Public evidence record
Published podcast speaker
Claim ledger
14 transcript-backed records
01 / commitment
“However, you know, what I would say is that this is not the the mass value per se, but it's what we need to producing and I intend to produce it on short order.”
02 / observation
“The point is that first we have to realize that there is 1, we have to prove what it does, and then we can refine it with the syntactic sugars and whatnot, and that's all good.”
03 / belief
“I've always thought, and you know, I think, you know, a lot of people in deep learning really believe this, that, you know gradient descent can do amazing things provided you give it the right architecture to operate on. And in a way what all these million papers are about is about finding the right architecture for gradient descent to operate on, and of course transformers are a great leap forward, but I think transfer I think, you know, Tensor Logic is an even greater leap forward.”
04 / belief
“I would say that you know in some sense discovering representation like that is the key problem in AI, is the holy grail.”
05 / belief
“Douglas Alstadter I think would like this because it's it's an analogical like he has this whole 500 page book arguing that all of cognition is just analogy.”
06 / evaluation
“First of all, tuning completeness doesn't matter at all whatsoever. Because the only difference between a turing machine and a finite state machine is the infinite tape.”
07 / recommendation
“I wrote this book that turned into a big bestseller surprisingly called The Master Algorithm that is precisely about this goal and where we are towards that goal. And my latest work which this this this podcast will talk about, is a new language called Tensor Logic that I would say for the first time brings this dream of a unified representation, a unified solution to AI within reach.”
08 / commitment
“I I would say the following is, and you know, from physics all the way to AI with biology in the middle.”
09 / preference
“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.”
10 / commitment
“You know, we're not then describing laws of the universe, and and I think tensor logic at least is my best attempt at having a language in order to do both this AI and this type of scientific discovery.”
11 / evaluation
“Another very important aspect and 1 that I think could prove decisive is that people don't use ions so much because it's not very efficient.”
12 / evaluation
“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.”
13 / prediction
“I have some suspicions as to what they might be, but I think, you know, we're not quite there yet. But I think once we have those regulators in some sense, you know, a a they will play in AI the role that the standard model plays in physics.”
14 / evaluation
“It's like there's an iterative loop of you set up the structure and then you learn, you get the results, and then you refine the structure. And what this does is it makes that more efficient much more because you just have to in your interpreter you write 1 more equation or you modify an existing equation, and also the entire stack is is of what you learn is much more interpretable than it was before.”