Episode Details

What happens when the world's top AI researchers build a tool that revolutionizes machine learning — then discover their entire explanation for why it works is wrong?

In this episode, we trace the wild decade-long saga of batch normalization, the 2015 breakthrough that made training neural networks dramatically faster and more stable. The original theory sounded airtight: standardize the data flowing between layers to fix a phenomenon called "internal covariate shift." Case closed. Except it wasn't.

We break down the MIT experiments that blew the theory apart, the paradox of gradient explosions that shouldn't exist if smoothness were the whole answer, and the cutting-edge mathematics of length-direction decoupling that's finally starting to explain what's really going on under the hood.

Along the way, we explore a question that extends far beyond AI: in fields governed entirely by rigid equations, how often is the accepted "why" just a placeholder story we tell ourselves until better math comes along?

No prior machine learning knowledge required — just curiosity about the messy, fascinating gap between building things that work and understanding why they work.