Wetwear

Summary

The Last Invention – Episode Summary

Podcast: The Last Invention
Episode: Wetwear
Date: April 9, 2026
Host: Longview (Andy Mills, Gregory Warner)
Special Guests: Han Wang Chong (CEO, Cortical Labs), Minas Liarokappas (CEO, Acumino; Director, New Dexterity Research Group)

---

Overview & Main Theme

This episode titled "Wetwear" explores the emerging frontier of "biological computers"—computing devices powered not by silicon chips, but by living brain tissue. Through interviews with pioneers in the field, including Han Wang Chong and roboticist Minas Liarokappas, the hosts unravel the strange, ethically fraught, and potentially world-changing journey towards merging biology and computing. The episode raises fundamental questions about intelligence, machine learning, embodiment, and what it could mean to literally bring 'life' to machines both intellectually and physically.

---

Key Discussion Points & Insights

1. A Brief History of the Computer and its Evolving Role

• Computers once seemed a 'mysterious,' niche tool—now, they're an essential part of modern society ([00:13-01:41]).
• The next evolutionary leap is "even more transformational"—computers powered by living brain tissue ([01:41]).

2. Han Wang Chong’s Wetware Computer: Origins & Design

• Han Wang Chong: Inspired by Demis Hassabis (DeepMind) to return to "our origins in neuroscience," he visits the University of Melbourne and discovers neurons growing on chips for drug research ([03:12-04:29]).
• Lab uses fetal mouse brain tissue on glass chips to observe neuron activity and responses to drugs ([04:29-05:16]).
• The leap: If neurons process information, can we teach them to 'compute' and learn?
  • Quote: “Why hasn’t anyone tried to get these neurons in a dish to try to do some sort of computer intelligence?” – Han Wang Chong ([06:13])
• Early experiments—like the "leechilator" using leech neurons to add numbers—couldn't teach the tissue to learn ([06:38-08:15]).
• Han builds a wetware computer the size of an Xbox, using human neurons, connected to wires that allow two-way communication ([08:33-09:59]).

3. Training Brain Cells to Play Pong

• Goal: Teach live brain tissue to learn via reward and punishment, starting with the game Pong ([10:29-12:03]).
• Biological computers don’t work with binary code; instead, neurons 'want' predictability, i.e., they minimize prediction error—“They seek to minimize surprise.” ([12:55-13:01])
• Analogy: Neurons prefer "a consistent beat" just like dancers in a club, hate being surprised ([13:30-14:04]).
• Reward: Give firing neurons a predictable sine wave (like a treat).
  Punishment: Blast them with white noise/static ([14:32-15:29]).
  • Quote: "It's bacon. Bacon treats for neurons." – Gregory Warner ([14:59])
• Remarkably, with this system, neurons "mastered Pong in five minutes" ([15:59-16:11]).

4. Ethics & Experience—Can Neurons Suffer?

• Question posed: Is this cruel? Are the brain cells suffering? ([16:35-16:55])
• Han doesn’t know for sure, but with only ~200,000 neurons, there’s no self-awareness—yet repeated punishment (“the silent treatment”) can force neurons into a coma-like state; at that point, “he just has to toss the whole thing in the garbage and start again.” ([18:10-19:43])
• Quote: "It would be like if you were in solitary confinement and there were no walls, no lights, no feeling, no bed that you were sitting on..." – Gregory Warner ([18:36])

5. Commercialization & Applications

• Han starts selling the wetware computer, which now can play Doom (a much harder game), not just Pong ([19:46-20:04]).
• Current customers ([21:31-22:35]):
  • Medical researchers: Use for advanced drug testing on live, computing cells (e.g., epilepsy drugs).
  • Crypto-gamers & quantum biology enthusiasts: Vague references, not detailed here.
  • Roboticists: The most consequential—aiming to use biological computers in embodied robots.

6. Robotics & The Embodied Intelligence Dream

• Classic AI paradox: Computers excel at math and chess, but struggle with 'easy' human tasks like walking or folding laundry ([26:56-28:55]).
• Roboticists (Minas Liarokappas):
  • Emphasize the limitations of current robots—they lack flexibility and can’t generalize tasks ([29:19-29:53]).
  • Quote: “I believe that in the future biological computing will facilitate [human-like dexterity].” – Minas Liarokappas ([29:53])
  • Idea: Biological material—not just in the 'brain' of robots, but perhaps in their 'skin' or 'hands'—could allow robots to sense and interact with the world more like humans ([30:42-32:14]).
  • Quote: “Maybe it’s the skin.” – Minas Liarokappas ([31:13])
• Theorizes that embodied knowledge—AI that physically interacts with the world—is vital for achieving AGI (artificial general intelligence) ([33:14-35:36]).
  • Quote: “You cannot model the complexity of the human world with text, video, or images.” – Minas Liarokappas ([35:13])
  • “Definitely. 100%. Yeah. You need to interact with the world. We feel the world around us.” – Minas Liarokappas ([35:36])

7. Wetware and Human Augmentation

• The same substrate could be used to build next-generation brain-machine interfaces—possibly giving us new skills, new knowledge, or even integrating 'superintelligence' into human minds ([37:34-38:38]).
• Quote: “Maybe we create a robot out of our wetware... AI that maybe hits AGI, maybe goes all the way to superintelligence, and then in turn, we take that super intelligence... and we stick it back in us?” – Andy Mills ([38:11])

---

Notable Quotes & Memorable Moments

• The 'Goopiest' Story Yet:

  • “Definitely the grossest, the most goopy for sure. It's a goop filled story.” – Andy Mills ([03:03])

• Wetware as Animal Training:

  • “To work with a biological computer, it's a lot more like training a puppy or like teaching...a baby, because you're using reward and punishment.” – Gregory Warner ([11:30])

• Neurons Seek Predictability:

  • “What they want is predictability.” – Gregory Warner ([12:55])
  • “It's bacon. Bacon treats for neurons. Like, they love it because it is predictable.” – Gregory Warner ([14:59])

• Computers That Smell:

  • “Does it smell like a pet shop or something like that?” – Andy Mills ([10:06])
  • “It smells like a hospital. Sort of like that faintly sweet, but also sterile. Also like kind of plasticky metal smell.” – Gregory Warner ([10:08])

• Purpose & Potential:

  • “There's no way that any hardware can ever match the efficiency of your brain.” – Gregory Warner ([20:54])
  • “If you could swap out some of that hardware for wetware, 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.” – Gregory Warner ([21:21])

• Reframing AGI:

  • “You cannot model the complexity of the human world with text, video or images.” – Minas Liarokappas ([35:13])
  • “But what Minas argues is that actually the path to superintelligence is not through sort of white collar work. The path to superintelligence is through finally getting an AI that knows what the rain feels like on its face...” – Gregory Warner ([36:19])

---

Timestamps for Major Segments

• Computers' Transformation: [00:06]–[01:41]
• Intro to Han Wang Chong & Biological Chips: [03:12]–[05:16]
• From Drug Testing to Computation: [06:09]–[08:15]
• The Device: Wetware Computer: [08:33]–[09:59]
• Teaching Neurons to Play Pong: [10:29]–[12:55]
• Reward, Punishment, and Neurons' 'Wants': [13:01]–[16:11]
• Ethics and Sentience: [16:35]–[19:43]
• Biological Computer Commercialization: [19:46]–[22:35]
• Robotics, Handling Embodiment: [26:56]–[32:14]
• Philosophy: The Brain vs Hand Debate: [33:14]–[34:23]
• Embodiment & Path to AGI: [35:09]–[36:19]
• Wetware Brain Implants & Augmentation: [37:34]–[38:38]

---

Final Thoughts

"Wetwear" takes listeners on a journey from the origins of computer science to the bleeding edge of biotech. The conversation blurs boundaries: between human and machine, intelligence and instinct, cruelty and progress. Whether in robotic 'skin' or as brain enhancements, wetware may not only create more efficient or more human-like AI—it may fundamentally change what it means to be intelligent and even what it means to be human.

---

Recommended for listeners seeking:

• The latest in neuroscience and AI crossover
• Reflections on the ethics and future potential of biotechnology
• Engaging, sometimes darkly humorous, explorations of the weirdest edges of tech
• Big questions about intelligence, consciousness, and the future of our species

Transcript

[Andy Mills]

Hey, this is Andy Mills.

[Narrator/Intro Voice]

And for today, just what are digital computers?

[Andy Mills]

The computer?

[Narrator/Intro Voice]

Are they man made monsters that perform mathematical miracles? In millionths of a second from where we sit today.

[Andy Mills]

It's actually kind of funny to look at how we were thinking and talking about the computer just decades ago.

[Narrator/Intro Voice]

Are they superhuman machines that can solve any kind of problem?

[Andy Mills]

How mysterious they seemed to to the general public.

[Narrator/Intro Voice]

No, nothing miraculous at all, nor monstrous. The working parts are transistors, vacuum tubes, magnetic devices and other electronic components.

[Andy Mills]

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. But that sink in 1%. 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.

[Gregory Warner]

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

[Andy Mills]

So this is Gregory Warner, the host of our series the Last Invention. And he's been reporting for the last year we 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.

[Han Wang Chong]

Yes.

[Andy Mills]

Thank you so much for returning to

[Gregory Warner]

the studio to tell us the weirdest story yet.

[Andy Mills]

Definitely the grossest, the most goopy for sure. It's a goop filled story.

[Gregory Warner]

It's little smelly too.

[Andy Mills]

All right, so where, where do we begin? What, how do you want to start this off?

[Gregory Warner]

Well, one place to Start is with this guy Han.

[Han Wang Chong]

Han Wang Chong, CEO and founder of Cortical Labs.

[Gregory Warner]

Han Wang Chun. 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? And he stumbled across this essay written

[Han Wang Chong]

by Demis Hassadas from DeepMind, where he advocated for machine learning and AI researchers to go back to our origins. Right. Which is in neuroscience. So he literally took that advice and went over to the neuroscience department at the University of Melbourne, and he asked for a tour.

[Gregory Warner]

And they're like, okay, no problem. They give him, like, maybe they give him, like a white coat and goggles, and they take him around, and they. 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 like a little cloud of tissue. And he's like, what is that? And they're like, well, that is mouse brain tissue on that chip that captures

[Han Wang Chong]

electrical activity between neurons and provide a little bit of a stimulus back.

[Gregory Warner]

But Han was a computer scientist. Like, he had never seen anything like this.

[Han Wang Chong]

And so I asked him, so how does that work? And they say, well, we get a mouse pregnant.

[Gregory Warner]

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. He's like, okay, thanks for letting me know.

[Andy Mills]

And you guys are disgusting, but keep going.

[Gregory Warner]

No, actually, he's kind of. He was like, this is awesome.

[Han Wang Chong]

And then we clicked them on the chip, and they grow intricate neural networks, and we get their activity and we test drugs on it.

[Gregory Warner]

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.

[Andy Mills]

So, like, shaking the drug onto the mouse brain cells, just like sprinkling it.

[Gregory Warner]

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.

[Andy Mills]

Gotcha.

[Gregory Warner]

So imagine you wanted to test a drug, you wanted 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.

[Andy Mills]

It's almost like, you know, going straight to the source.

[Gregory Warner]

Yeah. So he looks at this, Han looks at this, and he's like, wait a minute.

[Han Wang Chong]

Why hasn't anyone tried to get these neurons in a dish to try to do some sort of computer intelligence?

[Gregory Warner]

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?

[Andy Mills]

And I'm sorry if I. If this is an obvious answer to this, but, like, why would he even think that's possible?

[Gregory Warner]

Right?

[Andy Mills]

It doesn't sound like a thing from, like a biological lab. That sounds like a thing from science fiction.

[Gregory Warner]

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 leech elator. It used leech neurons attached to wires to basically perform simple addition. So they could like, send in a 2 and send in a 4, and the leech neurons would send out a 6.

[Andy Mills]

We're talking like blood sucking leech wormy things.

[Gregory Warner]

Oh, yeah. And they chose those neurons because those leeches apparently have, like, this huge neuron, so they were easy to work with.

[Andy Mills]

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.

[Gregory Warner]

Yeah. The way Han 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. And 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 leechilator 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 leechi lator could add numbers, but it didn't, like, become better at adding numbers over time. It didn't, like, suddenly learn multiplication. So Han was like, okay, I need to just figure out a way to not just talk to these neurons, but actually get them to learn.

[Andy Mills]

Amazing. Well, before we get into, like, 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. The device that he puts together here. I don't even know the right words for it.

[Gregory Warner]

It's like. I mean, well, it sort of looks like an Xbox. I mean, it's the size of one.

[Andy Mills]

Okay.

[Gregory Warner]

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. Have you 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 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, just

[Andy Mills]

a little bit of living brain.

[Gregory Warner]

Right. Although in his case, he did not use mouse neurons. He used human neurons.

[Andy Mills]

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, right?

[Gregory Warner]

And. And under those 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. They're sending electricity out. And so you have a two way electrical communication with human brain cells.

[Andy Mills]

Wow. And Greg, before we continue on with the story, I have to ask, does it smell.

[Gregory Warner]

It kind of smells.

[Andy Mills]

Does it smell like a pet shop or something like that?

[Gregory Warner]

It smells like. I think it smells like a hospital. Sort of like that faintly sweet, but also sterile. Also like kind of plasticky metal smell.

[Andy Mills]

Interesting. So, okay, he builds this contraption with the living human brain tissue in it. And what does he actually do with this device?

[Gregory Warner]

So Han 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 Hassabis, with a game. And he starts with a very, very simple game, a game called Pong, invented by Atari. Pong.

[Narrator/Intro Voice]

Now at last, you can play at

[Gregory Warner]

home, you know, Pong, right?

[Andy Mills]

Classic.

[Gregory Warner]

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.

[Andy Mills]

Okay, so how. How is he going to go about doing that? Like, walk me through this. Like, what does one do? I don't even know where you begin in this situation.

[Gregory Warner]

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.

[Han Wang Chong]

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.

[Gregory Warner]

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, right? 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.

[Andy Mills]

Interesting. We're using the word want here, but, like, it appears as if the cells seek.

[Gregory Warner]

They seek to minimize. Well, the technical term is 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, where to.

[Andy Mills]

Where to shimmy.

[Gregory Warner]

Absolutely. But so imagine the DJ changed 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, I'm gonna give you the same consistent music. Then you're gonna predict what's gonna happen next. Then you'd be. Then you'd know what's happening.

[Andy Mills]

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.

[Gregory Warner]

Yes.

[Andy Mills]

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?

[Gregory Warner]

Okay, so this is where it gets a little weird.

[Han Wang Chong]

If, for instance, the neurons are doing the right thing, we want to give them a very predictable piece of information.

[Gregory Warner]

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,

[Andy Mills]

Just.

[Gregory Warner]

It goes like that forever. And that is like. It's bacon. Bacon treats for neurons. Like, 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 like a. It's the equivalent of static. Totally random. It's totally unpredictable.

[Andy Mills]

So that's kind of like the punishment.

[Gregory Warner]

Yeah. So if you can imagine, here is Han 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 played. You. 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 gonna keep giving you that sine wave. Oh, you messed up again. Sorry, guys.

[Han Wang Chong]

Bam.

[Gregory Warner]

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.

[Andy Mills]

Wow, that's amazing. Well, you know, at the risk of, you know, over anthropomorphizing these brain cells. Well, I mean, they are human cells, right?

[Gregory Warner]

Right. These are human cells.

[Andy Mills]

Yeah. Is there. Is there any cruelty in this? Like, Are these living cells experiencing something like. Like suffering or pain?

[Gregory Warner]

You mean not being in a brain, not doing what evolution told them to do, but instead getting zapped by Han?

[Andy Mills]

Yeah. And forced to play Pong. Or else they get zapped.

[Gregory Warner]

No, no, they love it. They absolutely love this.

[Andy Mills]

Who wouldn't?

[Gregory Warner]

No, I mean, the truth is, I talked at length to Han about this, and he's. This really. He doesn't know. Like, he does not know for sure. The science is really new on this. Like, 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, you know, that's enough there to have consciousness. 200,000 neurons, which is what Han is dealing with. That's about three poppy seeds worth of brain.

[Andy Mills]

So it's tiny.

[Gregory Warner]

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 Han 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.

[Han Wang Chong]

We call it the silent treatment.

[Gregory Warner]

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.

[Andy Mills]

So essentially, this is like a cellular form of solitary confinement for these neurons.

[Gregory Warner]

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.

[Andy Mills]

And you're saying on a cellular level, like, humans obviously hate that, but it seems as if these cells also hate that.

[Gregory Warner]

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 is 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.

[Andy Mills]

This is crazy. 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.

[Gregory Warner]

Precisely.

[Andy Mills]

Okay, but this works. He pulls this off.

[Gregory Warner]

He pulls this off, starts a company, and he starts building his computer.

[Andy Mills]

Like a computer he's gonna sell to people.

[Gregory Warner]

Like, computer. He's gonna 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 harder game.

[Andy Mills]

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.

[Gregory Warner]

So one hope of actually not just Han, but a lot of folks looking into alternative computing is that this could change the energy equation of AI. Right? Because right now, ChatGPT, 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 in an oat bar.

[Andy Mills]

I had a smoothie, but I hear you.

[Gregory Warner]

I mean, that's efficiency. That is, like, that computers can only dream of. There's no way that any hardware can ever match the efficiency of. Of your brain. So, like, if you could swap out some of that hardware for wetware, 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.

[Andy Mills]

Holy shit. That'd be amazing.

[Gregory Warner]

So that's one of the dreams. Right now, though, this biological computer is for sale, and people are actually buying it.

[Andy Mills]

And who exactly is buying this? What are they. What are they doing with it, like, right now?

[Gregory Warner]

Right, so Han 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. 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 a seizure. So it's super useful in developing new drugs.

[Andy Mills]

All right, so first people, medical researchers.

[Gregory Warner]

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's, are the most interesting and honestly, maybe far more consequential.

[Han Wang Chong]

I think it's definitely the robotics people that are most exciting for me.

[Gregory Warner]

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.

[Han Wang Chong]

You know, you could feed in sensory information from the external world and have outputs from the neurons actually control some sort of wheel or an arm.

[Andy Mills]

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.

[Gregory Warner]

Right. And some of them believe this is actually how we get to AGI.

[Andy Mills]

This is how we get the superintelligence.

[Gregory Warner]

Superintelligence.

[Andy Mills]

And on that cliffhanger, after a short break, we will meet one of these roboticists. Stay with us.

[Matt (Co-founder at Longview)]

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 long view. 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 Longview investigations.com until then, here is a brief message from our sponsors. The last invention is brought to you by Quints. 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 quints. 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 activewear. 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 Quints 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 Q-U-I-N-C-E.com lastinvention.

[Lizzie O'Leary]

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.

[Gregory Warner]

I thought, hey, if you interview me, it'd be good for your publication.

[Matt (Co-founder at Longview)]

And that's not ego, I just have

[Gregory Warner]

a lot of followers. But it's that stigma.

[Andy Mills]

It's like YouTubers, they're not real.

[Lizzie O'Leary]

Join me, Lizzie 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.

[Andy Mills]

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.

[Gregory Warner]

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, for years, decades have expected robot workers around us to be, you know, doing the laundry and, you know, 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 upstairs 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, we have standard uses. You see bare claim. Just like in AI, they are playing games. In this case, they are playing sports. Think in San Francisco, they have like robot fight club where these little robots are punching each other, trying to beat each other up. And then there was an Olympics. The games are meant to showcase how

[Andy Mills]

far robotics have come.

[Gregory Warner]

There's been a couple of Olympics, actually four robots throwing the javelin, of course, track and field. Some of these robots move along at quite a clip, but when they crash, the 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 them fall over because they can't run a straight line. Not, not.

[Andy Mills]

I mean, I couldn't build them. I don't want to show any disrespect. No, no, it's, it's cool, it's amazing,

[Gregory Warner]

but it's not like, scary. It's not like these robots are going to replace humans anytime soon.

[Andy Mills]

It's 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.

[Gregory Warner]

Yes. But there are some people who think that that is about to change. One of them is Minas Liarokappas, CEO

[Minas Liarokappas]

and CTO of Acumino and Director of the New Dexterity Research Group.

[Gregory Warner]

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, because we don't really

[Minas Liarokappas]

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.

[Gregory Warner]

Meaning if we could possibly start putting biological material inside these robots, that's when

[Andy Mills]

we start actually able to have truly a Jetsons future.

[Gregory Warner]

That's what he's going for. Yeah.

[Andy Mills]

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, is that what he's thinking might happen?

[Gregory Warner]

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

[Minas Liarokappas]

might be the skin. It might be something local in the hand alone.

[Gregory Warner]

Maybe it's the skin.

[Andy Mills]

Just like just the mental image of that is amazing.

[Gregory Warner]

Yeah. Like maybe we coat these humanoid robots in sort of pre programmed neurological tissue.

[Andy Mills]

And why would that, like, why would it be skin over brain? Just because it's super gross, it's grosser. And we got to go with what's grosser?

[Gregory Warner]

Well, if you think about it, like, in you, like, 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 immediately pull it off. Before your brain even knows about it, your hand is. Your hand is gone.

[Andy Mills]

Right? We call it a reflex, right? It's like a reflexive impulse in the

[Gregory Warner]

sense that it is your nerve cell in your skin making a decision right away. It 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 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.

[Andy Mills]

And I feel like, just as a quick refresher, AGI, that is this benchmark that all of the AI labs 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 superintelligence and all that stuff. But why is it that Menas thinks that we get there when we are wrapping a robot in this biological computer skin?

[Gregory Warner]

The way Minas puts it is like, for him, this goes back to an ancient debate. It's an old philosophical argument.

[Minas Liarokappas]

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 led to the development of the superior human brain?

[Gregory Warner]

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.

[Andy Mills]

Hmm, I love that. What's the answer, do you know? Was it the brain or was it the hand with the thumb? What's the verdict?

[Gregory Warner]

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, everything we

[Andy Mills]

ever published on the Internet, right?

[Gregory Warner]

They are awash in all that and they're doing well with it.

[Minas Liarokappas]

But you cannot model the complexity of the human world with text, video or images.

[Gregory Warner]

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?

[Minas Liarokappas]

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 bridge human like or superhuman levels of intelligence.

[Gregory Warner]

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 AI can do, like impressive bits of writing and math and chess playing, et cetera, that those are all maybe what we think of as intelligence. But what Minas argues is that actually the path to superintelligence is not through sort of white collar work. The path to superintelligence 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.

[Andy Mills]

Meaning what? Exactly.

[Gregory Warner]

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 brains, brain computer interfaces, where we can communicate with some kind of chip in our head and give, 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 not.

[Andy Mills]

Just 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 super intelligence and its organic based, you know, AI system and we stick it back in us. Is that what you're saying?

[Gregory Warner]

I mean, I think what I'm saying is.

[Andy Mills]

I think what I'm saying, we've become the super intelligence.

[Gregory Warner]

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? So let's.

[Andy Mills]

Right, it's early days.

[Gregory Warner]

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. Foreign.

[Matt (Co-founder at Longview)]

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. 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 substack. Or you can support our work directly by going to longviewinvestigations.com and becoming a subscriber. We'll be back with more stories soon.

[Gregory Warner]

Thanks.