and focusing on key factors such as sleep. 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. Increasing work hours, working people harder, forgoing vacations, and so on are all tunneling responses, like borrowing at high interest.

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: 407 words: 103,501

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

Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, disintermediation, Frederick Winslow Taylor, 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, Mark Zuckerberg, Marshall McLuhan, means of production, meta analysis, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer, pets.com, Results Only Work Environment, Saturday Night Live, 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 Wisdom of Crowds, Thorstein Veblen, web application

Vendors who are competing with a winnertakes-all mind-set would be advised to join together to enable systems built from the best-of-breed data subsystems of cooperating companies. >>> 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. In other cases, meaning is “taught” to the computer. That is, the application is given a mapping between one structured data set and another.

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: 359 words: 96,019

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

Airbnb, Albert Einstein, Amazon Web Services, Apple's 1984 Super Bowl advert, augmented reality, Bernie Sanders, Black Swan, citizen journalism, Clayton Christensen, computer vision, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, Frank Gehry, Google Glasses, Hyperloop, information asymmetry, Jeff Bezos, Justin.tv, Lean Startup, Long Term Capital Management, Mark Zuckerberg, Menlo Park, minimum viable product, Nelson Mandela, Oculus Rift, paypal mafia, Peter Thiel, QR code, Sand Hill Road, Saturday Night Live, side project, Silicon Valley, Silicon Valley startup, Snapchat, social graph, sorting algorithm, speech recognition, stealth mode startup, Steve Jobs, too big to fail, Y Combinator, young professional

He spends four minutes awkwardly using slides written on a pad of paper to discuss the history of social media and how to use Snapchat. 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.

While researching other projects on crowdfunding platforms, Miller ran across Jon Rodriguez, a Stanford student who was looking to build virtual reality hardware and software for x-ray vision. Miller convinced Rodriguez to team up with him on Vergence Labs. 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. Snapchat never announces its acquisitions. One day the startup is fully functioning independently; the next, employees are telling their friends that they are moving to LA and can’t say any more.


pages: 346 words: 97,330

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

Affordable Care Act / Obamacare, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, basic income, 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, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, crowdsourcing, data is the new oil, deindustrialization, deskilling, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, market friction, Mars Rover, natural language processing, new economy, passive income, pattern recognition, post-materialism, post-work, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, 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, The Future of Employment, The Nature of the Firm, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, women in the workforce, Works Progress Administration, Y Combinator

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.” Wonkblog (blog), Washington Post, May 8, 2015. https://www.washingtonpost.com/news/wonk/wp/2015/05/08/the-next-labor-fight-is-over-when-you-work-not-how-much-you-make.

It has officially been in beta since its launch in 2005. [back] 8. Kevin P. 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.” See Alexander Wissner-Gross, “2016: What Do You Consider the Most Interesting Recent [Scientific] News?


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, 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, job automation, loss aversion, microservices, 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 vision, conceptual framework, cuban missile crisis, different worldview, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, Electric Kool-Aid Acid Test, general-purpose programming language, Golden Gate Park, Hacker Ethic, hypertext link, informal economy, information retrieval, invention of the printing press, Jeff Rulifson, John Markoff, John Nash: game theory, John von Neumann, Kevin Kelly, knowledge worker, Mahatma Gandhi, Menlo Park, Mother of all demos, Norbert Wiener, packet switching, Paul Terrell, popular electronics, 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, Ted Nelson, The Hackers Conference, Thorstein Veblen, Turing test, union organizing, Vannevar Bush, 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. He told McCarthy he thought the notion of an executive officer was a great idea.

The demonstration had a far greater impact than any of the participants could imagine. 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

3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, blue-collar work, 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, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, 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, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Network effects, 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, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Data is from an ImageNet presentation on the 2017 challenge; see http://image-net.org/challenges/talks_2017/ILSVRC2017_overview.pdf (accessed July 2018). 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?v=nXrTXiJM4Fg. 27.  Warren McCulloch and Walter Pitts, for instance, the first to construct one in 1943, were trying to describe “neural events” in the brain as “proposition logic” on paper.

New York: Prometheus Books, 1996. Ruger, Theodore W., Pauline T. Kim, Andrew D. Martin, and Kevin M. Quinn. “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. “Income Inequality in the United States, 1913–1998.” Quarterly Journal of Economics 118, no. 1 (2003), 1–39.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, Berlin Wall, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, corporate governance, corporate personhood, creative destruction, cryptocurrency, dogs of the Dow, don't be evil, Donald Trump, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, Ford paid five dollars a day, future of work, game design, Google Earth, Google Glasses, Google X / Alphabet X, hive mind, impulse control, income inequality, 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, knowledge economy, 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, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Shoshana Zuboff, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social graph, social web, software as a service, speech recognition, statistical model, Steve Jobs, Steven Levy, structural adjustment programs, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck

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. Initially valued for its ability to monitor and diagnosis health conditions from Parkinson’s disease to sleep disorders, smart skin is now hailed for its promise of ultra-unobtrusive ubiquity.

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. Silver-plated fabrics reflect thermal radiation, “enabling the wearer to avert overhead thermal surveillance.”

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.” How is an interface that foregrounds our friend count changing our conceptions of friendship?


pages: 197 words: 35,256

NumPy Cookbook by Ivan Idris

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

As usual, we can install using either of the following two commands: pip install -U scikits-image easy_install -U scikits-image Again, you might need to run these commands as root. 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. If you are on Windows, use the installer; otherwise, download the distribution, unpack it, and build from the top folder with the following command: .


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, 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, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, 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, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, 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, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, off grid, offshore financial centre, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Future of Employment, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, zero day

More impressive is the fact that it works right out of the box and can be up and running in just an hour, as opposed to the eighteen months it took to integrate the previous generations of industrial robots into a factory operation. 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. In more than 150 medical centers, Aethon’s TUG robots can be summoned by a smart-phone app to autonomously travel throughout the corridors to deliver medicines, patient meals, and laundry, replacing work previously done by orderlies.

Traditional military contractors such as Northrop Grumman, Boeing, and Lockheed Martin were early entrants into the world of robotics, followed by smaller specialized firms such as Boston Dynamics and iRobot (yes, the same people who make your Roomba vacuum make the IED-disposal PackBot). 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.

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, computer vision, information retrieval, p-value

He blogs at http://infiniteloop.in. 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

anti-communist, Bay Area Rapid Transit, Berlin Wall, blockchain, Broken windows theory, Burning Man, cashless society, Chelsea Manning, citizen journalism, computer vision, crowdsourcing, Donald Trump, Edward Snowden, Elon Musk, Ferguson, Missouri, Filter Bubble, Firefox, Internet of things, Jeff Bezos, Julian Assange, license plate recognition, Mark Zuckerberg, mass incarceration, medical malpractice, Occupy movement, off grid, pattern recognition, Peter Thiel, Robert Bork, Shoshana Zuboff, Silicon Valley, Skype, social graph, Steven Levy, 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. Automated license-plate readers are also an insidious tool used by law enforcement agencies to track how we travel.


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

agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, 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, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, 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, 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!, Y2K

The big question back then was how much the performance of neural networks could improve with the size and depth of the network. 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. Deep recurrent networks with short-term memory were trained to translate English sentences into French sentences at high levels of performance.

More structure means more preconceptions, which can be useful in making sense of limited data but can result in biases that reduce performance. 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. The recent breakthroughs using artificial neural networks come down firmly on the side of flexibility: They use a set of principles that can be applied in the same way to many different kinds of data—meaning that they have weak preconceptions about any particular kind of data—and they allow the system to discover how to make sense of its inputs.


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, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, 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, 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, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, full employment, future of work, 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, lifelogging, lump of labour, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, optical character recognition, Paul Samuelson, personalized medicine, 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, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, WikiLeaks, young professional

Cep, ‘Big Data for the Spirit’, New Yorker, 5 Aug. 2014. 109 ‘Catholic Church gives blessing to iPhone app’, BBC News, 8 Feb. 2011 <http://www.bbc.co.uk/news> (accessed 27 March 2015). 110 Susan Elizabeth Prill, ‘Sikhi through Internet, Films and Videos’, in The Oxford Handbook of Sikh Studies, ed. Pshaura Singh and Louis E. 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?’, Digital Humanities (2014). <http://www.genizah.org/professionalPapers/MyOtherHalf.pdf> (accessed 7 March 2015). 118 Idan Dershowitz et al., ‘Computerized Source Criticism of Biblical Texts’, published online (2014).

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, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, Dominic Cummings, Donald Trump, Edward Snowden, Elon Musk, Filter Bubble, future of work, gig economy, global village, Google bus, hive mind, Howard Rheingold, information retrieval, Internet of things, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, mittelstand, move fast and break things, move fast and break things, Network effects, Nicholas Carr, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, 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 ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Jobs, Steven Levy, strong AI, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, 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

Our destination was a depot in Deerfield Beach, around 200 miles south. 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. Tony flicked a little blue switch, and we went from ‘manual’ to ‘auto’.


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

4chan, Amazon Mechanical Turk, augmented reality, bash_history, bitcoin, cloud computing, computer age, computer vision, crowdsourcing, dematerialisation, Donald Knuth, Douglas Hofstadter, en.wikipedia.org, Everything should be made as simple as possible, finite state, Gödel, Escher, Bach, Jacques de Vaucanson, Larry Wall, late capitalism, means of production, natural language processing, new economy, Norbert Wiener, Occupy movement, packet switching, peer-to-peer, Richard Stallman, Ronald Coase, Slavoj Žižek, social software, social web, software studies, speech recognition, stem cell, Stewart Brand, The Nature of the Firm, Turing machine, Turing test, Vilfredo Pareto, We are Anonymous. We are Legion, We are the 99%, WikiLeaks

Take, for another instance, the confounding effects of the esoteric Brainfuck programming language, which offers a challenge to normative source code interpretation by consisting entirely of punctuation, with each of the eight characters “><+-.,[ ]” representing a single elementary operation. “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. The point can be demonstrated with writing more generally as, in wordprocessing a text (like this), the writer is also processed into the choice of software and operating system that prescribes or allows certain tasks.


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, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, digital Maoism, digital map, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Network effects, new economy, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, 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, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

First, a small child is generally intelligent, but most would probably fail the test. 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, database schema, en.wikipedia.org, loose coupling, natural language processing, Skype, statistical model

With spaCy, you can customize existing models or individual model components, and you can train your own models from scratch to meet your application’s requirements (you’ll learn how to do this in Chapter 10). 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: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, 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.

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: 561 words: 163,916

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

4chan, airport security, Anne Wojcicki, Asian financial crisis, augmented reality, barriers to entry, Bernie Sanders, bitcoin, call centre, computer vision, cryptocurrency, disruptive innovation, Donald Trump, drone strike, Elon Musk, financial independence, game design, Grace Hopper, illegal immigration, invisible hand, Jaron Lanier, Jony Ive, Kickstarter, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, move fast and break things, Network effects, Oculus Rift, Peter Thiel, QR code, sensor fusion, side project, Silicon Valley, skunkworks, Skype, slashdot, Snapchat, software patent, stealth mode startup, Steve Jobs, unpaid internship, white picket fence

“I was in a double,” Antonov explained, “but there were three people in it. So they moved me to different dorm. 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. Mike really liked graphics. But between school and his current job, he wasn’t necessarily looking for anything else to do.

“You’re crazy!” Dycus chided. “Eh, maybe,” Luckey replied. “But do you know of a more efficient solution?” 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? We’ll start it out at a safe distance and then just keep rotating the screw until, basically, it ever-so-slightly pokes you in the eye!”


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, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, 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, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, 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 Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

I am grateful to Richard Susskind for his assistance in formulating this definition, although his preferred definition would be wider than mine (including manual and emotional tasks as well). 3. Yonghui Wu et al. ‘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. Peter Campbell, ‘Ford Plans Mass-market Self-driving Car by 2021’, Financial Times, 16 August 2016 <https://www.ft.com/content/ d2cfc64e-63c0-11e6-a08a-c7ac04ef00aa#axzz4HOGiWvHT> (accessed 28 November 2017); David Millward, ‘How Ford Will Create a New Generation of Driverless Cars’, Telegraph, 27 February 2017 <http://www.telegraph.co.uk/business/2017/02/27/ford-seeks-­ pioneer-new-generation-driverless-cars/> (accessed 28 November 2017). 5.

NY Mag, 27 Dec. 2016 <http://nymag.com/selectall/2016/12/can-an-amazonecho-testify-against-you.html> (accessed 1 Dec. 2017). 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). Taplin, Jonathan.


pages: 271 words: 62,538

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

Airbnb, computer vision, crossover SUV, en.wikipedia.org, fear of failure, impulse control, Inbox Zero, Internet Archive, Internet of things, Jeff Bezos, Jony Ive, Kickstarter, Mark Zuckerberg, new economy, Oculus Rift, pattern recognition, QR code, RFID, self-driving car, Silicon Valley, Skype, Snapchat, Steve Jobs, technoutopianism, 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. TV broadcasters will get new stats to show during games. More importantly, for team doctors, SportVU provided an opportunity to avoid Kobe-like injuries.


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, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, 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, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, 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. This is done by human workers now because it takes human judgment to decide how to grasp and stack randomly shaped boxes in delivery vehicles and shipping containers.


pages: 834 words: 180,700

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

8-hour work day, anti-pattern, bioinformatics, c2.com, cloud computing, collaborative editing, combinatorial explosion, computer vision, continuous integration, create, read, update, delete, David Heinemeier Hansson, Debian, domain-specific language, Donald Knuth, en.wikipedia.org, fault tolerance, finite state, Firefox, friendly fire, Guido van Rossum, linked data, load shedding, locality of reference, loose coupling, Mars Rover, MITM: man-in-the-middle, MVC pattern, 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

., a research and development company based in the US. 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.

That system consisted of a configure script for Unix and an executable called pcmaker for Windows. pcmaker was a C program that read in Unix Makefiles and created NMake files for Windows. The binary executable for pcmaker was checked into the VTK CVS system repository. 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. Failure to have the correct environment caused the system to fail in ways that were difficult to debug, especially for end users.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, 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 space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, 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!, Whole Earth Catalog, working-age population

Eighty per cent of today’s professions existed a century ago, with the number of people employed in the 20 per cent of new occupations comprising only one in ten jobs. 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. Several months later Amazon acquired Whole Foods Market for $13.7 billion.


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, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, 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, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

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. Josh Chin and Liza Lin, ‘China’s all-seeing surveillance state is reading its citizens’ faces’, Wall Street Journal, 6 June 2017, https://www.wsj.com/articles/the-all-seeing-surveillance-state-feared-in-the-west-is-a-reality-in-china-1498493020. 67.


pages: 685 words: 203,949

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

airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, Bayesian statistics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, haute cuisine, impulse control, index card, indoor plumbing, information retrieval, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, longitudinal study, meta analysis, 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, shared worldview, 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, Turing test, ultimatum game, zero-sum game

Most assumed the winning team would use satellite imagery, but that’s where the problem gets tricky. 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. And what about spectrograms, chemical sensors, lasers? Tom Tombrello, physics professor at Caltech, favored a sneaky approach: “I would have figured out a way to get to the balloons before they were launched, and planted GPS tracking devices on them.

solving the Where’s Waldo? problem Buchenroth, T., Garber, F., Gowker, B., & Hartzell, S. (2012, July). 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. Retrieved from http://www.kickstarter.com the group average comes Surowiecki, J. (2005).


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, 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: 301 words: 85,126

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

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, 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, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

Previous efforts at doing this sort of thing had used small data sets, with fewer than a thousand images of skin lesions. 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. They can do this, moreover, without ever being told by a programmer what to look for.


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, Cass Sunstein, computer vision, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, Donald Trump, Edward Glaeser, Filter Bubble, game design, happiness index / gross national happiness, income inequality, Jeff Bezos, John Snow's cholera map, longitudinal study, Mark Zuckerberg, Nate Silver, 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. Vernon Henderson, Adam Storeygard, and David N.


pages: 330 words: 83,319

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

active measures, anti-communist, barriers to entry, Berlin Wall, blood diamonds, cognitive dissonance, commoditize, computer vision, corporate governance, corporate raider, cuban missile crisis, Donald Trump, double helix, drone strike, European colonialism, failed state, hive mind, index fund, invisible hand, John Markoff, joint-stock company, moral hazard, mutually assured destruction, Nash equilibrium, offshore financial centre, pattern recognition, Peace of Westphalia, plutocrats, Plutocrats, private military company, profit motive, RAND corporation, ransomware, Ronald Reagan, Silicon Valley, South China Sea, Stuxnet, technoutopianism, 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. Another example of life imitating art. William Gibson, Burning Chrome (New York: Ace Books, 1987). 7.


pages: 301 words: 89,076

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

agricultural Revolution, Airbnb, AltaVista, Amazon Web Services, augmented reality, autonomous vehicles, basic income, 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, David Ricardo: comparative advantage, declining real wages, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, hiring and firing, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, knowledge worker, laissez-faire capitalism, low skilled workers, 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, 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, Ronald Reagan, 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, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income

Yet instead of replacing doctors, white-collar robots are acting as yet another diagnostic device that doctors employ in doing their jobs. 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: 85,263

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

AI winter, Airbnb, Alfred Russel Wallace, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, British Empire, Brownian motion, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, drone strike, Edward Snowden, fear of failure, Flash crash, Google Earth, Haber-Bosch Process, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, John von Neumann, Julian Assange, Kickstarter, late capitalism, lone genius, mandelbrot fractal, meta analysis, 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, self-driving car, 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, 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: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, 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, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

By then, Lexus had released a car that could parallel-park itself under specific conditions. “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. Then, Little Ben had to identify the object as an obstacle and develop a path around it.


pages: 287 words: 95,152

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

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

If you have a car accident, it is easy to pull out your smartphone, take a photo, and use image recognition to determine the damage and file an insurance claim. 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: 302 words: 90,215

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

Apple II, augmented reality, computer vision, deliberate practice, experimental subject, game design, Google Glasses, income inequality, Intergovernmental Panel on Climate Change (IPCC), iterative process, Jaron Lanier, low earth orbit, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, Milgram experiment, nuclear winter, Oculus Rift, randomized controlled trial, Silicon Valley, Skype, Snapchat, Steve Jobs, Steve Wozniak, Steven Pinker, telepresence, too big to fail

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. Hillary Grigonis, “Lytro Re-Creates the Moon Landing to Demonstrate Just What Light-field VR Can Do,” Digital Trends, August 31, 2016, http://www.digitaltrends.com/virtual-reality/lytro-immerge-preview-video- released/. 5.


pages: 442 words: 94,734

The Art of Statistics: Learning From Data by David Spiegelhalter

Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Carmen Reinhart, complexity theory, computer vision, correlation coefficient, correlation does not imply causation, dark matter, Edmond Halley, Estimating the Reproducibility of Psychological Science, Hans Rosling, Kenneth Rogoff, meta analysis, meta-analysis, Nate Silver, Netflix Prize, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, speech recognition, statistical model, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus

‘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.


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

algorithmic trading, asset allocation, backtesting, 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, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, 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: 336 words: 93,672

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

23andMe, Albert Einstein, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, Drosophila, epigenetics, global pandemic, Google Glasses, iterative process, 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, 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.


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

23andMe, Affordable Care Act / Obamacare, Airbnb, American Legislative Exchange Council, Anne Wojcicki, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, Burning Man, call centre, carbon footprint, Charles Lindbergh, clean water, Colonization of Mars, computer vision, David Attenborough, Donald Trump, double helix, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Flynn Effect, Google Earth, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Nelson Mandela, obamacare, off grid, oil shale / tar sands, pattern recognition, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, technoutopianism, The Wealth of Nations by Adam Smith, traffic fines, Travis Kalanick, urban sprawl, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

Paulina Borsook, Cyberselfish: A Critical Romp through the Terribly Libertarian Culture of High Tech (New York: PublicAffairs, 2000), pp. 2–3. 7. Ibid., 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. Michio Kaku, The Future of the Mind: The Scientific Quest to Understand, Enhance, and Empower the Mind (New York: Doubleday, 2014), p. 271. 8.


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

Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, disintermediation, epigenetics, failed state, financial innovation, Firefox, food miles, future of work, global pandemic, global supply chain, global village, hive mind, 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, peak oil, pensions crisis, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, white flight, women in the workforce, 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. But Cyc still isn’t very intelligent, which is possibly why author, scientist and futurist Ray Kurzweil made a public bet with Mitchell Kapor, the founder of Lotus, that a computer would pass the Turing test by 2029.


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

4chan, affirmative action, Affordable Care Act / Obamacare, availability heuristic, Berlin Wall, Bernie Sanders, big-box store, Boris Johnson, British Empire, call centre, Chelsea Manning, chief data officer, cognitive bias, cognitive dissonance, colonial rule, computer vision, conceptual framework, cryptocurrency, Daniel Kahneman / Amos Tversky, desegregation, Dominic Cummings, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Etonian, first-past-the-post, Google Earth, 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, 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, uber lyft, unpaid internship, Valery Gerasimov, web application, WikiLeaks, zero-sum game

In my early years in London, many of my friends were fashion students at Central Saint Martins, which is one of the constituent colleges of the University of the Arts London. 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: 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, game design, information retrieval, iterative process, natural language processing, pattern recognition, performance metric, sentiment analysis, social web, speech recognition, statistical model, text mining

In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. 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. Clicking on any link will take you directly to the place in the text in which the marker appears. Symbols (κ) Kappa scores, Annotate with the Specification, Cohen’s Kappa (κ)–Cohen’s Kappa (κ), Fleiss’s Kappa (κ)–Fleiss’s Kappa (κ) Cohen’s Kappa (κ), Cohen’s Kappa (κ)–Cohen’s Kappa (κ) Fleiss’s Kappa (κ), Fleiss’s Kappa (κ)–Fleiss’s Kappa (κ) Χ-squared (chi-squared) test, Other evaluation metrics A A Standard Corpus of Present-Day American English (Kucera and Francis), A Brief History of Corpus Linguistics (see Brown Corpus) active learning algorithms, Active Learning adjudication, Creating the Gold Standard (Adjudication), MAI User Guide–Saving Files MAI as tool for, MAI User Guide–Saving Files Allen, James, Using Models Without Specifications, Related Research Amazon Elastic Compute Cloud, Distributed Computing Amazon’s Mechanical Turk (MTurk), The Infrastructure of an Annotation Project American Medical Informatics Association (AMIA), Organizations and Conferences American National Corpus (ANC), A Brief History of Corpus Linguistics Analysis of variance (ANOVA) test, Other evaluation metrics Analyzing Linguistic Data: A Practical Introduction to Statistics using R (Baayen), Corpus Analytics annotated corpus, The Importance of Language Annotation annotation environments, Choosing an Annotation Environment, Choosing an Annotation Environment, Choosing an Annotation Environment, Tools, MAE User Guide–Frequently Asked Questions annotation units, support for, Choosing an Annotation Environment chosing, Choosing an Annotation Environment MAE (Multipurpose Annotation Environment), MAE User Guide–Frequently Asked Questions process enforcement in, Choosing an Annotation Environment revising, Tools annotation guideline(s), Model the Phenomenon, Specification Versus Guidelines, Be Prepared to Revise, Writing the Annotation Guidelines, Writing the Annotation Guidelines, Example 1: Single Labels—Movie Reviews, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 3: Extent Annotations—Named Entities, Example 4: Link Tags—Semantic Roles, Example 4: Link Tags—Semantic Roles, Guidelines, Specifications, Guidelines, and Other Resources–Specifications, Guidelines, and Other Resources categories, using in, Example 2: Multiple Labels—Film Genres classifications, defining and clarifying, Example 1: Single Labels—Movie Reviews labels, importance of clear definitions for, Example 2: Multiple Labels—Film Genres limits on number of labels, effects of, Example 2: Multiple Labels—Film Genres link tags, Example 4: Link Tags—Semantic Roles list of available, Specifications, Guidelines, and Other Resources–Specifications, Guidelines, and Other Resources multiple lables, use of and considerations needed for, Example 2: Multiple Labels—Film Genres named entities, defining, Example 3: Extent Annotations—Named Entities and outside information, Example 2: Multiple Labels—Film Genres reproducibility, Example 2: Multiple Labels—Film Genres revising, Guidelines revising, need for, Be Prepared to Revise semantic roles, Example 4: Link Tags—Semantic Roles specifications vs., Specification Versus Guidelines writing, Writing the Annotation Guidelines annotation standards, Different Kinds of Standards–Other Standards Affecting Annotation, ISO Standards–Annotation specification standards, Community-Driven Standards, Other Standards Affecting Annotation, Other Standards Affecting Annotation, Other Standards Affecting Annotation, Applying and Adopting Annotation Standards–ISO Standards and You, Unique Labels: Movie Reviews, Multiple Labels: Film Genres, Linked Extent Annotation: Semantic Roles, Linked Extent Annotation: Semantic Roles, ISO Standards and You community-driven, Community-Driven Standards data storage format and, Other Standards Affecting Annotation date format and, Other Standards Affecting Annotation ISO standards, ISO Standards–Annotation specification standards LAF (Linguistic Annotation Framework) standard, ISO Standards and You linked extent annotation, Linked Extent Annotation: Semantic Roles naming conventions and, Other Standards Affecting Annotation and semantic roles, Linked Extent Annotation: Semantic Roles sources of error, Unique Labels: Movie Reviews XML and, Multiple Labels: Film Genres annotation(s), Kinds of Annotation–Kinds of Annotation, Kinds of Annotation–Kinds of Annotation, Kinds of Annotation, Kinds of Annotation, Kinds of Annotation, Semantic Roles, Semantic Roles, Different Kinds of Standards–Other Standards Affecting Annotation, Applying and Adopting Annotation Standards–ISO Standards and You, Metadata Annotation: Document Classification–Multiple Labels: Film Genres, Text Extent Annotation: Named Entities–Stand-off Annotation by Character Location, Annotation and Adjudication–Creating the Gold Standard (Adjudication), The Infrastructure of an Annotation Project, The Infrastructure of an Annotation Project, Specification Versus Guidelines, Be Prepared to Revise, Preparing Your Data for Annotation–Writing the Annotation Guidelines, Splitting Up the Files for Annotation, Writing the Annotation Guidelines–Example 4: Link Tags—Semantic Roles, Evaluating the Annotations–Creating the Gold Standard (Adjudication), Creating the Gold Standard (Adjudication), Matching Annotation to Algorithms, About Your Annotation Task and Annotators, Annotation: The Creation of TimeBank–Annotation: The Creation of TimeBank, Automatic Annotation: Generating TimeML–Cross-Document Analysis, Afterword: The Future of Annotation–And Finally..., Handling Big Data–Semi-Supervised Learning automatic, Automatic Annotation: Generating TimeML–Cross-Document Analysis Big Data, handling, Handling Big Data–Semi-Supervised Learning data preperation for, Preparing Your Data for Annotation–Writing the Annotation Guidelines distributed method of, The Infrastructure of an Annotation Project evaluating, Evaluating the Annotations–Creating the Gold Standard (Adjudication) future of, Afterword: The Future of Annotation–And Finally...


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, Bernie Madoff, Burning Man, butterfly effect, Cass Sunstein, collateralized debt obligation, 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, lake wobegon effect, late fees, loss aversion, market bubble, Murray Gell-Mann, payday loans, placebo effect, price anchoring, Richard Thaler, second-price auction, Silicon Valley, Skype, The Wealth of Nations by Adam Smith, Upton Sinclair

Accordingly, we found that the first person to order beer in the sequential group was the happiest of his or her group and just as happy as those who chose their beers in private. 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?” Instead of giving me his order, he asked how I was doing. “Very well, thank you,” I said.


Smart Mobs: The Next Social Revolution by Howard Rheingold

A Pattern Language, augmented reality, barriers to entry, battle of ideas, Brewster Kahle, Burning Man, business climate, citizen journalism, computer vision, conceptual framework, creative destruction, Douglas Engelbart, Douglas Engelbart, experimental economics, experimental subject, Extropian, Hacker Ethic, Hedy Lamarr / George Antheil, Howard Rheingold, invention of the telephone, inventory management, John Markoff, John von Neumann, Joi Ito, Joseph Schumpeter, Kevin Kelly, Metcalfe's law, Metcalfe’s law, more computing power than Apollo, New Urbanism, Norbert Wiener, packet switching, Panopticon Jeremy Bentham, pattern recognition, peer-to-peer, peer-to-peer model, pez dispenser, planetary scale, pre–internet, prisoner's dilemma, RAND corporation, recommendation engine, Renaissance Technologies, RFID, Richard Stallman, Robert Metcalfe, Robert X Cringely, Ronald Coase, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Silicon Valley, skunkworks, slashdot, social intelligence, spectrum auction, Steven Levy, Stewart Brand, the scientific method, transaction costs, ultimatum game, urban planning, web of trust, Whole Earth Review, zero-sum game

We walked down the hall to the office of Ismail Haritaoglu, who handed me the prototype of what he called the “InfoScope: Link from Real World to Digital Information Space.”29 Haritaoglu gave me an off-the-shelf hand-held computer with an off-the-shelf digital camera attachment, connected to a stock model digital cellular phone. Haritaoglu pointed out some signs on the wall outside his office. 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. In the near future, there will be sufficient processor power to enable the device itself to crunch the translation, but that won’t matter so much when all the processing power you want is available online, wirelessly.


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, Donald Trump, failed state, 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, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

., ‘Piloting IBM Watson Oncology within Memorial Sloan Kettering’s Regional Network’, Journal of Clinical Oncology 32:15 (2014), e17653; creation of original texts in natural language from massive amounts of data: Jean-Sébastien Vayre et al., ‘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. Ripley, Pattern Recognition and Neural Networks (Cambridge: Cambridge University Press, 2007); Christopher M.


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, bioinformatics, computer vision, correlation does not imply causation, crowdsourcing, distributed generation, 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, 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, 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. You specify k by selecting centers. It defaults to a certain number of iterations, which is an argument you can change.


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

A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Swan, blockchain, borderless world, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, DevOps, don't be evil, Donald Trump, Edward Snowden, Exxon Valdez, global village, immigration reform, Infrastructure as a Service, Internet of things, Jeff Bezos, Julian Assange, Kubernetes, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, ransomware, Richard Thaler, 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, Tim Cook: Apple, undersea cable, WikiLeaks, Y2K, zero day

AI was, thus, originally meant to be the simulation by machines of certain human cerebral activity. 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. AI can “see” someone doing something on a digital video feed and recognize that the action requires an alarm: a package has been left unattended on a train platform, alert a guard.


pages: 448 words: 117,325

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

23andMe, 3D printing, autonomous vehicles, barriers to entry, bitcoin, blockchain, Brian Krebs, business process, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, Donald Trump, drone strike, Edward Snowden, Elon Musk, fault tolerance, Firefox, Flash crash, George Akerlof, industrial robot, information asymmetry, Internet of things, invention of radio, job automation, job satisfaction, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, move fast and break things, national security letter, Network effects, pattern recognition, profit maximization, Ralph Nader, RAND corporation, ransomware, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, Stanislav Petrov, Stephen Hawking, Stuxnet, The Market for Lemons, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, web application, WikiLeaks, 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.


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

Asian financial crisis, banking crisis, barriers to entry, beat the dealer, Black-Scholes formula, commodity trading advisor, computer vision, East Village, Edward Thorp, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John Meriwether, John von Neumann, locking in a profit, Long Term Capital Management, margin call, money market fund, Myron Scholes, paper trading, passive investing, pattern recognition, random walk, risk tolerance, risk-adjusted returns, short selling, Silicon Valley, statistical arbitrage, the scientific method, transaction costs, Y2K

Although there were a lot more processors, they had to be much smaller and cheaper. 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." The This type of bottleneck does not exist in the brain because memory storage goes on in millions of different units that are connected to each other through an enormous number of synapses.


The Future of Technology by Tom Standage

air freight, 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, full employment, hydrogen economy, industrial robot, informal economy, information asymmetry, interchangeable parts, job satisfaction, labour market flexibility, Marc Andreessen, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, railway mania, rent-seeking, RFID, 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, technology bubble, telemarketer, transcontinental railway, 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. Now, he says, a second generation of more flexible and intelligent robots will be able 333 THE FUTURE OF TECHNOLOGY to do many more jobs.


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

Airbnb, Airbus A320, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, 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, uber lyft, upwardly mobile, urban planning, Zipcar

., 2016: Autonomous Driving and Urban Land Use, in: Maurer, M., Gerdes, C. J., Lenz, B., Winner, H., 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, computer vision, continuous integration, en.wikipedia.org, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, 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

This ecosystem of readily available packages in Python reduces time and efforts taken for development. 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/ . To find out various popular applications developed using Python, see www.python.org/about/apps/ and https://wiki.python.org/moin/Applications , where you will definitely find some applications you have used—some of them are indispensable.


pages: 402 words: 126,835

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

3D printing, affirmative action, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, 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, computer vision, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, deskilling, disruptive innovation, Donald Trump, Downton Abbey, Elon Musk, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, glass ceiling, hiring and firing, immigration reform, income inequality, industrial robot, invisible hand, Jeff Bezos, job automation, job satisfaction, 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, move fast and break things, new economy, Norbert Wiener, obamacare, offshore financial centre, Paul Samuelson, precariat, Ralph Waldo Emerson, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, The Chicago School, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, white picket fence, working poor, Y Combinator, young professional, zero-sum game

By garbage, Lindner meant human error, the alternative to which is apparently robotic precision. And yet robots are far from precise. 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. Robot arms cut that cost drastically.” Sawyer, an industrial robot created by Boston-based Rethink Robotics, offers an impressive illustration of how all-embracing those arms can be.


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, crossover SUV, cryptocurrency, defense in depth, demographic transition, distributed ledger, drone strike, easy for humans, difficult for computers, game design, index fund, Jaron Lanier, life extension, microbiome, Network effects, off grid, offshore financial centre, pattern recognition, planetary scale, ride hailing / ride sharing, sensible shoes, short selling, Silicon Valley, telepresence, telepresence robot, telerobotics, The Hackers Conference, Turing test, Works Progress Administration

“Identity” had been forever changed by the Internet; formerly it had meant “who you really are” but now it meant “any one of a number of persistent faces that you can present to the digital universe.” 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.

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. The security cameras on the front of Zula’s condo building had looked them up.


pages: 476 words: 132,042

What Technology Wants by Kevin Kelly

Albert Einstein, Alfred Russel Wallace, Buckminster Fuller, c2.com, carbon-based life, Cass Sunstein, charter city, Clayton Christensen, cloud computing, computer vision, Danny Hillis, dematerialisation, demographic transition, double entry bookkeeping, Douglas Engelbart, en.wikipedia.org, Exxon Valdez, George Gilder, gravity well, 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, meta-analysis, new economy, off grid, out of africa, performance metric, personalized medicine, phenotype, Picturephone, planetary scale, RAND corporation, random walk, Ray Kurzweil, recommendation engine, refrigerator car, Richard Florida, Rubik’s Cube, Silicon Valley, silicon-based life, Skype, speech recognition, Stephen Hawking, Steve Jobs, Stewart Brand, Ted Kaczynski, the built environment, the scientific method, Thomas Malthus, Vernor Vinge, wealth creators, Whole Earth Catalog, Y2K

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?” As a way of answering, Hillis offered a metaphor. He views the bias toward simultaneity as a funnel.


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, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, G4S, information retrieval, Isaac Newton, John Conway, Loebner Prize, Menlo Park, natural language processing, Occam's razor, p-value, pattern recognition, performance metric, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K

However, it does not mean that thinking must be grounded in human sensorimotor experience. 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. Since the language uses an experience-grounded semantics, the truth-value of each statement and the meaning of each term in the language are determined by the system’s relevant experience.


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, 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, disintermediation, disruptive innovation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, 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, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, 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. K. Streams, “How to Turn a Smartphone Into a Digital Microscope Using Inexpensive Materials,” Laughing Squid, 2013, http://laughingsquid.com/how-to-turn-a-smartphone-into-a-digital-microscope-using-inexpensive-materials/.


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, blockchain, Boycotts of Israel, Branko Milanovic, British Empire, call centre, capital controls, carbon footprint, cashless society, clean water, cloud computing, colonial rule, computer vision, connected car, corporate governance, crony capitalism, currency peg, deindustrialization, Deng Xiaoping, Dissolution of the Soviet Union, Donald Trump, energy security, European colonialism, factory automation, failed state, falling living standards, family office, fixed income, flex fuel, gig economy, global reserve currency, global supply chain, haute couture, haute cuisine, illegal immigration, income inequality, industrial robot, informal economy, Internet of things, Kevin Kelly, Kickstarter, knowledge worker, light touch regulation, low cost airline, low cost carrier, low skilled workers, Lyft, Malacca Straits, Mark Zuckerberg, megacity, 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, Scramble for Africa, self-driving car, Silicon Valley, smart cities, South China Sea, sovereign wealth fund, special economic zone, stem cell, Steve Jobs, Steven Pinker, supply-chain management, sustainable-tourism, trade liberalization, trade route, transaction costs, Travis Kalanick, uber lyft, upwardly mobile, urban planning, Washington Consensus, working-age population, Yom Kippur War

Investments by Tencent and Hanwha in the Montreal-based Element AI will help expand that company’s presence across Asia from Singapore to Tokyo. 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. Rather than one country or company dominating AI, then, the model of “AI as a service” is spreading across Asia, giving governments and companies a choice of whom to work with at the best price and on the most favorable data-sharing terms.


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

affirmative action, Albert Einstein, Asilomar, Bayesian statistics, computer age, computer vision, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, selection bias, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

A few months later it played sixty online games against top human players without losing a single one, and in 2017 it was officially retired after beating the current world champion, Ke Jie. 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. We do not understand in detail how the human brain works, and yet it runs well, and we forgive our meager understanding.


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, Apple II, barriers to entry, Bill Gates: Altair 8800, Byte Shop, cloud computing, commoditize, computer vision, Douglas Engelbart, Douglas Engelbart, Dynabook, Google Chrome, I think there is a world market for maybe five computers, Internet of things, Isaac Newton, Jaron Lanier, job automation, John Markoff, John von Neumann, Jony Ive, 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 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

The machine was interesting, but there were a lot of sharp engineers at Homebrew. 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. Jobs didn’t even know what net 30 meant. Terrell later received several calls from parts suppliers who wondered whether Jobs and Woz really had the guarantee from Terrell that they claimed they did.


pages: 416 words: 129,308

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

Airbnb, animal electricity, Apple II, Apple's 1984 Super Bowl advert, citizen journalism, Claude Shannon: information theory, computer vision, conceptual framework, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, Ford paid five dollars a day, Frank Gehry, global supply chain, Google Earth, Google Hangouts, Internet of things, Jacquard loom, John Gruber, John Markoff, Jony Ive, 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, ride hailing / ride sharing, rolodex, Silicon Valley, Silicon Valley startup, skunkworks, Skype, Snapchat, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Tim Cook: Apple, Turing test, uber lyft, Upton Sinclair, Vannevar Bush, zero day

By processing immense amounts of data about, say, Van Gogh’s paintings, a system like this can be instructed to create a Van Gogh painting—and it will spit out a painting that looks kinda-sorta like a Van Gogh. 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.” But there is, of course, a deficiency in that approach. “The machine-learned models, no one really has any idea of what the models know or what they mean; they just perform in a way that meets the objective function of a training set”—like producing the Van Gogh painting.


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, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, 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. So-called “next generation sequencing” shifts even more of the burden away from the wet lab to large-scale parallel reassembly algorithms.


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, 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, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, 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, speech recognition, Steve Jobs, supply-chain management, text mining, 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

"Robert Solow", 3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, big-box store, bioinformatics, bitcoin, business process, Chris Urmson, clean water, cleantech, cloud computing, collaborative consumption, collaborative economy, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, en.wikipedia.org, Frederick Winslow Taylor, global supply chain, global village, Hacker Ethic, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta analysis, meta-analysis, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off 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, RFID, Richard Stallman, risk/return, Ronald Coase, 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 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, transaction costs, urban planning, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, 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. Arguably, the IoT’s most dramatic impact thus far has been in security systems.


pages: 514 words: 152,903

The Best Business Writing 2013 by Dean Starkman

Asperger Syndrome, bank run, Basel III, call centre, clean water, cloud computing, collateralized debt obligation, Columbine, computer vision, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Erik Brynjolfsson, eurozone crisis, Exxon Valdez, factory automation, fixed income, full employment, Goldman Sachs: Vampire Squid, hiring and firing, hydraulic fracturing, income inequality, jimmy wales, job automation, John Markoff, Kickstarter, late fees, London Whale, low skilled workers, Mahatma Gandhi, market clearing, Maui Hawaii, Menlo Park, Occupy movement, oil shale / tar sands, Parag Khanna, Pareto efficiency, price stability, Ray Kurzweil, Silicon Valley, Skype, sovereign wealth fund, stakhanovite, Stanford prison experiment, Steve Jobs, Stuxnet, the payments system, too big to fail, Vanguard fund, wage slave, Y2K, zero-sum game

Some jobs are still beyond the reach of automation: construction jobs that require workers to move in unpredictable settings and perform different tasks that are not repetitive; assembly work that requires tactile feedback like placing fiberglass panels inside airplanes, boats, or cars; and assembly jobs where only a limited quantity of products are made or where there are many versions of each product, requiring expensive reprogramming of robots. But that list is growing shorter. 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. The robot uses a technology pioneered in Microsoft’s Kinect motion sensing system for its Xbox video game system.


pages: 499 words: 144,278

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, 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, 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, TaskRabbit, 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!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

Awakening,” New York Times Magazine, December 14, 2016, accessed August 19, 2018, https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html. “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: 573 words: 157,767

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

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Andrew Wiles, Bayesian statistics, bioinformatics, bitcoin, Build a better mousetrap, Claude Shannon: information theory, computer age, computer vision, double entry bookkeeping, double helix, Douglas Hofstadter, Elon Musk, epigenetics, experimental subject, Fermat's Last Theorem, Gödel, Escher, Bach, information asymmetry, information retrieval, invention of writing, Isaac Newton, iterative process, John von Neumann, 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, 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. In 1978 I wrote a short commentary entitled “Why not the whole iguana?”


pages: 513 words: 152,381

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

3D printing, agricultural Revolution, Albert Einstein, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, Columbian Exchange, computer vision, cosmological constant, cuban missile crisis, decarbonisation, defense in depth, delayed gratification, demographic transition, Doomsday Clock, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Norbert Wiener, nuclear winter, p-value, Peter Singer: altruism, planetary scale, race to the bottom, RAND corporation, Ronald Reagan, self-driving car, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, the scientific method, uranium enrichment

Achieving Human Parity on Automatic Chinese to English News Translation. ArXiv, http://arxiv.org/abs/1803.05567. Haub, C., 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. Herfst, S., et al. (2012). “Airborne Transmission of Influenza A/H5N1 Virus Between Ferrets.” Science, 336(6088), 1,534–41.


pages: 561 words: 157,589

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

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, 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 Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, 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, 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, strong AI, TaskRabbit, telepresence, the built environment, 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, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, 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

Foursquare had just been launched, and its magical ability to detect where you were and offer a location for “check-in” made me think that it could also be used for “checkout.” 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. It would let you see the rooms in the hotel, and even the view from each room.


pages: 596 words: 163,682

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

activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, 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, 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, disruptive innovation, Donald Trump, Edward Glaeser, facts on the ground, financial innovation, financial repression, full employment, future of work, global supply chain, high net worth, housing crisis, 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, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, means of production, moral hazard, Network effects, new economy, Nicholas Carr, obamacare, Productivity paradox, profit maximization, race to the bottom, Richard Thaler, Robert Bork, Robert Gordon, Ronald Reagan, Sam Peltzman, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, 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: 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. The automated system does it all. Of course, it has not done away entirely with the need for humans.


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, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, endowment effect, extreme commuting, fundamental attribution error, Google Earth, hedonic treadmill, hindsight bias, hive mind, 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, Sam Peltzman, Silicon Valley, statistical model, the built environment, The Death and Life of Great American Cities, traffic fines, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor

Hoffman notes, “Several problems that Richman faced are evident from this picture: clutter, trees moving in the wind, shadows dancing on the road, cars in front hiding cars behind. 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: 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

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

TensorFlow has its own model zoo available at https://github.com/tensorflow/models. 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: 626 words: 167,836

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

"Robert Solow", 3D printing, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, David Graeber, David Ricardo: comparative advantage, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, falling living standards, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, Gini coefficient, Hyperloop, income inequality, income per capita, 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, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, labor-force participation, labour mobility, Loebner Prize, low skilled workers, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Norbert Wiener, oil shock, On the Economy of Machinery and Manufactures, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, social intelligence, speech recognition, spinning jenny, Stephen Hawking, 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, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

The machine could release the package it’s carrying, alter its flight path to avoid crashing, [and] ask humans a question or abort the delivery, the patent says.”37 Aided by AI, engineers have also come up with clever ways of reducing labor requirements within stores, without offloading the tasks done by cashiers onto consumers through complicated self-service checkout procedures. One example is Amazon Go, an archetypical example of a replacing technology. 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

air freight, Apple II, Bill Gates: Altair 8800, Buckminster Fuller, Byte Shop, computer age, computer vision, corporate governance, Donald Knuth, El Camino Real, game design, Hacker Ethic, hacker house, Haight Ashbury, John Conway, John Markoff, Mark Zuckerberg, Menlo Park, 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, 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. While the grad students were complaining about how David Silver was never going to amount to anything, how David Silver wasn’t doing proper AI, and how David Silver was never going to understand things like recursive function theory, David Silver was going ahead with his bug and PDP-6.