👈 This Machine Learning Street Talk interview discusses our research,
Also available on Apple podcasts 👉
Our group is developing an Agent Foundation Model — a general-purpose learning and reasoning agent that learns to understand novel environments through active exploration, by forming explanatory world models and executing its own experiments to refine them.
Core Research Themes
• Universal modelling: Developing an increasingly expressive hypothesis space, enabling the agent to model anything from physical to social interactions.
• Model discovery: Inferring plausible models based on agent-environment interaction history, online, and under suitable priors (cf. compression & core knowledge).
• Active learning: Select actions to gain information about the world, thereby testing hypotheses through active environment interaction.
Why This Matters
• Open-ended, fully adaptive, generalist AI agents.
• Extreme sample efficiency & generalisation.
• Uncertainty quantification, safety and robustness.
• AI for scientific discovery.
The successful completion of this research program may be the key to artificial general intelligence.
Keywords
Active model discovery, active learning & model based planning.
Interested in joining?
We are looking for highly motivated students and researchers who enjoy working on theory, large-scale experiments or both.
Interested in joining this research program? ➡️ Please read this and contact me 📨.
This is a short, non-exhaustive list from our and other research groups:
Toward Universal and Interpretable World Models for Open-ended Learning Agents
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Bayesian Models of Conceptual Development: Learning as Building Models of the World
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning
...