The Bitter Lesson of Biology: Why Scaling AI Isn't Enough — Pushmeet K, DeepMind & Sal C, Biohub
In this panel discussion moderated by Brandon Anderson, Google DeepMind's Pushmeet Kohli and CZ Biohub's Sal Candido discuss whether the 'Bitter Lesson' of AI scaling applies directly to biological data and modeling. They explore the real limitations of AlphaFold, the nuance between artisanal architecture inductive biases and pure compute scaling, the true nature of model interpretability versus uncertainty calibration, and why solving translational medicine requires looking for 10x paradigm shifts rather than incremental 10% gains.
- DeepMind's Pushmeet Kohli argues AlphaFold 2 cannot be interpretable to people because biological mechanisms exceed the cognitive capacity of the human brain. Read →
- Sal Candido warns that scaling laws do not appear automatically in biology; discovering the exact regimes where data volume predictably drives performance is an open empirical problem. Read →
- DeepMind designed AlphaFold 2 by hardcoding biophysical rules into the network rather than relying on pure compute scaling. Read →
- Machine learning models already operate across every stage of target validation and screening, yet clinical pipelines remain stalled without mechanistic biology models. Read →