Foundation Models' Brain Body

Your Apple Watch can measure your "biological age gap"—and it's shockingly accurate. Smokers appear 4-6 years older. Pregnancy temporarily ages you 3.5 years. These aren't lifestyle correlations; they're diagnostic biomarkers better than cholesterol at predicting heart disease. You'll discover how self-supervised learning unlocks this power from noisy brain and body signals without requiring expensive manual labels. An elegantly simple trick—teaching models which EEG windows are close or far apart in time—achieves massive data efficiency. But the real breakthrough? Brain-computer interfaces that read your subconscious "oops" signal. When you intend to click but your brain detects an error, the system suppresses it—boosting accuracy from 90% to 99%. The scaling is imminent: from dozens of hours of brain data to millions. Topics Covered - Self-supervised learning (SSL): learning data structure without labels - The relative positioning task for EEG: elegantly simple, incredibly powerful - Scaling laws: more hours per subject > more subjects (depth > breadth) - Dual-branch cognitive decoding: brain activity → semantic meaning - Image reconstruction from brain signals (proving semantic decoding works) - PPG age gap biomarker: 2x heart disease rate in young adults, better than cholesterol - BrainJapa and BrainHarmony for MCI prediction - Synchron's Stentrode: minimally invasive BCI via jugular vein - Error detection primitive: subconscious "oops" signal for 90% → 99% accuracy