ML / AI
A structured layer for frontier AI maps, theoretical foundations, and implementation-oriented materials. This is where I organize the technical substrate behind the rest of the site.
How To Use This Section
This section is for readers who want the AI-specific layer of the site without jumping directly between isolated projects or essays. The emphasis is on maps, references, and durable technical scaffolding.
- Frontiers gives the research landscape: philosophical roots, AI schools, lab bets, and strategic debates.
- Foundations holds course maps and theory-oriented notes that build mathematical and conceptual depth.
- Frameworks is the implementation bridge: practical engineering pathways, model-building workflows, and applied technical judgment.
Frontiers
Start here if you want a high-level but rigorous map of the current AI frontier: what the main schools are, where labs are placing bets, and how research arguments connect back to deeper philosophical commitments.
The Philosophical Roots of Frontier AI: A Primer
A structured primer on the philosophical foundations, AI schools, lab landscape, training pipeline, and major debates shaping frontier AI, organized as a readable multi-page survey.
Frontiers Knowledge Graph
A navigable graph of the survey: philosophers, AI schools, methods, debates, and labs, clustered so the interconnections are visible rather than buried in linear prose.
Foundations
These pieces strengthen mathematical maturity, intellectual orientation, and historical grounding. They are meant to make later frontier work more legible rather than remain detached theory.
AI / ML Course Atlas Across Leading Universities
A structured reference to public AI, ML, statistics, deep learning, systems, and frontier-research courses across leading universities.
The Origins of World Models
A compact lineage from Craik to Sutton to modern world-model work, useful for understanding why the concept matters now.
Energy as the Universal Organizing Principle
A unifying note on energy landscapes across statistical physics, machine learning, protein folding, behavior, and active inference.
Frameworks
The dedicated frameworks layer is still being built. For now, the most implementation-oriented material lives across the systems work in Projects and the conceptual build-up in Notes.