transcript

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Speaker 6:
[01:01] Hey, this is Andy Mills. And for today.

Speaker 7:
[01:04] Just what are digital computers? The computer. Are they manmade monsters that perform mathematical miracles in millions of a second?

Speaker 6:
[01:13] From where we sit today, it's actually kind of funny to look at how we were thinking and talking about the computer just decades ago.

Speaker 7:
[01:20] Are they superhuman machines that can solve any kind of problem?

Speaker 6:
[01:24] How mysterious they seemed to the general public.

Speaker 7:
[01:27] No, nothing miraculous at all, nor monstrous. The working parts are transistors, vacuum tubes, magnetic devices and other electronic components.

Speaker 6:
[01:38] But we've got to remember that the computer is still a relatively new normal in the world. For example, in 1980, only 1% of Americans owned a computer. Let's think again. When the personal computer industry first got started, there was robust discourse for many years about why an individual would even want a computer, let alone need one. And of course, now, it's almost impossible to think of what would happen to the global economy, to our personal connections, to the very infrastructure of our modern society without these technological marvels. But our story today is about how the computer itself may be about to take its most radical and bizarre step forward in its evolution, and how that might prove to be even more transformational to the future of the human race.

Speaker 8:
[02:42] Imagine opening up your computer, breaking it open, like unscrewing the back and looking about the guts inside. And instead of seeing, you know, wires and chips and hardware and things like that, you see living brain tissue, human brain tissue, powering the computer.

Speaker 6:
[03:07] So this is Gregory Warner, the host of our series The Last Invention, and he's been reporting for the last year on this new kind of computer. And before we jump in with him, I just want to say that this is kind of a special crossover piece for us here at Longview. If you're listening to this on the Last Invention feed, great. We highly recommend that you go and check out our other show, Reflector, where this episode is also being hosted. And if you're listening on Reflector and you are curious about artificial intelligence, about why there is so much excitement, so much hype, so much fear about this new technology, we highly recommend that you go start back at episode one and listen to The Last Invention. And now, without any further ado, here we go. All right, Greg Warner.

Speaker 8:
[03:57] Yes.

Speaker 6:
[03:57] Thank you so much for returning to the studio to tell us.

Speaker 8:
[04:02] The weirdest story yet.

Speaker 6:
[04:04] Definitely the grossest.

Speaker 8:
[04:05] The most goopy, for sure.

Speaker 6:
[04:07] It's a goop-filled story.

Speaker 8:
[04:08] It's a little smelly, too.

Speaker 6:
[04:09] All right, so where do we begin? How do you want to start this off?

Speaker 8:
[04:13] Well, one place to start is with this guy, Hon.

Speaker 9:
[04:15] Hon Weng Chong, CEO and founder of Cortical Labs.

Speaker 8:
[04:19] Hon Weng Chong, he's a computer engineer down in Australia. I got to say down in Australia. I don't know why you always do need to say that. But he was really interested in this question of, how do we get a better way to teach computers to learn? He stumbled across this essay.

Speaker 9:
[04:34] Written by Demis Hasadas from DeepMind, where he advocated for machine learning and AI researchers to go back to our origins, which is in neuroscience. So he literally took that advice and went over to the Neuroscience Department at the University of Melbourne.

Speaker 8:
[04:52] And he asked for a tour. And they're like, okay, no problem. Maybe they give him a white coat and goggles, and they take him around. And they show him at one point in the tour this tiny glass chip. It's about the size of a fingernail, marked with these metal dots attached to wires. And on the metal dots are a little cloud of tissue. And he's like, what is that? And they're like, oh, that is mouse brain tissue on that chip.

Speaker 9:
[05:22] That captures electrical activity between neurons and provide a little bit of a stimulus back.

Speaker 8:
[05:29] But Hon was a computer scientist. Like, he had never seen anything like this.

Speaker 9:
[05:34] And so I asked them, so how does that work? And they say, well, we get a mouse pregnant.

Speaker 8:
[05:40] They're like, oh, well, first we get a mouse pregnant. We abort the fetus. We decapitate the head. We smear the brain tissue or the stem cells of the mouse fetus onto this piece of glass. And he's like, okay, thanks for letting me know.

Speaker 6:
[05:54] And you guys are disgusting, but keep going.

Speaker 8:
[05:57] No, actually he's kind of, he was like, this is awesome.

Speaker 9:
[05:59] And then we put them on the chip and they grow intricate neural networks and we get their activity and we test drugs on it.

Speaker 8:
[06:08] Then the way that the lab was using it is that they would kind of douse these brain cells with drugs and then zap it and sort of see how the cells responded.

Speaker 6:
[06:16] Like shaking the drug on to the mouse brain cells, just like sprinkling it.

Speaker 8:
[06:23] Well, the cells are living in a solution. They're living in a Petri dish that's feeding them glucose and whatever stuff the neurons like to eat. But it's also, you could stick drugs in there.

Speaker 6:
[06:33] Gotcha.

Speaker 8:
[06:33] So imagine you want to test a drug. You want to see, okay, if I pour this anti-seizure medication into the little Petri dish in which the neuron cells are living, will those neurons fire erratically? Will they fire more calmly? Well, I can even induce a seizure in those neurons, which is pretty amazing. But I can induce a seizure and then see if the drug will calm that seizure. So it's a really useful way. Instead of like giving a mouse a seizure and then feeding it the drug, I could just see real time what's happening in the brain or to the brain cells, rather.

Speaker 6:
[07:07] It's almost like, you know, going straight to the source.

Speaker 9:
[07:09] Yeah.

Speaker 8:
[07:10] So he looks at this, Hon looks at this, and he's like, wait a minute.

Speaker 9:
[07:13] Why hasn't anyone tried to get these neurons in a dish to try to do some sort of computing intelligence?

Speaker 8:
[07:20] What if we don't just zap it and see how it behaves? Like, what if we could send it electrical information and have it respond? What if we could get it to compute?

Speaker 6:
[07:33] And I'm sorry if this is an obvious answer to this, but like, why would he even think that's possible? It doesn't sound like a thing from a biological lab that sounds like a thing from science fiction.

Speaker 8:
[07:44] Well, there have been biological computers in the past. Kind of one of the earliest experiments of this was in the late 90s. It was something called the leechulator. It used leech neurons attached to wires to basically perform simple addition. So it could send in a two and send in a four, and the leech neurons would send out a six.

Speaker 6:
[08:09] We're talking like blood sucking leech wormy things.

Speaker 8:
[08:12] Oh yeah, and they chose those neurons because those leeches apparently have like these huge neurons, so they were easy to work with.

Speaker 6:
[08:18] Okay, so some people had already made a calculator out of the neurons of leeches, and he's seeing what's going on with this mouse brain, and he's like, let's build off this.

Speaker 7:
[08:28] Yeah.

Speaker 8:
[08:30] The way Hon puts it is, you could think about a neuron as like a little mini-computer. It takes in electrical information, it processes that information, kind of does something with it and responds. Of course, when there's a whole bunch of them in our brain, they could do a lot of big-time thinking. But the thing about the leecholator and some other later biological sort of type experiments, yes, they showed that there could be some kind of computing that happens in terms of input outputs, but nobody had figured out a way to actually teach a neuron to learn a skill. I mean, the leecholator could add numbers, but it didn't become better at adding numbers over time. It didn't suddenly learn multiplication. So Hon was like, okay, I need to just figure out a way to not just talk to these neurons but actually get them to learn.

Speaker 6:
[09:15] Amazing. Well, before we get into how all that works and how he goes about it, I just would love a mental picture here. What does he actually build? What should I have in my mind's eye? Is the contraption, the device that he puts together here? I don't even know the right words for it.

Speaker 8:
[09:33] It's like, I mean, well, it sort of looks like an Xbox. I mean, it's the size of one. But it's got a glass cover where you can actually peer inside and what you see inside, it doesn't look like any computer you've ever seen. There's no circuit boards. There's no sticks of RAM in there. Instead, it kind of looks like a little fish tank, honestly. I mean, it's got a tube for humidity, an oxygen pump. There's a heater. And then at the bottom of it, there's a Petri dish. And inside the Petri dish is a little glass chip. And there's that smear of neurons on the glass chip. You wouldn't even know it's there. It's just a little filmy cloud.

Speaker 6:
[10:16] Just a little bit of living brain.

Speaker 8:
[10:19] Right. Although in his case, he did not use mouse neurons. He used human neurons.

Speaker 6:
[10:26] All right. So he's got these human brain tissue, these real human neurons alive, living inside of this thing. And they're sitting on a chip?

Speaker 8:
[10:35] Right. And under that chip are all these wires coming out to the back of the computer. So basically, when you type into that computer, when you program into that computer, you are sending electricity to those neurons. And those neurons are talking back. And they're sending electricity out. And so you have a two-way electrical communication with human brain cells.

Speaker 6:
[10:59] Wow. And Greg, before we continue on with the story, I have to ask, does it smell?

Speaker 8:
[11:05] It kind of smells.

Speaker 6:
[11:06] Does it smell like a pet shop or something like that?

Speaker 8:
[11:08] It smells like, I think, like it smells like a hospital. Sort of like that faintly sweet, but also sterile.

Speaker 6:
[11:15] Right.

Speaker 8:
[11:16] Also like kind of plastic-y metal smell.

Speaker 6:
[11:19] Interesting. So, okay, he builds this contraption with the living human brain tissue in it. What does he actually do with this device?

Speaker 8:
[11:29] So Hon was like, okay, I need to just figure out a way to not just talk to these neurons, but actually get them to learn. And he decided to start, again, maybe inspired by his icon Demis Asabas, with a game. And he starts with a very, very simple game, a game called Pong, invented by Atari. Pong.

Speaker 7:
[11:52] Now at last you can play at home.

Speaker 8:
[11:54] You know Pong, right? Classic. It's a classic. It's actually still fun, even though it was the first ever commercially successful video game. And for those who maybe are not familiar with Pong, basically it's like table tennis. You have a paddle on each side and you have a little digital ball that's bouncing back and forth. The only job is to keep that ball alive. Follow that ball and just make sure it doesn't pass you. And that's what he's going to teach these brain cells to do.

Speaker 6:
[12:21] Okay, so how is he going to go about doing that? Walk me through this. What does one do? I don't even know where you begin in this situation.

Speaker 8:
[12:30] I mean, if you think about how you program a computer, actually I don't even know how this works, but you basically send it binary code, ones and zeros, and it's able to decode that information and make all these kind of amazing outputs. But the human brain cells will not understand ones and zeros, and if they do, they will not respond to ones and zeros. So, Hans has, what he realizes early on is that to work with a biological computer, it's a lot more like training a puppy or like teaching, I don't know, a baby because you're using reward and punishment.

Speaker 9:
[13:03] So, for instance, as it moves further to the target, it gets rewarded. If it moves further away, it gets punished and all that kind of stuff.

Speaker 8:
[13:10] And so, then it's like, okay, well, that's fine, but what are the rewards and punishments that will work on a little neuron in a dish? Like, you can't give it a dog treat, you can't pet it, you can't tell it, you know, good boy.

Speaker 9:
[13:21] Right.

Speaker 8:
[13:22] And if you think about like, well, how humans respond, okay, we have lots of rewards and punishments all the time in our brain, but we also have drives. We are seeking pleasure, we're seeking warmth, safety, sex, I mean, social acceptance, like all these reasons that we make the decisions we are making. Neurons smeared in a chip have none of that. But they do have one core thing that they want. And they want it really badly. What they want is predictability.

Speaker 6:
[13:56] Interesting. We're using the word want here, but like it appears as if the cells seek.

Speaker 8:
[14:01] They seek to minimize, well, the technical terms, they seek to minimize prediction error. They seek to minimize surprise. And I mean, the closest analogy I could think of was being in like a club, a dark club that is crowded and you listen to the music, you look how people are behaving and you basically dance along with them. Like you anticipate where to slide, where to back up.

Speaker 6:
[14:29] Where to shimmy?

Speaker 8:
[14:30] Absolutely. But so imagine the DJ change the music every second. And you were constantly trying to figure out, is it house, is it techno? Like what are we doing? And you would get all confused until the DJ was like, okay, no, we're going to give you the same consistent music. Then you're going to predict what's going to happen next. Then you'd be, then you'd know what's happening.

Speaker 6:
[15:04] So you're saying that he learns that on a cellular level, our neurons, they like predictability, or at least they seek predictable information, kind of like a predictable beat that they can dance to.

Speaker 8:
[15:17] Yes.

Speaker 6:
[15:19] And so with that baseline of knowledge about what the cells seek or what they want, how does he build off that to get them to do what he wants?

Speaker 8:
[15:29] Okay, so this is where it gets a little weird.

Speaker 9:
[15:33] If, for instance, the neurons are doing the right thing, we want to give them a very predictable piece of information.

Speaker 8:
[15:41] So what is the most predictable pattern you can give a pile of neurons? Turns out there is something. It is a very beautiful sine wave and it goes like. It just goes like that forever. And that is like-

Speaker 3:
[16:03] It's bacon!

Speaker 8:
[16:05] Bacon treats for neurons. They love it because it is predictable. Now, what is the most unpredictable pattern you can give a pile of neurons? White noise. Just random, it's the equivalent of static. It's totally random, it's totally unpredictable.

Speaker 6:
[16:22] So that's kind of like the punishment? Yeah.

Speaker 8:
[16:24] So if you can imagine, here is Hon in the lab, essentially watching in real time as these neurons are playing pong, and he can see if they are playing the game well. And if they're playing the game well, oh, get more sine waves. Oh, you hit the ball again. Great job. Sine wave, sine wave. Oh, man, you missed the ball. You didn't move the paddle. Blast them with static. You know, blast them with white noise. And then, okay, now you're playing good again. Okay, you're playing better. All right, we're going to keep giving you that sine wave. Oh, you messed up again. Sorry, guys. Bam, you know, like blast them with the white noise. And just with this reward and punishment, really simple, really familiar sort of method. These neurons mastered Pong in like five minutes.

Speaker 6:
[17:13] That's amazing. Well, at the risk of over-anthropomorphizing these brain cells, I mean, they are human cells, right?

Speaker 8:
[17:35] Right. These are human cells.

Speaker 6:
[17:39] Is there any cruelty in this? Are these living cells experiencing something like suffering or pain?

Speaker 8:
[17:49] I mean, not being in a brain, not doing what evolution told them to do, but instead getting zapped by Hon.

Speaker 6:
[17:55] Yeah, and forced to play pong or else they get zapped.

Speaker 8:
[17:58] No, no, they love it. They absolutely love this.

Speaker 6:
[18:01] Who wouldn't?

Speaker 8:
[18:03] No, I mean, the truth is, I talked to Hon about this, and he says, really, he doesn't know. Like, he does not know for sure. The science is really new on this. One of the things that I learned doing this, which I found quite interesting, is that the number of neurons actually matters. So, for example, in our brain, I think there's like 86 billion neurons, and that's enough there to have consciousness. 200,000 neurons, which is what Hon is dealing with, that's about three poppy seeds' worth of brain.

Speaker 6:
[18:34] So, it's tiny.

Speaker 8:
[18:36] Yeah, and apparently, that's just not enough to do any real higher level thinking like, am I happy right now? However, that said, there is a technique that Hon uses to train these neurons that I think definitely starts to cross the line and make you think, maybe these things are feeling something. Basically, if he blasts them with white noise, and if they don't listen, like if they're still, I guess, misbehaving or not playing pong very well, he could give them...

Speaker 9:
[19:10] We call it the silent treatment.

Speaker 8:
[19:13] The silent treatment. There's no information at all. It's just darkness. Because the only thing that neurons hate more than white noise is no information at all. Like, zero.

Speaker 6:
[19:29] So, essentially, this is like a cellular form of solitary confinement for these neurons.

Speaker 8:
[19:36] Well, even worse, because it would be like, if you were in a solitary confinement and there were no walls, no lights, no feeling, no bed that you were sitting on, no sensation at all.

Speaker 6:
[19:46] And you're saying, on a cellular level. Like, humans obviously hate that. But it seems as if these cells also hate that?

Speaker 8:
[19:55] Well, let's just say, they react very negatively to this. And either they fall into line and start playing pong a lot better, or, and this is the other odd thing, sometimes they just go into what is kind of like a coma. So, a comatose brain has a very distinctive electrical firing signal, and these neurons start to fire in that coma way. And once they're locked into that state, he can't reach them. Like, there's nothing he can do. He just has to toss the whole thing in the garbage and start again.

Speaker 6:
[20:30] Oh, this is crazy. It's, if you leave them in solitary too long, they can't recover, so it's into the trash and a fresh batch of brain into the box.

Speaker 8:
[20:43] Precisely.

Speaker 6:
[20:45] Okay, but this works. He pulls this off.

Speaker 8:
[20:47] He pulls this off, starts a company, and he starts building his computer.

Speaker 6:
[20:51] Like a computer he's going to sell to people.

Speaker 8:
[20:53] Like a computer he's going to sell. Like a real life biological computer. I think the most recent video they just put out was that now it's not just playing Pong, it plays Doom. Which is a much, like a much harder game.

Speaker 6:
[21:05] Who is buying these computers? Like what, why would anyone want this computer? I mean, especially in a world where like you can buy a Mac computer that doesn't smell like a hospital room or need oxygen.

Speaker 8:
[21:16] So one hope of actually not just Hon, but a lot of folks looking into alternative computing is that this could change the energy equation of AI, right? Because right now, Chachi BT, Claude, all these AIs, they consume an enormous amount of electricity, of resources. Whereas the human brain cell, I mean, it is remarkably efficient. I mean, think about all the decisions you made this morning on like coffee and an oat bar.

Speaker 6:
[21:52] I had a smoothie, but I hear you.

Speaker 8:
[21:54] I mean, that's the efficiency that is like, the computers can only dream of. There's no way that any hardware can ever match the efficiency of your brain. So, like if you could swap out some of that hardware for wet wear, then it would be so much more efficient that essentially the data centers that are currently like four or five football fields in size would only need to be the size of a Hershey bar.

Speaker 6:
[22:21] Holy shit. That would be amazing.

Speaker 8:
[22:24] So, that's one of the dreams. Right now, though, this biological computer is for sale and people are actually buying it.

Speaker 6:
[22:31] And who exactly is buying this? What are they doing with it like right now?

Speaker 8:
[22:36] Right. So, Hans says that there's three categories of buyers, in his words. One are actually medical researchers. Remember, the medical researchers were interested before in testing their drugs on these things. So, now they're like, great, we have a new tool to test drugs and not only see how the cells behave, we can actually see how they compute under these different drugs. So, like, we could get epileptic human cells, put them on one of these slides, basically put epilepsy medication in the petri dish, feed them epilepsy medication and see if they learn Pong faster. And if they don't have a seizure, you can see the neurons having seizures. It's super useful in developing new drugs.

Speaker 6:
[23:14] All right. So, first people, medical researchers.

Speaker 8:
[23:16] Category two is what he calls the crypto gamers. I'm not sure what to say about this category other than something to do with quantum biology. I don't know. But nevertheless, that's another episode. But the third group, Hans says, are the most interesting and honestly, maybe far more consequential.

Speaker 9:
[23:36] I think it's definitely the robotics people that are most exciting to me.

Speaker 8:
[23:40] Roboticists, who are trying to figure out a way to put these biological computers into humanoid robots. And so if you could imagine the future robots, instead of having silicon brains or just silicon brains, they would have brain cells inside them.

Speaker 9:
[24:02] You know, you could feed in sensory information from the external world and have outputs from the New York, actually control some sort of wheel or an arm.

Speaker 6:
[24:14] Wow, so this would be that you essentially are a god in a new Garden of Eden, creating an entirely new organic-based intelligent life form.

Speaker 8:
[24:26] Right. And some of them believe this is actually how we get to AGI.

Speaker 6:
[24:31] This is how we get the super intelligence.

Speaker 8:
[24:33] Super intelligence.

Speaker 6:
[24:36] And on that cliffhanger, after a short break, we will meet one of these roboticists. Stay with us.

Speaker 1:
[25:03] Hello, everyone, this is Matt, co-founder here at Longview, where we report stories that are grounded in curiosity and context, not political bias. As we say, it's not the left view, not the right view, but the Longview. One way we sustain this business is by advertising, but we are also listener-supported. And if you would like to go ad-free and support us at any dollar amount that you'd like, you can do that by clicking on the link in our show notes or by going to longviewinvestigations.com. Until then, here is a brief message from our sponsors. The Last Invention is brought to you by Quince. Every spring, I feel the same urge to clean things out. Closets, habits, routines, and with clothes, I keep coming back to the same idea. Fewer of them, but better quality. That's what I like about Quince. The materials feel elevated, and the cuts are clean, and the prices, they aren't insane. They make everyday staples using premium fabrics like 100% European linen, and this incredibly soft flow-knit fabric for their active wear. The kind of gear you end up wearing way beyond the gym. What surprised me most is the pricing. It's about 50 to 60% less than what you'd expect from comparable brands, and that's because Quince goes straight to the source, working directly with ethical factories and skipping the middlemen. So you're actually paying for the quality itself instead of the markup. My favorite piece these days is one of their blue chore jackets. It fits great, it's really comfortable, and I know it'll hold up even though I wear it all the time. Refresh your wardrobe with Quince. Go to quince.com/lastinvention for free shipping and 365 day returns. Now available in Canada too. Again, go to quince.com/lastinvention.

Speaker 4:
[27:00] Now more than ever, technology is a dominating force in our lives. Then there's the threat of AI everywhere. And yet, tech can be inspiring and help level playing fields. I mean, a YouTuber with a self-funded debut movie just dominated the box office.

Speaker 10:
[27:18] I thought, hey, if you interview me, it would be good for your publication. That's not ego. I just have a lot of followers. But it's that stigma. It's like YouTubers, they're not real.

Speaker 4:
[27:29] Join me, Lizzy O'Leary, the host of What Next TBD, Slate's podcast focused on technology, power and the future. Follow What Next TBD now wherever you get your podcasts.

Speaker 6:
[27:49] All right, I'm Andy Mills. You are listening to both Reflector and The Last Invention, very special episode, and we are back with Gregory Warner.

Speaker 8:
[27:57] So there's this classic conundrum in robotics, which says that things that are really hard for humans, like chess and math, things like that, are really easy for computers, but things that are super easy for humans, like catching a ball or walking upstairs, are really, really hard for computers, or robots. I mean, we've for years, decades have expected robot workers around us to be doing the laundry and doing all the messy labor in our factories, but that's just not happened. Instead, we have AI replacing cognitive labor, doing a lot of legal briefs, medical diagnoses, but we're not living in a world really with humanoid robots among us. And it's not that we can't build robots that can walk up stairs or fold laundry. It's just the amount of data and shifting data that's necessary to do those really seemingly effortless tasks is quite immense for a robot. So like one of the ways in which you see this really well is if you watch the humanoid robot space.

Speaker 10:
[29:03] We have Stanford, UC Berkeley.

Speaker 8:
[29:06] Just like an AI, they are playing games. In this case, they are playing sports. I think in San Francisco, they had like a robot fight club. Where these little robots are punching each other, trying to beat each other up, and then there was an Olympics.

Speaker 6:
[29:21] The games are meant to showcase how far robotics have come.

Speaker 8:
[29:25] There's been a couple of Olympics actually for robots. Throwing the javelin, of course, track and field. Some of these robots move along that's quite a clip. But when they crash, crowd loves it. If you watch it, I mean, it's not that it's not impressive, but they're sort of running some of the fall over because they can't run a straight line.

Speaker 6:
[29:45] Not, I mean, I couldn't build them. I don't want to show any disrespect.

Speaker 8:
[29:49] No, no, it's cool.

Speaker 6:
[29:50] It's amazing.

Speaker 8:
[29:52] But it's not like scary. It's not like these robots are going to replace humans anytime soon.

Speaker 6:
[29:56] This dream of us living life alongside these, you know, very intelligent, very helpful humanoid robots, we have failed again and again to reach that dream.

Speaker 8:
[30:06] Yes, but there are some people who think that that is about to change. One of them is Minas Liarokapis.

Speaker 11:
[30:15] CEO and CTO of Acumino and Director of the New Dexterity Research Group.

Speaker 8:
[30:19] Minas builds robots, specifically these very, very dexterous robot hands. And he's really one of the world's experts in this. His hands can sew, they can thread a needle, they can pack a box. However, they only can do all these things because there has been a human who has trained it on every specific action. It cannot generalize this intelligence. Yes, it can pack an egg, but give it a light bulb? It's confused, right?

Speaker 11:
[30:51] Because we don't really have the capability to mimic the human dexterity, to have human-like robot dexterity. And I believe that in the future, biological computing will facilitate that.

Speaker 8:
[31:04] Meaning if we could possibly start putting biological material inside these robots, that's when we start actually able to get...

Speaker 6:
[31:16] Truly a Jetson's future.

Speaker 8:
[31:21] That's what he's going for, yeah.

Speaker 6:
[31:23] Wow. And is his idea then to take the human brain or human brain cells, human neurons, and essentially create a wetware brain that he puts into the heads of these humanoid robots? Is that what he's thinking might happen?

Speaker 8:
[31:43] I mean, yes. That already sounds crazy, like putting brains in robots. I do want to say that he would say, okay, it's some combination, probably, of organic matter and silicon. But here's where it gets interesting. He says that because what biological computers are so good at is sensing and responding to sensory information, maybe instead of thinking of the brain cells as the robot's brain, maybe...

Speaker 11:
[32:13] It might be the skin. It might be something local in the hunt alone.

Speaker 8:
[32:17] Maybe it's the skin.

Speaker 6:
[32:22] Just the mental image of that is amazing.

Speaker 8:
[32:24] Yeah, like, maybe we coat these humanoid robots in sort of pre-programmed neurological tissue.

Speaker 6:
[32:34] And why would it be skin over brain? Just because it's super gross? It is grosser? And we got to go with what's grosser?

Speaker 8:
[32:43] Well, if you think about it, in you, you have neurons in your brain, sure, but you also have lots of neurons that are inside your nerves and your skin is full of nerves. What do nerves do? Well, they're kind of like these supercomputers that can both sense and respond. Meaning, like, if you put your hand on a hot stove, right, you don't think about it. You just you just immediately pull it off before your brain even knows about it. Your hand is gone.

Speaker 6:
[33:10] Right. It's called a reflex, right? It's like a reflexive impulse.

Speaker 8:
[33:14] In the sense that it is your nerve cell in your skin making a decision right away that doesn't even bother consulting with your brain about it. And so maybe that's how we get to the robot that can finally fold laundry and do the dishes and walk into a strange kitchen and actually know where to put away the groceries. But he also believes, and this is sort of where he, what drives him, he also believes that this is how we get to AGI.

Speaker 6:
[33:39] And I feel like, just as a quick refresher, AGI, that is this benchmark that all of the AI labs are working towards, where they're essentially trying to build a digital mind as intelligent and as capable as a very smart human mind. And the thought is that once we reach that benchmark, it may prove to be like the most impactful technological breakthrough in human history and lead to super intelligence and all that stuff. But why is it that Minas thinks that we get there when we are wrapping a robot in this biological computer skin?

Speaker 8:
[34:14] The way Minas puts it, it's like for him, this goes back to an ancient debate. It's an old philosophical argument.

Speaker 11:
[34:22] I mean, this is a question that has been asked for millennia. Did the human brain, the superior human brain, develop the dexterity of the human hand? Or did the human hand lead to the development of the superior human brain?

Speaker 8:
[34:39] Which came first, the human brain or the human hand, with its remarkable, opposable thumb? It's this sort of thought experiment, like was it our bigger brains that allowed us to imagine all kinds of tools we could design and inventions that led to technological progress and led us to where we are today? Or was it our earliest ancestors whose brains were still at that point the size of chimps that started exploring, kind of figuring out what their hands could do? Like, oh, wow, I could bang these two rocks together and kind of make a sharper rock. And then, oh, wow, I can cut things and stab somebody. And all of that increased data flowing to our brains forced our brains to evolve. Hmm.

Speaker 6:
[35:25] I love that. What's the answer? Do you know that? Was it the brain or was it the hand with the thumb? What's the verdict?

Speaker 8:
[35:33] I mean, in some sense, we now know the answer. It was neither the hand nor the brain. They co-evolved. You needed both. You needed both superior brain capacity to understand what to do with all this data, but you needed all the data to prompt more cognitive reasoning. And similarly, like in AI, this question of compute and data are both the key questions. If you look at today, LLMs, Large Language Models, or any of the kind of newer AI models that are out there, they are fed on this huge diet of what? Text, images.

Speaker 6:
[36:09] Everything we ever published on the internet.

Speaker 8:
[36:10] Right. They are awash in all that, and they're doing well with it, but...

Speaker 11:
[36:14] You cannot model the complexity of the human world with text, video or images.

Speaker 8:
[36:21] They do not have any embodied knowledge of the world. And he believes that this kind of embodied intelligence, like interacting with the world, is actually essential to creating AGI.

Speaker 11:
[36:36] Definitely. 100%. Yeah. You need to interact with the world. We feel the world around us. We go out on a rainy night, in a rainy night, and we feel the droplets on our skin, right? We become part of the environment. And I really believe that biological computing can do something so as to allow the robots to learn from the physical interactions and reach human-like or superhuman levels of intelligence.

Speaker 8:
[37:19] I think what's kind of amazing about this is it makes us think differently about work and intelligence. Because if you think about the kinds of things that AI can do, like impressive bits of writing and math and chess playing, et cetera, those are all maybe what we think of as intelligence. But what Minas argues is that actually the path to super intelligence is not through sort of white collar work. The path to super intelligence is through finally getting an AI that knows what the rain feels like on its face, knows what temperature change feels like and can respond instantly, knows how to hold a tool and what it feels like in its hand, and is more interacting with the world instead of just interacting with text and video. And maybe I could just add one little thing, because the other thing about a biological computer, let's not forget, is that because it is neuron-based, it can communicate with our brains.

Speaker 6:
[38:34] Meaning what, exactly?

Speaker 8:
[38:36] So if you think about the dream of, say, Neuralink, Elon Musk's Neuralink, or a number of different companies out there that are trying to create these next-level brain-computer interfaces where we can communicate with some kind of chip in our head, and, Gav, like the power of AI but in our brain, well, what if that chip was actually a biological substrate? They might be programmed to do anything. Like, they might know how to speak Portuguese. And suddenly, I just stick that chip in my brain. I mean, I'm not trying to get sci-fi. This is very far off, but, I mean, this is the hope.

Speaker 6:
[39:12] Let me say this back to you. You're saying that maybe we create a robot out of our wetware. We develop this AI that maybe hits AGI, maybe goes all the way to superintelligence. And then, in turn, we take that superintelligence in its organic-based, you know, AI system, and we stick it back in us? Is that what you're saying?

Speaker 8:
[39:38] I mean, I think what I'm saying is...

Speaker 6:
[39:42] And then we become the superintelligence.

Speaker 8:
[39:44] Well, it's like... I mean, look, Minas is testing this thing out right now. The only thing he's trying to get this biological computer to do is hold a pen. Right?

Speaker 6:
[39:56] Right, it's early days.

Speaker 8:
[39:57] This is early days. This is where we're at now. But where is this trend line going? A fusion of biology or biological substrate and machine intelligence augmenting our own brains.

Speaker 1:
[40:35] The Last Invention is produced by Longview. Like Andy said, this was a crossover episode with our other show, Reflector. On Reflector, we investigate the surprising stories behind the most consequential issues that we face today. Immigration, the rise of political violence, free speech and rap music, the transformation of the LGBTQ movement and much more. To find it, just search for Reflector in whatever app you are using to listen to this show right now. We appreciate all of you who share our stories with your friends and your family. And if you'd like to spread the word about Longview and our podcasts, there are a few ways that you can help. You can leave a review of one of our shows on Apple or Spotify. You also can leave a comment on our sub stack. Or you can support our work directly by going to longviewinvestigations.com and becoming a subscriber. We'll be back with more stories soon. Thanks.