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Lecturer(s)
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Schuster Radek, Mgr. Ph.D.
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Course content
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Natural and artificial: homunculi and thinking machines. Super-intelligence and singularity. Symbolic and sub-symbolic intelligence. Formal systems and self-reference: Gödel theorems, Turing machines and the halting problem, Lucas-Penrose argument. Algorithms, (deep) neural networks and machine learning. Turing test and Searle's Chinese chamber. Predictive language models: GPT3 vs. natural language semantics.
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Learning activities and teaching methods
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Lecture supplemented with a discussion, Group discussion, Self-study of literature
- Contact hours
- 26 hours per semester
- Preparation for an examination (30-60)
- 234 hours per semester
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| prerequisite |
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| Knowledge |
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| to describe examples of the use of neural networks and machine learning in practice |
| to explain the basic definitions and principles of artificial intelligence |
| to list the key theories of the philosophy of language and mind |
| Skills |
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| to use with understanding the terminology of philosophy of science and technology and philosophy of language and mind |
| to interpret abstract philosophical texts in English |
| to use AI-based information technology |
| Competences |
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| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| to demonstrate an understanding of basic neural network principles and AI paradigms |
| to systematically describe traditional and current philosophical arguments in the AI debate |
| to comprehensively explain key thought experiments related to AI |
| Skills |
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| to innovatively perform a logical-semantic analysis of the concept of AI in different discourses |
| to critically evaluate the current technological possibilities of AI and lay perceptions of it |
| to conceive new philosophical approaches to neural networks and machine learning |
| Competences |
|---|
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Self-study of literature |
| One-to-One tutorial |
| Textual studies |
| Skills |
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| One-to-One tutorial |
| Individual study |
| Competences |
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| Self-study of literature |
| Individual study |
| assessment methods |
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| Knowledge |
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| Oral exam |
| Skills |
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| Oral exam |
| Competences |
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| Oral exam |
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Recommended literature
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BOSTROM, N. Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press, 2014. ISBN 1501227742.
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CAPPELEN, H. DEVER, J. Making AI Intelligible: Philosophical Foundations. Oxford: Oxford University Press, 2021. ISBN 978-0192894724.
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Copeland, B. Jack. The essential Turing seminal writings in computing, logic, philosophy, artificial intelligence and artificial life plus the secrets of enigma. 1st pub. Oxford : Clarendon Press, 2004. ISBN 978-0-19-152028-0.
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KURZWEIL, R. The Singularity Is Near: When Humans Transcend Biology. New York, NY: Penguin USA, 2006.
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MITCHELL, M. Artificial Intelligence: A Guide for Thinking Humans. Pelican, 2020. ISBN 978-0241404836.
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RUSSELL, S. & NORVIG, P. Artificial Intelligence: A Modern Approach. Harlow : Pearson, 2022. ISBN 978-1-292-40113-3.
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WOOLDRIDGE M. The Road to Conscious Machines: The Story of AI. Pelican Books, 2021. ISBN 9780241333907.
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