Google PAIR's AI Explorables: research-grade interactive essays from SAEs to differential privacy
techNmak · x · 2026-09-04
A recommendation of Google PAIR's AI Explorables: research-grade interactive essays covering sparse autoencoders for LLM hidden representations, the Patchscopes inspection framework, memorization vs generalization (grokking, mechanistic interpretability), calibration and confidently incorrect models, plus privacy topics like why models leak data and the fairness side-effects of differential privacy.
Also featured: TensorFlow Playground, where you tweak layers, activations and learning rates and watch decision boundaries evolve live.
Related event: Stop learning AI from static diagrams: a curated visual learning roadmap(13 posts)→
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