Bibliography

  1. Copeland, Jack B., ed. (2006). Colossus: The Secrets of Bletchley Park’s Codebreaking Computers. Oxford University Press. ISBN 978-0-19-284055-4.
  2. Rojas, R.; Darius, F.; Göktekin, C.; Heyne, G. (2005). "The reconstruction of Konrad Zuse’s Z3". IEEE Annals of the History of Computing. 27 (3): 23–32. doi:10.1109/mahc.2005.48.
  3. Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind, LIX (236): 433–460, ISSN 0026-4423, doi:10.1093/mind/LIX.236.433, retrieved 2008-08-18.
  4. Sharkey, Noel (2012), Alan Turing: The experiment that shaped artificial intelligence, http://www.bbc.com/news/technology-18475646.
  5. http://cyberneticzoo.com/mazesolvers/1951-maze-solver-minsky-edmonds-american
  6. http://shelf1.library.cmu.edu/IMLS/MindModels/logictheorymachine.pdf
  7. McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (31 August 1955), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
  8. Simon & Newell 1958, p. 7−8 quoted in Crevier 1993, p. 108; see reference [40]. See also Russell & Norvig 2003, p. 21; see reference [41], p. 21.
  9. Greenberger, Martin (1962), p. 118; https://mitpress.mit.edu/books/computers-and-world-future
  10. Minsky, Marvin (1967), "Computation: finite and infinite machines," Prentice-Hall, Inc. page 2. ISBN:0-13-165563-9.
  11. "Scientist on the Set: An Interview with Marvin Minsky," Archived November 14, 2007, at the Wayback Machine.
  12. "AI pioneer Marvin Minsky dies aged 88". BBC News. 26 January 2016.
  13. Leondes, Cornelius T. (2002). Expert systems: the technology of knowledge management and decision making for the 21st century. pp. 1–22. ISBN 978-0-12-443880-4.
  14. Samuel, Arthur L. (July 1959), "Some studies in machine learning using the game of checkers", IBM Journal of Research and Development, 3 (3): 210–219, doi:10.1147/rd.33.0210.
  15. McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115–133.
  16. Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley & Sons.
  17. Rosenblatt, Frank (1957), The Perceptron–a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory.
  18. Ivakhnenko, A. G. and Lapa, V. G. (1965). Cybernetic Predicting Devices. CCM Information Corporation.
  19. Ivakhnenko, A. G., Lapa, V. G., and McDonough, R. N. (1967). Cybernetics and forecasting techniques. American Elsevier, NY.
  20. Ivakhnenko, A. G. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics, (4):364–378.
  21. "Noam Chomsky’s Theory of Universal Grammar Is Right; It’s Hardwired into Our Brains". Medical Daily. 2015-12-07.
  22. Winograd, Terry (February 1971) "Procedures as a Representation for Data in a Computer Program for Understanding Natural Language", MIT AI Technical Report 235.
  23. Gruber, T. (2008). Liu, Ling; Özsu, M. Tamer, eds. Ontology. Encyclopedia of Database Systems. Springer-Verlag. ISBN 978-0-387-49616-0.
  24. Juang, B. H.; Rabiner, Lawrence R. (2004) "Automatic speech recognition–a brief history of the technology development"(PDF): pg. 6.
  25. "History of Speech Recognition" (2015). Dragon Medical Transcription. Archived from the original on 13 August 2015.
  26. Rabiner, Lawrence. "First Hand: The Hidden Markov Model". IEEE Global History Network. Also, see http://ethw.org/First-Hand:The_Hidden_Markov_Model
  27. Papert, Seymour (July 1966). "The Summer Vision Project". MIT AI Memos (1959 – 2004).
  28. Szeliski, Richard (September 2010). Computer Vision: Algorithms and Applications. Springer Science & Business Media. pp. 10–16. ISBN 978-1-84882-935-0.
  29. Weizenbaum, Joseph (1976), "Computer Power and Human Reason: From Judgment to Calculation". New York: W.H. Freeman and Company. pp. 2,3,6,182,189. ISBN 0-7167-0464-1. Also see, "Alan Turing at 100". Harvard Gazette. Retrieved 2016-02-22.
  30. Güzeldere, Güven; Franchi, Stefano (July 1995) "Dialogues with Colorful Personalities of Early AI". Stanford Humanities Review, SEHR, volume 4, issue 2: "Constructions of the Mind," Stanford University.
  31. Shimon, Nof Y. (1999). Handbook of Industrial Robotics (2nd ed.). John Wiley & Sons. pp. 3 – 5. ISBN 0-471-17783-0.
  32. http://www.humanoid.waseda.ac.jp/booklet/kato_2-j.html
  33. Macias, Nathanael; Wen, John, (2014), "Vision Guided Robotic Block Stacking," published in: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 Sept. 2014, ISBN: 978-1-4799-6934-0. DOI: 10.1109/IROS.2014.6942647
  34. http://www.nytimes.com/1982/09/14/business/cray-cuts-price.html
  35. Moravec, Hans (1976), "The Role of Raw Power in Intelligence".
  36. http://www.umsl.edu/~siegelj/information_theory/projects/Bajramovic/www.umsl.edu/_abdcf/Cs4890/link1.html
  37. https://countryeconomy.com/gdp?year=1974
  38. Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences, 3 (3): 417–457, doi:10.1017/S0140525X00005756, retrieved May 13, 2009.
  39. Minsky, Marvin; Seymour, Papert (1969), Perceptrons: An Introduction to Computational Geometry, The MIT Press
  40. Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: Basic Books. ISBN 0-465-02997-3.
  41. Russell, Stuart; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach. London, England: Pearson Education. ISBN 0-137-90395-2.
  42. McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1, OCLC 52197627.
  43. http://www.nytimes.com/1977/08/27/archives/man-and-machine-match-minds-at-mit-5th-conference-on-artificial.html
  44. http://www.nytimes.com/2005/10/13/business/worldbusiness/ai-reemerges-from-a-funding-desert.html
  45. Bryson, A. E.; Yu-Chi, Ho (January 1975). Applied Optimal Control: Optimization, Estimation and Control. CRC Press. ISBN 978-0-89116-228-5.
  46. Linnainmaa, Seppo (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master’s Thesis (in Finnish), Univ. Helsinki, 6-7.
  47. Werbos, Paul (1974). "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences". Harvard University. Retrieved 12 June 2017.
  48. Werbos, Paul (1982). "Applications of advances in nonlinear sensitivity analysis". System modeling and optimization (PDF). Springer. pp. 762–770.
  49. Rumelhart, D.E; Hinton, G. E.; Williams, R. J. (1986), "Learning representations by back-propagating errors," Nature 323, pp. 533-536, October 9, 1986.
  50. Kevin, Guerney (2002). An Introduction to Neural Networks. Routledge. ISBN 1857285034.
  51. Sathasivam, Saratha (2008), "Logic Learning in Hopfield Networks". arXiv:0804.4075.
  52. Shiller, Robert (2005). "Definition of Irrational Exuberance".www.irrationalexuberance.com Princeton University Press. Retrieved 23 August 2014.
  53. https://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/what-should-we-learn-past-ai-forecasts
  54. Buchanan, Bruce G. (2005), "A (Very) Brief History of Artificial Intelligence" (PDF), AI Magazine, pp. 53–60, retrieved 30 August 2007
  55. Little, W. A. (1974). The existence of persistent states in the brain. Math. Biosci., 19, 101-120.
  56. Aggarwal, A. (January 2018), "Genesis of AI: The First Hype Cycle," available at www.scryanalytics.com/articles
  57. Aggarwal, A. (January 2018), "Resurgence of Artificial Intelligence During 1983-2010," available at www.scryanalytics.com/articles
  58. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT Press ISBN 9780262018258.
  59. Hastie, Trevor, Robert Tibshirani, Friedman, Jerome (2009). The Elements of Statistical Learning: Data mining, Inference, and Prediction. New York: Springer. pp. 485–586. ISBN 978-0-387-84857-0.
  60. Bertsekas, Dimitri P.; Tsitsiklis, John (1996). Neuro-Dynamic Programming. Nashua, NH: Athena Scientific. ISBN 1-886529-10-8.
  61. Pavlov, I. P. (1897/1902). The work of the digestive glands. London: Griffin. Also, see Pavlov, I. P. (1928). Lectures on conditioned reflexes. (Translated by W.H. Gantt) London: Allen and Unwin.
  62. Tesauro, Gerald (March 1995). "Temporal Difference Learning and TD-Gammon". Communications of the ACM. 38 (3). doi:10.1145/203330.203343.
  63. Igor Aizenberg, Naum N. Aizenberg, Joos P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media.
  64. Rina Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory.
  65. Fukushima, K. (1980). "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biol. Cybern. 36: 193–202. doi:10.1007/bf00344251. PMID 7370364.
  66. Hochreiter, Sepp; Schmidhuber, Jürgen (1997-11-01). "Long Short-Term Memory". Neural Computation. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. ISSN 0899-7667. PMID 9377276.
  67. Hinton, G. E.; Osindero, S.; Teh, Y. W. (2006). "A Fast Learning Algorithm for Deep Belief Nets" (PDF). Neural Computation. 18 (7): 1527–1554. doi:10.1162/neco.2006.18.7.1527. PMID 16764513.
  68. https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf.
  69. "From Spiders to Elephants: The History of Hadoop". http://www.economist.com/node/15557443
  70. "Spark News". apache.org.
  71. John R. Mashey (25 April 1998). "Big Data … and the Next Wave of InfraStress" (PDF). Slides from invited talk. Usenix.
  72. "MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges" yann.lecun.com.
  73. https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/
  74. https://www.seagate.com/files/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf
  75. http://www.history.com/news/in-1950-alan-turing-created-a-chess-computer-program-that-prefigured-a-i
  76. Hans Berliner (1989). "Deep Thought wins the $10,000 Fredkin Prize". AI Magazine Volume 10 Number 2.
  77. http://illumin.usc.edu/188/deep-blue-the-history-and-engineering-behind-computer-chess/
  78. https://stanfordhealthcare.org/stanford-health-now/2014/cyberknife-technology-20th-anniversary.html
  79. Sawyer, Kathy (November 13, 1993). "One Way or Another, Space Agency Will Hitch a Ride to Mars". Washington Post.
  80. https://www.chatbots.org/chatbot/a.l.i.c.e/
  81. http://www.cleverbot.com/ and   http://www.jabberwacky.com/
  82. Christopher D. Manning and Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. The MIT Press. ISBN 978-0-262-13360-9.
  83. Daniel Jurafsky and James H. Martin (2008). Speech and Language Processing, 2nd edition. Pearson Prentice Hall. ISBN 978-0-13-187321-6.
  84. Heck, L.; Konig, Y.; Sonmez, M.; Weintraub, M. (2000). "Robustness to Telephone Handset Distortion in Speaker Recognition by Discriminative Feature Design". Speech Communication. 31 (2): 181–192. doi:10.1016/s0167-6393(99)00077-1.
  85. Baker, J.; Deng, Li; Glass, Jim; Khudanpur, S.; Lee, C.-H.; Morgan, N.; O’Shaughnessy, D. (2009). "Research Developments and Directions in Speech Recognition and Understanding, Part 1". IEEE Signal Processing Magazine. 26 (3): 75–80. doi:10.1109/msp.2009.932166.
  86. Deng, L.; Hinton, G.; Kingsbury, B. (2013). "New types of deep neural network learning for speech recognition and related applications: An overview (ICASSP)" (PDF).
  87. Francesco Ricci and Lior Rokach and Bracha Shapira (2011), Introduction to Recommender Systems Handbook, Recommender Systems Handbook, Springer, pp. 1-35.
  88. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324, Nov. 1998
  89. http://www.worldsbestchatbot.com/The_Loebner_Prize
  90. Bengio, Yoshua; LeCun, Yann; Hinton, Geoffrey (2015). "Deep Learning". Nature. 521: 436–444. doi:10.1038/nature14539. PMID 26017442.
  91. Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828 Freely accessible. doi:10.1016/j.neunet.2014.09.003. PMID 25462637.
  92. Santiago Fernandez, Alex Graves, and Jürgen Schmidhuber (2007). An application of recurrent neural networks to discriminative keyword spotting. Proceedings of ICANN (2), pp. 220–229.
  93. Google Ngram chart of the usage of the expression "deep learning" posted by Jürgen Schmidhuber (2015) Online
  94. "AlphaGo: Mastering the ancient game of Go with Machine Learning". Research Blog. Also, see "Innovations of AlphaGo | DeepMind". DeepMind.
  95. Minsky, Marvin (1974), A Framework for Representing Knowledge. Also, see Minsky, Marvin (1986), The Society of Mind, Simon and Schuster
  96. https://www.computerhope.com/jargon/c/chatchal.htm – chatterbox challenge
  97. Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang X., Wang L., Wang G., Cai, J.; Chen, T. (2017), "Recent Advances in Convolutional Neural Networks," arXiv:1512.07108v6.
  98. Lipton, Z.; Berkowitz, J.; Elkan, C. (2015), "A Critical Review of Recurrent Neural Networks for Sequence Learning," arXiv:1506.00019v4.
  99. Graves, A.; J. Schmidhuber, J. (2009), "Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks." Advances in Neural Information Processing Systems 22, NIPS’22, pp 545–552, Vancouver, MIT Press, 2009.
  100. Keyvanrad, M. A.; Homayounpour. M. M. (2016), "A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet)," arXiv:1408.3264v7.
  101. "Is Watson the smartest machine on earth?". Computer Science and Electrical Engineering Department, University of Maryland Baltimore County. February 10, 2011.
  102. Rennie, John (February 14, 2011). "How IBM’s Watson Computer Excels at Jeopardy!". PLoS blogs.
  103. Ferrucci, David; et al. (2010) "The AI Behind Watson – The Technical Article". AI Magazine.
  104. https://seekingalpha.com/article/4087604-much-artificial-intelligence-ibm-watson
  105. "Mastering the game of Go without human knowledge," by David Silver, Julian Schrittwieser, et. Al., Nature, October 2017, Vol. 550, pp. 354-359.
  106. "Carnegie Mellon". Navlab: The Carnegie Mellon University Navigation Laboratory. The Robotics Institute, 1984. http://www.cs.cmu.edu/afs/cs/project/alv/www/index.html
  107. Ramsey, John (1 June 2015). "Self-driving cars to be tested on Virginia highways". Richmond Times-Dispatch.
  108. "Waymo is first to put fully self-driving cars on US roads without a safety driver". The Verge. https://www.theverge.com/2017/11/7/16615290/waymo-self-driving-safety-driver-chandler-autonomous
  109. "Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records" (2016). https://www.nature.com/articles/srep26094
  110. Bosker, Biance (January 24, 2013). "SIRI RISING: The Inside Story Of Siri’s Origins — And Why She Could Overshadow The iPhone". HuffPost. AOL.
  111. http://files.chinagoabroad.com/Public/uploads/content/files/201612/Sync_142_Voice_as_the_next_computing_platform.pdf
  112. http://www.trustedreviews.com/news/top-robotic-and-humanoid-tech-2947542
  113. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/digital-blog/burned-by-the-bots-why-robotic-automation-is-stumbling
  114. Moore, Gordon E. (1965). "Cramming more components onto integrated circuits". Electronics.
  115. Moore, Gordon (2006). "Chapter 7: Moore’s law at 40". In Brock, David. Understanding Moore’s Law: Four Decades of Innovation (PDF). Chemical Heritage Foundation. pp. 67-84. ISBN 0-941901-41-6. https://www.intel.com/content/www/us/en/silicon-innovations/intel-14nm-technology.html
  116. https://www.linkedin.com/pulse/difference-self-driving-driverless-car-explained-tijmen-mekel/
  117. Moore, Gordon (March 30, 2015). "Gordon Moore: The Man Whose Name Means Progress, The visionary engineer reflects on 50 years of Moore’s Law". IEEE Spectrum: Special Report: 50 Years of Moore’s Law (Interview)
  118. "Torch: a modular machine learning software library". 30 October 2002. Retrieved 24 April 2014.
  119. https://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html
  120. https://webdocs.cs.ualberta.ca/~chinook/matches/1995/Petal/
  121. https://partners.nytimes.com/library/cyber/week/080997othello.html
  122. Hsu, Feng-hsiung (2002). "Behind Deep Blue: Building the Computer that Defeated the World Chess Champion". Princeton University Press. ISBN 0-691-09065-3.
  123. https://www.theverge.com/2016/6/9/11893002/google-ai-deepmind-atari-montezumas-revenge
  124. https://techcrunch.com/2017/06/15/microsofts-ai-beats-ms-pac-man/
  125. https://www.cmu.edu/news/stories/archives/2017/april/ai-beats-chinese.html
  126. http://blog.kaggle.com/2012/10/31/merck-competition-results-deep-nn-and-gpus-come-out-to-play/
  127. http://blog.kaggle.com/2012/10/31/merck-competition-results-deep-nn-and-gpus-come-out-to-play/
  128. http://on-demand.gputechconf.com/gtc/2017/presentation/s7822-andre-esteva-dermatologiest-level-classification-of-skin-cancer.pdf. Also, see: Esteva, A.; Kuprel, B.; Novoa, R.; Ko, J.; Swetter, S.; Blau, H.; Thrun, S. (2017), "Dermatologist-level classification of skin cancer with deep neural networks," Nature 542, 115–118.
  129. Taigman, Y.; Yang, M.; Ranzato, M. A.; Wolf, L. (2014), "DeepFace: Closing the Gap to Human-Level Performance in Face Verification", Conference on Computer Vision and Pattern Recognition (CVPR), Facebook Research Group.
  130. https://www.nytimes.com/2015/12/11/science/an-advance-in-artificial-intelligence-rivals-human-vision-abilities.html. Also, see https://www.edge.org/response-detail/26671 http://science.sciencemag.org/content/350/6266/1332.full
  131. https://blogs.microsoft.com/ai/microsoft-researchers-win-imagenet-computer-vision-challenge/
  132. Rajpurkar, P.; Zhang, J.; Lopyrev, K.; Liang, P. (2016), "SQuAD: 100,000+ Questions for Machine Comprehension of Text," arXiv:1606.05250v3.
  133. https://www.theguardian.com/business/2016/feb/19/hsbc-rolls-out-voice-touch-id-security-bank-customers
  134. https://medium.com/@jichangchunhan/speech-recognition-system-comparison-microsoft-vs-ibm-2016-2488ad51aa36
  135. http://www.businessinsider.com/googles-latest-robot-can-do-your-dishes-while-being-adorable-2016-6
  136. https://www.ald.softbankrobotics.com/en/robots/pepper
  137. http://world.honda.com/ASIMO/
  138. http://news.mit.edu/2016/marvin-minsky-obituary-0125
  139. http://www.independent.co.uk/news/obituaries/john-mccarthy-computer-scientist-known-as-the-father-of-ai-6255307.html
  140. Beavers, Anthony (2013). "Alan Turing: Mathematical Mechanist". In Cooper, S. Barry; van Leeuwen, Jan. Alan Turing: His Work and Impact. Waltham: Elsevier. pp. 481–485.ISBN 978-0-12-386980-7.
  141. Aggarwal, A. (January 2018), "Domains in which AI Systems Are Rivaling Humans," available at www.scryanalytics.com/articles
  142. Aggarwal, A. (January 2018), "The Current Hype Cycle in Artificial Intelligence," available at www.scryanalytics.com/articles
  143. "Artificial Intelligence – The Next Digitial Frontier," McKinsey Global Institute, 2016. PDF.
  144. Mirhaydari, A. (October, 2017), "Rise of AI excites VC investors, challenges society".; https://pitchbook.com/news/articles/rise-of-ai-excites-vc-investors-challenges-society
  145. https://www.cbinsights.com/research/artificial-intelligence-startup-funding/
  146. https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
  147. https://www.mckinsey.com/global-themes/future-of-organizations-and-work/what-the-future-of-work-will-mean-for-jobs-skills-and-wages?cid=other-eml-alt-mgi-mgi-oth-1711
  148. https://www.pwc.com/gx/en/services/people-organisation/workforce-of-the-future/workforce-of-the-future-the-competing-forces-shaping-2030-pwc.pdf
  149. Musk, Elon, Elon Musk’s deleted message: Five years until ‘dangerous’ AI; see https://www.cnbc.com/2014/11/17/elon-musks-deleted-message-five-years-until-dangerous-ai.html
  150. Kurzweil, Ray (2014), "Don’t Fear Artificial Intelligence," Time Magazine; http://time.com/3641921/dont-fear-artificial-intelligence/
  151. Barrat, J. [2015), "Our Final Invention: Artificial Intelligence and the End of the Human Era." Thomas Dunne Books.
  152. https://www.forbes.com/sites/bernardmarr/2017/07/25/28-best-quotes-about-artificial-intelligence/#603fd5724a6f
  153. Kurzweil, Ray (2005), "The Singularity is Near," Viking Press.
  154. Dreyfus, H.L. (2008), "Skilled Coping as Higher Intelligibility in Heidegger’s ‘Being and Time’", Spinoza lectures, ISSN 1387-0971, Uitgeverij Van Gorcum, ISBN No. 9023243781, 9789023243786;
  155. Kolata, G. (1982), "How Can Computers Get Common Sense?" Science, 217, page 1237.
  156. http://www.mobihealthnews.com/content/ibm-watson-manager-academics-describe-challenges-potential-healthcare-ai
  157. https://www.statnews.com/2017/09/05/watson-ibm-cancer/
  158. Nguyen, A; Yosinski, J; Clune, J. (2015) "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images". In Computer Vision and Pattern Recognition (CVPR’15), IEEE
  159. https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf
  160. https://www.theverge.com/2016/11/3/13507542/facial-recognition-glasses-trick-impersonate-fool
  161. https://spectrum.ieee.org/cars-that-think/transportation/sensors/slight-street-sign-modifications-can-fool-machine-learning-algorithms
  162. "AI Is Easy to Fool-Why That Needs to Change". Singularity Hub. 2017-10-10.
  163. Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv:1312.6199 (2013).
  164. Clarke, Arthur C. (1973). Profiles of the Future: An Inquiry into the Limits of the Possible. Popular Library. ISBN 9780330236195
  165. https://itif.org/publications/2017/09/19/artificial-intelligence-robotics-and-future-work-myths-and-facts
  166. https://www.mckinsey.com/global-themes/employment-and-growth/technology-jobs-and-the-future-of-work
  167. http://www.universityherald.com/articles/75720/20170614/the-most-advanced-ai-algorithms-dont-follow-humans-at-all.htm
  168. https://www.cnsnews.com/news/article/terence-p-jeffrey/7231000-lost-jobs-manufacturing-employment-down-37-1979-peak
  169. https://venturebeat.com/2017/07/01/for-ai-startups-more-funding-is-often-not-the-answer/
  170. https://www.iarpa.gov/index.php/research-programs/microns?%20%20%20%20option=com_content
  171. Goertzel, Ben (2015). "Are there Deep Reasons Underlying the Pathologies of Today’s Deep Learning Algorithms?" (PDF).
  172. Gibney, Elizabeth. "The scientist who spots fake videos". Nature. doi:10.1038/nature.2017.22784.
  173. Dreyfus, H. (1965), "Alchemy and Artificial Intelligence". Rand Paper.
  174. Dreyfus, H. (1972), What Computers Can’t Do: The Limits of Artificial Intelligence. ISBN 0-06-011082-1
  175. https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-redefined-future/
  176. https://www.researchgate.net/figure/270290881_fig4_Figure-4-Electrophysiology-assessing-the-corticospinal-tract-before-and-after


Interesting Quotes Regarding Hype Cycles in Artificial Intelligence

https://www.forbes.com/sites/bernardmarr/2017/07/25/28-best-quotes-about-artificial-intelligence/#603fd5724a6f

  • "I visualize a time when we will be to robots what dogs are to humans, and I'm rooting for the machines." —Claude Shannon
  • "Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on." —Larry Page
  • "I'm more frightened than interested by artificial intelligence – in fact, perhaps fright and interest are not far away from one another. Things can become real in your mind, you can be tricked, and you believe things you wouldn't ordinarily. A world run by automatons doesn't seem completely unrealistic anymore. It's a bit chilling." —Gemma Whelan
  • "Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement – wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league." —Eliezer Yudkowsky
  • "Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver." —Diane Ackerman
  • "Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower." —Alan Kay
  • "Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition." —Sebastian Thrun
  • "A year spent in artificial intelligence is enough to make one believe in God." —Alan Perlis
  • "The upheavals [of artificial intelligence] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease." — Nick Bilton, tech columnist wrote in the New York Times
  • "I don't want to really scare you, but it was alarming how many people I talked to who are highly placed people in AI who have retreats that are sort of ‘bug out’ houses, to which they could flee if it all hits the fan."—James Barrat, author of Our Final Invention: Artificial Intelligence and the End of the Human Era, told the Washington Post

Behavioral economist Dan Ariely described the big data hype thusly; the same is true about AI today:

Big data is like teen age sex: everyone talks about it,nobody knows how to do it,everyone thinks everyone else is doing it,so everyone claims they are doing it…

Download as PDF

First Name * Last Name