From Apples to Strawberries - by Trevor Chow - Bunnyhopping

This publicly marks the start of the “search” paradigm in modern ML, just as ChatGPT’s launch in 2022 marked the arrival of the “learning” paradigm. With this new paradigm, you should expect progress in ML performance over the next two years to be at least as fast as it was in the last two years. In fact, there are reasons to expect it to be even faster. Here’s why! When I talk about “learning” and “search”, I mean the two “general methods that leverage computation” which Rich Sutton named in The Bitter Lesson. Loosely, “learning” is fitting to patterns in the world, while “search” is finding the best option in a space of possibilities. The Bitter Lesson explains why a single model launch, like o1’s, can catalyse such rapid progress. Since “the most effective” methods in ML are these general methods, progress in ML doesn’t occur steadily, but instead comes in fits and spurts. This is because it relies on researchers finding a technique which gets predictably better with more computing

This publicly marks the start of the “search” paradigm in modern ML, just as ChatGPT’s launch in 2022 marked the arrival of the “learning” paradigm. With this new paradigm, you should expect progress in ML performance over the next two years to be at least as fast as it was in the last two years. In fact, there are reasons to expect it to be even faster. Here’s why! When I talk about “learning” and “search”, I mean the two “general methods that leverage computation” which Rich Sutton named in The Bitter Lesson. Loosely, “learning” is fitting to patterns in the world, while “search” is finding