Pedro Domingos: preference

Transcript context

…Professor Pedro Domingos, it's amazing to have you back on MLST. I've lost count of how many times we've had had you on the show now, it's it's amazing to have you back. The main reason that we've invited you today is you've just released a brand new paper, a very exciting paper called Tensor Logic, the language of AI. And fields, you said, take off when they find their language. So you gave the example of calculus in physics and, you know, Boolean logic when designing circuits. What's the idea behind this paper? Well, tensor logic in many ways is the goal that I've been working towards my entire professional life because I really do strongly believe that a field cannot take off until it has really found its language. And and tensor logic, I believe, is the first language that really has all the key properties that you need in the language of AI. For example, it has automated reasoning right out of the box. Like for example, prologue has, right? The classic AI languages had the number of things that we just took for granted. The transparent and reliable reasoning you didn't even have to worry about. It was just already available. Right? At the same that you don't have that in in PyTorch at all. Right? You have all these hacks to try and do reasoning on top of it. At the same time, the lisps and the prologues, they never had the auto differentiation, the ability to learn. Right? 1 of the beauties of the current moment in many ways is that you barely have, you look at most papers, people barely talk about the learning because it's already implemented under the hood, so you want that as well. Right? And you want the scalability on GPUs is the other thing that things like like, you know, PyTorch and TensorFlow and whatnot give you. There was no language before that had all of these, and there's a number of others, but these maybe are some of the key ones. So tensor logic is is basically a language which as the name implies is a marriage, a very deep unification, not just some superficial combination of the tensor algebra that deep networks are all built out of and the logic programming that symbolic AI is built out of. There's only 1 construct in tensor logic and it's the tensor equation. You can do everything with with tensor equations. Are you saying that there's only 1 language of AI? Because certainly in in some fields like physics, you gave the example of calculus. I mean, yeah, like, you know, almost all of calculus I mean, almost all of physics, you know, involves quite a bit of calculus. There are other fields where actually there are kind of multiple multiple languages that that play, you know, almost equal roles. So I'm wondering if you think that is tensor logic gonna be 85, 90 plus percent of the way that we should be talking about and thinking about AI, or will be will be kind of a mixture of different different languages?…

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