Course Identification

This is a interdisciplinary course showing the relevance of the humanities to cutting-edge technologies, such as Artificial Intelligence. This relevance is to the core concern of developing novel algorithms, not peripheral (like Ethical issues).

This course presents a history of ideas relating to computing and Artificial Intelligence, beginning with the pre-Socratic philosophers, going through an overview of existing AI, and ending with blueprints for future programming, with videos of example runs. The course can also serve as an introduction to the history of ideas, using computation and AI as an example.

Opening

1. Introduction – How can we “think outside the box”? Examining unspoken assumptions. What are History and Philosophy? Introduction to the two fields using the story of Positivism form the French revolution, via Auguste Comte to current secular thought.

2. Overview, Dreams, Background – Overview – the interaction between 3 carriers of intelligence. Dreams - The fascination with thinking machines from ancient theologies through medieval Islam into some failed attempts of the 19^th and early 20^th century. Background - Pan-computational theories, catching up on logic, mathematics and algorithms.

Cultural Background

3. Rationalism – About “isms”. The temptation to over-simplify. The irrational belief that rationality (Logic, Mathematics) will solve all problems.

4. Science and Secularization – The rise of modern critical thinking (Protestantism). The canonical story of the evolution of modern astronomy. The myth of a “scientific clean sweep” (Descartes and Bacon). The rise of science up to scientific psychology (1913).

5. The Modern Mindset - Kant and the Analytic-Continental split. Recap reformation and enlightenment. Romanticism and the Victorian era. Recap of Positivism and WW1. Modernism: Focusing on efficiency, from architecture to agriculture. The rise of the MBA way of thinking (Simon).

   The rise of AI

6. Pre-AI fields – Mathematics: state automata, boolean logic. Cybernetics: Tortoises, Punched cards, telegraph. Psychology and Linguistics: Behaviourism, review of Cognitive psychology and Linguistics. Electrical engineering: oscilloscopes and telephone exchanges. Biology – Neural networks (1943).

7. USA in the 1940-70s - birth of computation and AI – Freedom of will (Weber & Andersen). WW2: America’s self image as sanity between the Nazis and Communists, Vietnam War and the Cold-war philosophy. Von Neumann and the first computers. Cognitive revolution, founding AI: Dartmouth conference of 1956.

Overview of AI

8. GOFAI – Good, Old-Fashioned, Artificial Intelligence – Definitions of Intelligence and AI. Logic, Fuzz, and experts. IBM, open-source. Herbert Simon’s pivotal role. Checkers, Chess, Go. Eliza. Winograd’s SHRDLU. The Nuclear bomb in the background.

9. Turing Test and problems with knowledge – Turing (1950). Variants and criticisms. Was it a joke? “Common sense” etc. The fear of X taking over from humanity. CYC. Knowledge that vs knowledge how. Atomism and rationalism. Knowledge as true, justified belief and the infinite regress of “justified justifications”.

10. Biologically-inspired AI – Passive walking robots. Genetic Algorithms. Neural nets. Deep learning / Big Data. Simulating insects (Brooks). GPT. Histomimetic robotics – Hockings.

Critiques of AI

11. Phenomenology (Dreyfus) – Philosophical background: phenomenology and existentialism. Heidegger. Three broken promises of AI, four assumptions necessary for AI, four distinctions, skills, sweeping pessimism. A fateful game of Chess. First-step fallacy.

12. Other Critiques – Replies to Dreyfus. Intelligence embedded and embodied. Winograd & Flores (1986). Visions of the future: Singularity, trans-humanism. Optimism and Pessimism. Fiction inspiring technology.

Future Directions and Conclusion

13. Post-rationalist AI – Technology vs Science. Human-like vs Rational AI. Papert. Anthropic AI. Subjectivity and Introspection. Legitimacy issues. A-priori causes for optimism. Steps for developing introspection-based algorithms. Examples + implementation videos.

14. Ethics and Conclusion – Ethics and Technophobia: Technology as a threat to workers, and to religions. Technology changing human thought. The future arriving prematurely (Toffler). Current social problems: leaks, social control, perpetuation of prejudices (in big data). AI safely.

              Conclusion – a synchronous review of the intellectual background to AI.

Upon successful completion of this course students will:

1. Become familiar with key issues in the history & philosophy of AI;

2. Develop an appreciation of how AI "Thinks in the box" and how we can think "outside the box";

3. Develop an appreciation of the historical accidents that lead to our current mindset;

4. Develop abilities to raise philosophical questions about AI.

Bolter, J. D. (1984). Turing’s man: Western culture in the computer age. Duckworth.

Costall, A. (2006). ‘Introspectionism’ and the mythical origins of scientific psychology. Consciousness and Cognition, 15(4), 634–654. https://doi.org/10.1016/j.concog.2006.09.008

Dreyfus, H. L. (1979). What computers can’t do / The limits of artificial intelligence (Revised). Harper & Row.

Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171(18), 1137–1160. https://doi.org/10.1016/j.artint.2007.10.012

Dreyfus, H. L. (2012). A history of first step fallacies. Minds and Machines, 22(2), 87–99.

Dyson, G. (2012). Turing’s Cathedral: The Origins of the Digital Universe. Penguin UK.

Freed, S. (2017). A role for introspection in AI research [University of Sussex]. http://sro.sussex.ac.uk/66141/

Freed, S. (2019). AI and Human Thought and Emotion. CRC Press.

Heidegger, M. (1962). Being and time (J. Macquarrie & E. Robinson, Trans.). Blackwell.

McCorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence (25th anniversary update). A.K. Peters.

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259

Piccinini, G. (2004). The First Computational Theory of Mind and Brain: A Close Look at Mcculloch and Pitts’s “Logical Calculus of Ideas Immanent in Nervous Activity.” Synthese, 141(2), 175–215. https://doi.org/10.1023/B:SYNT.0000043018.52445.3e

Simon, H. A. (1996). Models of my life. MIT P. http://capitadiscovery.co.uk/sussex-ac/items/547214

Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. J. of Math, 58, 345–363.

Watson, P. (2001). Terrible Beauty: A Cultural History of the Twentieth Century: The People and Ideas that Shaped the Modern Mind: A History. Phoenix.

Watson, P. (2006). Ideas: A History of Thought and Invention, from Fire to Freud. Harper Perennial.

Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20(2), 158–177. https://doi.org/10.1037/h0074428

Watson, J. B. (1920). Is Thinking Merely Action of Language Mechanisms1? (V.). British Journal of Psychology. General Section, 11(1), 87–104. https://doi.org/10.1111/j.2044-8295.1920.tb00010.x

Winograd, T. (1991). Thinking Machines: Can There Be? Are We? In J. J. Sheehan & M. Sosna (Eds.), The Boundaries of Humanity: Humans, Animals, Machines. University of California Press.

Winograd, T., & Flores, F. (1986). Understanding computers and cognition: A new foundation for design. Ablex. http://prism.talis.com/sussex-ac/items/272586