Watch a 27-minute lecture from Harvard CMSA's Mathematics and Machine Learning Closing Workshop where DeepMind researcher Matej Balog presents groundbreaking research on FunSearch, a novel approach combining large language models with systematic evaluation for mathematical discovery. Learn how this evolutionary procedure overcomes traditional LLM limitations like confabulation by searching for programmatic solutions rather than direct answers. Explore practical applications in extremal combinatorics, specifically the cap set problem, where FunSearch discovered new constructions surpassing previous records. Understand how this methodology extends to algorithmic challenges like online bin packing, producing interpretable programs that outperform existing baselines. Discover the potential for creating productive feedback loops between domain experts and AI systems, while gaining insights into deploying these solutions in real-world scenarios.
FunSearch: Mathematical Discoveries from Program Search with Large Language Models
Harvard CMSA via YouTube
Overview
Syllabus
Matej Balog | FunSearch: Mathematical discoveries from program search with large language models
Taught by
Harvard CMSA
Related Courses
-
Large Language Models, Mathematical Discovery and Search in the Space of Strategies - An Exploration of the Cap Set Problem
-
Abstraction and Evolution with Large Language Models
-
Mathematical Structure Computed by Large Language Models
-
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
-
How Can Large Language Models Learn From Humans? - Understanding Psycholinguistic Predictions and Sheaf Theory
