The Physics Reason AI Works When It Shouldn’t - #568

About this episode

The episode explores the physics behind why AI works despite its overparameterization, drawing connections to magnetism and the origins of life.

The same mathematics that explains why a fridge magnet sticks to your refrigerator also explains why neural networks work when they have no right to. His group did the calculation. Nobody else had. Subscribe if you want science with evidence, not speculation. Goldenfeld is a Professor of Physics at UC San Diego, a Fellow of the Royal Society, and a Member of the National Academy of Sciences who spent 36 years at the University of Illinois applying condensed matter physics to problems everyone else had given up on: why the genetic code is optimal, why early life evolved impossibly fast, and why AI works despite being overparameterized beyond anything classical statistics can explain. The thread connecting all three is one idea: that what emerges from many things together is qualitatively different from the sum of its parts. That idea explains magnetism, AI, the origin of the genetic code, and why life may be inevitable wherever the laws of physics apply. He also argues that what is happening to science right now is not a disagreement about facts but a fracture in how people decide what is true, and that is a more dangerous problem. What you’ll hear: -Why the same phase transition…

People in this episode

Host: Brian Keating

Guest: Goldenfeld

Topics covered

• AI
• magnetism
• genetic code
• phase transition
• life origins
• science communication

Keywords

• neural networks
• condensed matter physics
• horizontal gene transfer
• life on other planets
• science evidence

Mentioned in this episode

Organizations: UC San Diego, Royal Society, National Academy of Sciences, University of Illinois

Places: Enceladus, Europa

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