Stephen Wolfram: ChatGPT and the Nature of Truth, Reality & Computation | Lex Fridman Podcast #376

You know, I can tell ChatGPT, "Create a piece of code," and then just run it on my computer. And I'm like, you know, that- that sort of personalizes for me the what could, what could possibly go wrong, so to speak.

Was that exciting or scary, that possibility?

It was a little bit scary actually, because it's kind of like, if you do that, right?

Yeah.

What is the sandboxing that you should have? And that's sort of a, that's a- a version of- of that question for the world. That is, as soon as you put the AIs in charge of things, you know, how much, how many constraints should there be on these systems before you put the AIs in charge of all the weapons and all these, you know, all these different kinds of systems?

Well, here's the fun part about sandboxes, is, uh, the AI knows about them and has the tools to, uh, crack them. The following is a conversation with Stephen Wolfram, his fourth time on this podcast. He's a computer scientist, mathematician, theoretical physicist, and the founder of Wolfram Research, a company behind Mathematica, Wolfram Alpha, Wolfram Language, and the Wolfram Physics & Metamathematics projects. He has been a pioneer in exploring the computational nature of reality. And so, he's the perfect person to explore with together the new quickly evolving landscape of large language models as human civilization journeys towards building super intelligent AGI. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Stephen Wolfram. You announced the integration of ChatGPT and Wolfram Alpha and Wolfram Language. So, let's talk about that integration. What are the key differences, from the high philosophical level, maybe the technical level, between the capabilities of, broadly speaking, the two kinds of systems, large language models and this computational, gigantic computational system infrastructure that is Wolfram Alpha?

Yeah. So what does something like ChatGPT do? It's- it's mostly focused on make language like the language that humans have made and put on the web and so on.

Yeah.

So, you know, its- its primary sort of underlying technical thing is you've given a prompt, it's trying to continue that prompt in a way that's somehow typical of what it's seen based on a trillion words of text that humans have written on the web. And the way it's doing that is with something which is probably quite similar to the way we humans do the first stages of that, using a neural net and so on, and just saying, "Given these, given this piece of text, let's ripple through the neural net one wo- and- and get one word at a time of output." And, uh, it's kind of a- a shallow computation on a large amount of kind of training data that is what we humans have put on the web. That's a different thing from sort of the computational stack that I spent the last, I don't know, 40 years or so building, which has to do with what can you compute many steps, potentially a very deep computation? It's not sort of taking the statistics of what we humans have produced and trying to continue things based on that statistics. Instead, it's trying to take kind of the formal structure that we've created in our civilization, whether it's from mathematics or whether it's from kind of systematic knowledge of all kinds, and use that to do arbitrarily deep computations to figure out things that- that aren't just, "Let's match what's already been kind of said on the web," but, "Let's potentially be able to compute something new and different that's never been computed before." So as a, as a practical matter, you know, the- the, um, what we're, you know, the- Our goal is to have made as much as possible of the world computable in the sense that if there's a question that in principle is answerable from some sort of expert knowledge that's been accumulated, we can, uh, compute the answer to that question. And we can do it in a sort of reliable way that's- that's the best one can do given what the expertise that our civilization has accumulated. It's a very, it's a- it's a much more sort of labor-intensive on the side of kind of being, creating kind of the- the computational system to do that. Um, obviously the, in- in the, the kind of the ChatGPT world, it's like take things which were produced for quite other purposes, namely the, all the things we've written out on the web and so on, and sort of forage from that things which were, are like what's been written on the web. So I think as, you know, as a practical point of view, I- I view sort of the ChatGPT thing as being wide and shallow and what we're trying to do with sort of building out computation as being this sort of deep, also broad, but- but, uh, most importantly, kind of deep type of thing. I think another way to think about this is if you go back in human history, you know, I don't know, 1,000 years or something, and you say, "What- what can the typical person, what's the typical person going to figure out?" Well, the answer is there are certain kinds of things that we humans can quickly figure out. That's sort of what- what our, uh, you know, at our neural architecture and the kinds of things we learn in our lives let us do. But then there's this whole layer of kind of formalization that got developed in which is, you know, the kind of whole sort of story of intellectual history and whole kind of depth of learning. That formalization turned into things like logic, mathematics, science, and so on. And that's the kind of thing that allows one to kind of build these towers of- of, uh, uh, of- of, uh, sort of towers of things you work out.