Can Human Neurons Really Play Doom? The Science Behind Wetware (EP. 33)

Hosted by Lester Nare and Krishna Choudhary, this episode is a deep dive into one of the strangest science stories of the year: a dish of human neurons allegedly learning to play Doom. We go back to the original 2022 DishBrain paper out of Cortical Labs, unpack how biological neurons can be read and written with multi-electrode arrays, and then compare the peer-reviewed Pong result to the much newer Doom claim. The result is a story that is both genuinely impressive and, in places, probably overhyped. Summary Wetware engineering — replacing artificial neurons with real biological neurons plus electronics, and why some people think this could become a new computing paradigm. How DishBrain worked — human stem-cell-derived cortical neurons grown on a multi-electrode array, trained through sensory encoding and a “minimize surprise” feedback loop. Where the Doom story gets messy — the newer system appears to include a reinforcement-learning layer in the loop, raising the key question: are the neurons actually doing the learning? The big idea underneath the hype — even if Doom is overstated, the broader platform is still a remarkable step toward programmable biocomputing. Support the showDonate: FFPod.com/donateFollow: @FFPod on X / Instagram / TikTok / Facebook

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Krishna Choudhary?

  It's called wetware engineering, and it's effectively neurons, human neurons, plus electronics. So you cut out the middleman. The learning mechanism is literally minimizing surprise. If, on the other hand, it misses, it's going to get four seconds at 20 volts of highly chaotic electrical activity. So now I can very easily ask, who is actually doing the learning?

• 0:25Lester Nare?

  Correct.

• 0:26Krishna Choudhary?

  Is it my reinforcement learning network? Or is it the actual biological neurons that I have?

• 0:30Lester Nare?

  Doesn't count. Hello, Internet. This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Choudhary. And today, we're going to be talking about a topic that sounds like it's something straight out of science fiction, which is, did a dish full of human neurons successfully play Doom, the video game? Now, this is something that has been hyped in the news over the last two to three weeks with everyone trying to understand, is this real or is this hype? We're going to dive into the origins of how this came about through a research paper in Neuron out of Cortical Labs in Melbourne, Australia,

• 1:15Lester Nare?

  published in 2022, and look at that paper versus the recent news. And we might even touch on some of the other big fundamentals that made this concept possible. We are going to learn about the science from the ground up today because this is From First Principles.

• 1:51Krishna Choudhary?

  So, in our world of AI, We are very much used to artificial neural networks, right? And these are biomimetic AI in the sense that they mimic biology, right? Biological brains became artificial neural networks. The subject of this deep dive today is about a possible transition from this biomimetic AI to straight up biocomputing. It's called wetware engineering and it's effectively neurons, human neurons, plus electronics. So you cut out the middleman. You don't even make any artificial neurons. You're using now biological neurons to help you in computing.

• 2:34Krishna Choudhary?

  Specifically, there's an Australian company called Cortical Labs. They were recently in the news because their wetware computer could play Doom. And it was all hype, right?

• 2:45Lester Nare?

  I've seen it literally everywhere.

• 2:49Krishna Choudhary?

  Yeah.

• 2:49Lester Nare?

  The tech bros are going crazy.

• 2:52Krishna Choudhary?

  Yeah, all the doomsday people are like, should we be doing this?

• 2:54Lester Nare?

  Right.

• 2:55Krishna Choudhary?

  So, you know, I naturally asked, how much of this is real? And how much of this is hype? And it turns out there's a lot of both.

• 3:03Lester Nare?

  Okay.

• 3:04Krishna Choudhary?

  And that's what we're going to get into.

• 3:05Lester Nare?

  Okay.

• 3:05Krishna Choudhary?

  Okay, so let's do a little bit of brief background. Let's talk about the biological neuron, the one that is the substrate of our own nervous system in our human brain. A biological neuron is a cell, a biological cell. We see that on the left here. It's got a cell body. It's got dendrites, which are where other neurons come in and give you input. And in the cell body, what you do is you integrate, meaning you sum up all the inputs. And if all of the incoming inputs from other neurons exceed some kind of threshold, then you fire what's called an action potential, which is, in other words, a spike.

• 3:46Krishna Choudhary?

  It's effectively saying my neuron went from off to on because all of the other neurons that were talking to me, there were enough, there were enough neurons that were talking to me with enough input that I decided to go from off to on. When I do that, I go downstream and the action potential goes downstream to other dendrites of other neurons. And that's how we get a biological neuron. Now, in the artificial neuron, very much similar. You've got You've got incoming neurons and their activity. The GPU of Nvidia sums that up and then fires if it fires like, you know,

• 4:26Krishna Choudhary?

  an output to downstream neurons if that output, if the input exceeds a certain threshold, right? So we've basically taken the biological thing and we've turned it into a very simple math equation. Now, obviously, in that approximation, we're not doing it complete justice because in biological neurons, there's structure, there's proteins, there's all sorts of little things and it's not really just straight up integrating fire, though that's the simplest kind of model that you can create. But it turns out for a lot of the AI that we see, I mean, it's doing pretty well, right?

• 5:01Lester Nare?

  So even though we've sort of have a simplification of this representation of biological neurons in our artificial neural network systems, even that simple approximation in and of itself has valuable productive outcomes.

• 5:19Krishna Choudhary?

  Yes.

• 5:19Lester Nare?

  We don't have to replicate the whole of the brain.

• 5:22Krishna Choudhary?

  Yes.

• 5:23Lester Nare?

  In order to get some benefits from the concept.

• 5:27Krishna Choudhary?

  Exactly. And the reason why is because of the neural network. The idea that we can connect a bunch of these neurons into a giant network where everything is connected to one another. Not everything is connected, but you know, you can have layers of connections from one to the other. So in the biological case, again, on the left, you've got one neuron that's connected to the other. So the output of one neuron is connected to the dendrites of the other through something called a synapse. That's the gap in between neurons that let the neurons talk to one another. And in the artificial case, you've got hidden layers of neurons. And from one layer to the next, you've got inputs going in. Again, each neuron is going to integrate, meaning sum up and then fire if it exceeds some threshold. So you've got these nonlinearities and then that's going to create the output,

• 6:12Krishna Choudhary?

  which is going to create the next output, which is going to create the next output. And if you have trillions of parameters and, you know, millions and billions of neurons, then you can actually approximate any function you want, including, let's say, thought. And that's how you get large language models and all sorts of very, very cool things. So that's how we've mostly done artificial neural networks, right? Now, How is the brain different from these artificial neural networks? That's something that we should focus on here. Because these guys are trying to use biological neurons. If the artificial neural network is just that good, why would we want to try to use biological neural networks?

• 6:50Krishna Choudhary?

  There is an advantage that cortical labs and other people doing this technology say. The idea is silicon models, which are the things in Nvidia and things like that, they use the von Neumann architecture of computing. The biological brain is just the biological brain. The von Neumann architecture is the following. Your processor is separate from your memory, right? The memory is where you store stuff. The processing is where you compute. Those are two separate things in a conventional computer, right? You've got your RAM and then you've got your CPU. And over the years, there's a huge gap between how fast you can compute things in your CPU or your GPU,

• 7:34Krishna Choudhary?

  and how fast you can access the memory where you store stuff. In the brain, both are co-located.

• 7:40Lester Nare?

  Okay.

• 7:41Krishna Choudhary?

  They're the same thing. They're all stored in the synapses, the weights of these things. The memory itself is stored in the weights. And the way that you process stuff is you go from one neuron to the other in this neural network. But in an artificial neural network, there's that difference, right? So the idea is that perhaps this gives the brain an advantage. Another advantage that the brain has is that it's massively parallel. And it only uses like 12 to 20 watts of power for 100 billion neurons. On the other hand, state-of-the-art GPUs and neural nets use gigawatts of power for computing, right? So there's a clear difference also in the amount of energy needed to create that computing.

• 8:25Lester Nare?

  So the differences between the two. One is efficiency. We've created a simplified abstraction of what the brain does. And it takes a lot more energy to even do some of the things that the brain does. So it's a highly inefficient, as compared to the brain naturally.

• 8:42Krishna Choudhary?

  Yeah.

• 8:43Lester Nare?

  Artificial intelligence is highly inefficient or highly energy consumptive.

• 8:47Krishna Choudhary?

  Yes.

• 8:49Lester Nare?

  Also, the way in which the brain is structured, it can do significantly larger amounts of parallel processing.

• 9:01Lester Nare?

  Which enables that energy efficiency in part.

• 9:04Krishna Choudhary?

  Yeah, and also like each neural, each neuron itself is like highly efficient as a computational unit, right? It doesn't require a lot of energy. We think, you know, our brain does consume 20% of the energy of our body, but still, that's not actually a lot when you think about things, right?

• 9:19Lester Nare?

  Yeah.

• 9:20Krishna Choudhary?

  So perhaps the idea is we can use this natural biological wetware in place of our von Neumann architecture hardware, the GPUs that take up all that energy. That's the argument, okay?

• 9:31Lester Nare?

  Okay.

• 9:31Krishna Choudhary?

  But in order to do that, we need to be able to read and write to biological neurons.

• 9:36Lester Nare?

  Right, and also where do these biological neurons exist?

• 9:39Krishna Choudhary?

  Yeah, yeah, where do they come from? They're usually in our brain, right? So let's get into how we actually read and write to real biological neurons in a dish. This was actually pioneered in 1972 by Thomas G. Pine at Caltech, when he developed the first multi-electrode array. Here's the idea. So neurons produce electricity when they talk to one another. That's how they talk. They use ion channels, which are little holes in their membranes, the boundary between the inside and the outside of the cell. And when those little membrane proteins, those channels open up or close, that facilitates the transfer of ions, like potassium ions, chlorine ions, sodium ions, from the inside

• 10:23Krishna Choudhary?

  to the outside of the cell. Ions are charged atoms, in some sense, and so when they move around, they're going to create electricity, they're going to create electric fields, and we can sense that if we have an electrode array, which is just a bunch of tips of metal that is connected to some kind of amplifier. And as each neuron produces a little electric field blip, I can sense that with my electrode array, right? I can also supply electricity to then influence the behavior of the neurons near my electrode. Like if I've got a little metal tip here, and I supply electricity, that is going to change the electric field around that metal tip, which is then going to influence the ions, and thus the proteins on that neuron that are nearby that metal tip. And so I

• 11:08Krishna Choudhary?

  can now toggle between on and off based on that stimulation. So I've got a read, which is when the neuron's fired, they're going to release a little electric field, you know, spike that I can read. And then I can write by supplying electricity, and that's going to influence the neurons around it.

• 11:24Lester Nare?

  I have a quick clarifying question.

• 11:26Krishna Choudhary?

  Yeah.

• 11:26Lester Nare?

  When you talked about the spike just now, that you're referring to the concept you talked about earlier of the action potential.

• 11:32Krishna Choudhary?

  Yes.

• 11:33Lester Nare?

  This sort of like surge of electrical signal.

• 11:38Krishna Choudhary?

  Yeah.

• 11:38Lester Nare?

  That then the neuron utilizes to do its next action.

• 11:42Krishna Choudhary?

  Yeah.

• 11:43Lester Nare?

  Or its next step.

• 11:43Krishna Choudhary?

  Yeah.

• 11:44Lester Nare?

  And so when we have the ability, we have the ability to provide that

• 11:52Lester Nare?

  energy or source material necessary for the neuron to trigger its action potential artificially. It's not only capable, it's not only a capability that is not reproducible artificially.

• 12:06Krishna Choudhary?

  Yes, exactly. Yeah, it's something that we can, we can provide a signal to whatever metal contact is near that neuron. And then, you know, that's going to create a change in electric field, which is going to then drive ions around that neuron. It's going to drive the proteins around that neuron. And then the neurons will be sensitive to that change in the electric field, right? They're going to think that it's another neuron somewhere that's like doing that.

• 12:29Lester Nare?

  So we've, to, to, to just make a fine point on it. We can translate into the language that these neurons speak.

• 12:37Krishna Choudhary?

  Yeah.

• 12:37Lester Nare?

  Using this kind of methodology.

• 12:39Krishna Choudhary?

  Yes, exactly. Yeah. So now we can read and write.

• 12:41Lester Nare?

  Yes.

• 12:42Krishna Choudhary?

  Okay, so can we do biocomputing? Well, early biocomputing actually started in the late 1990s, early 2000s. There was this guy Steve M. Potter. He was at the Georgia Tech, Georgia Institute of Technology. He developed this thing called a hybrid, which is a hybrid robot. He used cultured cortical neurons from rats on a multi-electrode array to actually do just, to control like a robot. Okay, so again, this is a little plate that has a bunch of silicon electrodes, let's say. It could be something else. I don't actually know what, what, but today we use a lot of silicon in our electrodes, obviously.

• 13:22Krishna Choudhary?

  And he could grow rat cortical neurons on that electrode, like in a Petri dish, and then sense and then write, you know? So you've got a Read in and then write out.

• 13:35Lester Nare?

  You can listen to what's happening and you can talk to it to tell it stuff.

• 13:39Krishna Choudhary?

  Yeah, yeah. So this was him controlling a robot. In 2004, this was in the news. That was pretty cool. Thomas de Mars cultured 25,000 rat cortical neurons in a Petri dish. And that acted as an autopilot for an F-22 flight simulator. This was all over the news as well back then. And so that's pretty cool, right? Like almost 20 years ago, more than 20 years ago, 30 years ago is when we're doing this stuff. So how come now is when we can actually play video games? Okay, the reason for that is, one, we've had a huge improvement in how many electrodes we can fit inside our multi-electrode array. Back then, we could only fit about 60.

• 14:22Krishna Choudhary?

  Now we can do thousands, so we can sense a lot finer detail.

• 14:26Lester Nare?

  The bandwidth is greater.

• 14:28Krishna Choudhary?

  Yeah, yeah, it's like the density of the electrodes is literally greater. We can make them smaller, we can make them more sensitive. So we can now sense individual neurons rather than global populations. The other thing is that we need to keep these neurons alive, right? In the Petri dish. And we've had a tremendous amount of scientific innovation in how to keep neurons in a Petri dish alive for an extended period of time. Right? So now with all of that, we can get to cortical labs, which is a new startup that came out of Australia that is doing this stuff, that is trying to make neurons in a Petri dish play Pong, and now, most recently, play Doom. But before that, allegedly, allegedly, we'll see, right? We'll see.

• 15:12Lester Nare?

  Before that? We are, we are just so grateful and excited for so many of you that continue to join us on this journey of curiosity and discovery. For those of you who are returning, welcome back. We greatly appreciate your attendance at our multi-week now show release schedule. As we mentioned in our previous episodes, we are now testing going from just doing one weekly episode to multiple, with the rundown being its own episode type. If you're looking at your podcast feed or your Spotify feed and you're like, what's going on? You're getting more of us more often, which has been in high demand. And if you do like what you hear, whether you've been here with us for a long time

• 15:55Lester Nare?

  or you're a new listener, one of the greatest ways that you can support the show is to give it a like, share, comment, a follow on the platform that you happen to discover us on or listen to us on. And if you want to go above and beyond, we do have a donation portal at the website, ffpod.com backslash donate, where you can support the show with a recurring or one-time donation. We are a two-man operation who've set up this unbelievable production through the ingenuity of the both of us. And it is really a pleasure to be able to talk to you guys about the wonders of science multiple times a week. And we just, again, really, really, really

• 16:39Lester Nare?

  cannot express how important it is to fight the billionaire algorithm, to just engage in any way that you can. Even if it's not financially, it's really helpful for us to get to more people so we can talk about the wonders of what the researchers and scientists all over the globe are providing us in terms of the future of humanity. And with that housekeeping note, we can jump back into cortical labs.

• 17:04Krishna Choudhary?

  All right, so now let's talk about cortical labs. This is the subject. This is a startup out of Melbourne, Australia. I just recently watched the Formula One Grand Prix in Melbourne. Ferrari, no, it didn't. Mercedes is.

• 17:19Lester Nare?

  Was that the one where Leclerc and Hamilton were battling each other?

• 17:23Krishna Choudhary?

  No, that was the Chinese Grand Prix very recently. Anyways, I don't want to talk about it. So cortical labs founded in 2019 by Hongwen Chong. The company gained international attention in 2022 because of a paper that we're going to cover. It was in the journal Neuron and. It was describing this thing called Dish Brain, which is a system in which in vitro neural cultures, so that's in vitro, meaning in a Petri dish, learned how to play the game Pong in a closed-loop environment. Okay, so that is the paper that we are going to be talking about, because that is the bedrock upon which their technology is built, and it'll help us understand how exactly they did do. Okay, and it's actually very cool, the, some of the, some of the stuff that they've done.

• 18:07Krishna Choudhary?

  Okay, so first of all, you gotta get neurons for your wetware engineering.

• 18:13Lester Nare?

  Yes.

• 18:14Krishna Choudhary?

  How do you get neurons? Well, first you get primary mouse cortical neurons. Those are fine to get. You know, mice are very much a animal that we use in biological research. So that's not that hard. Human cortical cells, on the other hand, are hard to come by. So instead of actually, you know, waiting for a patient who has epilepsy, let's say, to have a little part of their brain excised and get that kind of cortical neuron, you never know with those kinds of samples, like, okay, it's a brain that has epilepsy. That's not a wild type, type brain. It's not a neurotypical, as we should say, type brain. So instead, what, what they did was they generated human-induced pluripotent stem cells. This is something that we cover a lot. It won the Nobel Prize, because it's a technology where we can take somatic cells,

• 18:59Krishna Choudhary?

  like stuff from the biopsy of a, of skin. We can reprogram them to become stem cells, which are blank slate cells that can be anything that they want. And then we can give them a cocktail of chemicals to differentiate them into cortical tissue. So we take skin cells, we make them a blank slate, and then we make them cortical neurons.

• 19:22Lester Nare?

  I just want to make a quick note that this was, I think this was the, the Yamanaka Nobel.

• 19:26Krishna Choudhary?

  Yes, yeah.

• 19:27Lester Nare?

  And this was really important, because prior to that embryonic stem cell research was highly controversial, and it was the only way to acquire stem cells. And it really unlocked the ability to actually be able to do this type of work

• 19:41Krishna Choudhary?

  Yeah.

• 19:41Lester Nare?

  by removing the sort of moral, ethical

• 19:44Krishna Choudhary?

  Yeah.

• 19:44Lester Nare?

  conundrum. So now, the point being, any human cell, you can reprogram to something else.

• 19:49Krishna Choudhary?

  Yeah.

• 19:49Lester Nare?

  That wasn't, that's where, you know,

• 19:51Krishna Choudhary?

  That's a huge.

• 19:52Lester Nare?

  That's a huge foundational discovery.

• 19:54Krishna Choudhary?

  Exactly.

• 19:55Lester Nare?

  Um, that, that now, because I, I know the sort of stem cell piece, for some folks, it's like they, they're that. Historical point, because some people still think, oh, aren't we still getting those from embryos?

• 20:06Krishna Choudhary?

  No, yeah, this is just your skin cell becoming a stem cell. It's pretty insane. Now, of course, with the pluripotent stem cells, it's not a complete blank slate, right? The embryonic stem cell is like the gold standard, because those things are built to be blank slates, right? So, but this, this comes pretty close for literally 90% of applications. So it's, it's done a huge service for the biomedical industry, because for 90% of applications, I don't need to worry about that. That's still a huge unlock.

• 20:35Lester Nare?

  Yes.

• 20:35Krishna Choudhary?

  I just want to be very clear here. So, let's talk about the culture that they made. They made 800,000 neurons, co-cultured with astrocytes. This is pretty incredible here.

• 20:45Lester Nare?

  Okay.

• 20:46Krishna Choudhary?

  Usually when we make stem cells, right, and we make a culture of cortical tissue, you're gonna have all neurons. That actually is not ideal.

• 20:55Lester Nare?

  Okay.

• 20:56Krishna Choudhary?

  For certain applications, it is. Like when we were discussing the ALS story.

• 21:00Lester Nare?

  Yes.

• 21:00Krishna Choudhary?

  And we wanted to create an ALS mini-brain on a Petri dish. We wanted it to be all motor neurons, right? Because then that's how we test the drugs and so on and so forth. In this case, we want like a live computing unit that kind of mimics the human brain. Like, or at least a small chunk of the human brain. And the human brain is not just neurons. The human brain has astrocytes. So if we look over here, what they've been able to do is grow neurons along with astrocytes and all of the supporting cells. Okay? So the blue, that DAPI.

• 21:38Lester Nare?

  Yes.

• 21:39Krishna Choudhary?

  Those are marking the neurons. The GFAP in the red, those are marking supporting astrocytes. And those are critical for long-term functioning.

• 21:48Lester Nare?

  Okay.

• 21:48Krishna Choudhary?

  Okay? So this is sort of a secret sauce that allows those cultures in that Petri dish to survive for a lot longer. So now they can learn over a longer time scale and, you know, actually... Now we can take neuro computing seriously.

• 22:04Lester Nare?

  If you have a professional sports team with these high skilled athletes.

• 22:09Krishna Choudhary?

  Yeah.

• 22:10Lester Nare?

  They're nothing without the training and medical staff.

• 22:12Krishna Choudhary?

  Exactly.

• 22:13Lester Nare?

  That enable them to continue to perform over a longer periods of time, given the amount of stress and things that are going on.

• 22:19Krishna Choudhary?

  Exactly. And so these are all these supporting cells, which are crucial to creating a compute, like, you know, a little piece of computation on that Petri dish. It's not just all neurons. You've got to have sort of a mimicked brain environment and they were very successful at doing that. So now that's the side of the cells.

• 22:38Lester Nare?

  Okay.

• 22:38Krishna Choudhary?

  We've, we've created really nice culture with 800,000 neurons. That's a lot of neurons.

• 22:42Lester Nare?

  Right.

• 22:43Krishna Choudhary?

  Okay. Now let's go into the multi electrode array.

• 22:47Lester Nare?

  I'm sorry. I just want to interrupt you really briefly.

• 22:49Krishna Choudhary?

  Yeah.

• 22:49Lester Nare?

  Is, is so the 800,000 neurons is that, would that be comparable to saying like the GPUs in your Nvidia cluster as like the corollary in an artificial system?

• 22:59Krishna Choudhary?

  Yeah, yeah. It's like the number of, um, it's the number of transistors one can think of.

• 23:04Lester Nare?

  Okay, okay.

• 23:04Krishna Choudhary?

  Maybe, you know, it's like, I mean, no, it's not, it's not quite because, you know, it's not one transistor to one artificial neuron. But in your artificial neural network, you have, you know, billions and billions of neurons with trillions of weights in between them. Here you've got 800,000 neurons. It's still small, but it's incredible what they're able to do with it.

• 23:23Lester Nare?

  Understood.

• 23:24Krishna Choudhary?

  You know, but that's the idea.

• 23:26Lester Nare?

  Yes.

• 23:26Krishna Choudhary?

  You know, okay. So that's our neuron piece. Now let's get into how are we going to sense them? That's our multi electrodes, right? Those are the little metal leads that let us read the neurons and then write to the neurons. So here they did a high density multi electrode array using CMOS technology. 26,000 platinum electrodes. What you can see over there, all of these little rectangles is an individual electrode.

• 23:49Lester Nare?

  Okay.

• 23:50Krishna Choudhary?

  So that can be individually tuned to have voltage, you know, five volts, negative two volts, whatever you want it to be. It can read the, the electric field around it and it can write an electric field around it.

• 24:02Lester Nare?

  Mm hmm.

• 24:02Krishna Choudhary?

  Okay. So, and you can see that the sort of, this is a, I think it's an electron microscope. And you can see the cell bodies and all the dendrites and all of the connections that are just sort of growing. Through that multi-electrode array. So when I have a connection, like a little axon that's connected from neuron A to B, I can follow that signal with my electrodes.

• 24:23Lester Nare?

  Yes.

• 24:24Krishna Choudhary?

  Very, very cool, right?

• 24:25Lester Nare?

  It looks like a map of LA from the sky.

• 24:27Krishna Choudhary?

  Yeah, yeah, it really does. Like, the Spanish are very good at gritting. And that's what this looks like. If you're in Barcelona, it also looks like that, you know?

• 24:35Lester Nare?

  Yes, yes.

• 24:37Krishna Choudhary?

  And out of the 26,000 platinum electrodes, there's only 1024 independent channels that are active.

• 24:44Lester Nare?

  Okay.

• 24:45Krishna Choudhary?

  So they can pick and choose which ones they want to read through, right? So that's, again, a huge thing. And one of the incredible things is that the latency between reading and then making a computation to figure out what to write, right? Because I'm going to read the activity from the neurons, then I'm going to, I don't know, I guess, like, see what the game state of Pong is or Doom is, and then write back to the neurons and give them feedback. They got it down to five milliseconds.

• 25:12Lester Nare?

  That's such a huge.

• 25:13Krishna Choudhary?

  Which is quite a small latency.

• 25:15Lester Nare?

  Yes.

• 25:16Krishna Choudhary?

  If you really think about it.

• 25:17Lester Nare?

  And that is, and if we're trying to mimic the brain, we need that latency as close to zero as possible.

• 25:23Krishna Choudhary?

  Exactly, because for us, our latency is around that.

• 25:27Lester Nare?

  Yeah, right.

• 25:27Krishna Choudhary?

  Right? One of the common things that we think about is like, our eyes are about 60 frames per second is the top level of how many, you know,

• 25:39Krishna Choudhary?

  little pictures it takes every second and relays back to our brain. And so 60 frames, let's just say 50 frames per second, because I want to make my math simpler.

• 25:47Lester Nare?

  Yes.

• 25:48Krishna Choudhary?

  Right? So 50 frames per second, that's about 20 milliseconds is the refresh rate

• 25:54Lester Nare?

  Yes.

• 25:55Krishna Choudhary?

  of my eye to the brain, right? And this is going down to five milliseconds. So it's even lower than that.

• 26:00Lester Nare?

  Yep.

• 26:01Krishna Choudhary?

  It's, that's the speed at which neurons talk. The action potential, which is this thing that I was telling you about, how when the neuron turns off, it actually immediately turns off right after. It turns on and off. And that action potential takes about one millisecond. So getting it down. Is mimicking the sort of computational latency that the brain already has and what these neurons in your dish are used to.

• 26:23Lester Nare?

  Yes.

• 26:23Krishna Choudhary?

  Right, so you don't, you don't have them like thinking harder than they need to.

• 26:27Lester Nare?

  Than they need to and be, which makes it more energy.

• 26:29Krishna Choudhary?

  Yeah, so now let's look at the dish brain protocol. How do we play pong with our video game?

• 26:37Lester Nare?

  With our DBP.

• 26:39Krishna Choudhary?

  Yes, okay, so over here, we've got in the middle, our Petri dish that has on the bottom of it, this multi electrode array. These are little tiny electrodes that are going to sense the neuron culture that is on top. The neuron culture on top is getting bathed with electrolytes with glucose to keep it alive. They can keep it alive for as long as six months. And as these neurons in my Petri dish turn on and off, my multi electrode array is going to sense that, it's going to read it in, it's going to sense the gameplay and it's going to read it out into the neuron. Now, what we don't want to do is completely jumble up our read in and our read out.

• 27:17Lester Nare?

  That was literally going to be my, the orchestration problem there is non trivial.

• 27:21Krishna Choudhary?

  Right, yeah, because we've got a bunch of electrodes and the brain is not really an all read in, all read out, right? There are regions in the brain, like the occipital lobe that has the visual cortex. There's the prefrontal cortex that makes decisions. There's the motor cortex that actually outputs my hand when I'm like, let's say, using a joystick, right? So there are segregated spots in my brain for where I'm reading in, that'll be my visual cortex, and where I'm going and reading out, that would be my motor cortex. So they decided to create an artificial visual cortex and a motor cortex on the chip itself. Okay, so at the very top, on the right hand side, what you can see is the electrode layout schematic.

• 28:05Lester Nare?

  Okay.

• 28:06Krishna Choudhary?

  On the very, so you've got your rectangular multi electrode array that has 1024 electrodes that I can actively read out and read in from. On the very top, I'm going to make a rectangular box that's in the upper half and eight of those electrodes, I'm going to use as my quote visual cortex. It's really quite a sensory cortex, I should say, because that's what I'm.

• 28:30Krishna Choudhary?

  Writing into, right? The sensory cortex is getting information. When I, when I play a video game, the sensory cortex is through my eyes and through, I guess, sound sometimes, getting information about the state of the game.

• 28:44Lester Nare?

  If you talk to Call of Duty players, sound is as equally important. Yeah. There are high quality headphones to sneak up on you as visual.

• 28:51Krishna Choudhary?

  Yeah.

• 28:51Lester Nare?

  I'm just being annoying.

• 28:52Krishna Choudhary?

  I mean, I, when I was playing Halo, I used to sometimes have to like play on mute because I was like 2am and my parents would like, like, what are you doing? So I, I'm actually quite good at playing Halo on mute. So I don't know about you guys. But anyways, the idea is there's a sensory cortex that is getting information.

• 29:10Lester Nare?

  Yeah.

• 29:10Krishna Choudhary?

  From the brain.

• 29:11Lester Nare?

  Yeah.

• 29:11Krishna Choudhary?

  So I'm gonna just arbitrarily make that top half of my electrode, the sensory cortex. And that's where I'm going to write information to my neurons.

• 29:20Lester Nare?

  And the idea here is we're segregating it from other section or sections such that it only has one thing to worry.

• 29:27Krishna Choudhary?

  Yeah, exactly. It's kind of like, I mean, I don't want to give too much agency to these neurons in a dish.

• 29:33Lester Nare?

  Sure.

• 29:34Krishna Choudhary?

  But what I'm saying is that those neurons in that upper half of my Petri dish are acting like the sensory neurons. They're gonna sort of understand that, hey, I'm getting information about something.

• 29:45Lester Nare?

  And I think this goes back to the point you brought up earlier, which is proximity creates stronger neural connections.

• 29:51Krishna Choudhary?

  Yes.

• 29:51Lester Nare?

  And so because you're putting them in proximity and having a similar input.

• 29:56Krishna Choudhary?

  Yeah.

• 29:57Lester Nare?

  The theory is, you know, they will naturally strengthen around that shared.

• 30:02Krishna Choudhary?

  Yes.

• 30:02Lester Nare?

  Idea.

• 30:03Krishna Choudhary?

  Very good. Yeah, exactly. Those neurons in that region, they're all connected to one another. And so they're gonna somehow figure it out.

• 30:09Lester Nare?

  Right.

• 30:10Krishna Choudhary?

  We're gonna get into how, but that's the idea. They're gonna somehow figure it out. And actually, if you, if we, if we put that back.

• 30:15Lester Nare?

  Yes.

• 30:16Krishna Choudhary?

  The bottom half of that Petri dish has those up and down arrows.

• 30:20Lester Nare?

  Yeah.

• 30:20Krishna Choudhary?

  That's my motor cortex.

• 30:22Lester Nare?

  Okay.

• 30:22Krishna Choudhary?

  Okay? So neurons in the down region, in that little block.

• 30:28Krishna Choudhary?

  If those fire, that's gonna trigger my Pong paddle, I guess. It's a paddle, right? It's like a digital paddle.

• 30:35Lester Nare?

  I know what you mean.

• 30:36Krishna Choudhary?

  You know what I mean, right? Like that, it's gonna, it's gonna trigger the paddle to go down. And if the neurons in the up region fire, if for those electrodes, then it's gonna trigger the neurons to go up. And sometimes you will have to calculate a difference between the up and down because, you know, maybe there's more neurons in the down region versus the up. So I also have to normalize based on like how many neurons there are because I don't want to just artificially keep going down if there's randomly more neurons in that region. So there's a bunch of mathematical tricks you have to do. But at the end of the day, segregated regions.

• 31:07Lester Nare?

  Yes.

• 31:07Krishna Choudhary?

  One set of neurons is going to fire if the culture wants to go down. And one set of neurons is gonna fire if that Petri dish culture is gonna want to go up.

• 31:16Lester Nare?

  Yes.

• 31:16Krishna Choudhary?

  Okay, so now we've, now we've figured out our read and our write mechanism. The right is the sensory cortex at the top, eight electrodes, and our read is getting it in.

• 31:26Lester Nare?

  And I think an interesting point about this is that geographical or locational, the, where it's located and how it's segregated, like form is function or form is following function in this context.

• 31:38Krishna Choudhary?

  Exactly, yeah. Now, there's one other thing that I want to touch upon, which is how exactly that writing mechanism is being implemented. Right? Because what do we need to give this Petri dish of neurons information about?

• 31:51Lester Nare?

  Yes.

• 31:51Krishna Choudhary?

  What is all the information we need to give it? Well, we need to give it a state of the game, right? Which is where is the paddle and where is the ball? Right? In relation to that.

• 32:00Lester Nare?

  Yes.

• 32:01Krishna Choudhary?

  Now, for us, when we play Pong, we've got a very nice visual system. So we see where the paddle is, we see where the ball is, and when we also see how it's moving. And so we can sort of, all of our billions of neurons in our brain can like understand spatially what's going on because we understand space because we've had so much pre-training. And we can figure it out.

• 32:23Lester Nare?

  Yeah.

• 32:23Krishna Choudhary?

  This is a neuron in a Petri dish. It does not have eyes, it does not have senses. We are creating the senses. So how are we gonna do it? Well, they use something called rate and place coding. This is something that is very commonly observed in.

• 32:39Krishna Choudhary?

  The human brain.

• 32:40Lester Nare?

  Okay.

• 32:40Krishna Choudhary?

  Okay. For example, let's take the example of the human cochlea, which is in our ear. That's how we sense sound. We sense frequency and we sense loudness, right? I've, um, I've touched upon the cochlea before. The idea with the cochlea is there's like this tube, a snail-like tube that is embedded in our ear. And what that tube is, if you were to unravel it, there would be neurons that are very thick at one end, and there would be neurons that are very thin at the other end. This thing gets wrapped up into kind of like a shell shape, right? But if I hear sound of a certain frequency, that is going to excite a neuron along this axis

• 33:25Krishna Choudhary?

  that is at exactly the right thickness, right? If the sound is very low frequency, like a bass sound, that's gonna excite the thicker neurons because they're bigger and the wavelength of the sound is bigger. So it's, it's kind of just like, you know, the resonance is happening. And if the sound is really high, then it's gonna excite the smaller neurons in my cochlea.

• 33:46Lester Nare?

  Yes.

• 33:47Krishna Choudhary?

  So that's a place coding.

• 33:48Lester Nare?

  Yep.

• 33:48Krishna Choudhary?

  That tells me the frequency.

• 33:50Lester Nare?

  Okay.

• 33:50Krishna Choudhary?

  Okay, that's my place because my place along the axis is telling me the frequency of the sound.

• 33:56Lester Nare?

  Yes.

• 33:56Krishna Choudhary?

  The rate coding is how loud is that sound?

• 33:59Lester Nare?

  There it is.

• 34:00Krishna Choudhary?

  Right? Because if it's very loud, then that neuron is gonna fire at a very high frequency.

• 34:05Lester Nare?

  Right.

• 34:05Krishna Choudhary?

  If it's not that loud, it's gonna fire at a low frequency. So I'm getting two pieces of information with place and rate. The place is telling me the frequency, the rate is telling me the loudness, right? And this is something that is very common in biology. Where the neurons are firing tells us one piece of information and how much the neurons are firing tells me another piece of information. And those are simultaneously present when biological neural networks create computation.

• 34:33Lester Nare?

  And just to continue to bring this back to the artificial comparison.

• 34:38Krishna Choudhary?

  Yeah.

• 34:38Lester Nare?

  I think what's interesting here is this is different than just being zeros or ones.

• 34:44Krishna Choudhary?

  Yes.

• 34:44Lester Nare?

  Because you actually have two levels of, of input. You have the, the, the place, which you could, uh...

• 34:52Lester Nare?

  Let me not make that direct comparison, but the point is it is not as simple as a binary system.

• 34:56Krishna Choudhary?

  Yeah.

• 34:56Lester Nare?

  It's more of like of a two-state modulator.

• 35:00Krishna Choudhary?

  Yeah, no, I think I think you're on to something here and that the neuron is kind of an analog system. Right in that sense. Now, some people will argue that the neurons are still digital because they're sending out spikes. And if you were to condense time and discretize it into smaller and smaller segments, then in each time window, the neuron is either active or inactive, right? And the idea of a high rate is just there's more time windows that it's active. Right? So there is, there is a really healthy debate in the neuroscience community about whether to interpret neurons as analog or digital, right? And I don't know where I fall on that.

• 35:38Lester Nare?

  Okay, that's fair.

• 35:39Krishna Choudhary?

  But it's, it's, I think it's a fair point that you're making. Like, it's a very important.

• 35:44Lester Nare?

  Right, right.

• 35:45Krishna Choudhary?

  Right? It's like, it's like that, that is, that is still up for grabs.

• 35:47Lester Nare?

  Because the answer to that will also then be determined, will be relevant to like our conversation about L L M's versus other models that we had in the last episode in terms of if, anyway, you get the idea.

• 35:59Krishna Choudhary?

  Yeah, yeah. So to be clear, I just want to reiterate here. What we're doing is using something that is in place and something that the brain is already used to, which is place and rate coding. And the idea of the cochlea, the place along this axis was the frequency and the rate at which that neuron fired was the loudness. In this case, how are we actually, if we go back to photo 14?

• 36:27Lester Nare?

  Yes.

• 36:28Krishna Choudhary?

  Actually, how are we actually going to use that sensory cortex? The eight electrodes that we're trying to write in our information? How are we going to use place and rate coding to inform my Petri dish brain about the state of the game? That's the key, right? What they did was they said something very interesting. The, um, where the ball is, or where the, um, sorry, where the paddle is?

• 36:57Lester Nare?

  Yes.

• 36:58Krishna Choudhary?

  Is going to be my place. Okay, so if my paddle is lower on the screen. I'm gonna fire the electrodes on the left. I'm gonna make the left electrodes give some electricity to my brain. If the paddle is up on the screen, I'm gonna give the right electrodes. Okay, so now I've got place. What's the other thing I need to code in? That's the distance to the ball. How close is the ball? How close is it getting? Right? And so for that, they use frequency.

• 37:26Lester Nare?

  Yeah.

• 37:27Krishna Choudhary?

  They went all the way from four hertz to forty hertz. Four hertz means it's very far away. Forty hertz means it's getting close. You need to figure out which way to go.

• 37:36Lester Nare?

  Yeah.

• 37:37Krishna Choudhary?

  Right?

• 37:38Lester Nare?

  And the idea here is we are basically creating a translation between what we experience as a visual input.

• 37:47Krishna Choudhary?

  Mm-hmm.

• 37:48Lester Nare?

  Where we can do the rate and place just through our visual input.

• 37:53Krishna Choudhary?

  That's right.

• 37:54Lester Nare?

  Into an electrical input such that, but that is regional.

• 38:01Krishna Choudhary?

  Mm-hmm.

• 38:01Lester Nare?

  As a means to create an extra layer of like degree of freedom in encoding more data in what is a very relatively flat kind of system.

• 38:10Krishna Choudhary?

  Yeah, exactly. And we need to be able to give those neurons in my Petri dish both of these pieces of information. Right? And one thing I do want to be clear here is the neurons in this Petri dish are not directly seeing the game.

• 38:22Lester Nare?

  Right.

• 38:22Krishna Choudhary?

  Like we are.

• 38:23Lester Nare?

  That is kind of what I'm trying to.

• 38:24Krishna Choudhary?

  Right?

• 38:25Lester Nare?

  Poke at.

• 38:25Krishna Choudhary?

  Yes. They're not seeing the game like humans do. Instead, they're sort of seeing the game based on the paddle's point of view in some sense. It's like, where am I? If I'm the paddle? And also, how far away is the ball?

• 38:37Lester Nare?

  Right.

• 38:38Krishna Choudhary?

  Those are the things. And the only, the resolution is only like eight pixels in some sense. Right? But from that, they're trying to make this Petri dish play pong. Okay? So the other thing that we need to think about is fine. Now we've got a read and write mechanism.

• 38:56Lester Nare?

  Yes.

• 38:57Krishna Choudhary?

  Now we need a training mechanism.

• 38:59Lester Nare?

  Right.

• 39:00Krishna Choudhary?

  Right?

• 39:01Lester Nare?

  Yeah.

• 39:01Krishna Choudhary?

  Because now all of a sudden, I mean, we're telling the Petri dish the state of the game and we have some way of reading what the Petri dish wants to do.

• 39:10Lester Nare?

  Yep.

• 39:10Krishna Choudhary?

  Which is move the paddle up or move the paddle down. How does the Petri dish know whether it's doing well or not?

• 39:15Lester Nare?

  Yes.

• 39:16Krishna Choudhary?

  Right? The challenge is actually quite non-trivial because in the brain, how do we get a reward?

• 39:24Krishna Choudhary?

  You know, we feel happiness. Maybe we got a cookie, literally. That is going to fuel dopaminergic neurons in our brain that are going to send signals of dopamine, pleasure, and all this other kind of stuff. So we've got like emotional signals for reward. We've got other crazy signals for reward. This is a brain in a Petri dish. It does not have dopaminergic neurons.

• 39:46Lester Nare?

  Right.

• 39:46Krishna Choudhary?

  It does not have these centers and this complex brain anatomy to have reward.

• 39:51Lester Nare?

  Yes.

• 39:51Krishna Choudhary?

  So how are we going to do that? The re- and this, I think, is actually the coolest part of the paper to me.

• 39:58Lester Nare?

  Okay.

• 39:58Krishna Choudhary?

  Okay. It's this idea of a closed-loop feedback architecture, and the learning mechanism is literally minimizing surprise.

• 40:08Lester Nare?

  Okay.

• 40:08Krishna Choudhary?

  Here's what they're doing. If there is a successful hit, meaning the pong paddle thing hit the, hit the ball.

• 40:18Lester Nare?

  Yeah.

• 40:18Krishna Choudhary?

  Okay, and we're good to go. What it's going to do is all of the electrodes in my multi-electrode array is going to send out a predictable electrical pulse. It's about 10 volts on every single electrode.

• 40:35Lester Nare?

  Okay.

• 40:35Krishna Choudhary?

  At 10 hertz, so 10 every second.

• 40:38Lester Nare?

  Yeah.

• 40:39Krishna Choudhary?

  For about 50 milliseconds. Very short, very highly structured.

• 40:43Lester Nare?

  Okay.

• 40:43Krishna Choudhary?

  Okay? If, on the other hand, it misses, it's going to get four seconds at 20 volts of highly chaotic electrical activity.

• 40:53Lester Nare?

  Mm-hmm.

• 40:54Krishna Choudhary?

  And the idea is, brains hate that.

• 40:57Lester Nare?

  Yes.

• 40:57Krishna Choudhary?

  They want to be able to predict the future.

• 41:00Lester Nare?

  Yes.

• 41:00Krishna Choudhary?

  Okay? And when you're giving them highly unpredictable stuff, even this naive network of biological neurons.

• 41:08Lester Nare?

  Yes.

• 41:08Krishna Choudhary?

  Wants to decrease the amount of times it gets that. So if we, if we look at the gif, I just wanted to show one thing.

• 41:15Lester Nare?

  Yes.

• 41:15Krishna Choudhary?

  So here, we've got the brain coming in. You see that bump in electric, electrical activity everywhere? And now it's about to miss.

• 41:21Lester Nare?

  Yep.

• 41:22Krishna Choudhary?

  It's gonna miss, oop, for four seconds. It's just that, like, there's noise everywhere.

• 41:26Lester Nare?

  Yes.

• 41:27Krishna Choudhary?

  Okay, we're feeding a bunch of electrical noise. The neurons hate that.

• 41:30Lester Nare?

  Yeah.

• 41:30Krishna Choudhary?

  Now, the, it's gonna go, it's gonna actually, oh, I guess it's gonna recycle. Let's recycle.

• 41:37Lester Nare?

  Recycle.

• 41:38Krishna Choudhary?

  So it'll recycle when it gets there. A bunch of predictable.

• 41:42Lester Nare?

  Yes.

• 41:42Krishna Choudhary?

  Electrical signal. That's all we're doing.

• 41:44Lester Nare?

  Yes.

• 41:45Krishna Choudhary?

  Our reward mechanism is simply, if you got it right, we're gonna reward you with a bunch of predictable activity.

• 41:52Lester Nare?

  Yep.

• 41:53Krishna Choudhary?

  10 hertz, 50 milliseconds. And if you get it wrong, four seconds of random chaotic activity.

• 42:01Lester Nare?

  And I think this dovetails with some sort of historical science research that's been done around the, you know, when we look at how do we get ordered systems with entropy? And this idea of everything goes to soup. And the way in which that works is by minimizing surprise.

• 42:21Krishna Choudhary?

  Yes.

• 42:21Lester Nare?

  This concept, it's like a biological, it is how life fundamentally is able to instantiate itself. And that, we're basically hijacking that natural kind of concept by artificially punishing the wrong decision.

• 42:40Krishna Choudhary?

  With chaos.

• 42:41Lester Nare?

  With chaos, which makes the system work harder. And it is naturally going to want to not work harder.

• 42:46Krishna Choudhary?

  Yeah.

• 42:47Lester Nare?

  And so that's why it chooses the right decision.

• 42:49Krishna Choudhary?

  Yeah.

• 42:49Lester Nare?

  It's not, it doesn't really know that it's the right decision.

• 42:53Krishna Choudhary?

  Exactly.

• 42:53Lester Nare?

  It is just optimizing for the less, the more energy efficient.

• 42:57Krishna Choudhary?

  Yes.

• 42:58Lester Nare?

  Pathway.

• 42:58Krishna Choudhary?

  Yes, the less surprising.

• 43:00Lester Nare?

  The less surprise, sorry, the less surprising.

• 43:01Krishna Choudhary?

  Yeah, it's really the less surprising outcome.

• 43:03Lester Nare?

  Right.

• 43:04Krishna Choudhary?

  Is what it wants, right? And now, let's look at the results.

• 43:07Lester Nare?

  Okay.

• 43:07Krishna Choudhary?

  Okay, with this training paradigm, let's look at the results. The apparent learning is demonstrated in five minutes. So here, what we're looking at is two sets of box plots. The green box plot is what happens from zero to five minutes.

• 43:20Lester Nare?

  Okay.

• 43:21Krishna Choudhary?

  Um, we're looking at the change in rally length, which is like the, you know, the, how many times the rally happens for the pong? It's just like in tennis, like how many times do I keep the ball alive? Um, and you can see from six to 20 minutes, there's a giant change in the size of the rallies from zero to five. So within five minutes, this thing is learning quite well. There's a. Up change in rally and that's for the stimulus for the controls which is silent and no feedback. There's no change. Okay, so we're getting a significant change only when they're actually playing the game and they have this feedback. Now, if we go to the next photo, what we're also seeing is that human cortical neurons do a lot better than mouse cortical neurons.

• 44:04Lester Nare?

  Yes.

• 44:04Krishna Choudhary?

  Okay, and that, I don't know, that makes me feel good, that my neurons are better than a mouse's neurons.

• 44:09Lester Nare?

  Better.

• 44:10Krishna Choudhary?

  Right, so and that kind of makes sense where we've had evolution and our brains arguably are probably better than mouse.

• 44:17Lester Nare?

  And the thing is, there was an improvement in both cases, it's just our improvement had a.

• 44:20Krishna Choudhary?

  Had a big, that's, that's a good point, very good, yeah, it's that ours had a much bigger improvement than the mice.

• 44:27Lester Nare?

  Compared to the mice.

• 44:28Krishna Choudhary?

  Right, so now that we have that, let's actually look at this free energy principle that you were alluding to a bit earlier.

• 44:36Lester Nare?

  Oh, interesting, okay.

• 44:38Krishna Choudhary?

  That's what it's called, the free energy principle, this is, was, it was first pioneered by Carl Friston. And the idea is, what you were saying, all self-organizing biological systems want to minimize something called the free energy, the variational free energy, in other sense, what we want to do is maximize the information entropy. And we want to minimize the surprise and the prediction error, okay? What we can look at is how much surprise there is for what is the probability of a certain state? If the probability of a certain state is very low, there's a lot of surprise, right? And you can quantify that in the same way that we actually quantify entropy in physics, which is the log of the probability of stuff.

• 45:24Krishna Choudhary?

  And effectively, what the free energy principle is saying is that biological systems want to maintain homeostasis, meaning like constancy over all the random stuff. And we want to minimize that surprise, right? And what do we, what do we do if we want to do that? Well, we've got some internal model of what the world is. And we are going to tune the parameters of that internal model such that that internal model mimics more closely the world that we live in. Right, that is the idea. It's a stark departure, I should say and highlight, from traditional reinforcement learning.

• 46:04Lester Nare?

  Okay.

• 46:05Krishna Choudhary?

  Okay? In traditional reinforcement learning, you've got some algorithm, and that algorithm explicitly gives negative feedback through backpropagation for wrong trials. Here, we are letting chaos itself be the negative feedback.

• 46:22Lester Nare?

  Yeah, yeah, yeah.

• 46:22Krishna Choudhary?

  Now, one of the things that's kind of crazy is that the algorithms for reinforcement learning require thousands of epochs of trial and error to update these weights through backpropagation.

• 46:33Lester Nare?

  Right.

• 46:33Krishna Choudhary?

  Biological systems will inherently self-organize to minimize this free energy, and they'll do it very, very quickly. They've just got really nice cellular mechanisms to find that sweet spot.

• 46:45Lester Nare?

  This goes back to-

• 46:45Krishna Choudhary?

  In their internal model.

• 46:46Lester Nare?

  The energy efficiency piece again.

• 46:48Krishna Choudhary?

  Yes, exactly, right? And so, maybe that's why we want to use the wetware in the first place, right? So, let's go back to this FEP in a dish, our free energy principle in a dish. Effectively, what's happening is, you've got predictions, and then you've got prediction errors. The predictions are where I want to move my paddle, and the prediction error is the chaos that I get when I get it wrong, right? I'm trying to change the connections in my neurons on my Petri dish such that I minimize prediction error, and that's effectively what these neurons are doing. It's actually pretty incredible when you think about it.

• 47:31Krishna Choudhary?

  We're not giving them a bad and good signal. We're only giving them a chaos versus non-chaos signal.

• 47:41Lester Nare?

  Right, right.

• 47:42Krishna Choudhary?

  And that itself, just because of the nature of information and the physics of information, is enough to like steer them like this is bad and that's good. I think that's like quite deep to think about.

• 47:55Lester Nare?

  The universe never chooses violence.

• 47:57Krishna Choudhary?

  Yeah.

• 47:57Lester Nare?

  It always chooses to bill him given. I'm being a little facetious.

• 48:02Krishna Choudhary?

  No, but I think, I think, I think that's a very good, that's a very poignant statement here. So now, let's look at like some of the hype that they created around it.

• 48:10Lester Nare?

  Okay.

• 48:10Krishna Choudhary?

  Let's look at some of the promo videos.

• 48:11Lester Nare?

  And this was for the Pong era?

• 48:13Krishna Choudhary?

  This is just 2022. This is, we're still on 2022.

• 48:16Lester Nare?

  Just confirming.

• 48:17Krishna Choudhary?

  Yeah.

• 48:17Lester Nare?

  Because I know people are listening, waiting for the doom.

• 48:19Krishna Choudhary?

  Yeah, but we need to really understand this in order to get to doom.

• 48:22Lester Nare?

  100%.

• 48:22Krishna Choudhary?

  Okay? So, this is one of their- Promo videos. Look at how many this this this is doing really well. It's already at like rallies of three. Yeah, maybe it'll get to four. Yeah, yep. Is it gonna get to four? Yeah, it got to four.

• 48:37Lester Nare?

  Four.

• 48:37Krishna Choudhary?

  Four rallies, right? Like this is it's really it's really doing it. Five. It was getting to five.

• 48:43Lester Nare?

  Yes.

• 48:43Krishna Choudhary?

  Now let's look at the results from their paper.

• 48:45Lester Nare?

  Okay.

• 48:45Krishna Choudhary?

  Okay.

• 48:46Lester Nare?

  Okay.

• 48:46Krishna Choudhary?

  From the results from their paper, if we look at the left hand side.

• 48:49Lester Nare?

  Yes.

• 48:51Krishna Choudhary?

  Figure B.

• 48:52Lester Nare?

  Yes.

• 48:52Krishna Choudhary?

  That's the average rally length.

• 48:54Lester Nare?

  Yeah.

• 48:54Krishna Choudhary?

  And look at the Y axis.

• 48:55Lester Nare?

  Yep.

• 48:56Krishna Choudhary?

  What is the Y axis on? It's on one, right? Most of their trials are on one, even on their box plot, the edge of their trials for human cortical neurons, which is the right hand side box plot.

• 49:08Lester Nare?

  Yep.

• 49:09Krishna Choudhary?

  Is going to 1.5, right? Which is that's the I think it's the third quartile.

• 49:16Lester Nare?

  Yeah.

• 49:16Krishna Choudhary?

  Usually for box plots. It's like the 75%

• 49:18Lester Nare?

  Yep.

• 49:19Krishna Choudhary?

  Of data falls within 1.5. So clearly they're like showing a very nice.

• 49:23Lester Nare?

  Yes.

• 49:24Krishna Choudhary?

  Example in their video, right? It's still statistically significant. If you look at the human cortical neurons, which is the right box plot in figure B.

• 49:33Lester Nare?

  Yes.

• 49:34Krishna Choudhary?

  From zero to five minutes, the average rally length is at 0.5 and now it's bumped all the way to one.

• 49:40Lester Nare?

  Yeah.

• 49:40Krishna Choudhary?

  And if you look at the spread of that data, clearly it's highly significant.

• 49:44Lester Nare?

  Yes.

• 49:45Krishna Choudhary?

  If you look at all the other metrics, you can see for human cortical neurons, they do the best compared to mouse cortical neurons and all the other controls. So there is clearly an effect, but the cortical labs team are a startup.

• 49:56Lester Nare?

  Yes.

• 49:56Krishna Choudhary?

  And so, you know, as is tradition in startups, not just in Silicon Valley, but around the world, even in Australia, they're going to hype up some of the results.

• 50:06Lester Nare?

  Of course.

• 50:06Krishna Choudhary?

  And that's fine. I still think it's a very cool thing that they did.

• 50:10Lester Nare?

  Yes. And we just walked through why it's incredibly cool.

• 50:13Krishna Choudhary?

  Yeah.

• 50:14Lester Nare?

  And if you think that AI is using too much energy, you should be all for wetware.

• 50:18Krishna Choudhary?

  Yeah. Yeah. I mean, and maybe it maybe has a lot of applications. So now we're going to scale this up.

• 50:24Lester Nare?

  Okay.

• 50:24Krishna Choudhary?

  We're going to scale it up to complexity level 1000 because we are now trying to play Doom. They built the CL1 platform cortical labs first.

• 50:37Krishna Choudhary?

  Code deployable biological computer in the world. That's what it looks like. It's a, it's a little box that at its center has the petri dish with all of the biomim- like the biological fluids and all the things to keep the neurons active. You've got the multi-electrode array and you've got a little screen on the side that shows you the health of the neurons, the amount of oxygen there is, the amount of CO2 there is and so on and so forth. So it can regulate temperature, it can manage gas exchange, it can clear metabolic waste because these neurons are alive. So they're going to be creating waste, all of this other stuff, and they can actually survive in that in vitro petri dish for up to six months. It's a very long time, okay, for a sort of little mini organoid to survive.

• 51:22Krishna Choudhary?

  So how about the software? This is very cool. So for the, for the Pong, for the Pong story, they had a custom-built C, C++ machine language type thing. Nobody wants to code in C++. I get that you want the base layer Yes. of your software to be in C++ because we want very low latency. Yes. Right? And C is an extremely fast language. Python is quite slow, but at the high level, I want to code in Python. Okay. And then maybe the Python gets transferred into C. And what they did was they created something called a cortical cloud and a Python API. So now you don't need C++ and you've got a cloud computer of a bunch of CL1 platforms

• 52:08Krishna Choudhary?

  that are each like, you know, little tiny brains on a dish that I can now just like get into my, you know, on my laptop, I can access its open, open platform. I mean, you have to like apply and say like, this is what I want to do with it. But this is, I think, very clever from cortical labs because even IBM, like one of the reasons why Kisket, which is the quantum computing platform for IBM, like their software platform, that's used a lot because they've created a cloud platform where theorists and like people who want to do testing of quantum algorithms and things like that, they can go into Kisket, they can apply for time on the IBM quantum Computer, they've got like a dilution refrigerator with that IBM quantum computer.

• 52:51Krishna Choudhary?

  And they can like test out their algorithms and like their air correction methodologies and things like that. So democratizing it is a really good way for like a startup or like a small computer lab to be like, hey, we've created the hardware. Why don't you guys go at it?

• 53:05Lester Nare?

  Yes.

• 53:05Krishna Choudhary?

  Right?

• 53:06Lester Nare?

  It's very matrix-esque. If you remember the scene where Neo unplugs and he sees all the pods of humans to capture heat for the machines. That's what that effectively wetware server rack, AWS Nvidia server rack.

• 53:20Krishna Choudhary?

  Exactly. Yeah, it's like AWS, but for like wetware.

• 53:23Lester Nare?

  For wetware.

• 53:24Krishna Choudhary?

  It's, it's pretty crazy. An AWS for a brain on a chip.

• 53:28Lester Nare?

  That is so outrageous. Amazon Web Services, for those who don't know, is the infrastructure layer that provides the backend for a, a large amount of all of the software applications that you use every day.

• 53:40Krishna Choudhary?

  Yeah, exactly. So with that in mind, now that we've got a cloud computing platform.

• 53:44Lester Nare?

  Yes.

• 53:44Krishna Choudhary?

  Now, independent researchers and developers can mess around with it. And this is where we get into Sean Cole. He's an independent developer.

• 53:53Lester Nare?

  Okay.

• 53:54Krishna Choudhary?

  Who wanted to use the CL one platform to play Doom Freedom.

• 53:59Lester Nare?

  Classic.

• 53:59Krishna Choudhary?

  Okay. And the, you know, one of the, I don't know why this is a meme. But like whenever you have like a new piece of computation or something, everyone's like, can it play Doom?

• 54:08Lester Nare?

  Play Doom.

• 54:09Krishna Choudhary?

  Hundred percent. Like, like even the ESA has like made satellites play Doom and things like that. So I, I guess it's a meme and the guys at cortical labs knew this was a meme.

• 54:22Lester Nare?

  Yes.

• 54:23Krishna Choudhary?

  And knew.

• 54:23Lester Nare?

  Yeah.

• 54:24Krishna Choudhary?

  That if they could make their little thing play Doom, it would get a lot of hype and it certainly worked.

• 54:29Lester Nare?

  And I just want to give a shout out to John Carmack, who was the inventor of Doom at Ed Software and literally was one of the pioneers that created all the video game. Like, like he, that, that was like the 3D platform, like that concept and what they did at the time is what created all first person shooter style or like all these types of games.

• 54:51Krishna Choudhary?

  So is Doom before, um, what's the James Bond?

• 54:55Lester Nare?

  Bond Goldeneye?

• 54:55Krishna Choudhary?

  Yeah.

• 54:56Lester Nare?

  I don't have a chronological

• 54:59Krishna Choudhary?

  Yeah.

• 54:59Lester Nare?

  knowledge.

• 55:00Krishna Choudhary?

  But I feel like James Bond Goldeneye was also kind of like, from what I've, I've never played Doom, but I've seen the videos while I was researching this and it looked a kind of like.

• 55:09Lester Nare?

  It's similar.

• 55:10Krishna Choudhary?

  Yeah.

• 55:11Lester Nare?

  But it's not Doom.

• 55:11Krishna Choudhary?

  Yeah, it's not Doom, I guess.

• 55:12Lester Nare?

  I was a sixty-four guy. I played Goldeneye all the time.

• 55:15Krishna Choudhary?

  Yeah.

• 55:16Lester Nare?

  I love the music, but Doom is sort of.

• 55:18Krishna Choudhary?

  Doom is kind of like this OG.

• 55:19Lester Nare?

  It's its own thing.

• 55:20Krishna Choudhary?

  Yeah, so it is way more complex than Pong. So, you know, one of the things that I, as I was like researching Doom and I was like watching some videos of people playing Doom, you know, it's really, you're transitioning from a 2D linear game like Pong to now it's kind of like this 2.5 dimensional. I wouldn't really say three dimensional.

• 55:38Lester Nare?

  Yeah.

• 55:38Krishna Choudhary?

  Because it's like first person and like the, it's not really fully three dimensional, I would say. But there's still, it involves complex navigation, threat detection.

• 55:49Lester Nare?

  Yep.

• 55:49Krishna Choudhary?

  And all of this other very cool stuff. So it's a way harder problem.

• 55:53Lester Nare?

  Yes.

• 55:54Krishna Choudhary?

  Than.

• 55:54Lester Nare?

  Up, down. How close?

• 55:57Krishna Choudhary?

  Yes, exactly, right? Oh, you're already getting there. It's like, those are the sort of salient things that I need to code in. You know, before in Pong, I needed to code in position of my paddle and where the ball is. Here, in this case, I need to figure out where, what direction the enemy is and how close they are.

• 56:12Lester Nare?

  Mm-hmm.

• 56:12Krishna Choudhary?

  Anyways, let's get to the stem, the cells. We got 200,000 human pluripots in stem cells.

• 56:21Lester Nare?

  Yes.

• 56:21Krishna Choudhary?

  So this is a fourth of what we had in Pong.

• 56:24Lester Nare?

  Oh, yeah.

• 56:24Krishna Choudhary?

  Fewer than what we have in Pong.

• 56:26Lester Nare?

  Yeah, exactly.

• 56:26Krishna Choudhary?

  Right?

• 56:26Lester Nare?

  Okay, yep.

• 56:27Krishna Choudhary?

  But because of like the cloud computing nature and the fact that this is all in Python, the guys were able to map the engine to the tissue in just a week. And this is what it looks like.

• 56:40Lester Nare?

  Jesus.

• 56:40Krishna Choudhary?

  So you've got, you've got your chip and it's sensing motor neurons or it's sensing the neurons and then it's playing Doom.

• 56:48Lester Nare?

  This is crazy.

• 56:49Krishna Choudhary?

  Right?

• 56:50Lester Nare?

  Yeah, yeah, yeah, yeah.

• 56:51Krishna Choudhary?

  It's pretty cool.

• 56:51Lester Nare?

  Yeah, and we're seeing this like, like, yeah, the real.

• 56:54Krishna Choudhary?

  No, yeah, those are the neurons that are coming in and then there, the, the petri dish is sort of controlling which way to point and which way to fire, right? So let's get into, let's get into how actually they did it. Because in Pong, remember, we've got a sensory cortex, we've got a motor cortex. They're gonna do very similar things.

• 57:13Krishna Choudhary?

  In this case, the.

• 57:17Krishna Choudhary?

  Proximity, we wanna, we wanna encode information about the game state, right? So there's proximity, which is how off, how close I am.

• 57:27Lester Nare?

  Yes.

• 57:28Krishna Choudhary?

  And there's direction, which is where, where I'm trying to fire. They're using place and rate to code that, right? Now, we wanna take the damage, so taking damage and dying is gonna be the chaos. And if we shoot someone, that's gonna be the predictive.

• 57:45Lester Nare?

  Ahhh.

• 57:46Krishna Choudhary?

  Reward, right?

• 57:47Lester Nare?

  Cause I was wondering how it was gonna map from Pong to Doom.

• 57:50Krishna Choudhary?

  Yeah, and that's how you do it. And for the motor neurons, um, there's specific firing patterns that are going to be moved forward. There's specific firing patterns that are gonna be turn, and there's specific firing patterns for fire a weapon. And the performance, after seven days of training, the agent's kill ratio was roughly twice that of pure chance. Now, I don't know what that means, and this is where it gets kind of annoying because there's no paper, right? So there's no methods, they just kinda said that.

• 58:17Lester Nare?

  Right.

• 58:17Krishna Choudhary?

  I'm like, I don't, I don't really know what that means.

• 58:20Lester Nare?

  Yep.

• 58:20Krishna Choudhary?

  But they're explicitly admitting that the cells played like a beginner who's never seen a computer. So it's not very good.

• 58:27Lester Nare?

  Yeah.

• 58:27Krishna Choudhary?

  Right? But it's still, I guess, higher than chance.

• 58:30Lester Nare?

  Yes.

• 58:30Krishna Choudhary?

  So maybe that's something. One of the things that I found kinda interesting was that during the study, the agents actually generally settled, settled for a strategy of survival, which is hiding rather than active combat. And this is something that is well-known in reinforcement learning, right? You just like go for the local minima, which is, I don't wanna deal with anything. I don't want any reward whatsoever, even the positive or the negative, because that positive or negative reward comes from a dense, like, chain of brittle action, right? I have to do stuff, I have to do stuff, and at the very end, I might just get chaos, and then it might just be bad. So why don't I just like go to a corner and try to hide?

• 59:09Lester Nare?

  I wish they gave the, uh, the, uh, nuke testing AI, uh, paper people the same wetware to, to go to the local minima. Cause as we talked in there two episodes ago, all the major model companies, Anthropic, Gemini, OpenAI, all used nukes 90% of the time.

• 59:27Krishna Choudhary?

  Yeah.

• 59:27Lester Nare?

  Which does not sound like what you're talking about.

• 59:29Krishna Choudhary?

  Yeah, yeah, no, this one is just, they just wanna.

• 59:31Lester Nare?

  They're like, we're just trying to hide.

• 59:32Krishna Choudhary?

  Yeah, I'm just gonna try and hide. So. Now we've covered what cortical labs is purporting they did.

• 59:39Lester Nare?

  Yes.

• 59:39Krishna Choudhary?

  In their YouTube video. This is where I got a lot of that information.

• 59:42Lester Nare?

  Yes.

• 59:43Krishna Choudhary?

  Now let's ask what's actually going on.

• 59:45Lester Nare?

  Yeah.

• 59:46Krishna Choudhary?

  Okay, cuz there's a lot less info about the Doom game versus the Pong game. The Pong game had a full ass, you know, peer reviewed paper in neuron. This one does not. This one is just like blogs.

• 59:59Lester Nare?

  Yes.

• 1:00:00Krishna Choudhary?

  Is all I got. Okay. Here's my concern, and this is something that I actually read Tommy Blanchard's Substack on. So if you guys wanna check out his Substack, he's got a lot more info on it. Here's the concern. There are eight input channels for Pong, right? But Pong is a very small game. I can reasonably put all of that information into eight electrodes. How do I encode everything that's going on in Doom with only eight inputs?

• 1:00:28Lester Nare?

  Yeah.

• 1:00:29Krishna Choudhary?

  Each of which can only have a few possible values, like either the electrode is on or off. And the answer is you actually don't. You use a reinforcement learning model.

• 1:00:37Lester Nare?

  Hmm.

• 1:00:38Krishna Choudhary?

  This is where it gets a little bit fishy.

• 1:00:40Lester Nare?

  Okay.

• 1:00:40Krishna Choudhary?

  Okay. Here, this is from their, this is from their Doom neuron, like, they've got a little, it's kinda like a GitHub where they have a documentation on what they did. This is, the architecture of what they're showing is they've got a computation unit, and that is talking to their CL1 computer.

• 1:00:58Lester Nare?

  Yeah.

• 1:00:58Krishna Choudhary?

  Okay. In that computational unit, on the left, right hand side, what you can see is there is a PPO policy.

• 1:01:04Lester Nare?

  Yeah.

• 1:01:04Krishna Choudhary?

  Which is a reinforcement learning paradigm.

• 1:01:07Lester Nare?

  Yeah.

• 1:01:08Krishna Choudhary?

  That is then encoding.

• 1:01:09Lester Nare?

  Yeah.

• 1:01:10Krishna Choudhary?

  The input of the game.

• 1:01:12Lester Nare?

  Yeah.

• 1:01:12Krishna Choudhary?

  That is then going as stimulation to the brain, to my brain in a Petri dish. And what I get back is action, and that is getting fed back into my reinforcement learning algorithm. So now I can very easily ask, who is actually doing the learning?

• 1:01:25Lester Nare?

  Correct.

• 1:01:26Krishna Choudhary?

  Is it my reinforcement learning? Network, or is it the actual biological neurons that I have?

• 1:01:30Lester Nare?

  Doesn't count.

• 1:01:31Krishna Choudhary?

  Right?

• 1:01:32Lester Nare?

  It's not pure.

• 1:01:33Krishna Choudhary?

  Really count.

• 1:01:34Lester Nare?

  It's not pure.

• 1:01:34Krishna Choudhary?

  And it's not, it's not that far away, like reinforcement learning. There's video games that have like perfected reinforcement learning. Dota two, OpenAI defeats Dota two world champions. So that's not that big of a deal.

• 1:01:46Lester Nare?

  Correct.

• 1:01:47Krishna Choudhary?

  So the question is, was the software decoder, which is that translation layer?

• 1:01:52Lester Nare?

  Yeah.

• 1:01:53Krishna Choudhary?

  Was that actually in the, the learning algorithm? Or is the actual neuron doing something?

• 1:01:58Lester Nare?

  Cause in the Pong case that we already talked about in the earlier.

• 1:02:01Krishna Choudhary?

  Very direct.

• 1:02:02Lester Nare?

  It was, it was direct and it was maybe incremental, but it was demonstrable learning.

• 1:02:06Krishna Choudhary?

  Yes. And, and we could very visibly say the neurons are doing the confrontation.

• 1:02:12Lester Nare?

  The neurons are doing.

• 1:02:13Krishna Choudhary?

  The neurons are doing the learning because there's no funny business going on where like I've got a reinforcement learning agent that, okay, fine. So now it's playing a video game. What's the big deal?

• 1:02:22Lester Nare?

  It knows all the history of Doom's lore internally.

• 1:02:25Krishna Choudhary?

  So here's how they addressed that. They actually did address it in their, um, frequently asked questions section.

• 1:02:32Lester Nare?

  Okay.

• 1:02:32Krishna Choudhary?

  They said, okay, isn't the decoder doing all of the learning? What they tested it was, how they tested it was through an ablation study. So they've got a metric called a breakthrough rate, which is the amount of episodes that scores above a negative 600 reward threshold. Now negative 600 is pretty low because negative 1,000 is the floor. I've never played Doom, Doom, so maybe somebody can tell me if negative 600 is like a lot or a little. But here's the key metric that they said. They said that if I were to use the neurons in the loop, I get 27% breakthrough rate. So my score is above that negative 600. If it's complete silence, so no input from the neurons, or if I get put random noise from the neurons, then I get only a seven to eight percent breakthrough.

• 1:03:17Krishna Choudhary?

  So the neurons are doing something.

• 1:03:19Lester Nare?

  Yes.

• 1:03:19Krishna Choudhary?

  Again. There's not a lot of trials. They're just spitting out these numbers. It's not, you know, I don't see data. I'm just like taking their word for it that they wrote in a blog post that it's 27% rather than 8%. So from baseline, okay, fine. Baseline is 8%, 27%, maybe I'll take your word for it. The next problem that they addressed in their frequently asked questions is that there's a, you know, is it the encoder? Is it, is it the thing that is encoding the neuron state? And then feeding it into the reinforcement learning thing, because that itself is a neural network.

• 1:03:54Lester Nare?

  Right.

• 1:03:55Krishna Choudhary?

  So is that doing all the learning? Well, let's see. So if you look at photo 30, this is their response. They say that, you know, well, the brain cells in the loop, they're effectively saying that assuming the brain cells are static, this is not correct. The policy in the cells are dynamical system. So the biological neurons have an internal state. And that is affecting the reinforcement learning. Now to me, all that saying is like, well, we actually made it harder for the reinforcement learning agent.

• 1:04:24Lester Nare?

  Yeah.

• 1:04:24Krishna Choudhary?

  That, you know, it's like, okay, the neurons are kind of like a noisy controller that I'm like using to, to, to create action in my game. And maybe, you know, to me, it's like, okay, well, that just means my reinforcement learning agent is like, okay with a noisy controller.

• 1:04:41Lester Nare?

  Right.

• 1:04:41Krishna Choudhary?

  The last sentence is what is key. During testing, encoder weights were frozen and still observed improvements in reward, which means that I, I froze the way that I am reading my neurons and still there was improvement, which means that the neurons were maybe doing some type of learning.

• 1:04:59Lester Nare?

  Yep. You know, but there's still the encoder in between, even if it's in a frozen state. And so how do you know that it's not attributed to the some base function, some base capability in the encoder, even if it's frozen?

• 1:05:11Krishna Choudhary?

  Yes. So that's where I'm like.

• 1:05:13Lester Nare?

  That's my naive.

• 1:05:14Krishna Choudhary?

  That's exactly right. Right? You know, it's like in the YouTube video, they talk about, oh, it's like, you know, if the neurons fire a certain way, the.

• 1:05:25Krishna Choudhary?

  Game was gonna turn left. If the neurons fire a certain way, the game will move for- the character in the game will move forward. And if they wanna fire, the neurons will fire a certain way. Well, not really. If the neurons fire one way or the other, it might just be that the reinforcement learning agent-

• 1:05:39Lester Nare?

  Yes.

• 1:05:39Krishna Choudhary?

  Is just like also interpreting the-

• 1:05:41Lester Nare?

  Yes.

• 1:05:42Krishna Choudhary?

  State of the game and just like overriding everything.

• 1:05:44Lester Nare?

  Exactly.

• 1:05:44Krishna Choudhary?

  So I don't think it's a very clean test, is what I'll be honest about, right? And perhaps they're working on a paper that is gonna elucidate some of these holes. I certainly hope so, because I do want-

• 1:05:57Lester Nare?

  Right.

• 1:05:58Krishna Choudhary?

  A putridish game.

• 1:05:59Lester Nare?

  Right.

• 1:06:00Krishna Choudhary?

  I'm sorry, I do want a putridish brain to play Doom. I think that would be hella cool.

• 1:06:05Lester Nare?

  Yes.

• 1:06:06Krishna Choudhary?

  But I'm, I'm, I think, I think the jury is still out, because, one, all of this is anecdotal.

• 1:06:11Lester Nare?

  Yes.

• 1:06:12Krishna Choudhary?

  So there's no hard data. They're not showing actual, you know, box plots about the spread and so on and so forth. There was another thing that I read just briefly that was saying that the, the, the seed in my neural network actually affects how the gameplay is done, right? The random seed that initializes weights and things like that. It's like, well, if it's that fragile, can you really say something about whether the neurons are doing anything? So, I don't know, you know? But that's my skepticism.

• 1:06:38Lester Nare?

  And but, and this is not to take away from the foundational, uh, uh, discovery that dish brain in the Pong context-

• 1:06:46Krishna Choudhary?

  Yes.

• 1:06:46Lester Nare?

  Which is why we took the time to talk about how we got to the Doom version, which is like, there is a there there.

• 1:06:52Krishna Choudhary?

  Yes.

• 1:06:53Lester Nare?

  But the way in which they're trying to scale it up for the increased complexity of the tasks they're trying to get the wetware to do.

• 1:07:00Krishna Choudhary?

  Yeah.

• 1:07:01Lester Nare?

  It is not clear that it is purely from improvements at the, uh, artificial, human, pleury potent, uh, stem cell level-

• 1:07:12Krishna Choudhary?

  Yeah.

• 1:07:12Lester Nare?

  That's doing the learning.

• 1:07:13Krishna Choudhary?

  Exactly.

• 1:07:14Lester Nare?

  It could just be the infrastructure that has now been built around it.

• 1:07:17Krishna Choudhary?

  Exactly.

• 1:07:17Lester Nare?

  That makes it kind of like an interesting thing.

• 1:07:20Krishna Choudhary?

  Exactly.

• 1:07:20Lester Nare?

  But not, brains on a chip are doing all of the heavy lifting.

• 1:07:24Krishna Choudhary?

  Exactly. And that's what all of the- That's what all of the news articles are saying, right? All of the news articles are like, uh, brain in a Petri dish, play Doom. I don't think anyone's mentioned this reinforcement learning paradigm in the middle.

• 1:07:38Lester Nare?

  Right.

• 1:07:38Krishna Choudhary?

  That is kind of the whole game, maybe.

• 1:07:40Lester Nare?

  Right. And because there's not a paper, again, like the Pong version, which they previously done, the jury is gonna still be out on this.

• 1:07:47Krishna Choudhary?

  And that's why it's really important to have peer reviewed journals because a reviewer would be like, what, you know, what are you doing?

• 1:07:53Lester Nare?

  We're, we're doing that here on the pod, let alone in a professional.

• 1:07:56Krishna Choudhary?

  Exactly. Yeah. Um, one last thing I want to, um, touch on is there was a lot of heat that the authors took for their 2022 paper because the 2022 paper in their title mentioned sentience. Learn and exhibit sentience. There was another paper that came out, um, that was a backlash. Okay. Again, published in Neuron as a rebuttal and they were effectively saying that you can't use sentience, right? Or the idea of perceiving because the behavior that you're mentioning and you're observing here when the Petri dish plays Pong is one of homeostatic feedback stabilization. There's no high level cognitive intent.

• 1:08:38Lester Nare?

  Yeah.

• 1:08:38Krishna Choudhary?

  Right? And so you can't really say things like that because then with sentience comes ethical concerns. In the public lexicon, the term sentience means there's some morality now that we have established onto this dish. And if that's the case, well, what are we actually doing?

• 1:08:58Lester Nare?

  Yes. Yeah. And I think we kind of, over the course of this episode, we've kind of like landed on that same point, which is, oh, this is an interesting fundamental understanding of how you can control neural networks through electrical, uh, like electrical read and write architecture to optimize for stability in the system.

• 1:09:21Lester Nare?

  That's cool, but that's effectively what it's and then we're just connecting that to an external thing we are interpreting and giving value to as like, oh, hit it with the paddle.

• 1:09:31Krishna Choudhary?

  Yeah.

• 1:09:32Lester Nare?

  But it fundamentally.

• 1:09:33Krishna Choudhary?

  Yeah, fundamentally, it's just trying to reduce surprise, but surprise itself is a mathematical construct that is trying to reduce, right? Just because of the way that the proteins and the

• 1:09:45Lester Nare?

  Right.

• 1:09:45Krishna Choudhary?

  and the neurons work when they when they get together.

• 1:09:48Lester Nare?

  Right.

• 1:09:48Krishna Choudhary?

  So it's it's a far leap to say this dish brain is sentient because it can sense and respond to an environment like Pong.

• 1:09:55Lester Nare?

  Right.

• 1:09:55Krishna Choudhary?

  Right? But this, again, I don't know.

• 1:09:58Lester Nare?

  Right.

• 1:09:58Krishna Choudhary?

  This is again, this this is, we're going beyond my pay grade, and I do want to be very clear about that, right? Because questions of consciousness, questions of sentience is something that I grapple with all the time. I have a PhD in like neural systems, right? Like, one of my PhDs was about how neural networks talk to one another and sleep and so on and so forth. You can create very simple neural systems that sleep and mimic behavior. I don't know what consciousness is, but for them to put that in the title is is a bit weird.

• 1:10:27Lester Nare?

  Yeah.

• 1:10:28Krishna Choudhary?

  Right? And and I think that's what that's what the rebuttal was about.

• 1:10:30Lester Nare?

  Which is, which is, which is, I think, is fair pushback.

• 1:10:33Krishna Choudhary?

  Yeah.

• 1:10:33Lester Nare?

  Not to minimize the work being done and just to be

• 1:10:36Krishna Choudhary?

  Yeah.

• 1:10:36Lester Nare?

  clear, right? Like, this is not trying to be like, oh, you didn't do anything.

• 1:10:39Krishna Choudhary?

  Sometimes

• 1:10:40Lester Nare?

  No, because it's it's pretty cool. Sometimes the reviewers are like, yeah, you didn't do anything.

• 1:10:43Krishna Choudhary?

  Oh, yeah.

• 1:10:43Lester Nare?

  That's not what we're saying.

• 1:10:44Krishna Choudhary?

  Yeah.

• 1:10:44Lester Nare?

  It's just that there's a nuance about the really cool thing that is being done. And then the CNN News headline that says, "Brain on a Chip Is Playing Doom."

• 1:10:54Krishna Choudhary?

  Yeah.

• 1:10:55Lester Nare?

  Which is not

• 1:10:56Krishna Choudhary?

  Yeah.

• 1:10:57Lester Nare?

  quite accurate.

• 1:10:59Krishna Choudhary?

  Yeah, exactly. And so, you know, and on this podcast, we're in the business of like, just talking about what was done.

• 1:11:04Lester Nare?

  Yes.

• 1:11:04Krishna Choudhary?

  What were the methods and what were the actual

• 1:11:06Lester Nare?

  Right.

• 1:11:07Krishna Choudhary?

  findings?

• 1:11:08Lester Nare?

  Right.

• 1:11:08Krishna Choudhary?

  And the results? And so, you know, it is what it is. They use the reinforcement learning paradigm. I don't know if that changes things. It seems to me that they have a lot more work to do

• 1:11:17Lester Nare?

  Yes.

• 1:11:18Krishna Choudhary?

  to convince someone like me

• 1:11:19Lester Nare?

  Yes.

• 1:11:20Krishna Choudhary?

  that it's the neurons that are playing Doom.

• 1:11:22Lester Nare?

  Yes.

• 1:11:22Krishna Choudhary?

  And not some small artificial neural network.

• 1:11:25Lester Nare?

  Yes.

• 1:11:25Krishna Choudhary?

  Right?

• 1:11:26Lester Nare?

  Hundred percent.

• 1:11:26Krishna Choudhary?

  There's not a lot of details, so I'm looking forward to hopefully a peer-reviewed scientific paper.

• 1:11:31Lester Nare?

  You know, cortical labs, if you want us to come over to Melbourne, I think it's Melbourne.

• 1:11:36Krishna Choudhary?

  Yeah, it's Melbourne.

• 1:11:36Lester Nare?

  Uh, come in and do some coverage as you prepare for release of a research paper. We are more than happy to join you.

• 1:11:42Krishna Choudhary?

  Oh, yeah.

• 1:11:43Lester Nare?

  And have the opportunity to learn more. Uh, if you, we will send this episode to you to get your feedback as well or corrections. So please let us know, um, if there's anything that we can do to clarify here. We are fit, like this is super interesting.

• 1:11:56Krishna Choudhary?

  Yeah.

• 1:11:56Lester Nare?

  We are just curious to learn more. And so I'm sure they are already well ahead of us.

• 1:12:00Krishna Choudhary?

  Yeah.

• 1:12:01Lester Nare?

  Uh, on that point, so we'll see what happens. We will cover, if there's a follow up, we will cover it again. This was a phenomenal episode around some of these details. I just, uh, we could talk about this forever. However, we said we were going to keep our episodes shorter, so I'm going to quickly wrap us up here. Uh, after that, let us know in the comments. We have a comment for the community today, for folks to comment on. What game do you want to see?

• 1:12:28Krishna Choudhary?

  Oh, that's a good one.

• 1:12:29Lester Nare?

  What game would you like to see the brain on a chip play next? Doom was number one. What's number two? I know what my thought is. Maybe we'll share it next episode.

• 1:12:36Krishna Choudhary?

  Yeah.

• 1:12:37Lester Nare?

  I'm your host, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Choudhary. Again, multiple episodes a week. We'll see you later this week.