Promo hype vs paper reality — what the box plots actually show

47:20connections in my neurons on my petri dish such that I minimize prediction error. And that's effectively what these neurons are doing. It's actually pretty incredible when you think about it. We're not giving them a bad and good signal. We're only giving them a chaos versus non- chaos signal. >> Right. Right. >> And that itself just because of the nature of information and the physics of information is enough to like steer them like this is bad and that's good. I think that's like quite deep. >> The universe the universe never chooses violence. >> Yeah. It always chooses to billy when given I I'm being a little facicious,

48:02but >> no, but I think I think I think that's a very good and that's a very poignant statement here. So now let's look at like some of the hype that they created around it. Let's look at some of the promo videos. >> And this was for the Pong. >> This is just 2022. This is we're still on 2022. >> Just confirm cuz I know people are listening waiting for the Doom. >> Yeah. But we need to really understand this in order to get to Doom 100%. >> Okay. So this is um one of their promo videos. Look at how many this this this is doing really well. It's already at like rallies of three. >> Yeah. >> Maybe it'll get to four. >> Yep. Yep. Is it going to get to four? >> Yeah, it got to four. >> Four rallies, right? Like this is it's really it's really doing it. Five. It

48:42was getting to five. Now, let's look at the results from their paper. >> Okay. >> Okay. From the results from their paper, if we look at the left hand side >> Yes. >> Um, figure B. >> Yes. >> That's the average rally length. Yes. >> And look at the y-axis. >> Yep. What is the y-axis on? It's on one, right? Most of their trials are on one. Even on their box plot, the edge of their trials for human cortical neurons, which is the right hand side box plot, >> is going to 1.5, >> right? >> Which is that's the I think it's the third quartile usually for box plots. So, it's like the 75% of data falls within 1.5. So clearly they're like showing a very nice example in their

49:25video right? >> It's still statistically significant. If you look at the human cortical neurons, which is the right box plot in figure B. >> Yes. >> Um from 0 to 5 minutes, the average rally length is at 0.5 and now it's bumped all the way to one. And if you look at the spread of that data, clearly it's highly significant. If you look at all the other metrics, you can see for human cortical neurons, they do the best compared to mouse cortical neurons and all the other controls. So there is clearly an effect, but the cortical labs team are a startup. >> Yes. >> And so, you know, >> as is tradition in startups, not just in Silicon Valley, but around the world, even in Australia, they're going to hype up some of the results. And that's fine.

50:07I still think it's a very cool >> thing that they did. >> Yes. And we just walked through why it's incredibly cool. Um, and if you think that AI is using too much energy, you should be all for wetwware. >> Yeah. Yeah. I mean, it maybe it maybe has a lot of applications. So, now we're going to scale this up. >> Okay. >> We're going to scale it up to complexity level 10,000 because we are now trying to play Doom. >> They built the CL1 platform, Cortical Labs >> first deployable biological computer in the world. >> I like that. >> That's what it looks like. It's a It's a little box that at its center has the petri dish with all of the biomime like

50:48the biological fluids and all the things to keep the neurons active. You've got the multi-elerode array. And you've got a little screen on the side that shows