Course Identification

History and Philosophy, Intelligence and Computing

Lecturers and Teaching Assistants

Dr. Sam Freed

Course Schedule and Location

First Semester
Sunday, 11:15 - 13:00, Wolfson Auditorium

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 2.00 points
Physical Sciences: Credit points must be approved by the Board of Studies
Chemical Sciences: Lecture; Elective; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (ExCLS Track): Lecture; Elective; Regular; 2.00 points
Mathematics and Computer Science: Lecture; Elective; Regular; 2.00 points


This course will be held by hybrid learning

The course is open to all faculties.





Language of Instruction


Registration by


Attendance and participation


Grade Type

Numerical (out of 100)

Grade Breakdown (in %)


Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



History and Philosophy, Intelligence and Computing

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

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.


  1. Introduction – Why 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 19th and early 20th century. Background: Pan-computational theories, some logic, mathematics, algorithms.

Cultural Background:

  1. Rationalism – About “isms”. The temptation to over-simplify. The irrational belief that rationality (Logic, Mathematics) will solve all problems. Predicate calculus.
  2. Science and Secularization – The rise of modern critical thinking (Protestantism). The canonical tale of the evolution of modern astronomy. The myth of a “scientific clean sweep” (Descartes and Bacon). The rise of science up to scientific psychology.
  3. The Modern Mindset - Kant and the Analytic-Continental split. Recap reformation and enlightenment. Romanticism and the Victorian era. Recap of Positivism and WW1. Modernism: worshipping efficiency, from architecture to agriculture. The rise of the MBA way of thinking (Simon).

The rise of AI

  1. 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 – The discovery of neural networks (1943).
  2. USA in the 1940-50s - 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:

  1. 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 of it all.
  2. 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”.
  3. Biologically-inspired AI – Passive walking robots. Genetic Algorithms. Neural nets. Deep learning / Big Data. Simulating insects (Brooks). Histomimetic robotics – Hockings.

Critics of AI

  1. Phenomenology – Philosophical background: phenomenology and existentialism. Four broken promises of AI, four assumptions necessary for AI, four distinctions, skills, sweeping pessimism. A fateful game of Chess. First-step falacy.
  2. Other Critiques – Replies to the above critique. Intelligence embedded and embodied. Winograd & Flores (1986). Visions of the future: Singularity, trans-humanism. Optimism and Pessimism. Fiction inspiring technology.

Future Directions and Conclusion

  1. 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.
  2. Ethics and Conclusion – Ethics and Technophobia: Technology as a threat to workers, and to religions. Technology changing human thought. The future arriving prematurely. Current social problems: leaks, social control, perpetuation of prejudices (big data). AI safely. Conclusion – a synchronous review of the strands discussed earlier.

Learning Outcomes

Upon successful completion of this course students will:

  1. Become familiar with key issues in the history & philosophy of AI;
  2. Will develop an appreciation of the historical accidents that lead to our current mindset;
  3. Develop abilities to raise philosophical questions about AI

Reading List