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Chapter 1: What is AI alignment and why is it important?
The more capacity for alignment you have, the more capacity for great good is in you, and the more capacity for great evil is in you. Because how do you do great evil? You don't generally do great evil by going around and like eviling a lot as an individual. To some degree, you can do some amount of evil, but you really can only do small scale evil that way.
Really big evil, like to be really evil, you gotta get people organized.
Chapter 2: How does theory of mind relate to AI systems?
You get some industrial scale evil going. And that requires actually quite a bit of skill and alignment. I hate to use the canonical example of evil, but like Hitler, Hitler is good at aligning the German, he aligned the German people around some really evil shit. That's a great example of how alignment's a very dangerous capability.
And anyone who tells you that we're gonna make an AI that's like aligned, I think if you're not careful, what they mean is I'm gonna make an AI that's aligned to me. And then you better hope that the person who's saying that you are aligned to them, because they're saying, I would like to be in charge, please.
Chapter 3: Why is continuous learning a challenge for AI?
Hi, everyone.
Chapter 4: What is the relationship between AGI and alignment?
Welcome to the Founders in Arms podcast with me, Imad Akan, co-founder and CEO of Mercury. And I'm Raj Suri, co-founder of Lima and Tribe. And today we have with us Emmett Shearer, founder and CEO of Softmax and previously co-founder and CEO of Twitch. And also was a Y Combinator partner and famously interim CEO of OpenAI. So welcome, Emmett.
Chapter 5: How can societies of smaller AIs outcompete larger AIs?
Yeah, happy to be here. Glad to finally make this happen. We'll be talking about this for a while.
Chapter 6: Why should AI be integrated with human society?
It's great to see you. And tell us about Softmax. You and I, I mean, I've never talked to you about it. I'm very curious about it. I know you're working with Adam Goldstein, who was my white combinator batch as well. Yeah, Softmax is an alignment research company working on the question of what it would mean to align learning thinking systems with each other at all scales.
And that sort of includes AI as well as people and societies, because in our view, the alignment question is not... It obviously has relevance in AI, and AI is a useful tool for studying it, but it's not really about AI. It's this question of how do complex learning systems align with each other? And what does that even mean?
Because most people talking about alignment don't seem to have a really clear theory of it. And yeah, I started it with Adam... Gosh, it was like... almost two years ago now, something like that. But for the first year or so, it was very heavily in the wilderness trying to figure out what is this alignment thing? How would we know if we were making progress?
What would be the conditions under which we could expect it to arise? When would we expect it to break? And about a year ago, we made pretty good progress on that, and we sort of started to have a direction.
And at that moment, the universe served up to us my third co-founder, I guess my second co-founder, the third co-founder of the team, David Blumen, who, unbeknownst to us, had been working on the exact same problem, but as a... I guess not even a solo founder, is it? By himself as an engineer on an open source project that he'd been working on where he'd seen the same thing.
He spent a year grinding on the same project. It was very clear when we met him, oh, you are our third co-founder. You just like, we just, both of us were just unaware that we had started this company together. a year ago, because you'd been off on a really intense programming component.
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Chapter 7: What lessons can AI startups learn from Twitch?
And so we added him to the team. Right around that time, the things had come together, because that both gave us a technical basis to build on, and also we had the theory at that point. And so the last year has been very much building that out. And we are making great progress on that, on our simulator environment and on the future. So what is the output, right?
Is it a new model that is like better aligned? Is it a series of research papers? Like what are you trying to produce? Yeah, ultimately what we are trying to produce is a training environment, a learning environment that is interactive and that then enables agents to learn the requisite things to be aligned and to flourish. And it turns out that like in some sense,
The major prerequisites are theory of mind, Because in order to be aligned to other agents, you have to understand those other agents. In retrospect, it's kind of obvious, but the idea that you could be aligned to someone without very strong theory of mind about them is totally crazy. How would you do that in any kind of sustainable way?
You can't possibly know what they're going to do next or why they'll do it or what their goals are without the ability to infer what's going on inside their head. And so... That's sort of one of these very deep prerequisites for alignment and not just for individuals, but theory of mind over groups. Like what do we want? What are we doing here?
There's a whole separate kind of group theory of mind that's required. And then the other prerequisite is sort of open-ended or continual learning. Not in the sense of you can pre-train forever, but in the sense of you have to stay plastic, stay flexible. Because people change, the world changes. You don't get to finish training and be done.
You always have to be in training, because in some sense the world's gonna be training. And if you can figure out a way to lift that training into a context window, so you're continually learning in context, all you've done is turn the context window into your weights, and you've learned the optimizer. which is actually a pretty good idea. Like it's probably the way to do it.
But like, ultimately you need to have a system that is capable of continuing to learn what's going on in the world, in a world that's non-stationary, where you can't guarantee you've figured out the state space, where things are a little ambiguous.
And so to learn those things, you need to be in an environment that has open-ended diversion dynamics, and you have to be around other agents who are also grappling with those things, so you can learn to model them in that context. And that's sort of what we're trying to make. It's a surprisingly complicated thing to make. Is it like pre-training learning environment, post-training?
And when you say learning environment, is it like a set of questions and answers, or is it like a 3D world? How does it manifest? Free your mind, Ahmad, free your mind. Pre-training, post-training, these are but labels. All the trainings. These are labels. What does it mean for something to be a pre-training or a post-training?
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Chapter 8: What advice does the guest have for first-time AI founders?
It's not one big cell. Like, because if it has to be doing theory of mind on itself, the bigger it is, the harder that is. And if it has to be integrating its experiences, the more diverse they are, the harder it is to integrate everywhere at once. And the more you want independent minds. And so I just think that, you know, societies of AIs will out-compete any giant singleton AI.
I think singletons are just a bad model. They're a good model for machines. You can scale machines up really big. And to the degree that the AI is a tool like the current AIs, that works great. Singleton, huge singletons, great.
You wanna make a self-modeling, self-directing, divergent AI capable of managing its own learning process and continual learning, you will find it's much easier to make a society of smaller ones. And that creates the same thing it does with humans, which is like robustness, robustness against one of them going crazy.
And I think that's like, I found that thought when I noticed it very reassuring. Now, that's no guarantee of safety. The collective of AIs could decide that the AIs are their family and we are not. And then that could be very, very bad. I'm not saying like this is like a guarantee of success or anything. I'm just saying like... it's kind of the normal problem, right?
The other people, the other society of people could also decide that you're bad. Like then you're their enemy, right? Like this is not, that's not a novel problem. That's just a problem. Like the kind of problem we have to deal with.
So the vision that you're building towards is like, okay, you have this society of AIs and you're building the capability that they can align for the needs of humans, but also each other, right? And And basically, and this continuous learning is, I think, critical, right, to development of AI. This is like very fun. Aren't all the big AI labs also working on continuous learning?
Isn't that like a big fundamental building block of AGI? Yeah, they are. They seem very confused to me, to be honest. They say they're working on continuous learning, but they don't seem to be in any meaningful sense working on continuous learning. They mean something closer to larger and larger training. Oh, I see.
Unless they have some secret projects that they aren't using and not talking about, not releasing, they mean how long can we run our training process on this thing, not can we train this thing on its own outputs indefinitely. Maybe I should say instead of continual learning, because it's a little bit confusing, reflective continual learning. or like auto-continual learning.
Continual auto-learning, that's a good term for it. Because it's like you're learning on your own inputs and outputs, not on some training regime separate from you. I feel like most of the current kind of companies are in a local maximo, right?
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