Nathan Lambert
speaker
1,814 appearances
3 recordings
2 series
first heard Feb 2025
last heard 1 Feb
Nathan Lambert’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Feb 2026 with 1.
Appearances
I mean, is that not what character AI has done?
But like it can be like, yeah, this is a risk, right? Like.
One of the hilarious things about technology over its history is that the illicit adult entertainment industry has always adopted technologies first. Right. Whether it was like video streaming to like where, you know, there's now the like sort of like independent adult illicit content creators who have their subscription pages.
And there they actually heavily utilize, you know, generative AI has already been like diffusion models and all that is huge there. But now these like these subscription based individual creators do use bots to approximate themselves and chat with their, you know, people pay a lot for it. And people pay a lot, right?
A lot of times it's them, but a lot of there are agencies that do this for these creators and do it like on a like mass scale. So the largest creators are like able to talk to hundreds or thousands of like people at a time because of these bots. And so it's already being used there. Obviously, you know, like video streaming and other technologies have gone there first.
It's going to come to the rest of society, too.
This is where the whole like hacking models comes from, right? Like GPT will not tell you how to make anthrax, but if you try really, really hard, you can eventually get it to tell you about anthrax because they didn't filter it from the pre-training data set, right?
I mean, people have been meaning on like games and other stuff, how to like say things that don't say Tiananmen Square. But, or like, yeah, so there's always like different ways to do it. There's, hey, the internet as a whole does tend to just have a slight left bias, right? Because it's always been richer, more affluent, right?
younger people on the internet relative to the rest of the population so there is already inherently a slight left bias right on the internet and so how do you filter things that are this complicated right is it like and and some of these can be like you know factual non-factual but like Tiananmen Square is obviously the example of a factual but it gets a lot harder when you're talking about aligning to a ideal right um
And so Grok, for example, Elon's tried really hard to make the model not be super PC and woke, but the best way to do pre-training is to throw the whole freaking internet at it and then later figure out. But then at the end of the day, the model at its core now still has some of these ideals.
You still ingested Reddit slash r slash politics, which is probably the largest political discussion board on the world that's freely available to scrape. And guess what? That's left leaning, right? And so, you know, there are some aspects like that you just can't censor unless you try really, really, really, really, really hard.
And I mean, it's like you can, you also have the ingested data of like Twitter or like Reddit slash r slash the Donald, which is like also super pro-Trump, right? And then you have like fascist subreddits or like you have communist subreddits. So the model in pre-training ingests everything. It has no worldview.
Now, it does have some skew because more of the text is skewed a certain way, which is general, slight left, but also somewhat intellectual. It's just the general internet is a certain way. And then as Nathan's about to describe eloquently, you can elicit certain things out.
And then maybe OpenAI does less and Anthropic does less. And then on the other end of the spectrum is XAI. But they all have different forms of RLHF trying to make them a certain way.
I think it's actually probably simpler than that. It's probably something related to computer user robotics rather than science discovery. Because the important aspect here is models take so much data to learn, they're not sample efficient, right? Trillions, they take the entire web, right? Over 10 trillion tokens to train on, right? This would take a human... thousands of years to read, right?
And humans know most of the stuff, a lot of the stuff models know better than it, right? Humans are way, way, way more sample efficient. That is because of the self-play, right? How does a baby learn what its body is? As it sticks its foot in its mouth and it says, oh, this is my body.
It sticks its hand in its mouth and it calibrates its touch on its fingers with the most sensitive touch thing on its tongue. This is how babies learn. And it's just self-play over and over and over and over again. And now we have something that is similar to that with these verifiable proofs, whether it's a unit test in code or...
mathematical verifiable task, generate many traces of reasoning, right? And keep branching them out, keep branching them out. And then check at the end, hey, which one actually has the right answer? Most of them are wrong. Great. These are the few that are right. Maybe we use some sort of reward model outside of this to select even the best one to preference as well.
But now you've started to get better and better at these benchmarks. And so you've seen over the last six months, a skyrocketing in a lot of different benchmarks, right?
So the thing here is that... These are only with verifiable tasks. We earlier showed an example of the, you know, the really interesting, like what happens when chain of thought is to a non-verifiable thing. It's just like a human, you know, chatting, right? With the, you know, thinking about what's novel for humans, right? A unique thought.
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