This Week in Startups
00:06:54 · Lon Harris, Alex Wilhelm
[00:06:54] S2: Yeah. We're in the middle of that meme that you are here, the world's greatest AI model. Like, we're we're right in the middle of another cycle. Yeah. [00:07:03] S1: But the question is, will this be just another iterative improvement? And if so, OpenAI is going to take a lot of egg on its face, or is it as big of a jump as maybe GPT three to four was or larger? And if that's the case, it does change the entire fabric and and landscape that the technology world, plays on. But, Lon, let's talk to some founders. [00:07:23] S2: I love it. Let's do it.
00:07:25 · Alex Wilhelm
[00:07:25] S1: Alright. So we have three interviews for you today. One, Cortical, then we're gonna talk to Turing, and then we're gonna talk to Merkor. Each one of these was an absolute treat. Cortical is probably the most out there in that we're talking about, kind of, like, hybrid biologic and digital computers. It's, it... It's an interview I I chased on for a long time. It's super fun. And then turning to Merkur, a bit more traditional in the software space, but incredibly interesting founders, incredibly interesting companies. And especially, I think, the case of Merkur, a a a vision for a company that could become incredibly instrumental to how labor functions in the future. So these are a treat. I had a blast. Please enjoy them, and say hello to three of our founders from the Twist five hundred. Hey. Welcome back to Twist. This is Alex. Today, we have another Twist five hundred interview with a company that has been my white whale for several months, but I'm so glad to have them on the show. We're going to talk about cortical labs. Now here at Twist, we love chips. We love talking about what Grok is building to handle AI inference. We loved that Etched is building ASICs just for LLMs. We've talked to XTropic and their thermodynamic computing paradigm, but all that's entirely silicon based, more or less. Cortical wants to bring the world of biology to the world of computing in a way that when I first heard about it, I thought was science fiction, but they've taken it out of the lab, and they're commercializing it. So please join me in welcoming to the show. It's Cortical Labs CEO and cofounder, Han Wangchong. Han, how you doing?
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.