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  • This Element is free online from 10th November - 17th November
  • Cited by 11
Publisher:
Cambridge University Press
Online publication date:
March 2022
Print publication year:
2022
Online ISBN:
9781009037853

Book description

Dr Viktor Dörfler combines his background in developing and implementing AI with scholarly research on knowledge and cultivating talent to address misconceptions about AI. The Element explains what AI can and cannot do, carefully delineating facts from beliefs or wishful thinking. Filled with examples, this practical Element provokes thinking. The purpose is to help CEOs figure out how to make the best use of AI, suggesting how to extract AI's greatest value through appropriate task allocation between human experts and AI. The author challenges the attribution of characteristics like understanding, thinking, and creativity to AI, supporting his argument with the ideas of the finest AI philosophers. He also discusses in depth one of the most sensitive AI-related topics: ethics. The readers are encouraged to make up their own minds about AI and draw their own conclusions rather than accepting opinions from people with vested interests or an agenda.

References

Aaronson, S. (2014, June 9). My Conversation with “Eugene Goostman,” the Chatbot That’s All Over the News for Allegedly Passing the Turing Test. Shtetle-Optimized. www.scottaaronson.com/blog/?p=1858
Ackermann, F. & Eden, C. (2011). Making Strategy: Mapping Out Strategic Success. London: SAGE Publications. http://books.google.co.uk/books?id=Ln1PQLi-flIC
Amabile, T. M. (1982). Social Psychology of Creativity: A Consensual Assessment Technique. Journal of Personality and Social Psychology, 43(5), 9971013. https://doi.org/10.1037/0022-3514.43.5.997
Amabile, T. M. (1983a). The Social Psychology of Creativity: A Componential Conceptualization. Journal of Personality and Social Psychology, 45(2), 357376. https://doi.org/10.1037/0022-3514.45.2.357
Amabile, T. M. (1983b). The Social Theory of Creativity. New York: Springer-Verlag.
Amabile, T. M. (1996). Creativity in Context: Update to the Social Psychology of Creativity. Boulder, CO: Westview Press.
Amabile, T. M. (2020). GUIDEPOST: Creativity, Artificial Intelligence, and a World of Surprises. Academy of Management Discoveries, 6(3), 351354. https://doi.org/10.5465/amd.2019.0075
Aron, J. (2011, September 6). Software Tricks People into Thinking It Is Human. New Scientist. www.newscientist.com/article/dn20865-software-tricks-people-into-thinking-it-is-human
Baer, J. (2020). The Consensual Assessment Technique. In Dörfler, & Stierand, M., eds., Handbook of Research Methods on Creativity. Cheltenham: Edward Elgar, pp. 166177. https://doi.org/10.4337/9781786439659.00020
Baracskai, Z. & Velencei, J. (2002, November 6–7). Important Characteristics for a Knowledge Engineer. 12th Annual Conference of Business Information Technology, Manchester, UK.
Bas, A., Sinclair, M., & Dörfler, V. (2022). Sensing: The Elephant in the Room of Management Learning. Management Learning. https://doi.org/10.1177/13505076221077226
von Bertalanffy, L. (1981). A Systems View of Man. Boulder, CO: Westview Press.
Boden, M. A. (1998). Creativity and Artificial Intelligence. Artificial Intelligence, 103(1), 347356. https://doi.org/10.1016/S0004-3702(98)00055-1
Boden, M. A. (2009). Creativity: How Does It Work? In Krausz, M., Dutton, D., & Bardsley, K., eds., The Idea of Creativity. Leiden: Brill, pp. 237250.
Bory, P. (2019). Deep New: The Shifting Narratives of Artificial intelligence from Deep Blue to AlphaGo. Convergence, 25(4), 627642. https://doi.org/10.1177/1354856519829679
Boulding, K. E. (1956). General Systems Theory: The Skeleton of Science. Management Science, 2(3), 197208. https://doi.org/10.1287/mnsc.2.3.197
Boulding, K. E. (1966). The Economics of Knowledge and the Knowledge of Economics. American Economic Review, 56(1/2), 113. www.jstor.org/stable/1821262
Chalmers, D. J. (1998). The Conscious Mind: In Search of a Fundamental Theory, paperback ed. New York: Oxford University Press.
Chomsky, N. (1957/2002). Syntactic Structures, 2nd ed. New York: Mouton de Gruyter.
Clarke, A. C. (1962/2013). Profiles of the Future: An Inquiry into the Limits of the Possible. London: Gollancz. https://books.google.co.uk/books?id=8_AcAQAAMAAJ
Coeckelbergh, M. (2020). Should We Treat Teddy Bear 2.0 as a Kantian Dog? Four Arguments for the Indirect Moral Standing of Personal Social Robots, with Implications for Thinking About Animals and Humans. Minds and Machines, 31, 337360. https://doi.org/10.1007/s11023-020-09554-3
Cunliffe, A. L. (2009). The Philosopher Leader: On Relationalism, Ethics and Reflexivity – A Critical Perspective to Teaching Leadership. Management Learning, 40(1), 87101. https://doi.org/10.1177/1350507608099315
Damasio, A. R. (1995/2005). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Avon Books.
Darling, K. (2019, March 27). Why We Should Show Machines Some Respect [Interview]. Forbes. www.forbes.com/sites/insights-intelai/2019/03/27/why-we-should-show-machines-some-respect
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, MA: MIT Press.
Davenport, T. H. & O’Dell, C. (2019, March 18). Explainable AI and the Rebirth of Rules. Forbes. www.forbes.com/sites/tomdavenport/2019/03/18/explainable-ai-and-the-rebirth-of-rules
Davenport, T. H. & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know, paperback ed. Boston, MA: Harvard Business School Press.
Daws, R. (2020, October 28). Medical Chatbot Using OpenAI’s GPT-3 Told a Fake Patient to Kill Themselves. AI News. https://artificialintelligence-news.com/2020/10/28/medical-chatbot-openai-gpt3-patient-kill-themselves
Dörfler, V. (2010). Learning Capability: The Effect of Existing Knowledge on Learning. Knowledge Management Research & Practice, 8(4), 369379. https://doi.org/10.1057/kmrp.2010.15
Dörfler, V. (2020). Artificial Intelligence. In Runco, M. A. & Pritzker, S. R., eds., Encyclopedia of Creativity, 3rd ed., Vol. 1. Oxford: Academic Press, pp. 5764. https://doi.org/10.1016/B978-0-12-809324-5.23863-7
Dörfler, V. (2021). Looking Back on a Framework for Thinking about Group Decision Support Systems. In Kilgour, D. M. & Eden, C., eds., Handbook of Group Decision and Negotiation, 2nd ed., Vol. 2. Cham: Springer, pp. 837860. https://doi.org/10.1007/978-3-030-49629-6_32
Dörfler, V. (2022). Artificial Intelligence. In Mattingly, J., ed., The SAGE Encyclopedia of Theory in Science, Technology, Engineering, and Mathematics. Thousand Oaks, CA: SAGE Publications.
Dörfler, V. & Ackermann, F. (2012). Understanding Intuition: The Case for Two Forms of Intuition. Management Learning, 43(5), 545564. https://doi.org/10.1177/1350507611434686
Dörfler, V., Baracskai, Z., & Velencei, J. (2009, August 7–11). Knowledge Levels: 3-D Model of the Levels of Expertise. AoM 2009: 69th Annual Meeting of the Academy of Management, Chicago, IL. The Academy of Management. www.researchgate.net/publication/308339223
Dörfler, V. & Bas, A. (2020a). Intuition: Scientific, Non-Scientific or Unscientific? In Sinclair, , ed., Handbook of Intuition Research as Practice. Cheltenham: Edward Elgar, pp. 293305. https://doi.org/10.4337/9781788979757.00033
Dörfler, V. & Bas, A. (2020b, August 7–11). Tools for Exploring the Unknowable: Intuition vs. Artificial Intelligence. AoM 2020: 80th Annual Meeting of the Academy of Management, Vancouver, BC. The Academy of Management. www.researchgate.net/publication/342135191
Dörfler, V. & Bas, A. (unpublished). Understanding Uncertainty: Known, Unknown, and Unknowable.
Dörfler, V. & Eden, C. (2017, August 4–8). Becoming a Nobel Laureate: Patterns of a Journey to the Highest Level of Expertise. AoM 2017: 77th Annual Meeting of the Academy of Management, Atlanta, GA. The Academy of Management. https://doi.org/10.5465/AMBPP.2017.12982abstract
Dörfler, V. & Eden, C. (2019). Understanding “Expert” Scientists: Implications for Management and Organization Research. Management Learning, 50(5), 534555, Article 135050761986665. https://doi.org/10.1177/1350507619866652
Dörfler, V. & Stierand, M. (2017). The Underpinnings of Intuition. In Liebowitz, J., Paliszkiewicz, J., & Gołuchowski, J., eds., Intuition, Trust, and Analytics. Boca Raton, FL: Taylor & Francis, pp. 3–20. https://doi.org/10.1201/9781315195551-1
Dörfler, V. & Stierand, M. (2018, August 10–14). Understanding Indwelling through Studying Intuitions of Nobel Laureates and Top Chefs. AoM 2018: 78th Annual Meeting of the Academy of Management, Chicago, IL. The Academy of Management.
Dörfler, V. & Stierand, M. (2019). Extraordinary: Reflections on Sample Representativeness. In Lebuda, & Glăveanu, V. P., eds., The Palgrave Handbook of Social Creativity Research. Cham: Palgrave Macmillan, pp. 569584. https://doi.org/10.1007/978-3-319-95498-1_36
Dörfler, V., Stierand, M., & Chia, R. C. H. (2018, September 4–6). Intellectual Quietness: Our Struggles with Researching Creativity as a Process. BAM 2018: 32nd Annual Conference of the British Academy of Management, Bristol, UK. The British Academy of Management.
Dörfler, V. & Szendrey, J. (2008, April 28–30). From Knowledge Management to Cognition Management: A Multi-Potential View of Cognition. OLKC 2008: International Conference on Organizational Learning, Knowledge and Capabilities, Copenhagen. www.researchgate.net/publication/253780221
Dreyfus, H. L. & Dreyfus, S. E. (1986/2000). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York: The Free Press.
Drucker, P. F. (1995). The Information Executives Truly Need. Harvard Business Review, 73(1), 5462. https://hbr.org/1995/01/the-information-executives-truly-need
Einstein, A. (2010). The Ultimate Quotable Einstein, edited by Alice Calaprice. Princeton, NJ: Princeton University Press. https://books.google.co.uk/books?id=G_iziBAPXtEC
Ericsson, K. A. & Charness, N. (1994). Expert Performance: Its Structure and Acquisition. American Psychologist, 49(8), 725747. https://doi.org/10.1037/0003-066X.49.8.725
Feigenbaum, E. A. (1977). The Art of Artificial Intelligence: I. Themes and Case Studies of Knowledge Engineering. 5th International Joint Conference on Artificial Intelligence,
Feigenbaum, E. A. (1992). A Personal View of Expert Systems: Looking Back and Looking Ahead (KSL 92–41). https://purl.stanford.edu/gr891tb5766
Feigenbaum, E. A. (2006). Ed Feigenbaum’s Search for AI. Feigenfest 70th, Stanford University, Stanford, CA. https://youtu.be/B9zVdU3N7DY
Feigenbaum, E. A. & Simon, H. A. (1984). EPAM-Like Models of Recognition and Learning. Cognitive Science, 8(4), 305336. https://doi.org/10.1207/s15516709cog0804_1
Finley, K. (2012, October 1). Did Deep Blue Beat Kasparov because of a Computer Bug? Wired. www.wired.co.uk/article/deep-blue-bug
Fromm, E. (1942). The Fear of Freedom. London: Routledge.
Gardner, H. (1995). Why Would Anyone Become an Expert? “Expert Performance: Its Structure and Acquisition”: Comment. American Psychologist, 50(9), 802803. https://doi.org/10.1037/0003-066X.50.9.802
Guo, E. & Hao, K. (2020, December 21). This Is the Stanford Vaccine Algorithm That Left Out Frontline Doctors. MIT Technology Review. www.technologyreview.com/2020/12/21/1015303
Handy, C. (2015). The Second Curve: Thoughts on Reinventing Society. London: Random House. https://books.google.hu/books?id=yztOBQAAQBAJ
Hao, K. (2019, February). Police across the US Are Training Crime-Predicting AIs on Falsified Data. MIT Technology Review. www.technologyreview.com/2019/02/13/137444
Heaven, D. (2019). Deep Trouble for Deep Learning. Nature, 574(7777), 163166. https://doi.org/10.1038/d41586-019-03013-5
Heaven, W. D. (2020, November 30). DeepMind’s Protein-Folding AI Has Solved a 50-Year-Old Grand Challenge of Biology. MIT Technology Review. www.technologyreview.com/2020/11/30/1012712/
Hobbes, T. (1651/2018). Leviathan. London: Strelbytskyy Multimedia Publishing. https://books.google.co.uk/books?id=X81qDwAAQBAJ
Hofstadter, D. R. (1979/1999). Godel, Escher, Bach: An Eternal Golden Braid, 2nd ed. London: Basic Books.
Hume, D. (1739). A Treatise of Human Nature. London: John Noon. https://books.google.co.uk/books?id=66S3DAEACAAJ
Kahneman, D. (2011). Thinking, Fast and Slow. London: Penguin Books. http://books.google.co.uk/books?id=ZuKTvERuPG8C
Kelly, G. A. (1955/1963). A Theory of Personality: The Psychology of Personal Constructs, paperback ed. New York: Norton.
Keyes, D. (1966). Flowers for Algernon. Boston, MA: Harcourt, Brace & World. https://books.google.co.uk/books?id=_oG_iTxP1pIC
Knight, F. H. (1921). Risk, Uncertainty and Profit. New York: Houghton Mifflin. https://books.google.es/books?id=9fHTAAAAMAAJ
Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin Publishing Group. https://books.google.co.uk/books?id=9FtnppNpsT4C
Lave, J. & Wenger, E. C. (1991/2003). Situated Learning: Legitimate Peripheral Participation. New York: Cambridge University Press. http://books.google.co.uk/books?id=CAVIOrW3vYAC
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436444. https://doi.org/10.1038/nature14539
Lenat, D. B. & Feigenbaum, E. A. (1991). On the Thresholds of Knowledge. Artificial Intelligence, 47(1), 185250. https://doi.org/10.1016/0004-3702(91)90055-O
Liu, C. (2020). The World’s First Trillionaires and More AI Predictions. AoM Insights. https://journals.aom.org/doi/abs/10.5465/ambpp.2019.12809symposium.summary
March, J. G. (1994). Primer on Decision Making: How Decisions Happen. New York: Free Press. http://books.google.nl/books?id=zydIx15DM2kC
McCorduck, P. (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd ed. Natick, MA: A. K. Peters. https://monoskop.org/images/1/1e/McCorduck_Pamela_Machines_Who_Think_2nd_ed.pdf
McCulloch, W. S. & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5(4), 115133. https://doi.org/10.1007/BF02478259
McGilchrist, I. (2019). The Master and His Emissary: The Divided Brain and the Making of the Western World, 2nd ed. New Haven, CT: Yale University Press. https://books.google.co.uk/books?id=alSIDwAAQBAJ
Mérő, L. (1990). Ways of Thinking: The Limits of Rational Thought and Artificial Intelligence. New Jersey, NJ: World Scientific.
Meyer, J., Land, R., & Baillie, C. (2010). Threshold Concepts and Transformational Learning. Rotterdam: Sense Publishers. https://books.google.co.uk/books?id=AOqaSQAACAAJ
Minsky, M. L. (1988). The Society of Mind. New York: Simon & Schuster.
Minsky, M. L. (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York: Simon & Schuster.
Moggridge, B. (2007). The Internet: Interviews with Terry Winograd, Larry Page and Sergey Brin of Google, Steve Rogers, and Mark Podlaseck. In Moggridge, B., ed., Designing Interactions. Cambridge, MA: MIT Press.
Musk, E. (2018, April 17). Elon Musk on Google DeepMind. YouTube. https://youtu.spenbe/MuWWZ91-G6w
von Neumann, J. & Morgenstern, O. (1953). Theory of Games and Economic Behavior, 3rd ed. New York: John Wiley & Sons.
Newell, A., Shaw, J. C., & Simon, H. A. (1963). Empirical Explorations with the Logic Theory Machine: A Case Study in Heuristics. In Feigenbaum, E. A. & Feldman, J., eds., Computers and Thought. New York: McGraw-Hill, Inc., pp. 109133.
Newell, A. & Simon, H. A. (1956). The Logic Theory Machine: A Complex Information Processing System. IRE Transactions on Information Theory, 2(3), 61–79. https://doi.org/10.1109/TIT.1956.1056797
Oliver, N., Calvard, T., & Potočnik, K. (2017a). Cognition, Technology, and Organizational Limits: Lessons from the Air France 447 Disaster. Organization Science, 28(4), 729743. https://doi.org/10.1287/orsc.2017.1138
Oliver, N., Calvard, T., & Potočnik, K. (2017b, September 15). The Tragic Crash of Flight AF447 Shows the Unlikely but Catastrophic Consequences of Automation. Harvard Business Review. https://hbr.org/2017/09/the-tragic-crash-of-flight-af447-shows-the-unlikely-but-catastrophic-consequences-of-automation
Pavlov, I. P. (1927). Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex. London: Routledge and Kegan Paul. http://psychclassics.yorku.ca/Pavlov
Polányi, M. (1946). Science, Faith and Society. London: Oxford University Press.
Polányi, M. (1959). The Study of Man. Chicago, IL: University of Chicago Press. http://books.google.rs/books?id=lbMkAQAAMAAJ
Polányi, M. (1962a/2002). Personal Knowledge: Towards a Post-Critical Philosophy. London: Routledge.
Polányi, M. (1962b). Tacit Knowing: Its Bearing on Some Problems of Philosophy. Reviews of Modern Physics, 34(4), 601616. http://link.aps.org/doi/10.1103/RevModPhys.34.601
Polányi, M. (1966a). The Logic of Tacit Inference. Philosophy, 41(155), 118. https://doi.org/10.1017/S0031819100066110
Polányi, M. (1966b/1983). The Tacit Dimension. Gloucester, MA: Peter Smith. https://books.google.co.uk/books?id=zfsb-eZHPy0C
Polányi, M. (1969). Knowing and Being. Chicago, IL: University of Chicago Press.
Popper, K. R. (1968/2004). The Logic of Scientific Discovery, 2nd ed. London: Routledge. https://archive.org/details/PopperLogicScientificDiscovery/page/n3
Pyrko, I., Dörfler, V., & Eden, C. (2017). Thinking Together: What Makes Communities of Practice Work? Human Relations, 70(4), 389409. https://doi.org/10.1177/0018726716661040
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017, September). Reshaping Business with Artificial Intelligence: Closing the Gap between Ambition and Action. MIT Sloan Management Review and The Boston Consulting Group.
Roszak, T. (1986/1994). The Cult of Information: A Neo-Luddite Treatise on High-Tech, Artificial Intelligence, and the True Art of Thinking. London: University of California Press.
Rumelhart, D. E. & Norman, D. A. (1988). Representation in Memory. In Atkinson, R. C., Herrnstein, R. J., Lindzey, G., & Luce, R. D., eds., Stevens’ Handbook of Experimental Psychology, 2nd ed., Vol. 2, Learning and Cognition. New York: John Wiley & Sons, pp. 511587.
Russell, S. & Norvig, P. (2020). Artificial Intelligence: A Modern Approach, 4th ed. Harlow: Pearson Education. http://aima.cs.berkeley.edu
Searle, J. R. (1980). Minds, Brains, and Programs. Behavioral and Brain Sciences, 3(3), 417424. https://doi.org/10.1017/S0140525X00005756
Searle, J. R. (1998). The Mystery of Consciousness. London: Granta Books.
Selfridge, O. G. (1955). Pattern Recognition in Modern Computers. Western Joint Computer Conference, Los Angeles, CA.
Shubik, M. (1954). Information, Risk, Ignorance and Indeterminacy. Quarterly Journal of Economics, 68(4), 629640. https://doi.org/10.2307/1881881
Simon, H. A. (1977). The New Science of Management Decision, 3rd ed. New Jersey, NJ: Prentice-Hall.
Simon, H. A. (1991). Models of My Life. New York: Basic Books. https://books.google.co.uk/books?id=dFgwBQAAQBAJ
Simon, H. A. (1995). Artificial Intelligence: An Empirical Science. Artificial Intelligence, 77(1), 95127. https://doi.org/10.1016/0004-3702(95)
Simon, H. A. (1996). The Sciences of the Artificial, 3rd ed. Cambridge, MA: MIT Press.
Simon, H. A. & Feigenbaum, E. A. (1964). An Information-Processing Theory of Some Effects of Similarity, Familiarization, and Meaningfulness in Verbal Learning. Journal of Verbal Learning and Verbal Behavior, 3(5), 385396. https://doi.org/10.1016/S0022-5371(64)80007-4
Simon, H. A. & Newell, A. (1958). Heuristic Problem Solving: The Next Advance in Operations Research. Operations Research, 6(1), 110. https://doi.org/10.1287/opre.6.1.1
Sinclair, M. & Ashkanasy, N. M. (2005). Intuition: Myth or a Decision-Making Tool? Management Learning, 36(3), 353370. https://doi.org/10.1177/1350507605055351
Skinner, B. F. (1950). Are Theories of Learning Necessary? Psychological Review, 57(4), 193216. https://doi.org/10.1037/h0054367
Sowden, P. T., Pringle, A., & Peacock, M. (2020). Verbal Protocol Analysis as a Tool to Understand the Creative Process. In Dörfler, V. & Stierand, M., eds., Handbook of Research Methods on Creativity. Cheltenham: Edward Elgar, pp. 314328. https://doi.org/10.4337/9781786439659.00033
Spender, J. C. (2014). Business Strategy: Managing Uncertainty, Opportunity, and Enterprise. Oxford, UK: Oxford University Press. https://books.google.co.uk/books?id=RNxMAgAAQBAJ
Spender, J. C. (2015, August 4). Stop Worrying about Whether Machines Are “Intelligent.” Harvard Business Review. https://hbr.org/2015/08/stop-worrying-about-whether-machines-are-intelligent
Spender, J. C. (2018). Managing: According to Williamson, or to Coase? Kindai Management Review, 6, 1334. www.kindai.ac.jp/files/rd/research-center/management-innovation/kindai-management-review/vol6_2.pdf
Spender, J. C. (2021). Towards a Firm for Our Time. Kindai Management Review, 9, 124137.
Stierand, M. (2015). Developing Creativity in Practice: Explorations with World-Renowned Chefs. Management Learning, 46(5), 598617. https://doi.org/10.1177/1350507614560302
Stierand, M. & Dörfler, V. (2016). The Role of Intuition in the Creative Process of Expert Chefs. Journal of Creative Behavior, 50(3), 178185. https://doi.org/10.1002/jocb.100
Tesla, N. (1919/2006). My Inventions: The Autobiography of Nikola Tesla. Milton Keynes: Filiquarian Publishing.
Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2– 42(1), 230265. https://doi.org/10.1112/plms/s2-42.1.230
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433460. https://doi.org/10.1093/mind/LIX.236.433
Ullman, S. (2019). Using Neuroscience to Develop Artificial Intelligence. Science, 363(6428), 692693. https://doi.org/10.1126/science.aau6595
Velencei, J. (2017, March 9–10). Modelling the Reality of Decision Making with the Doctus Knowledge-Based System. 20th International Scientific Conference, “Enterprise and Competitive Environment,” Brno, Czech Republic.
Warwick, K. & Shah, H. (2016). Can Machines Think? A Report on Turing Test Experiments at the Royal Society. Journal of Experimental & Theoretical Artificial Intelligence, 28(6), 9891007. https://doi.org/10.1080/0952813X.2015.1055826
Weizenbaum, J. (1966). ELIZA – A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communications of the ACM, 9(1), 3645. https://doi.org/10.1145/365153.365168
Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation. New York: W. H. Freeman & Co. https://books.google.co.uk/books?id=3yfyAAAACAAJ
Whitehead, A. N. & Russell, B. A. (1927). Principia Mathematica, 2nd ed., Vol. 1. Cambridge, UK: Cambridge University Press. https://books.google.co.uk/books?id=ke9yGmFy24sC
Wiklund, J. (2020). Working in Bed – A Commentary on “Automation, Algorithms, and Beyond: Why Work Design Matters More than Ever in a Digital World” by Parker and Grote. Applied Psychology. https://doi.org/10.1111/apps.12261
Wilczek, F. (2015). A Beautiful Question: Finding Nature’s Deep Design: Penguin Books Limited. https://books.google.co.uk/books?id=Oh3ICAAAQBAJ
Winograd, T. (1980). What Does It Mean to Understand Language? Cognitive Science, 4(3), 209241. https://doi.org/10.1207/s15516709cog0403_1
Winograd, T. (1990). Thinking Machines: Can There Be? Are We? In Partridge, & Wilks, Y., eds., The Foundations of Artificial Intelligence: A Sourcebook. Cambridge, UK: Cambridge University Press, pp. 167–189. https://doi.org/10.1017/CBO9780511663116.017
Wittgenstein, L. J. J. (1969). On Certainty, trans. D. Paul & G. E. M. Anscombe. Oxford: Blackwell.
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3), 338353. https://doi.org/10.1016/S0019-9958(65)90241-X

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