Explainability
2 published articles
AIFeatured5 min read
Foundations of AI
DeepMind's new framework shows why AI is brilliant at some things and terrible at others
A Google DeepMind paper introduces a three-part framework for thinking about rigor in AI: conceptual, epistemic, and operational. It argues that deep learning's rapid progress has come from prioritizing performance-driven iteration over scientific understanding, and that closing the gaps will require more than better benchmarks.
2026-07-20
AI4 min read
Nature Neuroscience paper
Microsoft's new method turns black-box brain AI into readable theories
GCT translates uninterpretable LLM-based brain models into short phrases like 'food preparation' or 'location names,' then uses an LLM to write stories that causally test those explanations in real subjects. The method promises to bridge predictive AI and human-readable scientific theory.
2026-07-05