This Week in Startups
00:08:51 · Hon Weng Chong, Alex Wilhelm
[00:08:51] S3: Hi. Good. Thanks. Thanks for having me on the show, Alex. [00:08:53] S1: I'm so excited. Okay. So you guys are fusing biology and, I would say, digital computing in a way that's really interesting. But I think to help everyone understand how you got to where you are today, we should go back to the earlier days of the company when you guys were experimenting with your technology, and you used it not to play Grand Theft Auto as everyone wants to do with generative AI, but instead to play Pong. So, Han, can you tell me about why you chose that and then how the experiment went to prove out what Cortical Labs wants to build? [00:09:24] S3: Yeah. So if you take a few steps back to, I guess, 2018... 2017, 2018, there was a... I guess the first AI boom that happened that was, I would call it the convolutional neural network reinforcement learning boom. I had just exited from my last business and as part of that, it was a med tech business that gathered a lot of data. And I was looking at using machine learning to do automated diagnostics and really got deep into the rabbit hole of machine learning and artificial intelligence. And I came across a paper written by Suru Demasuras Sabes from DeepMind, who wrote that the machine learning and AI community had to go back to its roots in neuroscience because that's where most of the initial discoveries were made.
00:10:23 · S4
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00:11:28 · Hon Weng Chong, Alex Wilhelm, Jonathan Siddarth
[00:11:28] S3: So I did exactly that. I went to the, neuroscience department at the University of Melbourne, which is my alumni, and spoke to the researchers there and asked them, what's exciting in your world? What are you working on these days? You know, I'm thinking applying machine learning and AI to the neuroscience world. And one thing that really kind of stood out to me was they said, oh, we have this device called the multi electrode array. It's not a new device, it's been around, I guess since the early 2000s where you can actually etch in electrodes, in petri dishes where you can grow neurons on top of them. And because neurons communicate electrically, and because chips run electricity, you now have this common language between the biological system and a compute system. [00:12:11] S1: Electricity is the shared language between both neurons and chips. Absolutely. [00:12:16] S0: Okay. Mean, if you think [00:12:17] S3: about it, that's how that's how Neuralink works. Right? I mean, joke we have at at Coracle Labs is that we are the inverted Neuralink. [00:12:24] S1: Yes. You've taken the brain out and put it into the computer versus putting the computer into the brain. [00:12:30] S3: Correct. Exactly. I had read the paper from Demis. I was inspired by it. And the first thing that Demis tried to do at DeepMind was he tried to get the machine to play pong. So I [00:12:40] S1: said Ah, okay. So when we do the pitting...
00:12:40 · Alex Wilhelm, Hon Weng Chong
[00:12:40] S1: said Ah, okay. So when we do the pitting... [00:12:43] S3: Yeah. Yeah. Yeah. Exactly. So quite a lot of the first few attempts weren't really successful. But fortunately, these neurons are very malleable and they're very dynamic and they self organize. And we realized that if we were trying to tweak the system while they were learning, you would end up with these very weird so called attractive dynamics where... Because, you know, what... It's like how to describe it when when you're trying to find somebody in a shopping mall, but you... The other person's also looking for you and you're looking for them, and so you almost never meet because they're always moving around in that same pattern. That's what we were doing. So what we ended up doing was we said, wait. We're not gonna change some parameters. We're just gonna leave it fixed. But we are going to, you know, flash them with different, like, signals and so forth. Maybe, hopefully, that way they won't be like... We wouldn't be wandering around trying to find it... Find each other.
00:12:43 · Hon Weng Chong, Alex Wilhelm
[00:12:43] S3: Yeah. Yeah. Yeah. Exactly. So quite a lot of the first few attempts weren't really successful. But fortunately, these neurons are very malleable and they're very dynamic and they self organize. And we realized that if we were trying to tweak the system while they were learning, you would end up with these very weird so called attractive dynamics where... Because, you know, what... It's like how to describe it when when you're trying to find somebody in a shopping mall, but you... The other person's also looking for you and you're looking for them, and so you almost never meet because they're always moving around in that same pattern. That's what we were doing. So what we ended up doing was we said, wait. We're not gonna change some parameters. We're just gonna leave it fixed. But we are going to, you know, flash them with different, like, signals and so forth. Maybe, hopefully, that way they won't be like... We wouldn't be wandering around trying to find it... Find each other. [00:13:35] S1: So so, Han, this is actually when I I fell in love with what Corticol was doing because according to at least NPR's reporting of this, you guys provided kind of a positive and negative response electrically to the neurons that were interacting with the chip, and you gave a burst of, I guess, unfortunate white noise to the Mhmm. Device if it was the wrong action and then a a positive organized burst of electrical activity if it got it right. So essentially, you very politely shocked it if it did bad pong, and you rewarded it if it did good pong?
00:13:35 · Alex Wilhelm, Hon Weng Chong
[00:13:35] S1: So so, Han, this is actually when I I fell in love with what Corticol was doing because according to at least NPR's reporting of this, you guys provided kind of a positive and negative response electrically to the neurons that were interacting with the chip, and you gave a burst of, I guess, unfortunate white noise to the Mhmm. Device if it was the wrong action and then a a positive organized burst of electrical activity if it got it right. So essentially, you very politely shocked it if it did bad pong, and you rewarded it if it did good pong? [00:14:07] S3: Kind of. I mean, I wouldn't say... It's not really a, you know, a a reward or a shock kind of thing. Because at the end of day, we're still giving them electrical signal. It's just the structure of it. And this was actually driven by us coming across a theory developed by a neuroscientist based in UCL in London by the name of Carl Friston, which is actually really interesting, really fascinating, the free energy principle. The free energy principle kind of posits is that the brain or all biologically intelligent systems are actually generative models. What we do in was now believed is happening in our brains is that we're not reactive machines, we're actually predictive machines. And so we're actually generating hypotheses about the world that is external to the brain in the brain as simulations. And then we're using the senses that we receive and the ability to affect the world as mini experiments to prove and disprove the hypotheses that are generated by the brain.