How Doom was mapped onto the neurons

56:42chip and it's sensing motor neurons or it's sensing the neurons and then it's playing Doom. >> This is crazy, >> right? >> Yeah. Yeah. Yeah. >> It's pretty cool. >> Yeah. And we're seeing this like like Yeah. The the real >> Yeah. Those are the neurons that are coming in and then there the the petri dish is sort of controlling which way to point and which way to fire. Right. So, let's get into let's get into how actually they did it. Because in Pong, remember, we've got a sensory cortex, we've got a motor cortex. They're going to do very similar things. In this case, the proximity, we want to we want to encode information about the game state, right? So, there's proximity, which is how off

57:26how close I am. >> Yes. >> And there's direction, which is where where I'm trying to fire. They're using place and rate to code that. Right >> now, we want to take the damage. So, taking damage and dying is going to be the chaos. >> And if we shoot someone, that's going to be the predicted >> ah reward. >> Okay. Interesting. Cuz I was wondering how it was going to map from pong to doom. >> Yeah, that makes sense. >> And that's how you do it. And for the motor neurons, um there's specific firing patterns that are going to be move forward. There's specific firing patterns that are going to be turn. and their specific firing patterns for fire a weapon. And the performance after 7 days of training, the agent's kill ratio

58:07was roughly twice that of pure chance. Now, I don't know what that means. And this is where it gets kind of annoying because there's no paper, right? So, there's no methods. They just kind of said that, >> right? >> I'm like, I don't I don't really know what that means. But they're explicitly admitting that the cells played like a beginner who's never seen a computer. So, it's not very good, >> right? But it's still I guess higher than chance. >> Yes. >> So maybe that's something. One of the things that I found kind of interesting was that during the study the agents actually generally settle settled >> for a strategy of survival which is hiding rather than active combat. And this is something that is well known in reinforcement learning right you just

58:47like go for the local minima which is >> I don't want to deal with anything. I don't want any reward whatsoever, even the positive or the negative. Because that positive or negative reward comes from a dense like chain of brittle action, right? I have to do stuff. I have to do stuff and at the very end I might just get chaos and then it might just be bad. So why don't I just like go to a corner and try to hide? I wish they gave the uh the uh nuke testing AI uh paper people the same wetwware to to go to the local minima because as we talked in our two episodes ago all the major model companies Anthropic Gemini Open AAI all used Nukes 90% of the time which

59:27does not sound like what you're talking about. >> Yeah. Yeah. No, this one is just they just want to >> they're like we're just trying to hide. >> Yeah. I'm just going to try and hide. So, now we've covered what Cortical Labs is purporting they did. Yes. >> In their YouTube video. This is where I got a lot of that information. >> Yes. >> Now, let's ask what's actually going on. >> Yeah. >> Okay. Cuz there's a lot less info about the Doom >> game versus the Pong game. The >> Pong game had a full ass, >> you know, peer-reviewed paper in Neuron. This one does not. This one is just >> like blogs is all I got. Okay. Here's my concern, and this is something that um I actually read Tommy Blanchard's

1:00:07Substack on. So, if you guys want to check out his Substack, he's got a lot more um info on it.