Artificial intelligence · Machine learning · Category theory

Exploring the Compositional Structure of Intelligence.

Over four decades, my work has moved from learning apprentices and autonomous agents to representation discovery, causal inference, categorical AI, and computational creativity. This portal gathers that trajectory—and the questions still unfolding.

Enter the categorical AI program Follow the research arc ↓

Intelligence is not only the minimization of a loss. It is the capacity to build a theory, recognize where that theory fails, and revise the structure through which the world is understood.

A research life in four movements

One question, repeatedly reframed.

The methods have changed. The search for learnable structure has remained.

01
1980s–1990s
Learning from explanation
Learning apprentices, explanation-based learning, and the question that has endured throughout my work: how can an intelligent system acquire structure rather than merely fit observations?
02
1990s–2000s
Agents that learn to act
Reinforcement learning, hierarchical decision-making, multi-agent systems, robot learning, and autonomous agents operating under uncertainty.
03
2000s–2010s
Discovering representation
Spectral methods, proto-value functions, manifold learning, transfer, optimization, and the geometry hidden inside high-dimensional decision problems.
04
2020s–
A categorical research program for AI
Categories, causality, trustworthy foundation models, learning from failures of compositionality, and infinitesimal approaches to creativity.

Research atlas

From appearances to compositional structure.

My current program asks how explicit mathematical structure can make learning systems more diagnosable, intervenable, and capable of controlled theory change.