Pedro Domingos: evaluation

Transcript context

…It was the language of embedded programs and web Like that was that was the only option right? So and you know, there's big arguments about this, but not relevant to us here. The the point I'm trying to make here is, we are precisely at a also very relevant. Why did languages like Lisp and ProGod, know, fall out? Right? Because they were better for AI than you know, Fortran or C or or or whatever, right? Or Java, is that like, they were niche languages. And the network effects of the more widely used languages and all their aspects just completely overrode that, right. We understand that very well now, people did in the eighties. But now we're in a different ballgame now. Now the big technology, the center of everything is AI, Right? If you have a better language for AI, that is the 1 that is gonna, you know, have the biggest users. And moreover, if you have a language that solves the big pains, right, to adopt a new language or a new anything, right, you know, a new app, right, it needs to solve some big pain. Right? Is there a big pain that Tensor Logic solves? Well, hell yeah. It solves hallucination. Okay? It solves all this is subject to empirical verification, but it potentially solves hallucination. It solves the opacity. Right? Like, we're in this world right now where there's like multi billion corporations and systems that are like, they're driven by this black box. And nobody I know I've talked with CEOs of big tech companies that say like, you know, I can't sleep at night I don't know what this thing is gonna do and the people who trained it have left the company and who knows, right? So if we can make a dent in that people will converge to it very very quickly. Also I think when people have the experience of how easy it is to use tensor logic compared to the big pile of stuff that is lies under PyTorch and whatnot, I think that they will actually be very very motivated to migrate very quickly. And then you know like, and there are several things, there's like developing the open source community and vendor competition and whatnot. But but but you know, like, there's there's a couple of other important things here, 1 of which is the following. Tensor logic is ideally suited for AI education. It's 1 language in which you which has very little, you know, extraneous stuff and you can just and and you can teach the entire gamut of AI very well and do the exercise. It it'll be a language that the professors, the TAs, and the students will like. Right? And and history shows, you know, going back to things like Unix that if you have something like that that ticks off in computer science education, then people go to industry and say like, I want to use this because it's what's good, it's what I like. And and a generation later it's it's it's what everybody is is using. And and 1 more thing is the following. There the you the transition to tensor logic from from from Python doesn't have to happen all at once. Right? You can have, for example, and I already have actually, another half, like, again, because it's very easy to do, right? to tensor logic from from from Python doesn't have to happen all at once. Right? You can have, for example, and I already have actually, another half, like, again, because it's very easy to do, right? You read the paper and you like, and you do that in the next whatever 30 minutes. You can write a preprocessor that just converts tensor equations into Python. And again, all it does is a 1 to 1 mapping between the syntax of of tensor logic and EINSUM, right, then making things efficiently as we discussed is another matter, but from this point of view of of developer, you know, uptake, right, All it and there's a long long history of people doing this with different languages, right? It's like, you have a preprocessor that that that lets you write in 3 equations, but then it converts you, it converts those equations into into PyTorch or Python or just NumPy, let's say Python, right? And then you do everything else in Python that you did before. You don't lose anything, you don't lose any existing code, it's just that a set of things, and in particular reasoning, have now become much easier than they were before. And then once you have this little bali people like, oh, but I can do this, and I let me add that piece of syntactic sugar, and before you know it, people are like, well, I don't need all that, you know, Python stuff anymore. I just rather live in Tensor Logic world.…

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