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.
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20 published records
01 / evaluation
“Whereas in the in this sense, we're we're now abstracting them because there's also this orthogonality thesis as well, which is this intelligence of final goals are are, you know, disconnected from each other.”
02 / evaluation
“Right? But that's a word and a phrase that comes from this computational analogy for cognition and I think there's been a real kind of ignorance or amnesia or just convenient forgetting that it's an analogy.”
03 / observation
“people aren't really talking much about the hallucination now because they're they're agentic and they can fix their own stuff.”
04 / commitment
“What I will say is, you know, I I I actually The thing about that that I wanna focus on, is the same thing I focus on in the relevant part of my book.”
05 / belief
“I mean, yeah, it is a it is a grotesque metaphor that you can take something which I believe is is a physical property of of stuff in the universe And we can create an abstraction that that seems to work reasonably well for abstract domains like playing chess and and and so on.”
06 / uncertainty
“I know. But but I I should push back a little bit because I don't know whether you played with Mythos before it got taken offline.”
07 / belief
“Except I wouldn't even I I would get more specific instead of saying an emergent force above them that they don't understand. I would say the venture capital startup ecosystem Silicon Valley.”
08 / belief
“I'm not diminishing the the functionality of calculators either. But it is quite deceptive, and and I think I I in terms of how to make sense of it, I think we just have to keep in mind what these systems are at the end of the day.”
09 / belief
“Oh, man. I mean, I think that I mean, that's a good question. I mean, it's 1 of the things I was hoping to try to address with my book.”
10 / belief
“Yep. But I I think I mean, there's this anthropic fiasco recently, you know, what it doesn't make sense.”
11 / evaluation
“I've lived in the Bay Area now for almost 15 years, and I have been to a lot of parties out here where I've heard a lot of people say a lot of bizarre things about the future of technology, AI, you know, space colonization, that kind of thing. And I always just kinda rolled my eyes, but as time went on, I realized, wait, these ideas are actually really influential and yet there's no good evidence for them and in most cases, lot of good evidence against them.”
12 / evaluation
“You know, I I think that if nothing else, the fact that an LLM takes in more text than any human could in a human lifespan in order to be worse at language in many ways than a 5 year old.”
13 / evaluation
“has created I mean, Musk did not originate the myth of colonizing Mars but he is, you know, in the same way that Kurzweil didn't create the idea of the singularity, Musk didn't create the idea of colonizing Mars but he's certainly, you know, mister occupy Mars these days. And and it creates this idea that we don't need to care about the problems here on Earth because we're leaving, you know, and that's just not going to happen.”
14 / prediction
“Moore's law has to stop sometime in the 20 twenties because eventually you get down to the size of individual silicon atoms and you can't really make transistors”
15 / preference
“I mean, I think I think that the that I mean, look, you you know I think that that my sympathies lie significantly more with people like Timnit than with someone like Yadkowsky. And and my issue is, like, I I I think that what's going on is you have people in the effective altruist rationalist community who, you know, want to or feel that they should be able to entertain ideas like human biodiversity without recognizing that actually this is an idea that has been entertained for a very long time and has a really horrifying history and has no good science behind it.”
16 / evaluation
“Maybe we can RL train it to refactor it, and we just have to kind of and it's very dangerous keeping going because now we're messing all of our code bases up and we're we're creating all of this slop everywhere.”
17 / prediction
“I would argue that we're learning some interesting things about how language works and what you can get out of the sort of mathematical structure and statistical properties of language.”
18 / observation
“Because we're we're not only attributing agency to these things, but in the case of AI psychosis, mean I don't know exactly what's going on there, but it certainly seems like it's people attributing agency to these things and then getting caught in a kind of feedback loop where these systems are sort of regurgitating themselves, like regurgitating the user back to themselves.”
19 / recommendation
“I'm also the host of the podcast Dreaming Against the Machine where we try to think about what a better future could look like outside of the grip of the tech industry. And you should listen to this episode of Machine Learning Street Talk because Tim and I have a great conversation about why some of the most powerful people in the world are wrong about so many things.”
20 / prediction
“I don't know if he's a true believer in whatever, but he has gone out and said things and he's not the only 1 who said things like this that, you know, AI is going to solve the climate crisis. And like, I I think that in the unlikely event that we built like a really intelligent conscious machine intelligence based on existing technologies in the near future, well, if we did that and asked it to solve climate crisis, the minute we turn it on, it would say, well, you shouldn't have built me.”