An AI system can crush the world's greatest chess grandmasters, processing millions of positions per second with superhuman precision. But show that same system a simple card game it's never seen before, and it's completely helpless — no better than random guessing. The gap between narrow expertise and genuine adaptability is the central challenge of modern AI, and meta-learning is the field trying to close it.

This episode explores meta-learning in computer science — the paradigm shift from AI that learns facts to AI that learns how to learn. We break down what it means for a machine learning system to acquire not just knowledge about a specific task, but generalizable strategies for rapidly mastering new tasks it has never encountered before, often from just a handful of examples.

We cover the major approaches to meta- ...