"But I think that’s why these large language models are so successful, is because good at form and form isn’t that hard in some sense. And meaning is tough still, and that’s why they don’t understand. We’re going to talk about that later maybe, but we can distinguish, forget about large language models, talking humans, maybe you’ll talk about that later too, is the difference between language, which is a communication system, and thinking, which is meaning. So language is a communication system for the meaning. It’s not the meaning. And there’s a lot of interesting evidence we can talk about relevant to that. Thinking and language"

"Well, you or anyone has to think of a task which they think is a good thinking task, and there’s lots and lots of tasks which would be good thinking tasks. And whatever those tasks are, let’s say it’s playing chess, that’s a good thinking task, or playing some game or doing some complex puzzles, maybe remembering some digits, that’s thinking, a lot of different tasks we might think. Maybe just listening to music is thinking. There’s a lot of different tasks we might think of as thinking."

"And so he’s like, “Oh, I guess it’s not learned. It’s innate.” And if you just throw out the movement and just think about that in a different way, then you get some messiness. But the messiness is human language, which it actually fits better. That messiness isn’t a problem. It’s actually, it’s a valuable asset of the theory. And so I think I don’t really see a reason to postulate much innate structure. And that’s kind of why I think these large language models are learning so well is because I think you can learn the form, the forms of human language from the input. I think that’s likely to be true."