Welcome to the course Philosophy and Theory of Artificial Intelligence! It is given during the winter semester 2024/5 at LMU Munich as part of the Master in Logic and Philosophy of Science.
Motivation
Despite the tremendous technological progress of modern artificial intelligence, we are still lacking a thorough theoretical understanding of it. The situation is sometimes compared to technologies in the past: that the artifact (e.g., the steam engine) came first and the theory (thermodynamics) later. In this course, we start with an introduction to deep learning and then discuss both classic and recent literature from the philosophy and theory of AI. In terms of content, the aim is to thus get an introduction to the current understanding of the foundations of AI, including the key open questions. In terms of skill, the aim is to learn to mathematically develop and philosophically assess the different approaches.
General information
We have one session per week during the semester:
- The lecture given by me, Levin Hornischer, on Thursdays from 14:15 bis 15:45 in room 021 (Ludwigstr. 31). Below you find a schedule of when we cover which topic.
Lecture Notes
The lecture notes are updated as the course progresses. You find the latest edition here: phil-theo-ai.pdf.
Coding Exercise
To philosophize and theorize about AI models, it is—I think—essential to also practically see one. This is what this exercise is for: to build an AI model step by step and without any coding experience required.
The easiest (and very common) way to open the exercise and be able to run the code in it (without install anything) is to do it online here:
https://colab.research.google.com/drive/1VDFm5iHMD2L57CisLdOqY9We7bYoCLV-?usp=sharing
But if you want to do this locally on your computer (which is more advanced), you find all the necessary material in this github repository.
Formalities
All the organizational details for the course are described in this file: formalities.pdf.
Schedule
The schedule below describes in which week we will cover which material in the seminar.
| Week | Date | Chapter | Reading | Lecture |
|---|---|---|---|---|
| 1 | 17.10.2024 | Preface | - | Intro to the course, coding exercise (introducing neural networks) |
| 2 | 24.10.2024 | 1 | Boden 2016, ch. 1 & 4; explainer videos, ch. 1-4; explainer videos as many as you like | Results coding exercise, key concepts from ch. 1 |
| 3 | 31.10.2024 | 2 | Turing 1950, Bender & Koller 2020 | Turing test, octopus test |
| 4 | 07.11.2024 | - | - | cancelled |
| 5 | 14.11.2024 | 2 | Smolensky 1988 | connectionism & subsymbols |
| 6 | 21.11.2024 | 3 | Buckner 2023 | Empiricism vs rationalism in AI |
| 7 | 28.11.2024 | 4 | Freiesleben et al 2024 | Machine learning for scientific inference |
| 8 | 05.12.2024 | TBA | TBA | TBA |
| 9 | 12.12.2024 | TBA | TBA | TBA |
| 10 | 19.12.2024 | TBA | TBA | TBA |
| 11 | 09.01.2025 | TBA | TBA | TBA |
| 12 | 16.01.2025 | TBA | TBA | TBA |
| 13 | 23.01.2025 | TBA | TBA | TBA |
| 14 | 30.01.2025 | TBA | TBA | TBA |
| 15 | 06.02.2025 | TBA | TBA | TBA |
Essay topic
You can find a list of potential essay topics here.