Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-662rr Total loading time: 0.497 Render date: 2022-05-28T10:00:49.942Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

What Every CEO Should Know About AI

Published online by Cambridge University Press:  04 March 2022

Viktor Dörfler
Affiliation:
University of Strathclyde Business School

Summary

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.
Get access
Type
Element
Information
Online ISBN: 9781009037853
Publisher: Cambridge University Press
Print publication: 05 May 2022
Copyright
© Viktor Dörfler 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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=1858Google Scholar
Ackermann, F. & Eden, C. (2011). Making Strategy: Mapping Out Strategic Success. London: SAGE Publications. http://books.google.co.uk/books?id=Ln1PQLi-flICGoogle Scholar
Amabile, T. M. (1982). Social Psychology of Creativity: A Consensual Assessment Technique. Journal of Personality and Social Psychology, 43(5), 9971013. https://doi.org/10.1037/0022-3514.43.5.997CrossRefGoogle Scholar
Amabile, T. M. (1983a). The Social Psychology of Creativity: A Componential Conceptualization. Journal of Personality and Social Psychology, 45(2), 357376. https://doi.org/10.1037/0022-3514.45.2.357CrossRefGoogle Scholar
Amabile, T. M. (1983b). The Social Theory of Creativity. New York: Springer-Verlag.Google Scholar
Amabile, T. M. (1996). Creativity in Context: Update to the Social Psychology of Creativity. Boulder, CO: Westview Press.Google Scholar
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.2019.0075Google Scholar
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-humanGoogle Scholar
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/10.4337/9781786439659.00020CrossRefGoogle Scholar
Baracskai, Z. & Velencei, J. (2002, November 6–7). Important Characteristics for a Knowledge Engineer. 12th Annual Conference of Business Information Technology, Manchester, UK.Google Scholar
Bas, A., Sinclair, M., & Dörfler, V. (2022). Sensing: The Elephant in the Room of Management Learning. Management Learning. https://doi.org/10.1177/13505076221077226CrossRefGoogle Scholar
von Bertalanffy, L. (1981). A Systems View of Man. Boulder, CO: Westview Press.Google Scholar
Boden, M. A. (1998). Creativity and Artificial Intelligence. Artificial Intelligence, 103(1), 347356. https://doi.org/10.1016/S0004-3702(98)00055-1CrossRefGoogle Scholar
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.Google Scholar
Bory, P. (2019). Deep New: The Shifting Narratives of Artificial intelligence from Deep Blue to AlphaGo. Convergence, 25(4), 627642. https://doi.org/10.1177/1354856519829679CrossRefGoogle Scholar
Boulding, K. E. (1956). General Systems Theory: The Skeleton of Science. Management Science, 2(3), 197208. https://doi.org/10.1287/mnsc.2.3.197CrossRefGoogle Scholar
Boulding, K. E. (1966). The Economics of Knowledge and the Knowledge of Economics. American Economic Review, 56(1/2), 113. www.jstor.org/stable/1821262Google Scholar
Chalmers, D. J. (1998). The Conscious Mind: In Search of a Fundamental Theory, paperback ed. New York: Oxford University Press.Google Scholar
Chomsky, N. (1957/2002). Syntactic Structures, 2nd ed. New York: Mouton de Gruyter.CrossRefGoogle Scholar
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_AcAQAAMAAJGoogle Scholar
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/s11023-020-09554-3CrossRefGoogle Scholar
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/10.1177/1350507608099315CrossRefGoogle Scholar
Damasio, A. R. (1995/2005). Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Avon Books.Google Scholar
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-respectGoogle Scholar
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
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-rulesGoogle Scholar
Davenport, T. H. & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know, paperback ed. Boston, MA: Harvard Business School Press.Google Scholar
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-themselvesGoogle Scholar
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.2010.15CrossRefGoogle Scholar
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/B978-0-12-809324-5.23863-7CrossRefGoogle Scholar
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_32CrossRefGoogle Scholar
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.Google Scholar
Dörfler, V. & Ackermann, F. (2012). Understanding Intuition: The Case for Two Forms of Intuition. Management Learning, 43(5), 545564. https://doi.org/10.1177/1350507611434686CrossRefGoogle Scholar
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/308339223Google Scholar
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/10.4337/9781788979757.00033CrossRefGoogle Scholar
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/342135191Google Scholar
Dörfler, V. & Bas, A. (unpublished). Understanding Uncertainty: Known, Unknown, and Unknowable.Google Scholar
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.2017.12982abstractCrossRefGoogle Scholar
Dörfler, V. & Eden, C. (2019). Understanding “Expert” Scientists: Implications for Management and Organization Research. Management Learning, 50(5), 534555, Article 135050761986665. https://doi.org/10.1177/1350507619866652CrossRefGoogle Scholar
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/10.1201/9781315195551-1Google Scholar
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.Google Scholar
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_36CrossRefGoogle Scholar
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.Google Scholar
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/253780221Google Scholar
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.Google Scholar
Drucker, P. F. (1995). The Information Executives Truly Need. Harvard Business Review, 73(1), 5462. https://hbr.org/1995/01/the-information-executives-truly-needGoogle Scholar
Einstein, A. (2010). The Ultimate Quotable Einstein, edited by Alice Calaprice. Princeton, NJ: Princeton University Press. https://books.google.co.uk/books?id=G_iziBAPXtECGoogle Scholar
Ericsson, K. A. & Charness, N. (1994). Expert Performance: Its Structure and Acquisition. American Psychologist, 49(8), 725747. https://doi.org/10.1037/0003-066X.49.8.725CrossRefGoogle Scholar
Feigenbaum, E. A. (1977). The Art of Artificial Intelligence: I. Themes and Case Studies of Knowledge Engineering. 5th International Joint Conference on Artificial Intelligence,CrossRefGoogle Scholar
Feigenbaum, E. A. (1992). A Personal View of Expert Systems: Looking Back and Looking Ahead (KSL 92–41). https://purl.stanford.edu/gr891tb5766Google Scholar
Feigenbaum, E. A. (2006). Ed Feigenbaum’s Search for AI. Feigenfest 70th, Stanford University, Stanford, CA. https://youtu.be/B9zVdU3N7DYGoogle Scholar
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_1CrossRefGoogle Scholar
Finley, K. (2012, October 1). Did Deep Blue Beat Kasparov because of a Computer Bug? Wired. www.wired.co.uk/article/deep-blue-bugGoogle Scholar
Fromm, E. (1942). The Fear of Freedom. London: Routledge.Google Scholar
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.50.9.802CrossRefGoogle Scholar
Guo, E. & Hao, K. (2020, December 21). This Is the Stanford Vaccine Algorithm That Left Out Frontline Doctors. MIT Technology Review. www.technologyreview.com/2020/12/21/1015303Google Scholar
Handy, C. (2015). The Second Curve: Thoughts on Reinventing Society. London: Random House. https://books.google.hu/books?id=yztOBQAAQBAJGoogle Scholar
Hao, K. (2019, February). Police across the US Are Training Crime-Predicting AIs on Falsified Data. MIT Technology Review. www.technologyreview.com/2019/02/13/137444Google Scholar
Heaven, D. (2019). Deep Trouble for Deep Learning. Nature, 574(7777), 163166. https://doi.org/10.1038/d41586-019-03013-5CrossRefGoogle Scholar
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/Google Scholar
Hobbes, T. (1651/2018). Leviathan. London: Strelbytskyy Multimedia Publishing. https://books.google.co.uk/books?id=X81qDwAAQBAJGoogle Scholar
Hofstadter, D. R. (1979/1999). Godel, Escher, Bach: An Eternal Golden Braid, 2nd ed. London: Basic Books.Google Scholar
Hume, D. (1739). A Treatise of Human Nature. London: John Noon. https://books.google.co.uk/books?id=66S3DAEACAAJGoogle Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. London: Penguin Books. http://books.google.co.uk/books?id=ZuKTvERuPG8CGoogle Scholar
Kelly, G. A. (1955/1963). A Theory of Personality: The Psychology of Personal Constructs, paperback ed. New York: Norton.Google Scholar
Keyes, D. (1966). Flowers for Algernon. Boston, MA: Harcourt, Brace & World. https://books.google.co.uk/books?id=_oG_iTxP1pICGoogle Scholar
Knight, F. H. (1921). Risk, Uncertainty and Profit. New York: Houghton Mifflin. https://books.google.es/books?id=9fHTAAAAMAAJGoogle Scholar
Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Penguin Publishing Group. https://books.google.co.uk/books?id=9FtnppNpsT4CGoogle Scholar
Lave, J. & Wenger, E. C. (1991/2003). Situated Learning: Legitimate Peripheral Participation. New York: Cambridge University Press. http://books.google.co.uk/books?id=CAVIOrW3vYACGoogle Scholar
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436444. https://doi.org/10.1038/nature14539CrossRefGoogle ScholarPubMed
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-OCrossRefGoogle Scholar
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.summaryGoogle Scholar
March, J. G. (1994). Primer on Decision Making: How Decisions Happen. New York: Free Press. http://books.google.nl/books?id=zydIx15DM2kCGoogle Scholar
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.pdfCrossRefGoogle Scholar
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/BF02478259CrossRefGoogle Scholar
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=alSIDwAAQBAJGoogle Scholar
Mérő, L. (1990). Ways of Thinking: The Limits of Rational Thought and Artificial Intelligence. New Jersey, NJ: World Scientific.CrossRefGoogle Scholar
Meyer, J., Land, R., & Baillie, C. (2010). Threshold Concepts and Transformational Learning. Rotterdam: Sense Publishers. https://books.google.co.uk/books?id=AOqaSQAACAAJCrossRefGoogle Scholar
Minsky, M. L. (1988). The Society of Mind. New York: Simon & Schuster.Google Scholar
Minsky, M. L. (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York: Simon & Schuster.Google Scholar
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.Google Scholar
Musk, E. (2018, April 17). Elon Musk on Google DeepMind. YouTube. https://youtu.spenbe/MuWWZ91-G6wGoogle Scholar
von Neumann, J. & Morgenstern, O. (1953). Theory of Games and Economic Behavior, 3rd ed. New York: John Wiley & Sons.Google Scholar
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.Google Scholar
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.1956.1056797Google Scholar
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.2017.1138CrossRefGoogle Scholar
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-automationGoogle Scholar
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/PavlovGoogle Scholar
Polányi, M. (1946). Science, Faith and Society. London: Oxford University Press.Google Scholar
Polányi, M. (1959). The Study of Man. Chicago, IL: University of Chicago Press. http://books.google.rs/books?id=lbMkAQAAMAAJGoogle Scholar
Polányi, M. (1962a/2002). Personal Knowledge: Towards a Post-Critical Philosophy. London: Routledge.Google Scholar
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.34.601CrossRefGoogle Scholar
Polányi, M. (1966a). The Logic of Tacit Inference. Philosophy, 41(155), 118. https://doi.org/10.1017/S0031819100066110CrossRefGoogle Scholar
Polányi, M. (1966b/1983). The Tacit Dimension. Gloucester, MA: Peter Smith. https://books.google.co.uk/books?id=zfsb-eZHPy0CGoogle Scholar
Polányi, M. (1969). Knowing and Being. Chicago, IL: University of Chicago Press.Google Scholar
Popper, K. R. (1968/2004). The Logic of Scientific Discovery, 2nd ed. London: Routledge. https://archive.org/details/PopperLogicScientificDiscovery/page/n3Google Scholar
Pyrko, I., Dörfler, V., & Eden, C. (2017). Thinking Together: What Makes Communities of Practice Work? Human Relations, 70(4), 389409. https://doi.org/10.1177/0018726716661040CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
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.Google Scholar
Russell, S. & Norvig, P. (2020). Artificial Intelligence: A Modern Approach, 4th ed. Harlow: Pearson Education. http://aima.cs.berkeley.eduGoogle Scholar
Searle, J. R. (1980). Minds, Brains, and Programs. Behavioral and Brain Sciences, 3(3), 417424. https://doi.org/10.1017/S0140525X00005756CrossRefGoogle Scholar
Searle, J. R. (1998). The Mystery of Consciousness. London: Granta Books.Google Scholar
Selfridge, O. G. (1955). Pattern Recognition in Modern Computers. Western Joint Computer Conference, Los Angeles, CA.CrossRefGoogle Scholar
Shubik, M. (1954). Information, Risk, Ignorance and Indeterminacy. Quarterly Journal of Economics, 68(4), 629640. https://doi.org/10.2307/1881881CrossRefGoogle Scholar
Simon, H. A. (1977). The New Science of Management Decision, 3rd ed. New Jersey, NJ: Prentice-Hall.Google Scholar
Simon, H. A. (1991). Models of My Life. New York: Basic Books. https://books.google.co.uk/books?id=dFgwBQAAQBAJGoogle Scholar
Simon, H. A. (1995). Artificial Intelligence: An Empirical Science. Artificial Intelligence, 77(1), 95127. https://doi.org/10.1016/0004-3702(95)CrossRefGoogle Scholar
Simon, H. A. (1996). The Sciences of the Artificial, 3rd ed. Cambridge, MA: MIT Press.Google Scholar
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)80007-4CrossRefGoogle Scholar
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.6.1.1CrossRefGoogle Scholar
Sinclair, M. & Ashkanasy, N. M. (2005). Intuition: Myth or a Decision-Making Tool? Management Learning, 36(3), 353370. https://doi.org/10.1177/1350507605055351CrossRefGoogle Scholar
Skinner, B. F. (1950). Are Theories of Learning Necessary? Psychological Review, 57(4), 193216. https://doi.org/10.1037/h0054367CrossRefGoogle ScholarPubMed
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/10.4337/9781786439659.00033CrossRefGoogle Scholar
Spender, J. C. (2014). Business Strategy: Managing Uncertainty, Opportunity, and Enterprise. Oxford, UK: Oxford University Press. https://books.google.co.uk/books?id=RNxMAgAAQBAJCrossRefGoogle Scholar
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-intelligentGoogle Scholar
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.pdfGoogle Scholar
Spender, J. C. (2021). Towards a Firm for Our Time. Kindai Management Review, 9, 124137.Google Scholar
Stierand, M. (2015). Developing Creativity in Practice: Explorations with World-Renowned Chefs. Management Learning, 46(5), 598617. https://doi.org/10.1177/1350507614560302CrossRefGoogle Scholar
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.100CrossRefGoogle Scholar
Tesla, N. (1919/2006). My Inventions: The Autobiography of Nikola Tesla. Milton Keynes: Filiquarian Publishing.Google Scholar
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/s2-42.1.230CrossRefGoogle Scholar
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433460. https://doi.org/10.1093/mind/LIX.236.433CrossRefGoogle Scholar
Ullman, S. (2019). Using Neuroscience to Develop Artificial Intelligence. Science, 363(6428), 692693. https://doi.org/10.1126/science.aau6595CrossRefGoogle ScholarPubMed
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.Google Scholar
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.2015.1055826CrossRefGoogle Scholar
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/10.1145/365153.365168CrossRefGoogle Scholar
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=3yfyAAAACAAJGoogle Scholar
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=ke9yGmFy24sCGoogle Scholar
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.12261CrossRefGoogle Scholar
Wilczek, F. (2015). A Beautiful Question: Finding Nature’s Deep Design: Penguin Books Limited. https://books.google.co.uk/books?id=Oh3ICAAAQBAJGoogle Scholar
Winograd, T. (1980). What Does It Mean to Understand Language? Cognitive Science, 4(3), 209241. https://doi.org/10.1207/s15516709cog0403_1CrossRefGoogle Scholar
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/CBO9780511663116.017Google Scholar
Wittgenstein, L. J. J. (1969). On Certainty, trans. D. Paul & G. E. M. Anscombe. Oxford: Blackwell.Google Scholar
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3), 338353. https://doi.org/10.1016/S0019-9958(65)90241-XCrossRefGoogle Scholar

Save element to Kindle

To save this element to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

What Every CEO Should Know About AI
  • Viktor Dörfler, University of Strathclyde Business School
  • Online ISBN: 9781009037853
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

What Every CEO Should Know About AI
  • Viktor Dörfler, University of Strathclyde Business School
  • Online ISBN: 9781009037853
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

What Every CEO Should Know About AI
  • Viktor Dörfler, University of Strathclyde Business School
  • Online ISBN: 9781009037853
Available formats
×