This Week in Startups
00:22:10 · Hon Weng Chong, Alex Wilhelm
[00:22:10] S3: We say six months because it's just easier to explain it that way. But actually, you can keep... You know, we as a lab and other people have kept them alive for months, even years kind of thing. When you're talking about trying to do large scale experiments, you need a lot of samples, you need a lot of units to get statistical significance and power through a large end. We wanted that scale, we wanted that longitudinal ability to study the neurons, but we also didn't want to have to spend a lot of effort doing it. So a bit of engineering went into a bit of plumbing to build this system that encapsulates the neurons in a sealed environment, which is really important because the neurons don't have an immune system. And so, if you had COVID and you coughed into it, they would actually just also get COVID. [00:23:03] S1: So your computer could literally catch the cold. That's really funny.
00:23:03 · Alex Wilhelm, Hon Weng Chong
[00:23:03] S1: So your computer could literally catch the cold. That's really funny. [00:23:05] S3: Yes, pretty much. Don't eat bread next to it because you'll get the yeast and mold that goes into it and they'll kind of kill the cells. But we also built in our own neural interfacing system. So there's actually, you don't really see it at the base of the CL1 has a compute unit. So we have FPGA and whatchamacallit, general purpose compute units in there as well. That's where we digitize the signal. We then put it through our very tight processing loops And, you know, we're developing APIs and SDKs for this so that, you know, as a as a programmer, you don't have to go deep into getting these things to stimulate the neurons with high precision, but a lot of code, you can just use our API and we've developed a DSL domain specific language to express how do you want to stimulate these neurons at individual channels at low latency loops. [00:24:09] S1: That that brings me to kind of, I think, the question on everyone's mind who's listening to us right now, which is, okay. This is awesome. You've made a a digital biologic brain. What's it good for? And I I think that this is actually where my my knowledge really kinda run short because I'm not sure what compute loads, what experiment types, what commercial applications, the CL one and its successors, are going to be best at. And so when we think about the market that Corticolax is [00:24:35] S3: going [00:24:35] S1: after how big is it?
00:24:35 · Alex Wilhelm, Hon Weng Chong
[00:24:35] S1: after how big is it? [00:24:38] S3: I think the two most interesting things that we've learned along the way about this system here is that they do two things really well. Well, actually three things, but that third thing is attached to the second one. Firstly, they use significantly less energy than your traditional compute. They're not a von Neumann based architecture. The energy is derived from the glucose in the actual cell culture media. So doing a back on the envelope calculation, we came across some really phenomenal numbers. So for our original Ecpona experiment, we grew about 800,000 to a million neurons. And we did a very rough estimate where we just said, what's the glucose content in the media? How often are we changing the media? So therefore if we change the media, know, X number of times a week and there's this much of glucose and they're combusting all of it assuming, because they never combust 100%, you know, how much energy were they using? It turned out it was actually 10 to the minus four watts of energy. So that's 0.0001 watt of energy. It's probably even less than that because that's a massive overestimation. [00:25:41] S1: But but still, even with massive overestimation, it's effectively zero. And glucose, last time I checked, is is sugar water, so it's nice and cheap. [00:25:50] S3: Exactly. I mean, if you think about it, you and I, walking reference, use only 20 watts in our brain.
00:25:50 · Hon Weng Chong, Alex Wilhelm
[00:25:50] S3: Exactly. I mean, if you think about it, you and I, walking reference, use only 20 watts in our brain. [00:25:55] S1: The guys at Extropic were telling me this. They're like, you know, we have this amazing probabilistic computer that runs on 20 watts, and it's literally inside your head. And they're working on thermodynamic committee, which is different, but I've been thinking about that ever since they brought it up. Okay. So, clearly, this is not gonna be used right now to train the next major LLM. But on the people who are purchasing the CL ones that will come out sometime next year, what what do they want to do with it?
00:26:20 · Hon Weng Chong
[00:26:20] S3: I think the thing to understand here as well is that LLMs could not exist twenty years ago, right? Even if we had the compute available, we could not make it work because LLMs required large datasets. I mean, they're called large for a reason, right? We... We've gotten to this point of, what is it, forty years? What are we now? Twenty, twenty five? Let's say forty years of of the public Internet being this Sure. Repository of, you know, language training data. There are lots of other domains that are not language based, that do not have large datasets that are publicly available. And this is the second point that we've discovered along the way is that if you do a head to head comparison and there's a paper coming out actually, this was in Europe, it's about two years ago where we put it in the RL workshop as one of the posters. Did you took reinforcement learning agents? So we we did an experiment where we took three of them, DQN, which is your classic alpha go. It's kind of, you know, the... I guess the gold standard, it's kind of old now. There are better algorithms out there that are more what's so called sample efficient. And we put that against the biological system and we get to the same game. But we said, here's the one thing, we're gonna we're gonna constrain the reinforcement learning agents so that they get the same amount of data that the biological system gets. Right? Because if you think about reinforcement learning systems, nobody really talks about this, but the way they actually learn in a simulation is that they spawn millions of parallel processes and they speed up the time of the game by, you know, 200, 300 fold. And so if you read the AlphaStar paper or something like that, DeepMind kind of said that, you know, if you were to train a a thing or human to play at the amount of data that AlphaStar was playing, it would take four hundred years of continuous gameplay to get to that level of performance. And so what we did was we said, we're gonna temple constrain it because if you think about this, the real world, you can't speed up time. Time ticks at the same rate that you and I have. And if data is a factor of time, right? Say bit rate and so forth, then if you wanted to get, say ten minutes of data, you would have to spend ten minutes collecting that data.
00:28:40 · Hon Weng Chong, Alex Wilhelm
Knowing that these systems use far less data, when we did the comparison with the reinforcement learning agents and data being a factor of time, we realized these things are actually potentially very good for problems that don't have a dataset, problems that are real time specific. And that is actually the vast majority of the domains that have not actually been touched by language. [00:29:03] S1: This is when I get really excited. And I I don't I don't wanna make you project the future too much because that's not really fair. But to me, with with neurons that we can interact with with software, it seems like we've managed to take out the best of both worlds, the programmability of of computers as we understand them, and also the probabilistic low power consumption and kind of generalized intelligence of brains and brought them together, Hans. So to me, in time, it feels like we should be able to use both, you know, sure, our mass GPU clusters that everyone's building, but also we should have probably large tanks of biologic, you know, neurons that are also helping us do a lot of a lot of work. Is that too kind of science fiction y, or am I on the right path?