Tim Scarfe: belief

Transcript context

…indeed maybe let's say, time to call knowledge priors as something somewhat vague, but not fully testable, but still usable. The thing is, if you replace LLM by human there, I think it's still true. We also don't have formal core knowledge priors that are, let's say, fully exact, and that we apply to the world and describe the world fully exactly. We have kind of vague, trained over our life understandings that evolve over time. So, at least if you want human intelligence, that does seem to be the paradigm you want there. And I think, yeah, the LLMs get kind of the bonus that they also can reason very well, and much more implicitly than the humans can encode. So if they want to get exact, they can. But I think if you want to understand the world is certainly beyond these, let's say, simple games, you will need more probably that, yeah, vibe understanding basically. I'm guilty of always like, you know, suggesting what Charle thinks. I don't really know what he thinks, but you know, I've I've got a pretty good simulation of Charle in my mind. But I I I think he's he's influenced by this kind of nativism psychology type thing. And he thinks that a lot of the reasoning we do do is almost platonistic. Know, that that you know somehow the laws of nature imputes these primitives into our mind and we compose these primitives together for certain classes of problems. So we can do abstract system 2 reasoning and for those types of problem we do compose these things together. But you're absolutely right, there's so many things in the world are actually really complicated. Right? You know, like navigating relationships or even, you know, path finding in a complex environment, you know, on the tube network or something like that. So we do a little bit of both, but there's there's at least a pocket of pure reasoning. I think that's what Charlotte thinks. Would you consider LLMs pre trained on the Internet? And then if you apply it to a new pattern using like in context learning, is that a form of okay. Let's say with reasoning as well, would that not be a form of like perhaps skill acquisition on the fly because it learns obviously, there's a lot of core knowledge as well, but it's also a different part where it has to adapt on the fly. So you can give some example that might not be on the internet and might adapt. Sometimes it fails, it is still a distribution, but at least you get some adaptability. And then for example, when you started prompting it, think of like a chain of thought prompting, it started improving because it had more time to reason and adapt to what you're asking. Then if you train it using reasoning, you can actually do way more because now it has more time to actually reason and figure out what you're actually asking and form new extractions for solving a specific problem case as opposed to just regurgitating what it's seen on the internet?…

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