Universität Wien

059100 VO Machines That Understand? Large Language Models and Artificial Intelligence (2023W)

5.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Moodle

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Details

Language: German, English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

Lecture website with more information:
https://dm.cs.univie.ac.at/teaching/machines-that-understand/

October 5, 2023
Eröffnung und Paneldiskussion:
“Was bedeutet Generative KI für Hochschule und Gesellschaft?” (Deutsch, Hörsaal 1)
Expert*innen:
Dagmar Gromann, Matthias Leichtfried, Claudia Plant
Moderation: Benjamin Roth

Opening and panel discussion
“Was bedeutet Generative KI für Hochschule und Gesellschaft?” (German, Hörsaal 1)
Experts: Dagmar Gromann, Matthias Leichtfried, Claudia Plant
Moderation: Benjamin Roth

October 12, 2023
Introductory Lecture (Anna Beer, Claudia Plant / Benjamin Roth, English, Hörsaal 1)

October 19, 2023
Invited Talk: Dirk Hovy, Bocconi University, Milan / Italy
“Unhumanizing Models. Why we Need to Change how We Think about AI” (English, online)

November 9, 2023
Invited Talk: Alexander Koller, Saarland University, Saarbrücken / Germany

November 16, 2023
Invited Talk: Sepp Hochreiter, JKU Linz
“Memory Architectures for Deep Learning” (English, Hörsaal 1)

November 23, 2023
Invited Talk: Nikolaus Forgo, Universität Wien
“Die EU als "regulatory superpower"? Überlegungen zur europäischen KI-Regulierung.” (German, Hörsaal 1)

November 30, 2023
Invited Talk: Plank Barbara, Ludwig-Maximilians-Universität München, Munich / Germany
(English, Hörsaal 1)

December 7, 2023
Invited Talk: Ondrej Dusek, Charles University, Prague / Czechia
Topic: Dialogue Systems
(English, Hörsaal 1)

December 14, 2023
Invited Talk: Pavlopolus John, Athens University of Economics and Business, Athens / Greece
“Machine Learning for Ancient Languages”
(English, Hörsaal 1)

January 11, 2024
Invited Talk: Asia Biega, Max Planck Institute for Security and Privacy, Bochum / Germany
“Data Protection in Data-Driven Systems”
(English, Hörsaal 1)

January 18, 2024
Revision of the lecture topics, question and answer session (Anna Beer / Claudia Plant / Benjamin Roth)
(English, Hörsaal 1)

January 25, 2024
Klausur, Hörsaal 1

  • Thursday 05.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 12.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 19.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 09.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 16.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 23.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 30.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 07.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 14.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 11.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 18.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

The aim of the lecture series is to make current developments in the field of generative artificial intelligence understandable and to stimulate an informed dialogue about the capabilities, limitations and societal relevance of these models.
Top-class international researchers are invited to present their current research to a broad university public. In addition to technical aspects, topics will include questions of fairness and responsibility in AI models, and the importance of AI for the broader university context, e.g. in the field of digital humanities.

One week before each lecture, the participants are given a reading recommendation with background information for the following lecture. On the Moodle learning platform, 2-4 self-assessment exercises are provided for each lecture, which can be answered from the lecture and the recommended reading, and for which a solution and explanations will be displayed if participants have attempted to solve them. In the last session of the semester there will be a written exam with questions based on the exercises from Moodle.

Assessment and permitted materials

At least 50% of the possible points must be achieved in the written examination at the end of the semester. The questions will be similar to the self-assessment exercises from Moodle.

Minimum requirements and assessment criteria

At least 50% of the possible points must be achieved in the written examination at the end of the semester. The following grading scheme applies depending on the points achievable:

90%-100% of the points: grade 1
80% - <90% of the points: grade 2
65% - <80% of the points: grade 3
50% - <65% of the points: grade 4
<50% of points: failed

Examination topics

In the written exam, questions on the topics of the lectures must be answered.

Reading list

Weekly reading recommendations will be announced as a preparation for the next lecture.

Association in the course directory

Last modified: Fr 16.02.2024 09:25