Machine Learning Street Talk / episode intelligence
Speakers in the public record
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Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
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21 published records
01 / evaluation
“We'll just turn up the temperature and we'll just sample tokens from the tail. And and you you really get garbage there because you're kind of, you know, you're you're a little bit out of distribution now.”
02 / preference
“Right? And I guess what I like about computer science is you put on 1 hat and you look for efficient algorithms for things, and then if you fail to find a good algorithm, you can switch hats and try to prove that the problem is hard.”
03 / prediction
“And so I don't know. This this to me is a really interesting frontier for AI where you take the problem and you invent on the fly what the what kind of variable you should use to address the problem.”
04 / evaluation
“1 of the reasons why we like sudoku is it's very easy to scan a row, scan a column, and scan a little 3 by 3 box so it fits with how we can address that data structure, if you will.”
05 / belief
“You know, you roll the tape over to this part. So, yeah, I I feel I guess for me, mathematically, when I think about computational universality, I think about things like our favorite programming languages and their relationship with the, the theory of partial recursive functions.”
06 / belief
“We think that means that our theory is breaking down somewhere, and historically, that's been true.”
07 / belief
“I I agree with you, and yet it seems to work surprisingly well at a lot of things, and we keep moving the goalposts, and we should move the goalposts.”
08 / belief
“I mean, I think, you know, we're at this workshop this week where there was a whole discussion yesterday about what do we actually need language for and, you know, and what do we actually need symbolic thinking for because that's where recursion seems to start.”
09 / belief
“In physics, we would say a very glassy landscape where there are many local optimates, hard to navigate, blah blah blah.”
10 / belief
“You better be doing it on purpose, but you shouldn't just be doing it because you've heard it before and because it has a high probability in the distribution. So I think his goal as a prose writer was to constantly produce new juxtapositions and in order to, well, to intrigue the reader and to access a space in the, you know, to access a region of the creative space, the writing space, which hadn't been accessed before.”
11 / uncertainty
“Therefore, if they were easy, these other problems would be easy too. And a funny thing though is there are a lot of systems where we don't know how to build a computer in them, but they still look really irreducible.”
12 / preference
“I'm just gonna go solve it anyway. And that's somehow because the real world presents us with examples of these problems where there is so much rich structure to sink your teeth into, whether that's the structure in text, the structure in images, and so on.”
13 / preference
“If you recommend movies to me using a black box and I like the movie, everybody's happy, that doesn't bother me.”
14 / recommendation
“I think that's a very interesting line of work. So I think that as humans, it would be very good for all of us, especially if we want a democratic society where we're making kind of informed collective decisions about when to use these things, if to use these things, and what settings to use them.”
15 / evaluation
“I think what's fascinating is that that real world structure seems very hard to mathematize.”
16 / prediction
“Then there can also be these interesting middle ranges where you can find the ground truth if you do an exhaustive search. But we actually believe that there is no efficient algorithm that will succeed in that regime because you're wandering around in this high dimensional landscape of possible fits to the data, and the accurate ones are kind of hidden behind what in physics we call an energy barrier.”
17 / evaluation
“Because, actually, formal logic is not something that we're built to do. So I guess I I expect I expect these systems to once they can really play with all of these modules, including ones that we don't have, like visualizing things in 7 dimensions, they'll be able to do a lot just as when they start I'm not sure if we should do this, but when we give them access to 3 d printers and fab labs so that they can start building things and seeing whether they work, well, maybe we should solve the alignment problem first.”
18 / evaluation
“Turing machines as an architecture, I think, are rather brittle. And, I mean, I think that the partly analog nature of neural networks and, and LLMs, this ability to, I mean, I know that they can be made discrete and so on, but somehow their ability to work in a continuous way with high dimensional vector spaces and embeddings, I I think that that is important to their trainability, even if it's not ultimately important to their cognitive abilities.”
19 / prediction
“If I want to know how some function behaves, I graph it and look at it. And I think once LLMs are given these various playgrounds and given the ability to fire them up to do literally doodle and look at it in a 2 dimensional way, the way we can with our eyes, as opposed to treating everything as 1 dimensional strings of text, I expect that we'll see much more multimodal abilities.”
20 / evaluation
“I mean, I don't think that at the fundamental level things are cellular automata because I think that doesn't really work with quantum mechanics.”
21 / preference
“Just as maybe a better music maybe a better music or or book recommendation system would challenge you the way a friend challenges you in that wonderful kind of directed way that friends do.”