Did they find the “boss’s office”?

59:33found >> may have found the boss's office. >> if you turn the lights on and off, the boss will wake up. >> Yeah, right. Right. This is I'm so mad because it's so clever. Each of the three layers, also in how we went through it, of like detection characterization and evaluation almost is like is is so good. No. >> Because the the the part that's getting me about this piece is because you've set up like the genomic algorithm and the the model, the brain simulator, the way they thought to set it up that way means that you almost have this like AlphaFold adjacent kind of thing where

1:00:15you can in silica like test >> Yeah. stuff to try to narrow the sandbox >> Mhm. of where to look. >> Exactly. >> Right. And like that's so valuable. >> Yeah. Yeah, exactly. >> necessarily have to be the exact answer. No. But if it's even somewhat directionally correct, that's unbelievably valuable. >> Yeah, dude, it's great. And one thing that one thing that's kind of funny is so Daniel, he he is himself a science communicator, right? So, he's got an Instagram and a TikTok. And he did like a short 3-minute video being like, "Hey, my paper came out." And he kind of explained it at a very high level. I mean, here we've gone really in-depth. So, at that high level he was describing

1:00:56his generative AI framework. And there was someone in the comment who was like, "That's not really AI, right? Because you're using a biophysical model." And it's like, "Dude, that's the point." That's the point. It's like without a biophysical model, I wouldn't be able to make these predictions of oh, it's the PV plus neurons in the cortex. It's this highway in between the striatum and the pallidum. It's the subthalamic nucleus that we need to probe, right? It's just going to some random neuron in some random layer of a trillion parameter model. What good is that? The the 100% I still come back to one of the There's so many interesting insights in this entire paper's architecture, like the experimental design architecture. But, I

1:01:38think a key piece was you you can get traceability and then subsequently reproducibility because of that brain simulator. >> Yeah. Um like and the way in which it is uh explicit it is um What's the I There's the the terminology and this may not be correct. There's a terminology where a probabilistic versus deterministic and it's more on the deterministic side and less on the probabilistic side, which in science That's a good thing. >> It's a good thing. >> Yeah. It's very much a good thing. And right and then finally we now have an