Chapter 1: What is the main topic discussed in this episode?
geometric deep learning is a big part of like is a big part of the stack if for no other reason than when we talk about like modeling the physical world that means like incorporating the symmetries that exist in the physical world it's like we're highly motivated to employ a lot of those methods and techniques
Chapter 2: What is the relationship between agency and intelligence?
But is the world written in code, or do you mean exploiting the regularities in the code that seem to have symmetry? Exploiting the regularities.
No, it's like, look, the world is translation invariant. The world is like rotation.
Chapter 3: How do we distinguish between simulated agency and true agency?
Well, not really, because there's gravity, but in principle... There is a principal axis, but it's certainly rotationally invariant in the XY plane. And if you want to have a good model of the world as it actually is, it should incorporate those features. Of course, you can discover it in a brute force-y way, but the mathematician in me really wants to build the symmetries in.
And fortunately, we've got a lot of great tools that were developed over the last several years that can do that.
What's your view on agency?
If I'm being, you know, like an FEP purist, I have to sort of say like, oh, well, there's no difference between, you know, an agent and an object in a very real way, or at least there's nothing structurally distinct between how we model an agent and how we model an object.
Chapter 4: What are energy-based models and how do they differ from neural networks?
It's really just a question of degrees, right? An agent is a really sophisticated object, right? It has internal states that represent things over very long timescales. It has sophisticated policies that are context-dependent, which is basically saying really long timescales again, and things like that.
Yeah, you know, there's the kind of philosophical highbrow notion of agency that we introduce notions of intentionality and self-causation and things like that. I mean, the really no-nonsense version of an agency is it's just... It's just a thing which acts and performs some kind of computation. And I guess you could almost model anything as an agent.
Chapter 5: How might the evolution of the brain relate to our sense of smell?
Yeah, well, so if your definition of an agent is something that executes a policy, then anything is an agent, right? A rock is an agent, right? Everything has, you know, it's an input. A policy is an input-output relationship, right? When many people talk about agents, they're adding a few additional elements that I think have a lot to do with how the policy is computed.
So for example, when we think of how the difference between us and amoebas, We often cite things like planning, counterfactual reasoning, goal-oriented behavior.
Chapter 6: What is the JEPA revolution in AI learning?
We're specifying things that are all related to how it is we compute our policies. They're latent variables that represent policies. that are compatible with reinforcement learning. And that's the defining characteristic of an agent.
But you could very easily just say from an outside perspective, if you can't look at how someone or something is doing the computations, if the only thing you observe is the policy, Does that mean that you can never conclude that something's an agent? And I would say no.
Chapter 7: How can AI safety be approached without fear of rogue superintelligences?
You'd still like to be able to conclude that this is an agent, even though the only thing I ever get to measure is its policy.
But do you think we should have some notion of the strength of an agent? The strength of an agent.
Is this like a measure of agency? Is that what you mean? Yes.
Chapter 8: What role does scientific discovery play in the future of AI?
So, I mean, I think you could use notions of transfer entropy and things like that in order to estimate the timetable for which something is incorporating information or the degree to which it exhibits a context-dependent behavior and things like that. And that would be a pretty good measure. Now, is it normative? No, it's not. But it is a measure and you could use things like that.
But at that point, you're really just talking, again, about policy sophistication. Right. Not does it have a reward function? Like, is it actually executing planning?
Yeah, I mean, certainly intuitively agents to me seem to be kind of causally disconnected because they're planning into the future. They are not impulse response machines. They're not just, you know, part of the mass of things going on around them. They are just obviously disconnected from the locality.
So the trick is that, okay, so I've got this agent, and I know exactly what it does, right? It takes into account information. Internally, it rolls out a whole bunch of future consequences of various different actions or plans that it could take. It selects the best one, and then it executes it, right? So all of those variables, all of those variables that occurred inside, right?
From the outside perspective, it just looked like a function transformation, right? Unless I'm somehow going in and recording and somehow demonstrating the fact that the manner in which it is calculating its policy involved doing those rollouts, I wouldn't be able to show that it's actually doing those rollouts. I would just be able to conclude it has a really sophisticated policy.
So the question is, how do you identify something that is actually doing planning? I think that's a really hard question, as opposed to having an incredibly sophisticated policy.
I think my intuition is, it feels to me that a function, a simple input-output mapping can't be an agent. And in a way, this is related to what we were talking about with grounding. It seems that when things are physically embedded in the world, then they're more likely to be agents.
This functionalist idea that just a bit of computer code running on a machine, it kind of feels like that can't be an agent.
Yeah.
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