The Wisdom Wall

“I think a big bottleneck these models have is their inability to learn on the job, to have continual learning. Their entire memory is extinguished at the end of a session. Um, there's a bunch of reasons why I think this actually makes it really hard to get human-like labor out of them.”

“Once continual learning is solved, you might have something that looks like a broadly deployed intelligence explosion, which is to say, That because, um, if these models are broadly deployed to the economy, every copy that's like this copy is learning how to do plumbing. And this copy is learning how to do, um,…”

“now if we're getting to the world where you got to like do a project for seven hours and then at the end of those seven hours, then we tell you, Hey, did you, um, did you get this right? Uh, then like the progress just goes on a bunch. Cause you've gone from like getting signal within the matter of like microseconds to…”

“The reason they're not being more widely used is not because people cannot afford a couple bucks for a million tokens. The reason they're not being more widely used is just like, they fundamentally lack some capabilities. So I disagree with this focus on the cost of these models. And I think it's much more. We're,…”

“More importantly, I think the future economy, once we do have these AI workers will be denominated in compute, right? Cause if computer's labor, right now, if you just think about like GDP per capita, because the individual worker is such an important component of production that you have to like split it, split up…”

“The thing with AI is if you buy scaling in this picture, that is, you make the models bigger, they get much smarter. Then I don't think it makes sense to hedge your bets in this way. I think you should just double down, give, give one of them a hundred billion dollars and just say like, go make me, go make me super…”

“it will make training more expensive because instead of just doing one backward pass, you now potentially have to do many forward passes because at each forward pass, you're going to come up with some output. Then the model has to decide which of those outputs was the best. Now we're going to train on the best of those…”

“you shouldn't be, like, sure that they're gonna, uh, we're on the track of AGI because, yeah, fundamentally we don't know what kinds of things these are.”

“If, let's say, GPT-Six isn't that much better than GPT-Four, and you had to look back on it and say, like, why, why did that happen? I think the most, the thing I'd expect to say is that right now we are We are, um, kind of fooled by, uh, how much data these models consume. Whereas, you know, they've like literally…”

“I just don't think you're going to train the AI so much on math that is going to learn how to do Henry Kissinger level diplomacy. I do think skills are somewhat more self-contained.”

“Look, if there is an intelligence explosion, it has a really tough dynamic because if you're a month ahead, you will kick off this loop much faster than anybody else. And what that means is that you will, uh, you will be a month ahead to super intelligence, but nobody else will have it right. Like you will get, you'll…”

“the key constraint with energy is not necessarily, is there enough energy in the world, but more so for training, is there enough energy in one place?”

“The way I've been thinking about it, which is, which has less to do with like maybe AGI in the world and more so about, I think it's long-term impact is the kind of model which can automate or significantly speed up AI research.”

“Fundamentally, it's a bunch of parameters, right? So it's much more interpretable than a human brain or something.”

“if AI substitutes for people, uh, can increase the effective population of a country, then you can imagine that it would just be a huge leverage that a country would have over other countries in terms of Its own economic output, or even its ability to withstand geopolitical competition.”

“One, one big one, and this is similar, is that they aren't yet useful in long, um, uh, when you need them to kind of go do a job. You can't be like GPT-IV. I'll be back in a while, but can you like manage my inbox for me in the meantime? Or can you go, uh, go book a trip for me? You know, just like things that require…”

“people have been talking about long horizon RL, which is the training method. You need to get something like this, where you go tell it to do something and then you reward it at the end for having achieved that outcome. But the difficulty with those kinds of approaches and the difficulty with RL in general is sparse…”

“I think the main flywheel, honestly, is that I make the podcast better, smarter people listen, some of those smart people I become friends with, and they teach me a bunch of things. And now I can do an even better interview. Now I have a bunch, I can get connected to a bunch of other smart people. They teach me more…”