Synthetic Biological Intelligence with Brett Kagan

Can you make a computer chip out of neurons? Neil deGrasse Tyson, Chuck Nice, & Gary O’Reilly explore organoid intelligence, teaching neurons to play Pong, and how biology can enhance technology with neuroscientist and Chief Scientific Officer at Cortical Labs, Brett Kagan.

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

• 0:00Neil deGrasse Tyson?

  Coming up on Startalk Special Edition, rather than attach computers to your brains, what happens if a computer is made of the building blocks of brains themselves? Coming up on Special Edition.

• 0:18Neil deGrasse Tyson?

  Welcome to Startalk, your place in the universe where science and pop culture collide. Startalk begins right now.

• 0:31Neil deGrasse Tyson?

  This is Startalk Special Edition. Neil deGrasse Tyson, you're a personal astrophysicist. I got with me my co-host, the dynamic duo.

• 0:41Speaker 01

  Oh.

• 0:42Neil deGrasse Tyson?

  Gary O'Reilly. Gary, how you doing, man?

• 0:44Speaker 01

  I'm good, Neil. Good to be on again.

• 0:46Neil deGrasse Tyson?

  Former soccer pro, you staying in shape a little bit?

• 0:48Speaker 01

  No.

• 0:49Neil deGrasse Tyson?

  Okay.

• 0:51Neil deGrasse Tyson?

  We'll do a separate episode on staying in shape. Just as it should be. As it should be. And that other voice is, of course, longtime Startalk co-host Chuck Nice. How you doing, Chuck? Hey, what's happening? So Gary, what did you and your producers cook up for today?

• 1:03Speaker 01

  Uh, well, once we came across this particular project and this particular guest, this was a given. We had to do it. So let me put it to you this way. Everyone wants to know what tomorrow's world will bring. We all know you can put technology into biologists think Elon and Neuralink. But is that the only route to the future? What if we flip the script?

• 1:28Neil deGrasse Tyson?

  Yeah.

• 1:29Speaker 01

  What if we introduce the biology to the technology?

• 1:34Neil deGrasse Tyson?

  Ooh.

• 1:35Speaker 01

  Then what happens? You get what I'm reliably informed is called synthetic biological intelligence, or as we will learn further into the show, SBI for short. And so during this show, Neil, we're going to get into AGI ethics. Comparing SBI with silicone intelligence and there's so much more. There's loads to unpack, so buckle up. We are headed to the future.

• 2:01Neil deGrasse Tyson?

  Wait, you mean silicon, silicone. The silicone you find in.

• 2:06Chuck Nice?

  We already have silicone intelligence.

• 2:10Neil deGrasse Tyson?

  I think.

• 2:11Chuck Nice?

  We already have that. It's called Hollywood. Silicone intelligence, baby.

• 2:18Speaker 01

  Are we in the weeds with tomato and tomato?

• 2:21Neil deGrasse Tyson?

  No, I think silicon is the.

• 2:23Speaker 01

  Silicon, alright, fine.

• 2:25Neil deGrasse Tyson?

  You're comparing biology introduced to technology and technology introduced to biology. And that's what we're going to explore here.

• 2:31Speaker 01

  Totally, yeah.

• 2:32Neil deGrasse Tyson?

  All right, all right. So let's get our guest, who may be uniquely qualified, to tell us about it. Brett Kagan, Brett, welcome to StarTalk, special edition.

• 2:41Speaker 03

  Thank you so much. Thanks so much for having me.

• 2:43Neil deGrasse Tyson?

  And you're dialing in from Australia, Melbourne, so.

• 2:47Speaker 03

  Yeah, yeah.

• 2:48Chuck Nice?

  Nice.

• 2:49Speaker 03

  Great to be able to organize the international calls at a time that can work for everyone.

• 2:53Neil deGrasse Tyson?

  Indeed. You have a PhD in neuroscience, which is, if I were to pick a field today, it might just about be that. Maybe, maybe. It's a close second to astrophysics, just for how endless that frontier feels, how ripe it may be for future discovery. You're also a chief scientific officer at the Cortical Labs in Melbourne.

• 3:16Speaker 03

  Yeah, yeah.

• 3:17Neil deGrasse Tyson?

  And who are these people? They want to build synthetic intelligence processors.

• 3:23Chuck Nice?

  Oh, oh.

• 3:25Neil deGrasse Tyson?

  Okay.

• 3:25Chuck Nice?

  A positronic brain, huh?

• 3:28Neil deGrasse Tyson?

  So let's just start out. You're making brain cells in a dish? That, that's, so does that mean you're growing neurons? And then, if that's what you're doing, you're going to put it into electronic circuitry? So what's up with that?

• 3:44Speaker 03

  Yeah, that's exactly what we're after. So when you get right down to it, I think it's interesting you bring up astrophysics as a parallel. Because when you look at the complexity that happens in the universe at that macro scale, infinite number of different bodies all interacting with each other, moving as a whole, you do actually get a similar level of complexity when you look at the brain. And when you look at the outcome of what brains can achieve, building everything that's around us, you can realize it's a pretty special system. And so we became really interested in this idea. Well, what if you could leverage that fundamental building blocks of brains to actually create a device that is intelligent?

• 4:26Speaker 03

  And so we set about figuring out a sustainable and ethical way to produce brain cells, which fortunately has been established through a lot of academic work previously.

• 4:34Neil deGrasse Tyson?

  Wait, when you say ethical way, you mean there's some brain cells that'll complain and protest your work?

• 4:39Speaker 03

  Well, for us, we were obviously interested in a lot of people in the work. They take them from animals. So you have to grow the animal, you have to kill the animal, you have to harvest it, which is for some work necessary. But we wanted to figure out, is there a more scalable and sustainable way to do that? And so we moved to synthetic biology. And so we found that what we could generate was something called an induced pluripotent stem cell, which is a type of stem cell that you can make from any adult donor's blood tissue or skin cells, or there's a number of ways you can do it. Then we could turn them into brain cells using a number of different methods. And then we could integrate them into devices like what you can see I've got next to me here, which allows us to interact with them.

• 5:19Neil deGrasse Tyson?

  Please describe it, describe what you have.

• 5:21Speaker 03

  Yeah, so this sort of device, we call it a CL1. Essentially, it's a device that allows you to record the small electrical pulses that happen when brain cells are active and then also supply small electrical pulses to communicate to them. So electricity here is a form of information transfer.

• 5:40Neil deGrasse Tyson?

  You're peeking at the playbook of nature and adopting elements from it, methods and tools, to improve your efforts to duplicate it in hardware. Is that a fair characterization?

• 5:56Speaker 03

  Not exactly. We're not trying to duplicate it in hardware. What we're trying to do is actually leverage the cells themselves. Part of the problem is we can't duplicate brain cells in hardware. The complexity that they display is something we can't achieve yet. And so we kind of adopted this idea of why mimic what you can harness? All right, so how are you getting from?

• 6:18Speaker 01

  Growing your neurons and your brain cells onto the multi-electrode arrays? And then how are they networking and beginning to function?

• 6:28Speaker 03

  Yeah, all of those are great questions. Sort of real short, real simplified. These neurons, what we can do is we can put down these extracellular matrices that the neurons are pretty happy to grow on. And we just put those above these multi-electrode arrays. So it's a dense platform with electrodes. And we can grow the neurons on that. And we can keep them alive with standard methods for up to a year, sometimes longer. And then how do we actually interact with them? Well, that's where the neurocomputational approach has come in. And this is really a question, this is a physics question. How do information systems work at a fundamental level?

• 7:05Chuck Nice?

  So now, do neurons, do they have a proclivity to start communicating with one another on this matrix that you set up? Or is that something that you are specifically designing and creating? Because our neurons are firing in our brain.

• 7:25Speaker 03

  Yeah, absolutely. And the answer is actually both. Neurons will, it's called spontaneous activity.

• 7:31Chuck Nice?

  Okay.

• 7:31Speaker 03

  When they're there, they will network up, they will talk to each other. Of course, without any information coming in, they don't have very much to talk about.

• 7:40Neil deGrasse Tyson?

  So you think.

• 7:43Speaker 03

  So you think, who knows what sort of quantum home is.

• 7:46Chuck Nice?

  Yo, yo, yo, did you hear what was happening over in the hippocampus? I just got back from the hippocampus, it's going crazy over there, man.

• 7:53Neil deGrasse Tyson?

  Are you a big man on the hippocampus?

• 7:56Speaker 03

  No, no, no, yeah, absolutely. There's a, there's a huge amount of complexity that arises there. But what's fascinating is when you shift it and you, you give them that information, the reorganization you see is dramatic. And it really suggests the ability to interact with these systems is something that is achievable.

• 8:13Neil deGrasse Tyson?

  You're saying the neurons intentionally organize themselves in a way in response to being laid down on your multi-electrode arrays?

• 8:20Speaker 03

  So you can pattern it, you can use a bunch of materials to create some intentional organization, which is fine, and that's a really neat area that we're investigating. But what I think is more exciting is the fact that they will, in response to the information you provide, these electrical signals that have structure and quality to them, they will reorganize their function rapidly.

• 8:39Neil deGrasse Tyson?

  Oh, whoa.

• 8:41Chuck Nice?

  Okay.

• 8:42Neil deGrasse Tyson?

  Very Frankenstein here.

• 8:43Chuck Nice?

  That's very cool, though, because our brains do the same thing.

• 8:47Neil deGrasse Tyson?

  I guess so, don't they?

• 8:48Speaker 03

  Exactly.

• 8:48Chuck Nice?

  Our brains do the exact same thing. That's wild.

• 8:51Neil deGrasse Tyson?

  But so you're not growing neurons, you are activating neurons.

• 8:56Speaker 03

  We have to do both. So we grow them, we plate them, and then we, and how can we activate them with information?

• 9:01Neil deGrasse Tyson?

  Wait, if you can grow neurons, why can't you cure spinal cord severed nerves, that sort of thing?

• 9:09Speaker 03

  Well, that's actually what a lot of this technology, so this synthetic biology, has arisen from. People looking at Parkinson's, um, yeah, spinal cord damage, Alzheimer's, a whole range of things. And so people have been trying to develop brain cells for just over decades from these, this material. But we kind of took it and went, well, great, we could also apply it to just build brain cells for an intelligent purpose. So we're kind of adapting it.

• 9:33Speaker 01

  In reading about your project here, Brett, I came across a term called embodied networks.

• 9:39Speaker 03

  Yes.

• 9:40Speaker 01

  Could you break that down for us, so as we'll understand if we get to this point? Because I think we've got a lot of distance to cover before we get to how this sort of intelligence is going to be used in the future. So if we can sort of build, build some basics as we understand a little bit more, please?

• 9:58Speaker 03

  Yeah, sure. So in the simplest possible terms, embodiment, we all have embodiment, right? There's a statistical or physical barrier between our bodies and the external world. The question is, how do you create that for a group of neurons in a dish, right? Most neurons sit there in the darkness chatting to themselves as we're saying, like, what's going on there? We don't know. What we try to do is by creating a type, what's called a closed loop, where we take information from the cells, we apply it to a virtual world, like we started with the game Pong. And then we feed back how that changes the world to the neurons. Suddenly, there's this barrier between the neurons' activity and how it affects the world, and they're informed of that. And so this is embodiment, because there's this barrier, there's a separation.

• 10:40Neil deGrasse Tyson?

  Before we get to the, the abilities of these things that you are creating in your Frankenstein lab, what? Tell me again, convince me that adding neurons to circuits is better than adding circuits to neurons, because you are bucking a trend here. You know, Neuralink, Elon Musk and others, they want to put the internet in our head and enhance our biology.

• 11:09Chuck Nice?

  You want to make a head and then put the internet in it.

• 11:16Neil deGrasse Tyson?

  So, why should I bet on you?

• 11:20Speaker 03

  Well, look, it's not a zero-sum game, right? They do different things. One's aiming to take humans and advance our capacity. The other one's trying to use the capacity of biology to enhance some other process. So both have their times in place and there's also the chance they could interact. It's one thing to put a BCI, a brain-computer interface into a head. How do you actually interpret those signals? This is an open question. Maybe brain cells are better at interpreting signals from brain cells.

• 11:50Chuck Nice?

  Wow.

• 11:51Speaker 03

  In terms of, yeah, so there's a lot of capability there. And it's supported. If you look at what we as humans or animals, bees, cats, rats, whatever, what they do well, they do well going into a novel environment, investigating it, optimizing, and they do it with a fraction of the power of machine learning, between hundreds of thousands, hundreds of millions of times less power consumption.

• 12:12Chuck Nice?

  Right.

• 12:12Speaker 03

  And they can do it quicker in terms of the amount of data they need as well. You know, you don't need to look at too many tigers to learn.

• 12:18Chuck Nice?

  To know what a tiger is.

• 12:19Speaker 03

  Run from the tiger, right? We have these innate predispositions to learn rapidly.

• 12:24Chuck Nice?

  So now, let me ask you, since you just brought that up, the reason why we are able to do that, say for instance, and like, computers don't, is because we are grounded in real-world knowledge of what we are experiencing.

• 12:39Speaker 03

  Yeah.

• 12:39Chuck Nice?

  So, how exactly do you transfer that into neurons that are embodied in something other than a circuit? How do you bring that real-world grounded, or is the real-world grounded knowledge that we have because of the network of neurons that we have in our brain?

• 13:01Speaker 03

  Our ability to acquire that real-world knowledge is what's fascinating. So, something like a tiger or a snake, that could be a genetic prior, right? Generations and generations of people who didn't run when they saw tigers got eaten. But you'll also learn to fear electrical shocks, let's say. It's very unlikely there's a predisposed genetic crier to make you scared of electricity because we just haven't interacted with it for that long. But we learn, don't we, rapidly? You show a child once or twice, you show them a video, they learn, oh, electricity bad. One or two samples is all that they'll typically need. And that's due to the absolute mass of paralyzation and flexibility of a neural system. We can make these connections.

• 13:42Speaker 01

  How long does it take your neurons, processors, to learn as they play pong? Or is this an experiment still ongoing?

• 13:52Speaker 03

  We're still just scratching the surface of this. This is science as it's happening. And we've wanted to be really, we kind of like to think of ourselves as like anti-hype scientists. So we wanted to show people like, look, here's this work. It's covered in warts, it's messy, it's janky, but it seems to be doing something. So we wanted to share it with people. So we were able to see some learning. We basically tested to say 20 minutes at a time and we could find like very big differences from the first five to the last 15. So within five minutes they were reorganized. And actually, we recently found out it could be even quicker than five minutes for them to reorganize. And then the learning appears over the next couple of minutes as they start to upregulate and improve.

• 14:29Chuck Nice?

  I'm still trying to figure out, is this just a natural order of neural function? Because there's a saying, neurons that fire together, wire together.

• 14:41Speaker 03

  Yeah, yeah.

• 14:42Chuck Nice?

  Is that what you're talking about when you said the learning aspect?

• 14:45Neil deGrasse Tyson?

  Is that what they say up in the hood? I mean, where'd you get this?

• 14:50Speaker 03

  No, that's a classic line for something called heavy plasticity. Neurons that fire together, wire together. It's been a mantra in neuroscience since, oh gosh, don't test me on the history of that, but for a long time.

• 15:01Neil deGrasse Tyson?

  Okay, maybe this started up in the hood, okay.

• 15:03Speaker 03

  Yeah, in our hood, in the hood we share, yeah.

• 15:06Chuck Nice?

  Yeah, it was a dude named Jamal.

• 15:10Speaker 01

  I know him.

• 15:13Neil deGrasse Tyson?

  The white Jamal the Wise.

• 15:14Speaker 01

  Jamal the Wise.

• 15:17Chuck Nice?

  Is that what you're seeing when you talk about this learning aspect that happens in this short period of time that you're observing?

• 15:24Speaker 03

  That's actually just one part of what we're seeing. So yeah, this thing called heavy in plasticity.

• 15:29Chuck Nice?

  Yeah.

• 15:30Speaker 03

  Absolutely massively upregulates incredibly rapidly. Once they're in these environments. But what's cool is that there's actually so much more that's going on. And you can break this down and find so many different processes that are interacting at different time scales. So that's why when I say like the complexity of these systems. One of the few things that really bears parallel to is those massive macro scale interactions that can happen on that galaxy level. It's absolutely mind blowing.

• 15:57Chuck Nice?

  What you're talking about now that I'm putting all this together.

• 16:00Speaker 03

  Hmm.

• 16:01Chuck Nice?

  Is fricking crazy because what you're talking about right now is a biological computer. Basically.

• 16:09Speaker 03

  Yeah.

• 16:10Chuck Nice?

  That has the ability to do what we do. Because computers right now can't do what we do. They can't do.

• 16:20Speaker 03

  Silicon.

• 16:20Chuck Nice?

  Silicon, thank you.

• 16:21Speaker 03

  Yeah.

• 16:21Chuck Nice?

  It can't do what we do. The real world grounded knowledge that is necessary to do what we do. It can make huge calculations and tons of associations. But it has to see all those associations in order to make them. And what you said earlier is what really makes sense. You show a child a ball. It will know a ball if you show it a baseball, if you show it a basketball, it's going to say ball. Whereas if you show a computer that you have to show that computer every single kind of ball for it to say that's a ball. What you're talking about right now with using neurons. You can turn these things on and off instead of zeros and ones.

• 17:02Chuck Nice?

  And instead of zeros and ones where you got to show every single thing that is. It can actually do what we do and it can start to make associations on its own. Am I right when I say this?

• 17:16Speaker 03

  That's certainly what we're hoping to be able to show. And you've got this neat thing here where you have a ground truth. Let's say people are going for artificial generalized intelligence. And we just, we don't know if you can achieve what you're talking about with silicon. It's never been done before. But you, me, as I said, cats, rats, birds, bees, to some extent have this generalized intelligence. You have this ground truth that using this hardware, this wetware. It is possible to have these effects. So the question isn't if it's possible, but how do you get there? And that's a very different place to start from.

• 17:49Chuck Nice?

  Let me just for once, I'm sorry, guys, because I'm freaking out right now. You're freak, you're freaking me out, man. I'm just saying. Okay, here's the question, Brett, and I'm not, I'm not trying to be disrespectful at all.

• 18:02Neil deGrasse Tyson?

  That means it's about to be disrespectful.

• 18:04Chuck Nice?

  Well, I don't mean to be. Why the hell would you want to do this? This, I mean, this could go horribly wrong in a lot of ways. Like, you could literally create the intelligence that becomes the next species. It, it won't be a computer. It'd be something much more. That's what I'm getting. You're scared, chap? I'm scared. Yeah. I'm sorry, I'm scared. You scared.

• 18:31Neil deGrasse Tyson?

  By the way, the Terminator had biological tissue affixed to its exoskeleton.

• 18:36Speaker 03

  But not, and I think this is the important thing, not a biological brain.

• 18:40Chuck Nice?

  Right.

• 18:40Speaker 03

  And I think when you, when you think about the risk, something that can self replicate rapidly, uh, be hard to, to, you know, get into the internet and all these things that we worry about AGI, all those fears are missing from biological intelligence. At the end of the day, even if you do create an incredibly intelligent system in a dish, let's say that happens, let's say, let's say we go completely out there, super intelligence in a dish, it's still not really going to be able to manage, you know, against a small court full of, of bleach. So, so these things are controllable. Like, they're, they're controllable. This is, this is one aspect of it, even if we achieve what you're saying.

• 19:15Neil deGrasse Tyson?

  It's not going to jump out at Petri dish and kick your ass.

• 19:18Speaker 03

  Exactly.

• 19:18Neil deGrasse Tyson?

  It's not gonna happen.

• 19:19Speaker 03

  No. Any capabilities we, we provided is something we have to provide it. And, and we have no intention of doing any of this in the near future. And in the day, it will be discreet, it'll be controllable. It'll be just one brain, much as we are.

• 19:44Speaker 04

  I'm Kais from Bangladesh and I support StarTalk on Patreon. This is StarTalk with Neil deGrasse Tyson.

• 19:59Neil deGrasse Tyson?

  So what have you taught it? Pong?

• 20:01Speaker 03

  We did, yeah, we started out with pong.

• 20:04Neil deGrasse Tyson?

  Wait, just wait, no, you can't just, we just started out with pong. How the hell you get from some circuits and some neurons to pong?

• 20:15Speaker 03

  So that was a great question and we were really interested in not just trying to leverage some basic, you know, reaction response thing, you know, you stimulate here and if you measure the same response, which is sort of what a lot of people have been looking at for a while. We wanted to know what's the fundamental basis of intelligence? And there's a lot of theories out there. So we started to work with this brilliant neuroscientist over in University College of London called Professor Carl Friston. And he proposed this idea called the free energy principle. And essentially what it states is that a system will work to minimize the uncertainty, the information entropy in its environment. And so we thought, well, this is great. If truth could be a generalized intelligence and if we find evidence against it, well,

• 20:58Speaker 03

  we found evidence for against a very prominent theory. That'll be a good thing to get people's attention as well. Either way, for us, we do good science, win-win. And so we tested it and we built an environment where when the system missed the ball, right? We gave the control of the paddle and we said, if you miss the ball, you're going to be injected randomness into your well. What's that look like? Just random stimulations all over the dish. A super simple idea to test a very complex idea. And what we found was that over time with this feedback loop that we created, which was worked in real time, the system actually did change its behavior rapidly. At all these levels I was talking about. So that was pretty exciting for us to see.

• 21:37Speaker 01

  But when the paddle misses the ball, how do you gauge the intelligence that it doesn't get as angry? And throw the paddle down and have a tantrum? Or does it? I mean, we talked about it reorganizing. So how do you get that metric to gauge this intelligence? Or is it just visual? Well, it hits the ball more accurately and better now?

• 21:56Speaker 03

  That's the simplest approach, right? If you were to train a person or a cat or a dog to do a trick, the question isn't what's really going on in its head. The question is, does it sit next time I tell it to sit? Or can it solve two plus two next time? And so that's exactly what we said. So we basically, and we disconnected them. We disconnected where the information went in and where the information went out. So that it actually have to be some sort of process going on through the system. And we just looked, would it learn over time?

• 22:26Speaker 01

  Wow. So mentioning learning, if you put synthetic biological intelligence and compared it to a machine learning algorithm, what's the effectiveness, the efficiency, the speed of how this compares and operates?

• 22:46Speaker 03

  Yeah, that's a question that seems to upset a lot of people because we actually have done that work in depth.

• 22:53Neil deGrasse Tyson?

  It's the answer that upsets them, not the question.

• 22:55Speaker 03

  Even the question can upset some people. There's been such a narrative of AI ML supremacy for so long. Unfortunately, in science, there can be a lot of gatekeeping in certain areas, but we've sort of very conclusively shown that these systems actually have better learning rates, better sample efficiency than machine learning. Now, of course, machine learning will continue to learn and you can speed it up and you do all these things. But if you're talking about the amount of power consumed and the amount of data consumed to get to a given point, these systems will outperform them.

• 23:26Chuck Nice?

  Well, that's because you need less of a sample size in terms of input. You don't need as much input with what you're talking about. With machine learning, it's only as good as the data set that trains it. So you need you need a tremendous amount of of data to put in in order to get something out.

• 23:47Neil deGrasse Tyson?

  And remind us of what AGI stands for.

• 23:49Speaker 03

  Artificial Generalized Intelligence.

• 23:51Neil deGrasse Tyson?

  Which means?

• 23:52Speaker 03

  The idea of having an AI that's able to solve, well, basically with human level capabilities, able to be generalized their their approach with, you know, a given set of data, opposed to having to be trained in a bespoke way on every single task, which is currently what you have to do.

• 24:08Speaker 01

  We use the algorithms and obviously if you want them to solve incredibly large problems, it takes a lot of energy. You've highlighted the fact that this is a low-energy intelligence. Do I need a football field size of biological processes that you're creating to solve big problems? Or is this something that's ultimately going to become scalable and handy to stick in your? Put them and think back to the telephone. Now we can stick one in a small pocket. There is before you had a telephone box that couldn't go anywhere.

• 24:41Speaker 03

  Yeah, great question. And actually, like, bigger isn't always better. You look at elephants, they their brains are about two and a half times as large as us. But unfortunately, most of them, unless they're killed by predators, die of starvation because they grind their teeth down. So bigger isn't always better for a brain. It's the connections inside it, and it's the method in which it's used that matters. Even, let's say, bumblebee intelligence. You know, bees are an amazing creature.

• 25:07Chuck Nice?

  Mm hmm.

• 25:08Speaker 03

  And they can do so much. If we could harness, that's only eight hundred to a million, eight hundred thousand to a million, I should say, neurons inside a bee. And they can achieve so much. What if we could just harness that level of intelligence? It would outperform any machine learning-based drone we have.

• 25:22Chuck Nice?

  Wow, that's pretty insane. I I just love the fact that elephants, uh, die because they don't have a dental plan.

• 25:32Speaker 03

  Yeah, yeah. Sorry to bring the tone down, guys.

• 25:35Chuck Nice?

  If only, yeah, if only elephants had learned to be dentists.

• 25:39Neil deGrasse Tyson?

  What they need are dentures, you know. Yeah, yeah, yeah.

• 25:43Speaker 01

  Is this synthetic biological intelligence going to solve problems in a similar way to silicon intelligence? And there's this worry about ethics of AI and does biological intelligence have a greater or lesser degree of ethical concerns for us as a species, which is talking back to the point Chuck was making earlier?

• 26:13Speaker 03

  Two very good questions. The first one is almost certainly not. And I could talk for hours on the differences we see. I'll give one example, though. When you look at a complex dynamic system like the brain and you inject information into it, you're going to see these very dramatic changes that you won't see in, say, silicon computing. For example, something called criticality. This is basically something that's balanced at the edge of chaos, between order and disorder.

• 26:43Speaker 01

  Mm hmm.

• 26:44Speaker 03

  And it's fascinating because that's the exact same sort of thing you'll see as bird flocks respond and change their behaviour, their flight patterns, in response to, say, a hawk or something like this. And so you start to get these parallel links between how these systems at a neuron level, at a bird level, perhaps even, you know, you can model this in city levels, are actually changing their behaviour. So it's very fundamentally a natural process opposed to zeros and ones. There will be overlaps, there will be links, but fundamentally there are also more differences, which does raise the ethical questions. So we work with like a lot of independent bioethicists around the world to look at this. And actually, one really exciting thing is that if you start to want to look at what

• 27:24Speaker 03

  a morally relevant state is, broad term, like consciousness, this tool can actually help you maybe understand what that even means. Because when you look at consciousness in a person, there's so much going on, right? But if you can break it down to a simpler level and start to look at metrics there, you can maybe actually understand what is the biological basis to some of these morally relevant states.

• 27:45Chuck Nice?

  Now, is it possible that you wouldn't even need to worry about consciousness? When you talk about the bee and you talk about the elephant, you know, I'm looking at it like a neural network. If you're looking like, when you look at AI neural networks, you know, if you were to take specific tasks for your biological computer and you were to link them all together, you could kind of make this, I don't know, ad hoc makeshift kind of brain, but it wouldn't necessarily require intel, um, not intelligence, um, consciousness, you know?

• 28:21Speaker 03

  Exactly, yeah. You're, you're spot on, and this is something I try to, try to communicate with people. Intelligence and consciousness, they're not inherently tied together. And you see examples of that in people as well. So there's a phenomenon called blindsight. I don't know if you're familiar with this.

• 28:36Chuck Nice?

  No.

• 28:38Speaker 03

  But in blindsight, essentially, you have a case where you've got damage to the visual cortex. And so if someone becomes like legally blind, they perceive they have no conscious experience of any vision. Yet, if you throw a ball at them or you pull a chair in front of them as they're walking, it's not nice to do, but people have done the tests, they'll move around the chair, they'll catch the ball, right? And, but they won't know it. And you'll say, well, how did you do this? Oh, it was luck. I don't, I didn't do it. And so, so you have this thing of intelligent action, catching a ball, moving around the chair, no conscious awareness. So it's certainly possible. And again, we're not necessarily trying to create a human brain in a dish. We're using neurons as an engineering substrate. So if we understand what causes consciousness, we can build around and away from that if that's desirable.

• 29:22Speaker 01

  You say you're not trying to create a brain, but it sounds a lot like you are.

• 29:27Speaker 03

  Not a human brain, not a human brain.

• 29:29Speaker 01

  But isn't there something, wasn't there research that cortical labs, your labs, did with Johns Hopkins University here in the US? Regards organoid intelligence, I can say that, but I can't give you an explanation of what it is. So would you help me understand that a bit better, please?

• 29:47Speaker 03

  Yeah, so there are the two. That's why I say not necessarily wanting to create a brain in addition. There are two directions. One is, well, the human brain is the most capable brain for doing complex tasks that we know of. We should just recreate it exactly, and that's going to come with certain benefits and certain risks. The other approach is to go the other way. So with the organoid intelligence work, yeah, we're looking to see more physiologically or biologically compatible systems, something that looks more like a human brain. Still far smaller, far more simple, but closer down that pathway. But as I was saying, if we do become really worried about consciousness, we can pivot and go the exact opposite direction and still have a lot of use. So make it less like a human brain, but leverage the underlying properties of these neurons.

• 30:29Neil deGrasse Tyson?

  Where precisely does the ethics concern land in this whole conversation?

• 30:34Speaker 03

  I think there's two broad or three broad areas. One of them being largely solved because of stem cell therapy-based work that's been going on in genetics work. That one is the donors for your tissue, for the blood. It's a simple process, but still you don't want people taken advantage of. You want to make sure it's nice and equitable access, you have genetic diversity, et cetera, et cetera. Fortunately, a lot of work's been done on that. The other two, one is applications, which needs to be done on a case by case basis. And then the other one is indeed this idea of what if they become conscious? So I think there's these sort of three pipelines or three pathways of ethics that we need to be aware of.

• 31:13Neil deGrasse Tyson?

  And do you get ethicists or are neuroscientists also good at that exercise?

• 31:19Speaker 03

  We work with both. We're a big proponent of multidisciplinary collaboration. So we work with ethicists to talk about ethical problems. We try to integrate them with neuroscientists.

• 31:29Neil deGrasse Tyson?

  Well, guess me is when people say, let's choose a priest, a rabbi and an imam, and they don't bring in a scientist into a conversation about the ethics of the science.

• 31:37Speaker 03

  Yeah, yeah.

• 31:38Neil deGrasse Tyson?

  So I'm delighted to learn that you've got a seat at the table.

• 31:40Chuck Nice?

  Well, that's wait a minute, that's because you guys are the problem.

• 32:07Neil deGrasse Tyson?

  Brett, you just casually mentioned consciousness, like that's something you know you can create in your dish. When there are people who are brain dead, but are they conscious? Do we know enough about consciousness to say whether what you're creating ever achieves it?

• 32:22Speaker 03

  Yeah, sorry, that was not my intention. I might have misspoke. It's not so much that we think we can create consciousness in a dish. In fact, my personal belief is that we're unlikely to do that because, as you said, there's so much complexity there. But we have to recognize that there are these possibilities, so we're making sure we progress this work in a sort of ethically sustainable way. So what I would say with that, though, is that if we begin to find that consciousness or anything like that does arise, it wouldn't just change how we treat these things in a dish, it would change how we interact with all of nature. You know, we're looking at things that are still more simple than a cockroach at the moment.

• 32:59Chuck Nice?

  For now. For now.

• 33:01Speaker 03

  For now, but there's so much complexity there. It could just, it could inform not, you know, not just the ethics of this research area or this application, this technology, but how we interact with the world. And I think that's exciting.

• 33:13Chuck Nice?

  But wait a minute, Brett. Let me, let me just push back for a second. When you talk about complexity, the kind of complexity that you're talking about could lead to consciousness and we wouldn't even know how. I mean, is consciousness something that for us, we know that, you know, we're born with it supposedly. But like Neil just said, there are people who are alive and not conscious. So is it something that is emergent? What is it? What is it? And then, once we figure out what it is, when you talk about the level of complexity that you're talking about, maybe it could happen the same way it happened in us, if it is indeed emergent. Maybe you will happen upon consciousness.

• 33:58Chuck Nice?

  And what do you do at that point?

• 33:59Speaker 03

  Look, it's a brilliant question. It's one we ask, we don't, we don't have the answer to it. What we'd like to think, though. That was a great answer, by the way. We don't have the answer. And I think it's, you know, as scientists, you have to be humble. You have to say, look, we don't know. As I said, I don't think it'll happen, at least not any time in the foreseeable future. But we don't know that. So we have to be humble. We have to approach it. We have to say, well, look, how do we test and make sure that we're able to know and sort of identify the road signs before we've come up to the turn? And so that's what we're actively working on with people is is not just blindly going into this and saying, well, maybe we create Frankenstein, maybe we don't, but someone else's problem. No, it's our problem and we need to bring in the people to work together to be able to figure out the best way to actually get to the result we want to get to, which is

• 34:44Speaker 03

  ultimately something that benefits people.

• 34:46Speaker 01

  Are we looking at the future of computing being synthetic biological intelligence? Or are we likely to find the hybrid between the biological and the silicon?

• 34:57Speaker 03

  Absolutely. So there's this idea called heterogeneous compute. We really have heterogeneous compute, by the way. I mean, CPUs and GPUs, they process data differently and they work together really well. What if we could bring about, you know, maybe one day you have your quantum processing unit that does photography very well, and you have your biological processing unit that does real time applications really well, and they all work together so that you have the right tool for the right job?

• 35:23Speaker 01

  Do they solve problems in different ways? Or did we explain that fully in the sense of will the biological intelligence find a different route to a solution than the silicon intelligence?

• 35:36Speaker 03

  That's interesting. Again, we we would almost certainly think so based on just what we've done from, say, human versus AI, which people, ML, machine learning, that people have looked at. We do seem to solve problems differently. Yeah. And sometimes humans don't always come up with an optimum answer. Often what they come up to is an answer that's good enough. And so you need to figure out, like, what do we want to? Do we want to know how to get from point A, B, and C? In a way that's you can do it with your time? Or do you want to figure out the exact optimum, you know, pathway that might take you hundreds of thousands, uh, you know, more amount of power consumption? So you just need to figure out like, what is it we actually need to know? And I think that's something that we've as a people not always done really well. We haven't always figured out the best approach, the most efficient approach to get things done.

• 36:21Chuck Nice?

  So, you know, that being said, is it possible that the computational power of the synthetic biological computer? Is it possible that that might be compromised by the fact that it's more, it thinks more like we do? In other words, if giving it a real world grounded knowledge, could that actual knowledge be an impediment?

• 36:50Speaker 03

  I'm not, I can't think of a case where knowledge of reality is an impediment to problem solving.

• 36:56Neil deGrasse Tyson?

  Yeah, I agree.

• 36:56Chuck Nice?

  Um, okay. I'm just looking at, I'm looking at the differences because those are the differences between how we think and computers think. Hmm. So I'm looking at those differences. Uh, you know, is there any possibility that could serve as a stumbling block in any way? That's all.

• 37:13Speaker 03

  You know, and it could be like, I, as a scientist, I never like to say, I think it's not possible. The possibilities out there are almost endless. Uh, but what I'd say is like, if you do find those edge cases, it just means that biological computation is not the right approach for that problem.

• 37:29Neil deGrasse Tyson?

  Yeah.

• 37:30Chuck Nice?

  That's super cool, man.

• 37:31Neil deGrasse Tyson?

  Yeah, we go through various versions of that.

• 37:33Chuck Nice?

  Okay.

• 37:34Neil deGrasse Tyson?

  We program computers. There's certain methods of programming, certain computing CPU wiring that doesn't marry well to the problem you want to solve.

• 37:44Chuck Nice?

  Gotcha.

• 37:45Neil deGrasse Tyson?

  And you just work on it, you work on something else. You could write code that overcomes that, but then that burns CPUs that you can use for the real thing you're trying to calculate.

• 37:54Chuck Nice?

  For something else. Right, right, right, right. Okay, that makes sense. Okay.

• 37:58Speaker 01

  So Brett, as this thing begins to develop and progress, where are you looking at it and thinking it's really gonna do well in this field? Is this field healthcare? Is this field data processing, autonomous? Whatever, I can't say. What are the first events, driving, flying, or whatever that might be?

• 38:22Speaker 03

  Yeah, the nice thing about this approach is it's a platform technology. So yeah, the initial use case is like just basic science research is a super interesting question and it's a huge field, billions, tens of billions of dollars get spent in it every year. And we're often using tools that aren't quite up to asking the questions or answering the questions we're asking. Beyond that, as you say, healthcare, drug testing.

• 38:43Neil deGrasse Tyson?

  Is there a tool you already know you need that you don't have that might be supplied by a medical engineer or a physicist?

• 38:49Speaker 03

  Well, we have medical, you know, we have medical engineers, we have, we work with physicists for this very reason, right? We're incredibly multidisciplinary. So, you know, this sort of this little box here, it combined, you know, all sorts of things, biomedical engineering, hardware, software, everything. We all have to come together and work. That's ultimately one of the biggest reasons we're a company, not an academic lab. Academic labs, silo, they focus on one area and they go deep. And I have a lot of respect for that, but we weren't able to build a platform technology with that approach. So we had to bring everybody together.

• 39:22Chuck Nice?

  That being said, do you ever envision yourself saying these words? It's alive! It's alive!

• 39:37Speaker 03

  It took that long?

• 39:38Chuck Nice?

  I've been resisting, I've been resisting, I've been resisting, I've been resisting!

• 39:45Speaker 03

  I know, you caved. We have to say that at least twice a day in the lab, actually. Yeah, it's compulsory, you're not allowed to leave before you've got your maniacal laugh out the way.

• 39:55Chuck Nice?

  It's alive!

• 39:57Neil deGrasse Tyson?

  I've seen it twice with Frankenstein and Frankenfurter. So these are the two people creating life out there. So you had to play Pong. Is there other games coming forward on it? Some other mental feats that we can look forward to?

• 40:13Speaker 03

  Yeah, we started, we started with Pong because DeepMind started that for some of the first RL work. You know, it's one of the first computer games, it met a bunch of other criteria. We moved on, we did try some other things and had some really interesting results. But what became really apparent to us is that we were using off-the-shelf hardware and we were sort of hacking a lot of it. Because we started out, we didn't have a lot of money or many resources, so just a couple of us. And so we had to make do with what we could. And we just realized, God, it was hard to use things not designed for that purpose. And so we set out to build the platforms that we're building that make it easier. So now instead of eighteen months of development to make pong, we can do it in a week or two. And so we're now, just now, these things are coming online and we're starting to iterate rapidly.

• 40:58Speaker 03

  And we're doing all sorts of things. And a lot, some of them are really basic neurocomputational questions that just haven't been able to be answered before. You know, trying to understand the music of neurons, the, the waveforms and what that means in terms of a computational approach.

• 41:11Neil deGrasse Tyson?

  So you're telling me this will help us understand our own brain? Or is this going to go off and do something else and become our overlords?

• 41:17Speaker 03

  Well, I was gonna say, why not both? I don't think it will become our overlords, but, but why not both? Why not?

• 41:24Neil deGrasse Tyson?

  No, no, yeah. Yeah, it got into his head, Chuck, you see? He's, he's.

• 41:27Speaker 03

  Yeah, yeah, it could be, look, we certainly think like for sure it's going to help us understand our own brains. And when you understand a system, you can build it. I forget who said the famous quote, but if I can't understand it, I can't build it. That's what we're trying to do, basically.

• 41:43Chuck Nice?

  Hmm.

• 41:43Speaker 03

  Um.

• 41:44Chuck Nice?

  I believe that was Field, I believe that was Field of Dreams.

• 41:47Neil deGrasse Tyson?

  If I can't understand.

• 41:49Chuck Nice?

  I'm pretty, I'm sure that was what he said before he came up with something a little catchier.

• 41:54Speaker 03

  Yeah.

• 41:55Chuck Nice?

  He came up with something a little catchier.

• 41:57Neil deGrasse Tyson?

  We build it, they will come. Right, okay.

• 41:59Speaker 03

  Yeah, exactly.

• 42:00Neil deGrasse Tyson?

  If we build it, they will kick our ass. You know, there are a lot of, there are a lot of ways that could have gone.

• 42:04Speaker 01

  But before we get our asses kicked, and I'm not overly, overly keen on that theory. Neil mentioned about spinal column damage. Is there a way that this will develop to treat diseased neurons and bring a healing process forward? If you've got it playing pong, it's not just sensory, it's motor, it's motor skills as well. There's a lot of complexity in this. Is that, are you able to articulate that forward?

• 42:32Speaker 03

  Yeah, absolutely. And that's what I was saying, like neuro, neuroclinical trials for psychiatric neurological disease, they fail nearly all of the time. You're looking between seven or eight percent down to less than one percent, depending on the area. And part of the reason is our pre-screening tools, our pre-trial testament, pre-clinical testing, is enough for the task. And that's because when you look at a neuron, the purpose of a neuron isn't to express a protein or to even, it's not even to fire action potential.

• 43:00Neil deGrasse Tyson?

  It's just electrochemical, that's it.

• 43:02Speaker 03

  But it's, it's not just to have electrical chemical activity, that's the thing, it's to process and do something with information. It needs the external information to do its job, to do its function.

• 43:12Neil deGrasse Tyson?

  But it's doing it electrochemically. Right.

• 43:15Speaker 03

  Yes, it's doing electrochemically, but it needs the external information. That's a marker of it, but it's not the whole story. And so if we can look at the response of these systems and how they actually change our information processing response to drugs, you're going to get a far better understanding of how that drug is affecting the system. And we've, we've done that. We've been using sort of very simple epilepsy models and finding that if you take an epilepsy model, it doesn't learn pong, unsurprisingly.

• 43:41Neil deGrasse Tyson?

  Oh, wow, okay.

• 43:42Speaker 03

  If you treat it with things that reduce that activity, not only can it improve its gameplay, but you get a wealth of information that was previously inaccessible.

• 43:52Speaker 01

  You said about criticality, that borderline between organized and chaotic, isn't that kind of like an epileptic fit?

• 44:02Speaker 03

  No, no, actually, um.

• 44:05Speaker 01

  When you go into that complete chaotic state of.

• 44:08Speaker 03

  No, yeah, that's what I'm saying. They don't go there, they, they balance between the order of chaos and.

• 44:13Speaker 01

  Uh huh.

• 44:14Speaker 03

  So that, that balancing act is actually incredibly important for information transfer. And it's implicated, so we, we actually had a paper on this. And a lot of people sort of said, oh, this is related to memory or to intelligence or to this and to that. And there's a lot of controversy. And what we found was actually, no, it, it underpins all of them because it's a fundamental pattern of dynamic systems in response to an external signal. And as I said, that's where we draw the parallel between flocks of birds and, and people in cities and all of this stuff. And you can see the same patterns arising again and again in nature.

• 44:44Neil deGrasse Tyson?

  Yeah, but you can't look at a bird and know that it flocks, can you?

• 44:47Speaker 03

  Well, no, you need to look at the flock.

• 44:49Neil deGrasse Tyson?

  Right, but you don't know that it even has the capacity to do so. So the flocking is itself an emergent.

• 44:58Neil deGrasse Tyson?

  Element of bird behavior, right?

• 45:01Speaker 03

  Exactly, yeah, yeah. And so we were trying to build the system so you can actually look at the emerging properties that happen from the collection.

• 45:08Neil deGrasse Tyson?

  Well, it might be a naive question. We learn in, you know, basic brain biology class, that different parts of the brain specialize in different activities, though there's quite a bit of overlap. But there's a portion that focuses on your vision and your name, facial recognition, and language, and we know that from brain injuries, they were, the person loses that ability. Does this tell us that if you sample neurons from different parts of the brain, they will behave differently in your circuit?

• 45:37Speaker 03

  Oh.

• 45:38Neil deGrasse Tyson?

  Or are all neurons identical? And it's just how they've been trained ever since they were born?

• 45:43Speaker 03

  Yeah, so no, there are different types of neurons that do different things. So we mainly work with cortical neurons.

• 45:50Neil deGrasse Tyson?

  Which means what?

• 45:52Speaker 03

  So cortical neurons are the sort of the neurons that sit on the outside layer of the brain. They're important for stuff like tension, you know, very high, higher order cognition.

• 46:02Neil deGrasse Tyson?

  The good stuff that makes us human.

• 46:03Speaker 03

  Yeah, and if you look at, say, like a human compared to a monkey or something else, like, the big difference is we have a whole lot more cortex, right? That's what gives us our human, humanness.

• 46:12Neil deGrasse Tyson?

  And, and some people have a much bigger reptilian brain inside.

• 46:17Neil deGrasse Tyson?

  Is there still talk of this, a reptilian brain?

• 46:19Speaker 03

  Look, it's, it's more of a metaphor or model, a way to think about how it's structured.

• 46:25Neil deGrasse Tyson?

  If there were one, I wouldn't want you to use those neurons for me.

• 46:28Speaker 03

  Yeah, I was gonna say.

• 46:29Neil deGrasse Tyson?

  Give me the good neurons, not the reptilian ones.

• 46:32Chuck Nice?

  Not the sleep stack neurons.

• 46:34Speaker 03

  Yeah, exactly. But then we can grow other types of cells as well. So we have some mitochondrial cells and, you know, really, the limitations we have on this is mostly at the moment due to, due to funding and time. I used to be as an example, because I think if we had sort of enough funding and time, we could recreate bee brain complexity with the synthetic biology and bioengineering tools we have available. I think it's possible.

• 46:57Neil deGrasse Tyson?

  We devoted an entire episode of Cosmos to bees.

• 47:00Speaker 03

  They're fascinating.

• 47:01Neil deGrasse Tyson?

  Just the waggle dance of a bee.

• 47:04Speaker 03

  Yeah, yeah.

• 47:05Neil deGrasse Tyson?

  How they communicate, how they, how they pick up camp and move to another location and how they scope it out. And, and their brain is this big, right? I mean, tiny.

• 47:13Speaker 03

  Tiny, tiny, but the complexity.

• 47:16Chuck Nice?

  You gotta love any species that communicates through twerking, okay?

• 47:22Neil deGrasse Tyson?

  And now I've got that song in my head. That's a new word. Chuck, that's on you. Chuck, now we'll never Chuck.

• 47:31Neil deGrasse Tyson?

  Can't unthink it. I need the thing they have in Men in Black, you know, where they, please, take that out of my head. Yeah, that's about it. Well, Brett, any future thoughts so that we can think nice things about your work instead of worry about how one day we'll become our overlord? I love it, though.

• 47:48Speaker 03

  Look, I don't think there's any worry about these things becoming our overlord.

• 47:52Neil deGrasse Tyson?

  He doesn't think, you hear that? He doesn't think there is.

• 47:54Speaker 03

  I don't think that.

• 47:57Speaker 03

  Hey, look, I'm a scientist. As I said, I have to leave possibility for the unknown always out there.

• 48:01Neil deGrasse Tyson?

  Yes.

• 48:01Speaker 03

  Helps me get up every day. I'm sure you you feel the same way. It's the unknown that drives us forward.

• 48:06Neil deGrasse Tyson?

  Of course, it's the only driver, yeah.

• 48:09Speaker 03

  I think it's these very features that make this work so exciting. The fact that there is going to be parallels, even if we do take it down that engineering pathway. And it's it's the fact that this drive to understand the unknown and to optimize. It gives us both a chance to understand ourself and the world better and also potentially to provide a a platform that can change the way we do so many things from drug discovery to maybe computation. One of the things that drove me to this company was the founder, Tom. When he approached me, he's like, hey, look, we're starting this. Do you want to come on board? I said, well, what do you want to do? I love the idea, but what do you want to do? It's just going to be to sell out? And he said, no, no, no, we want to create a legacy. We want to change the way things are done. We don't care about the money.

• 48:55Speaker 03

  There's easier ways to make money than this. This is not a convenient way to make money. But it is our chance, we think, to, you know, make an impact on the world and for the better.

• 49:04Neil deGrasse Tyson?

  All right, Brett, we got to call it quits there. Thank you for dialing in from Melbourne, Australia. For this call. It seemed like you were just right across the street. He's the man from the future, Neil.

• 49:16Speaker 01

  Literally, a man from the future.

• 49:20Neil deGrasse Tyson?

  Oh, it's tomorrow.

• 49:21Speaker 01

  Thank you.

• 49:22Neil deGrasse Tyson?

  Okay, fine. Fine. Fourteen hours ahead of midnight.

• 49:28Speaker 03

  Yeah, thank you very much Chuck Neil and Gary. A pleasure chatting to you.

• 49:32Speaker 01

  No, you've opened our eyes to, and our minds to a number of things. Thank you.

• 49:37Neil deGrasse Tyson?

  You got it. All right. Gary, good to have you. Chuck?

• 49:40Speaker 01

  Pleasure.

• 49:41Neil deGrasse Tyson?

  Such a pleasure. Keep it going, guys. All right, this has been StarTalk Special Edition, the SBI version. Neil deGrasse Tyson here, as always, your personal astrophysicist. Keep looking up.