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
32 published records
01 / recommendation
“important that the checker was stronger than the actual instruction creator, which I I think is, interesting.”
02 / prediction
“I I think you're generally correct that it's not happening at the moment, but I still think, like, fundamentally, I don't think there is a, like, a fundamental blocker physically for why they won't be able to do it in the future.”
03 / evaluation
“I was actually inspired by Ryan Greenblatt who had a solution earlier, which was to generate a ton of Python programs that would encapsulate the transformation role. And Python programs are great because they're deterministic, and you can pretty quickly check whether or not the Python program works or not, which is really cheap, so it's cheap to verify.”
04 / evaluation
“In the first 1, I used Python programs because Python programs are deterministic and it's really easy to verify whether or not, you know, it's correct.”
05 / recommendation
“The problem is, you know, for my v 1 solution, you also have to generate code, And Sonnet 3 5 is really good at thinking about code and generating code, and I I prefer Sonnet to Grok, for code generation.”
06 / belief
“I think you can generally you should be able to encapsulate a symbolic system with a neural network in the same way I think that you can do the I think you can do the same thing with the brain as well.”
07 / belief
“Will be will be logarithmic. So I wouldn't expect using these basically, using the language models we have today, I would not expect anyone to break, let's say, 40%, but you could probably make my solution twice as efficient, I would say.”
08 / commitment
“I think fun I think fundamentally taking a step back, the fact that our brains can do it and our brains are generally running similar algorithms, to me, this means that we will eventually be able to, inject general reasoning into the language models.”
09 / belief
“Like, reasoning is that meta skill. And so, to put it another way, I think if you fundamentally learn the skill of reasoning, you should be able to then, apply that skill to learn all the other skills.”
10 / commitment
“I believe the brain is much more composable than neural networks biologically, but I think there's no reason why we can't, you know, we we won't be able to figure this out.”
11 / belief
“I think, my guess is and, you know, NVIDIA just put in a $100,000,000,000 into OpenAI.”
12 / uncertainty
“I don't know if this this fits in, but this is kind of how I think about reinforcement learning, which is replacing knowledge web with an with a knowledge tree.”
13 / belief
“The first is I think if you have a large enough neural network, I think generally almost everything is you could you could represent a symbolic system, but of course, it's not Turing complete.”
14 / belief
“I think the algorithm that runs in our brain is a is a Turing machine algorithm.”
15 / belief
“I I would say intelligence is the efficiency that you can acquire the tree, and reasoning is building the tree.”
16 / belief
“I think OpenAI, generally, they wanna do the right thing, and they want their solutions to be very general and broad.”
17 / belief
“I think your editors can't build the the tree because they don't have the deductive footing.”
18 / belief
“I I'm for the record, I would define understanding as being able to I mean, I think understanding the spectrum, I think on 1 end, it's memorization, and then which is 0, understanding.”
19 / commitment
“I think that I think that what you're describing I'm actually not even totally convinced that continual learning is fundamentally the blocker, but I think if it is the fundamental blocker, that's actually incredible because we will solve continual learning.”
20 / uncertainty
“I don't know if you've seen the recent couple of papers that are applying it to transformers, you know, where essentially it's it's kind of a step towards probabilistic models where you actually have this uncertainty quantification.”
21 / belief
“I think the fundamental reason why I got higher and also, I could match his efficiency, and I would still get higher because I was using natural language.”
22 / belief
“I think generally, when models think in natural language and they output a natural language, they, they are higher entropy.”
23 / preference
“There's this phylogeny of of knowledge, and you need to respect it as much as possible because if you don't respect it, you're not grounded anymore. So it kind of feels to me that intuitively code is great because it means that I'm actually respecting the constraints and and the semantics are correct and it's grounded in in the real world.”
24 / commitment
“It was using Sonnet 3.5, and you had about 4 iterations, I think. And and, essentially, you you know, you were working on the ARC challenge, you were producing these programs through evolution.”
25 / preference
“I think I think with SGD because the fascinating thing is that, you know, if you look at all of the FSA algorithms, a a tiny sliver of those algorithms are capable of controlling, you know, a Turing machine and expanding their memory and so on.”
26 / prediction
“It's it's Turing complete. And our brains, even though they are finite, they run a Turing complete algorithm, which means our brains know how to expand their memory.”
27 / preference
“So on the first 1 as well, you were generating Python programs explicitly. And because of all the things that we're just talking about, I'm a big fan of that because I feel intuitively, and I think you did, that there's something special about Python programs.”
28 / evaluation
“We need not detain us now, but I think in traditional machine learning, transduction means that the test example is a function of your prediction.”
29 / evaluation
“The reason for that, as we discuss in today's show, is that current AI does not understand the world in a grounded way. It doesn't have a deep abstract understanding of the world, which is why the only way that we can make AI work effectively is by grounding the generation and supervising the training of AI models with human data.”
30 / preference
“Absolutely. And I I remember I read in in the first version of your blog post that you were talking about, we need to do this kind of deduction where we synthesize hypotheses, and then we we test them, and we do this kind of generate test loop.”
31 / evaluation
“Because there is a bit of an elephant in the room and in the scene at the moment, I think so many people just just don't have such a crisp understanding.”
32 / evaluation
“He's talking about things at a level of abstraction which, you know, can refer to anything, but it's beyond most people's cognitive horizon. But there is something to be said for that when when you can respect the history deep down into the epistemic tree, the creative stepping stones you take, because they respect the history, they actually have more evolvability.”