Keith Duggar: belief

Transcript context

…Oh, yes. Sorry. I I I see. I I understand your question. So so again, it's more than that. It's it's a DNF. Right? It's not just DNF is It's a of the normal form. Yeah. Exactly. And so what happens is that like, you know, like the Einstein is, So in numeric land, right, think of a dot product, right, a dot product, right, is just the sum of products, right, which in boolean land will be a disjunction of conjunctions. If more than 1 is true, get an m that is greater than 1, which is you need to pass it through a step function to reduce all the values greater than 1 back to 1. Yes. But my question was more like, okay. I have these 2 different representations of the same the same operation at least at the element level. Like an ein sum over an index followed by a on that element is equivalent to an or overall the same boolean values of that of that index. And I guess and my question or per element. So my question to you is, I I give up 1 thing, which is instead of having a single symbol, which is kind of like an or, I've now got 2 operations, you know, Einsom, Heaviside. And there are many examples of that. Right? Like, I can build every single circuit out of NAND gates. I think we discussed this like Yeah. Exactly. Once actually or or I can have, like, other kinds of gates, and and it's useful to have other kinds of gates. So in your in your language, do you foresee people not having syntactic sugar like an or operator, which under the hood is einsom hevicide or would they still retain those? It's just that the fundamental, you know, the most basic, you know, constructs of the language are tensor logic. We can do everything with NAN. So why do we need high level programming languages at all? Right? The the point so there's 2 things that you want a language to be. First of all, you want it to be universal so you can for some things you don't, but in general, right, for AI surely you want the universal language. You want something Turing complete and Turing log and and tensor logic is that. But then this is actually the most important and most difficult part. You want something that is at the right level of abstraction for the things that you want to do. And NAND definitely is not. And I can show with a lot of examples that I have in the paper for example, you can you know, code the transformer in a dozen tensor equations as opposed to a vast massive code. Right? And then what happens when people have a language that suits their needs is that then they just get used to that. They often wind up using it for even for things that it wasn't the perfect thing for, but at that point it's what they're comfortable with. So my guess is at the end of the day people are just gonna do everything in tensor logic, and and and they know, you know, in the back of their heads that yes, there are oars going on here and you could think of them as oars, but the the the they just think of them as joints and projects and tensor equations.…

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