Reasoning in artificial intelligence
MATH-700 / 3 credits
Teacher: Abbé Emmanuel
Language: English
Remark: Fall 2025. The class discusses theoretical and applied developments on AI reasoning; the main class output is an experimental project (including typically training/finetuning of foundation models)
Frequency
Every year
Summary
Large language models have raised the potential of artificial intelligence in various applications, including science and mathematics, but their reasoning capabilities remain under investigations. This class focuses on defining, measuring and improving the reasoning capabilities of such AI models.
Content
The class overviews some of the main concepts and developments concerning reasoning in AI, such as Transformers, LLMs, Step-by-Step Reasoning, Tool Use, Planning, Logical Reasoning, Self-Improvement, Generalization on the Unseen or Theorem Proving.
Keywords
Artificial intelligence, foundation models, large language models, reasoning, generalization, logic, proving.
Learning Prerequisites
Required courses
Basic machine learning concepts.
Learning Outcomes
By the end of the course, the student must be able to:
- Recognize the basic concepts of AI reasoning and implementing methods in a project.
Resources
Bibliography
List of papers provided when the class starts.
Moodle Link
In the programs
- Exam form: Oral (session free)
- Subject examined: Reasoning in artificial intelligence
- Courses: 20 Hour(s)
- Project: 40 Hour(s)
- Type: optional
- Exam form: Oral (session free)
- Subject examined: Reasoning in artificial intelligence
- Courses: 20 Hour(s)
- Project: 40 Hour(s)
- Type: optional