The Exchanges

Q I'm always curious. I mean, you were mentioning second sight sort of, you know, flashes of light, and yet, you know, here, you know, how did you figure out the API? I mean, if I was, you know, trying to reverse engineer it, I guess I would, like, try to measure the signals. Is it similar with, you know, biology?

A It's just, it's difficult to measure the signals. So, brain-computer interface research and development is limited by your ability to record and stimulate these signals. The neuroscience, comparatively, is actually pretty simple. As soon as you can record the signals, we've very quickly figured out what, we talk about neural representations, what they are. Second site's instructive. So in the retina, there's three layers of cells that matter. There's a hundred fifty million rods and cones. This connects to a hundred million bipolar cells, bipolar because they've got two ends, and that connects the rods and cones to 1.5 million optic nerve cells. We call them retinal ganglion cells. Ganglion is like a fancy word for, like, reaches a far distance and connects to somewhere. We stimulate the hundred million bipolar cells. Second sight stimulated the 1.5 million ganglion cells. And so they were trying to get the signal into the brain past that 100 X compression. And the retina was doing a lot of computation there. The eyes of camera light shines in from the front. It hits the rods and cones like that. The representation in the rods and cones is a bit mapped image. It's just like you take the image, you tile it across the rods and cones. That's what it is now. And the The 1.5 million optic nerve cells. It's not like that. Like if you just project an image onto them, you get a bunch o…

AI assessment note: “As soon as you can record the signals, we've very quickly figured out”

Q You're one of the best examples of someone who came from a pure software world and then went into hard tech and now is actually doing real breakthrough type of research and work that is also commercializable. The people watching, they might be on a similar track. Knowing what you know now, like, what would you tell to the sort of 2016 version of yourself?

A So I think there's two things. The first is, um, like the thing that I did, and then there's the thing I didn't do. The thing that I did, I think, was I had, I had a, A clear sense of what I wanted, and then I was very high agency towards that. When I was in college, I knew that I wanted to work in brain computer interfaces. There was a great lab that was doing that work at Duke where I went. And I was pretty persistent in figuring out how to like place myself into that lab. It was in the medical center. They didn't usually take undergrad. They were like, it took me a little while to get in there. I eventually figured out that I could sneak in by taking an independent study in the chemistry department that would like be a backdoor into this like primate neuroscience group. But then really most of my education in college happened in that lab. So yeah, I, I grew up programming in my, my deepest hard skill is software, but I, I've been doing primate brain computer interface, like closely neural decoding stuff since And so that was just like, you had to be pretty high agency and, and like, um, persistent in trying to like, if like follow through on that, but that only works if you have a sense of where you want to go. And so the first is like, figure out what you want. The thing I didn't do was my, so after college, I started a company, um, called Transcriptic. That was a, the, it …

AI assessment note: “So I think there's two things. The first is... figure out what you want.”

Q like, a million questions, honestly. I mean, one of the things that I'm super curious about is, like, well, what is the qualia of the person who has Prima, and what is, you know, I'd be curious, like, with the bio-hybrid approach, like, what does it feel like? And, you know, is it like having a second screen? Like, you know, is there an input or output? I'm very curious.

A Yeah, so for Prima, actually, on the topic of plasticity, In the time that the patients are blind, the brain, the brain wants to see. Like again, you, the thing you experience is this world model constructed by the brain, and that is this, is this generative model that is conjuring your reality. And so when it's not getting input from the, from the optic nerve, it is still trying to see things. So it kind of turns up the noise. And so, um, blind patients often report like hallucinations and these like internally generated percepts. When you first turn on the implant in these patients, like you hit it with the laser, Um, they'll, they'll say, oh, I see a flash, but then you can do a thing where you'll, you'll turn on the laser, they'll see a flash, and you'll play a tone, and you do this a couple times, and then you, like, don't turn on the laser, but you play the tone, and they're like, I see the flash. And so for the first couple hours of rehab, they kind of just have to, like, learn to, like, dissociate the real percepts from the phantom percepts, because the brain is, like, so, it is, like, so turned up the gain, like, turned up Turn down the noise for that. Um, just like getting, learning how to discriminate real information coming in from the optic nerve takes a little bit of rehab. The qualia of Prima is, is normal sight. Um, it's black and white. It's only a, it's a smal…

AI assessment note: “The qualia of Prima is, is normal sight. Um, it's black and white.”

Q I mean, like anything, you sort of bootstrap with the thing that works, which I think, you know, what, what you have is a clear breakthrough as it is. And then if you look at like the PC revolution, for instance, it's like, could you believe that all of this that we have today started with like a little blue box, like in Altair?

A It still takes some suspension of disbelief because I think biotech has just been so incremental. Like it's been so, like there's, there's been big advances, but at the same time, these time constants historically, I mean, you could easily spend 10 years on something that feels very incremental. And I think that One of the things that's so exciting about what's happening now is that no longer really feels so incremental to me. To me, it feels like we're firmly in like the takeoff era now, like something new has happened on earth. But I think it's also important to remember that this didn't start in like, 2019 or 1999. This started in the late 1800 with the industrial revolution. Just a few years before the industrial revolution really kicked off, I mean, life was more or less unchanged in a fundamental sense for several thousand years. And they, Didn't really even have like a concept of progress in many ways. And I don't think there's any way they could have imagined like the way that their life would have changed over the course of the like first 1015 years of the steam engine. And that is how I feel like looking at the next 15 years right now.

AI assessment note: “It still takes some suspension of disbelief because I think biotech has just been so incremental.”

Q What if the technology just isn't good enough yet, and it needs to be improved?

A It needs to be improved, definitely, but that wasn't even the response that I got. The response that I got was just, like, shouting and throwing things, and so I was like, something feels wrong here, but I wasn't really in a position then to pursue it, but this was always a thing that was kind of, I saw that there was a really important unification here. It also, this, the same fundamental type of technology has really transformed organ transplantation. So there, they call it NMP, normothermic machine perfusion, rather than ECMO, but it's the same idea. Um, so, 20 years ago, if you needed a, like a, Kidney transplant or liver. If the car crash happened at three in the morning, the surgery would happen at four or five in the morning. But now it gets scheduled for like the afternoon or the next day, and over 75% of liver transplants in the U.S. use this type of perfusion technology now. But like the, the systems that exist for this are like 500,000 dollars. They can only be moved by private jet. Like one of the big companies in the space, it turns out that they're like private jet logistics business is bigger than their medical device business. And it just like, there was just like clearly an engineering that could refine this. And so we looked at this and we thought like, well, what if you could refine this to the point where you could check a kidney as luggage on a United fligh…

AI assessment note: “It needs to be improved, definitely, but that wasn't even the response that I got.”

Q For those watching who have never heard of a brain-computer interface, what is it, and what have people been able to do, what are they able to do now?

A So the brain is this powerful computer, but it's encased in the skull, like it is not magically connected to things, and so, um, it has these, these handful of connections to the world, and these give you the senses that you know, and the motor control that you know, but You can kind of ask like, is that, so either do we want to replace these with something else? So for example, like the simulated reality or the matrix use case. Another is restoring lost functionality. So this is, I mean, this is how they're deployed today. So if someone has gone blind, you can restore the ability to see. If they've gone deaf, you can restore the ability to hear. If they're paralyzed, you can restore the ability to move. And then you can think about structural neural engineering. And this is the, this is the thing that people haven't really, we haven't gotten to as a field as much, but looking at how, how does the brain process information? Can you add new brain areas? Are there ways to understand how the brain is like, what, What is going on either to use this to build smarter machines or to think about how to treat things like depression or addiction.

AI assessment note: “Another is restoring lost functionality. So this is, I mean, this is how they're deployed today.”