Jeremy Howard: evaluation

Transcript context

…First of all, I'm I'm exasperated by what I see as the tech bro predilection to misunderstand cognitive science and philosophy and and what not. Because we've we've spoken to so many really interesting people on MLST like for example César Hidalgo, he wrote this book, The Laws of Knowledge. And and even Mazviita Chirimuuta, she's a a philosopher of neuroscience and she was talking all about, you know, like flipping basically that knowledge is protean. So yeah, I think that knowledge is perspectival. I don't think that knowledge can be this abstract perspective free thing that can exist on Wikipedia. And I also think that knowledge is embodied and it's alive. It's something that exists in us. And the purpose of an organization is to preserve and evolve knowledge. So when you start delegating cognitive tasks to language models, you actually have this weird paradoxical effect that you erode the knowledge inside the organization. Well, that's true. And that's terrifying. There's often these these arguments online between people who are like, LLMs don't understand anything. They're just pretending to understand. Mhmm. And then other people are like, don't be ridiculous. Look what this LLM just did for me. Right? And the funny thing is they're both right. LLMs cosplay understanding things. Like, they pretend to understand things. And this is the interesting thing about the early kind of work with, like, cognitive science work with, like, Daniel Dennett. That's basically what the Chinese room experiment is. Right? It is you've got a guy in a room who can't speak Chinese at all, but he sure looks like he does because you can feed in questions and he gives you back answers, but all he's actually doing is looking up things in a huge array of books or machines or whatever. The difference between pretending to be intelligent and actually being intelligent is entirely unimportant, as long as you're in the region in which the pretense is actually effective, you know. So so it's actually fine for a great many tasks that LLMs only pretend to be intelligent, because for all intents and purposes, it it it just doesn't matter until you get to the point where it can't pretend anymore. And then you realize, like, oh my god. This thing's so stupid. I'm a fan of Searle, by the way. So, know, he said that understanding is causally reducible but ontologically irreducible. And he was saying there was a phenomenal component to understanding. You don't even need to go there. Like the interesting thing about knowledge being protean is this idea that, you know, it's basically this Kantian idea. The world is a complex place. None of us understand it. It's like the blind men and the elephant. We all have different perspectives. It's a very complex thing. And so we all we all do this kind of modeling. But the the interesting thing is that the language model, sometimes they seem to understand. And they understand because the supervisor places them in a frame. So inside that frame, so when you have that perspective of the elephants, they're actually surprisingly coherent. But we discount the supervisor placing the models in that frame.…

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