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(IN0014, IN2107, IN4875) Seminar Computational Social Science: Generative AI and Democracy

Lehrveranstaltung 0000003195 im SS 2024

Basisdaten

LV-Art Seminar
Umfang 2 SWS
betreuende Organisation Professur für Computational Social Science and Big Data (Prof. Pfeffer)
Dozent(inn)en Daniel Matter
Jürgen Pfeffer
Termine

Zuordnung zu Modulen

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weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise The seminar will cover the following contents:1. Fundamentals of AI: This section addresses the basic concepts of AI, emphasizing the idea that everything is represented as numbers. It includes a brief introduction to linear algebra and the fundamentals of gradient descent.2. Fundamentals of Generative AI: This covers the distinction between discriminative deep neural networks (DNNs) and generative DNNs. We discuss the most common archictures, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs) and Variational GANs (VGANs), as well as the application of diffusion models for images and Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers for text processing.3. Types of Training: This part explores different training methodologies, specifically focusing on supervised, semi-supervised, and unsupervised learning, in contrast with fine-tuning and Reinforcement Learning via Human Feedback (RLHF).4. Fundamentals of Evaluation: This includes examples like the Turing Test, offering insights into how AI models are assessed and evaluated.5. The Alignment Problem and Control Problem: This section delves into the challenges of aligning AI systems with human values and controlling their actions within ethical and practical constraints.6. Fundamentals of Democracy and its History: An exploration of the basic principles and possibly the historical development of democracy.7. Risk Landscape: This part addresses the potential risks and dangers posed by AI in the context of democratic societies.8. Solution Ideas: The seminar concludes with a discussion of various ideas and approaches to mitigate the risks and harness the potential of AI in a democratic framework.
Links E-Learning-Kurs (z. B. Moodle)
TUMonline-Eintrag

Gleiche Lehrveranstaltungen (z. B. in anderen Semestern)

SemesterTitelDozent(en)Termine
WS 2022/3 (IN0014, IN2107, IN4875) Seminar Computational Social Science Pfeffer, J. Voggenreiter, A. Fr, 09:45–13:00, H.004
SS 2022 (IN0014, IN2107, IN4875) Seminar Computational Social Science Ghawi, R. Pfeffer, J. Fr, 09:45–12:45, virtuell
WS 2021/2 (IN0014, IN2107, IN4875) Seminar Computational Social Science Ghawi, R. Pfeffer, J. Fr, 09:45–13:00, H.004
WS 2020/1 Seminar Computational Social Science (IN0014, IN2107, IN4875) Ghawi, R. Pfeffer, J. Fr, 09:45–11:15, virtuell
WS 2019/20 Seminar Computational Social Science (IN0014, IN4875) Ghawi, R. Pfeffer, J. Schönfeld, M.
WS 2017/8 Seminar Computational Social Science (IN0014, IN4875) Pfeffer, J. Schönfeld, M.
SS 2017 Seminar Computational Social Science (IN0014, IN4875) Pfeffer, J. Schönfeld, M.
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