Content (Syllabus outline):

Philosophy of AI:

• Historical overview: from automata to AI;
• The concept of natural and artificial intelligence;
• Artificial General Intelligence (AGI);
• Existential risks, “technological singularity”, and “superintelligence”.
• Cognitive mechanisms:
• Cognitive architectures of AI;
• Theory of mind;
• Understanding and simulating human thinking and behaviour;
• From artificial intelligence to artificial “consciousness”.

Ethics of AI:

• Ethics for artificial intelligence;
• (Un)biased algorithms;
• Privacy and democracy in the age of AI;
• Dystopias and utopias of AI and AGI;
• The balance between innovation and safety.

Readings:

• Boden, M. A. 2006. Mind as machine: A history of cognitive science. Oxford: Clarendon Press.
• Bostrom, Nick. 2014. Superintelligence: Paths, Dangers, Strategies (1st ed.). Oxford: Oxford University Press, Inc.
• Brockman, John. 2019. Possible Minds: Twenty-Five Ways of Looking at AI. New York, NY: Penguin Press.
• Brooks, R. A. 1990. “Elephants don’t play chess”, Robotics and autonomous systems, 6(1-2), pp. 3-15.
• Churchland, P. M. 1995. The Engine of Reason, the Seat of the Soul: A Philosophical Journey into the Brain. Cambridge, MA: MIT Press.
• Coeckelbergh, Mark. 2020. AI Ethics. Cambridge: The MIT Press.
• Deacon, T. W. 1997. The symbolic species: the co-evolution of language and the brain. New York: W.W. Norton and Company.
• Deleuze, Gilles. 1992. “Postscript on the Societies of Control”, October, 59, pp. 3–7.
• Dennett, Daniel C. 1991. Consciousness Explained. London: Penguin Books.
• Dreyfus, H. 1972. What Computers Can’t Do. New York: Harper and Row.
• Gärdenfors, P. 2000. Conceptual Spaces: The Geometry of Thought. Cambridge, MA: MIT Press.
• Haugeland, J. 2000. Having thought: essays in metaphysics of mind. Cambridge, MA: Harvard University Press.
• Hinton, G. E. 1989. “Connectionist Learning Procedures”, Artificial Intelligence, 40: 185–234.
• Heidegger, Martin. 1977. The Question Concerning Technology and Other Essays, trans. William Lovitt, 1st Harper pbk. ed. New York: HarperPerennial.
• Hofstadter, Douglas R. 1979. Gödel, Escher, Bach: an Eternal Golden Braid. New York: Basic Books.
• Humphreys, Paul. 2004. Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.
• Kurzweil, R. 1992. The Age of Intelligent Machines. Cambridge, MA: The MIT Press.
• Malabou, C., and C. Shread. 2019. Morphing Intelligence: From IQ Measurement to Artificial Brains. New York: Columbia University Press.
• Massimi, Michela. 2011. “From Data to Phenomena: A Kantian Stance”, Synthese, 182(1): 101–116.
• Mitchell, Melanie. 2021. “Abstraction and analogy‐making in artificial intelligence”, Annals of the New York Academy of Sciences, 1505 (1). https://doi.org/10.1111/nyas.14619.
• McCarthy, John, Marvin Minsky, Nathan Rochester, Claude Shannon. 1955. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
• McCarthy, J., and P. J. Hayes 1969. “Some Philosophical Problems from the Standpoint of Artificial Intelligence”, in B. Meltzer and D. Michie (eds.), Machine Intelligence 4. Edinburgh, Edinburgh University Press, pp. 463-502.
• Minsky, Marvin L. 1986. The Society Of Mind. New York: Simon & Schuster.
• Newell, A., and H.A. Simon. 1959. The simulation of human thought. Santa Monica, CA: Rand Corp.
• Russell, S., and P. Norvig. 1995. Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall.
• Searle, J. 1989. “Artificial Intelligence and the Chinese Room: An Exchange”, New York Review of Books, 36: 2.
• Turing, A.M. 1950. “Computing machinery and intelligence”, Mind 59, pp. 433–460.
• Varela, F. J., E. Thompson, and E. Rosch. 2016. The Embodied Mind. Cognitive Science and Human Experience (Revised). Cambridge, MA: MIT Press.
• Winograd, T., and F. Flores 1987. Understanding Computers and Cognition: A New Foundation for Design. Norwood, NJ: Addison-Wesley.

Objectives and competences:

• The ability to provide historical context in the evolution of AI;
• The ability to gain knowledge of the core mechanisms of AI: representation, cognitive modelling, machine-learning, etc.;
• The ability to extricate a philosophical context for AGI: the human-machine relationship, “technological singularity”, and “superintelligence”;
• The ability to engage in the epistemological challenges of AGI, including an assessment of existential risks;
• The ability to critically articulate the coming transformations of AI and AGI in relation to the individual and society.

Intended learning outcomes:

Students will comprehend the necessity of a new philosophical grounding of the notions assumed, while in parallel also seeking determination of cognitive, ethical, economic, social and political dimensions of AGI.

Learning and teaching methods:

Types of learning/teaching:

• Frontal teaching
• Work in smaller groups or pair work
• Independent students work
• e-learning

Teaching methods:

• Explanation
• Conversation/discussion/debate
• Work with texts
• Case studies
• Different presentation
• Solving exercises
• Field work (e.g. company visits)
• Inviting guests from companies

Assessment

• 20 % Short written assignments,
• 80 % Long written assignments.