computer vision

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pages: 138 words: 27,404

OpenCV Computer Vision With Python by Joseph Howse

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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.

 

pages: 215 words: 56,215

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

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Amazon Web Services, clean water, cloud computing, computer vision, en.wikipedia.org, full employment, income inequality, job automation, knowledge worker, 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: 413 words: 119,587

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

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A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, augmented reality, autonomous vehicles, 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 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, 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 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, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, 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

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: 205 words: 20,452

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

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4chan, call centre, computer vision, discrete time, 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).

 

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

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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, Grace Hopper, inventory management, John von Neumann, linear programming, Menlo Park, 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: 326 words: 74,433

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

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augmented reality, computer vision, corporate governance, crowdsourcing, disintermediation, hiring and firing, Inbox Zero, Jeff Bezos, 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.

 

pages: 49 words: 12,968

Industrial Internet by Jon Bruner

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autonomous vehicles, barriers to entry, 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: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

Geoff Hinton, Yoshua Bengio, and I started what you could call a conspiracy—the deep learning conspiracy, basically—where we attempted to rekindle the interests of the community in learning representations as opposed to just learning classifiers. For the first few years, it was very difficult for us to get any papers published anywhere, be it computer vision conferences or machine learning conferences. The work was labeled “neural nets” and basically not interesting for that reason. People just didn’t seem interested in digging past the title essentially. It’s only around 2007 or so that things started to take off. For deep learning, it was still a bit of a struggle for a while, particularly in computer vision. In computer vision, the transition to deep learning happened just last year. In speech recognition, it happened about three years ago, when people started to realize deep learning was working really well and it was beating everything else, and so there came a big rush to those methods.

It’s relatively small, but it’s very focused and very interesting. So those are conferences for machine learning–type things. There are other areas of machine learning that I’m not very active in. Things like reinforcement learning, which is actually very important for industry and is used in things like ad placement, so there’s AAAI and similar conferences. I also have a foot in computer vision. In computer vision, the main conferences are CVPR, ICCV, and ECCV. CVPR is more into images, videos, and similar things. Gutierrez: Is there an area today that you feel is somewhat analogous to deep learning when you started, in that you think it’s going to be giant in the future but people just aren’t looking at it right now? LeCun: I think it goes in cycles. We have a new set of techniques that comes up, and for a while the technique is under the radar and then it kind of blows up, and everybody explores how you can milk this technique for a while until you hit a wall.

After his postdoc work in Geoff Hinton’s group at the University of Toronto developing the theory of artificial neural networks with back-propagation, he joined AT&T Bell Labs, where he later became the head of the Image Processing Research Department. LeCun then worked briefly as a Fellow of the NEC Research Institute in Princeton before joining NYU in 2003. Over his career to date, he has published more than 180 technical papers and book chapters on machine learning, computer vision, handwriting recognition, image processing and compression, and neural networks. He is particularly well known for his work on deep learning methods, which are used by companies to understand images, video, documents, human-computer interactions, and speech. www.it-ebooks.info 46 Chapter 3 | Yann LeCun, Facebook LeCun is a peerless example of a data scientist with a transformational vision—in his case, using deep learning to teach machines to perceive the world—who strives to actuate that vision in both academic and industrial research laboratories.

 

pages: 696 words: 143,736

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

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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, 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, Jacquard loom, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, 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: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, 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 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, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

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: 294 words: 81,292

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

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3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, 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, 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

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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: 144 words: 43,356

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

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3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, 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, 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, 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, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E

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: 291 words: 81,703

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

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Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, 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, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Narrative Science, Netflix Prize, Nicholas Carr, 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.

 

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

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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, 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: 486 words: 132,784

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

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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, 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

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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, pattern recognition, Pluto: dwarf planet, Richard Feynman, Richard Feynman, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day

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: 494 words: 116,739

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

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Albert Einstein, Berlin Wall, Bernie Madoff, blood diamonds, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, computer vision, conceptual framework, delayed gratification, Edward Glaeser, en.wikipedia.org, 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, libertarian paternalism, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, microcredit, mobile money, Nicholas Carr, North Sea oil, pattern recognition, Peter Singer: altruism, Peter Thiel, post-industrial society, randomized controlled trial, rent-seeking, RFID, Richard Florida, Richard Thaler, school vouchers, self-driving car, Silicon Valley, Simon Kuznets, 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: 339 words: 88,732

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

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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, Baxter: Rethink Robotics, British Empire, business intelligence, business process, call centre, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, crowdsourcing, David Ricardo: comparative advantage, 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, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, 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, Mark Zuckerberg, Mars Rover, 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, payday loans, 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.

 

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

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Any sufficiently advanced technology is indistinguishable from magic, Apple II, back-to-the-land, Bill Duvall, Bill Gates: Altair 8800, Buckminster Fuller, California gold rush, card file, computer age, computer vision, conceptual framework, cuban missile crisis, Douglas Engelbart, Dynabook, El Camino Real, general-purpose programming language, Golden Gate Park, Hacker Ethic, hypertext link, informal economy, information retrieval, invention of the printing press, Jeff Rulifson, 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, 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.

 

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

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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.

 

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

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Amazon Mechanical Turk, Andrew Keen, 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, Network effects, new economy, Nicholas Carr, PageRank, 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: 197 words: 35,256

NumPy Cookbook by Ivan Idris

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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

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23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, 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, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, 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, Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, offshore financial centre, optical character recognition, pattern recognition, 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, 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 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, 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.

 

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, 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 von Neumann, knowledge worker, 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

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.

 

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

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3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, 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, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, 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, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, 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

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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, computer age, computer vision, conceptual framework, corporate governance, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Frank Levy and Richard Murnane: The New Division of Labor, 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, lump of labour, Marshall McLuhan, Narrative Science, natural language processing, Network effects, optical character recognition, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, semantic web, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, 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!, 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: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

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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, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, 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.

 

pages: 237 words: 64,411

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

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Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, 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, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, 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, 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: 361 words: 83,886

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

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carbon-based life, computer age, 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: 685 words: 203,949

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

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airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, 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, meta analysis, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Skype, Snapchat, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Turing test, ultimatum 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: 834 words: 180,700

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

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8-hour work day, anti-pattern, bioinformatics, c2.com, cloud computing, collaborative editing, combinatorial explosion, computer vision, continuous integration, create, read, update, delete, Debian, domain-specific language, en.wikipedia.org, fault tolerance, finite state, Firefox, friendly fire, linked data, load shedding, locality of reference, loose coupling, Mars Rover, MVC pattern, premature optimization, recommendation engine, revision control, 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: 502 words: 107,510

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

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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: 336 words: 93,672

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

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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, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, 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.

 

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

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Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, 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 supply chain, global village, hive mind, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, linked data, low skilled workers, M-Pesa, 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.

 

pages: 383 words: 108,266

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

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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, 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.

 

pages: 476 words: 132,042

What Technology Wants by Kevin Kelly

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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, 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, John Conway, John von Neumann, Kevin Kelly, knowledge economy, Lao Tzu, life extension, Louis Daguerre, Marshall McLuhan, megacity, meta analysis, meta-analysis, new economy, out of africa, performance metric, personalized medicine, phenotype, Picturephone, planetary scale, RAND corporation, random walk, Ray Kurzweil, recommendation engine, refrigerator car, Richard Florida, 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, 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: 588 words: 131,025

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

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23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, computer vision, conceptual framework, connected car, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, disintermediation, 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, interchangeable parts, Internet of things, Isaac Newton, job automation, Joseph Schumpeter, Julian Assange, Kevin Kelly, license plate recognition, 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, Watson beat the top human players on Jeopardy!, 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: 303 words: 67,891

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

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AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, 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: 565 words: 151,129

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

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3D printing, 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 vision, crowdsourcing, demographic transition, distributed generation, en.wikipedia.org, Frederick Winslow Taylor, global supply chain, global village, Hacker Ethic, industrial robot, informal economy, intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, labour mobility, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, means of production, meta analysis, meta-analysis, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, oil shale / tar sands, pattern recognition, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, 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, 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: 310 words: 34,482

Makers at Work: Folks Reinventing the World One Object or Idea at a Time by Steven Osborn

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3D printing, A Pattern Language, additive manufacturing, air freight, Airbnb, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, c2.com, computer vision, crowdsourcing, dumpster diving, en.wikipedia.org, Firefox, future of work, Google Chrome, Google Glasses, Google Hangouts, Hacker Ethic, Internet of things, Iridium satellite, Khan Academy, Kickstarter, Mason jar, means of production, Minecraft, minimum viable product, Network effects, Oculus Rift, patent troll, popular electronics, Rodney Brooks, Shenzhen was a fishing village, side project, Silicon Valley, Skype, slashdot, social software, software as a service, special economic zone, speech recognition, subscription business, telerobotics, urban planning, web application, Y Combinator

So what happened was that I accidentally got off on ITP’s floor, but didn’t realize it right away. I went to the reception desk to ask for information, and they were starting to give me some information, when I realized I was in the wrong place. But they were so nice that I didn’t have the courage to say I had made a mistake. I just looked around and then something really strange happened. I saw the work that the students were doing there, which was electronics and computer vision—this was back in 1998 and the web was still very young. We still used modems to connect. Basically, something clicked, and it felt like I had found my place. So I never even applied to the film school. I actually never even went there. I basically went home, applied for ITP, and I got accepted. Luckily, I also got a scholarship because I couldn’t possibly afford NYU otherwise. ITP is a very special place.

 

pages: 514 words: 152,903

The Best Business Writing 2013 by Dean Starkman

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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, full employment, Goldman Sachs: Vampire Squid, hiring and firing, hydraulic fracturing, income inequality, jimmy wales, job automation, late fees, London Whale, low skilled workers, Mahatma Gandhi, market clearing, Maui Hawaii, Menlo Park, Occupy movement, oil shale / tar sands, price stability, Ray Kurzweil, Silicon Valley, Skype, sovereign wealth fund, stakhanovite, Steve Jobs, Stuxnet, the payments system, too big to fail, Vanguard fund, wage slave, Y2K

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.

 

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

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Asian financial crisis, banking crisis, barriers to entry, Black-Scholes formula, commodity trading advisor, computer vision, East Village, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John von Neumann, locking in a profit, Long Term Capital Management, margin call, 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

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air freight, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, disintermediation, distributed generation, double helix, experimental economics, full employment, hydrogen economy, industrial robot, informal economy, interchangeable parts, job satisfaction, labour market flexibility, 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.

 

pages: 598 words: 183,531

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

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air freight, Apple II, Bill Gates: Altair 8800, Buckminster Fuller, Byte Shop, computer age, computer vision, corporate governance, El Camino Real, game design, Hacker Ethic, hacker house, Haight Ashbury, John Conway, Mark Zuckerberg, Menlo Park, non-fiction novel, 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, 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.

 

pages: 1,079 words: 321,718

Surfaces and Essences by Douglas Hofstadter, Emmanuel Sander

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affirmative action, Albert Einstein, Arthur Eddington, Benoit Mandelbrot, Brownian motion, Chance favours the prepared mind, cognitive dissonance, computer age, computer vision, dematerialisation, Donald Trump, Douglas Hofstadter, Ernest Rutherford, experimental subject, Flynn Effect, Georg Cantor, Gerolamo Cardano, Golden Gate Park, haute couture, haute cuisine, Henri Poincaré, Isaac Newton, l'esprit de l'escalier, Louis Pasteur, Mahatma Gandhi, mandelbrot fractal, Menlo Park, Norbert Wiener, place-making, Silicon Valley, statistical model, Steve Jobs, Steve Wozniak, theory of mind, upwardly mobile, urban sprawl

If we then add to the human side of the ledger our easily distractable attention, the fatigue that often seriously interferes with our capacities, and the imprecision of our sensory organs, we are left straggling in the dust. If one were to draw up a table of numerical specifications, as is standardly done in comparing one computer with another, Homo sapiens sapiens would wind up in the recycling bin. Given all this, how can we explain the fact that, in terms of serious thought, machines lag woefully behind us? Why is machine translation so often inept and awkward? Why are robots so primitive? Why is computer vision restricted to the simplest kinds of tasks? Why is it that today’s search engines can instantly search billions of Web sites for passages containing the phrase “in good faith”, yet are incapable of spotting Web sites in which the idea of good faith (as opposed to the string of alphanumeric characters) is the central theme? Readers will of course have anticipated the answer — namely, that our advantage is intimately linked to categorization through analogy, a mental mechanism that lies at the very center of human thought but at the furthest fringes of most attempts to realize artificial cognition.

 

pages: 898 words: 266,274

The Irrational Bundle by Dan Ariely

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accounting loophole / creative accounting, air freight, Albert Einstein, banking crisis, Bernie Madoff, Black Swan, Broken windows theory, Burning Man, business process, cashless society, Cass Sunstein, clean water, cognitive dissonance, computer vision, corporate governance, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, endowment effect, Exxon Valdez, first-price auction, Frederick Winslow Taylor, fudge factor, George Akerlof, Gordon Gekko, greed is good, happiness index / gross national happiness, Jean Tirole, job satisfaction, knowledge economy, knowledge worker, lake wobegon effect, late fees, loss aversion, Murray Gell-Mann, new economy, Peter Singer: altruism, placebo effect, price anchoring, Richard Feynman, Richard Feynman, Richard Thaler, Saturday Night Live, Schrödinger's Cat, second-price auction, shareholder value, Silicon Valley, Skype, software as a service, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, ultimatum game, Upton Sinclair, Walter Mischel, young professional

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.