Dr. Jeff Beck

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

No, it's like, look, the world is translation invariant.

The world is like rotation.

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.

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.

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, 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.