Description

This lecture introduces students to cognitive science—an interdisciplinary endeavor that draws upon research from psychology, AI, neuroscience, linguistics and philosophy to develop an integrated framework of the mind. This framework aims, on the one hand, to explain how the mind works. On the other hand, it distills lessons that AI researchers can incorporate into their research so to develop more resilient agents that can navigate dynamic and complex environments.

Knowledge of cognitive science is becoming ever more important to AI researchers as the structural limitations of the machine learning paradigm are getting clearer. It is undoubtedly true that ML is exerting a transformative impact over several industrial branches. Yet ML applications remain limited either to performing pattern recognition or making predictions based on some variation of regression analysis. ML applications are perhaps best described as executing a highly more accurate and reliable version of what Daniel Kahneman termed as fast thinking. The cognitively more challenging slow thinking that allows humans to develop strategies tailored to prevailing circumstances lies beyond ML’s capabilities. Neither a growth in data volume nor an increase in processing power can overcome ML’s structural limits.

Future AI agents will probably operate via some form of a synthesis between ML models and cognitive science components. This lecture will introduce students to this future.

Prerequisites

Students should have visited an introductory course to AI. Ideally the course should have covered the topics elaborated on by Russell and Norvig (2020). 

Instructor

Dr. Nabil Alsabah is Head of Arti cial Intelligence at Europe’s largest tech association, Bitkom. He is also head of the annual Big-Data.AI Summit which brings together thousands of European business leaders and AI researchers.

Alsabah also edits Bitkom’s regular publications on AI and ML that address technical topics as diverse as operationalizing explainable AI, data anonymization and pseudonymization for ML projects, and leveraging data science in industrial applications. In addition, Alsabah is also a member of the Industrial Liaison Board at the Department of Computing at Imperial College London. In this capacity, he advices the department on teaching plan, hirings, and industry outreach. Alsabah has thought a cognitive science seminar at the Center for Machine Learning and Robotics at the Free University of Berlin in the summer term 2020.

Alsabah holds a PhD in psychology and a Master’s degree in computer science. In the past, he spent research stays at Peking University, National University of Singapore and Stanford University. Alsabah looks back at a 15-years experience researching and working at the intersection of arti cial intelligence and psychology.

Suggested reading

• Baars, B. (1989). A cognitive theory of consciousness. Cambridge, Mass.: Cambridge University Press.
• Bermúdez, J. (2014). Cognitive Science: An Introduction to the Science of the Mind. 2nd ed.
• Cambridge: Cambridge Univ. Press.
• Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. New York, Oxford: Oxford University Press
• Damasio, A. (2005). Descartes' Error: Emotion, Reason, and the Human Brain. New York: Penguin Books.
• Dennett, D. 1991. Consciousness Explained. Little, Brown
• Dennett, D. (1996). Kinds of minds: towards an understanding of consciousness. London: Weidenfeld and Nicholson.
• Donald M, Origin of the Modern Mind, Harvard University Press, Cambridge, 1991.
• Dörner, D. (2001). Bauplan für eine Seele. Zweite Auflage. Hamburg: Rowohlt Taschenbuch Verlag.
• Engeler, E. (2019). Neural algebra on “how does the brain think?”. Theoretical Computer Science, 777, pp. 296-307. doi:10.1016/j.tcs.2019.03.038
• Frith, C. (2007). Making up the mind. How the brain creates our mental world. London: Blackwell.
• Goleman, D. (1996). Emotional Intelligence: Why It Can Matter More than IQ. London: Bloomsbury Publishing.
• Haugeland, J. 1981. Semantic engines: An introduction to mind design. In Mind Design:
• Philosophy, Psychology, Artificial Intelligence, ed. J. Haugeland. MIT Press
• LeDoux, J. E. (1996). The Emotional Brain. New York: Simon & Schuster.
• Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an Integration of Deep Learning and Neuroscience. Frontiers in computational neuroscience, 10, 94. doi:10.3389/fncom.2016.00094
• Penrose R, The Emperor’s New Mind: Concerning Computers, Mind, and the Law of Physics, Oxford: Oxford University Press, 1989.
• Pinker, S. (2009). How the Mind Works. New York: W. W. Norton & Company.
• Searle J. (1980). Brains and Programs, Behavioral and Brain Sciences, 3:417-57
• Searle J (1992). The Rediscovery of the Mind, the MIT Press, Cambridge, 1992
• Sloman, A. (1978). The Computer Revolution in Philosophy. Hassocks, Sussex: Harvester Press (and Humanities Press).
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  Wittgenstein, L, Philosophical Investigations, Basil Blackwell Ltd, 1953   It is also useful to consult the journal Cognitive Science, published on behalf of the CognitiveScience Society.