Machine Learning Street Talk / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
17 published records
01 / evaluation
“We make prediction and we test them. So we predict how many data you need to learn how many different level of abstraction.”
02 / belief
“I think you're leaning towards there being some kind of a universal learning algorithm.”
03 / belief
“I think I'm just trying to understand what the gap is because it would be consistent with your argument.”
04 / commitment
“I will use this term in a very narrow sense of being able to generate new sentences that satisfy hard constraint syntactic rules that the child would never have heard before.”
05 / belief
“What we argue is very important to look at and that we could finally measure with LLMs or other architecture and we find consistent result is what is the entropy left after a sentence of N token.”
06 / belief
“I think maybe we need to do more introspection of how we function as scientists to come up with a good data set and the good procedures to teach machines to be good scientists.”
07 / prediction
“Are we doing the wrong thing? And I'm very interested in, you know, should we predict in token space at a very low level or more should we train machine to predict abstractions.”
08 / evaluation
“similar context around them. And this is very pertinent because you've got a paper out basically saying that we should predict in the latent space, not the token space.”
09 / evaluation
“The reason why deep architecture cancels those tasks is precisely because they understand just like the physicists understood about pressure, velocity, field.”
10 / evaluation
“It doesn't mean that what he inferred is incorrect. It's not because I think an argument is incorrect that the statement is incorrect.”
11 / prediction
“Just to kind of play that back just so that everyone The idea is that there is I mean, we're talking about grammar here. But more broadly, we think that there are structured generative processes in the world.”
12 / prediction
“I mean the only way you will extrapolate and have power to generalize is if you bring those points together, it means you have an exponentially larger number of data, you have more data than atoms in the universe.”
13 / evaluation
“My take is that those are more academic problems that you never encountered in in practice because sentences that loop for 50 times are extremely rare.”
14 / evaluation
“I mean, certainly they are interacting with the world because those theories theories in physics are super useful to build technology.”
15 / evaluation
“I will still say a word of caution that those LLMs are generative models that's very important for them to be because you can interact with them and they produce reasoning and so on.”
16 / commitment
“Okay. So I think we will be talking about creativity and then I will also be discussing a lot about constraint, but there will be in my way of thinking in another space.”
17 / preference
“I think the analogy I like is industrial revolution when actually heat engine emerged before.”