Here's a bit from an episode of This American Life that I think is relevant, cut into a post on my blog: https://danangell.com/blog/posts/gpt-understands/ http

Here's a bit from an episode of This American Life that I think is relevant, cut into a post on my blog: https://danangell.com/blog/posts/gpt-understands/ https://danangell.com/blog/posts/gpt-understands/ > The idea is that back when GPT-4 was being trained for it to really consistently get the next word correct, to do that reliably, it had to do more than just bullshit. It had to do more than guess based on patterns. To get the next word right, it had to truly understand the words coming before it. Imagine if you were training a transformer to predict numbers in a sequence that came from a sine function. Eventually it would get a pretty good test score and you'd find that internally it had built a decent replication of the sine function. Now do the same with English text. Throw enough data and computation at it and eventually it'll model a rough understanding of gravity and the shapes of objects, as seen in the different answers between GPT-3.5 and GPT-4.