Synthetic Biological Intelligence with Brett Kagan

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Neil deGrasse Tyson
Welcome to Startalk, your place in the universe where science and pop culture collide. Startalk begins right now. This is Startalk special edition. Neil degrasse Tyson, you're a personal astrophysicist. I got with me my co host, the dynamic duo, Gary O'Reilly.

Gary, how you doing, man? I'm good now. Good to be on again. Former soccer pro. You staying in shape a little bit?

Gary O'Reilly
No. Okay. We'll do a separate episode on staying. In shape, just as it should be. As it should be.

Neil deGrasse Tyson
And that other voice is, of course, long time star talk co host Chuck. Nice. How you doing, Chuck? Hey, what's happening? So, Gary, what did you and your producers cook up for today?

Gary O'Reilly
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? Yeah. What if we introduce the biology to the technology? 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. Wait, you mean silicon? That's silicone.

Neil deGrasse Tyson
The silicone you find in Brazil. We already have silicone intelligence. I think we. We already have that. It's called Hollywood.

That's right. Silicone intelligence. Are we in the weeds with tomato and tomato? No, I think silicon is the silicon dips. Okay.

You're comparing biology introduced the technology, and technology introduced to biology, and that's what we're going to explore here. Totally, yeah. 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. Thank you so much. Thanks so much for having me. And you're dialing in from Australia, Melbourne, so. Yeah, yeah.

Chuck Nice
Nice. Great to be able to organize the international calls at a time that can work for everyone. Indeed. Do you have a PhD in neuroscience, which is, if I were to pick a field today, it might just about be that. Maybe.

Neil deGrasse Tyson
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. Yeah. Yeah. And who are these people?

They want to build synthetic intelligence processors. Okay. A positronic brain, huh? So let's just start out. You're making brain cells in a dish.

Does that mean you're growing neurons? And then, if that's what you're doing, you're gonna put it into electronic circuitry. So what's up with that? 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.

Brett Kagan
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, the fundamental building blocks of brains, to actually create a device that is intelligent. 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. Wait, when you say ethical way, you mean there's some brain cells that'll complain and protest your work?

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 donors, blood tissue or skin cells, or there's a number of ways you can do it. And 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. Please describe it. Describe what you have. Yeah, so this sort of device, we call it a cl one.

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 the form of information transfer. You're peaking 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? 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?

Gary O'Reilly
All right, so how are you getting from growing your neurons and your brain cells onto the multielectrode arrays, and then how are they networking and beginning to function? Yeah, all of those are great questions. So, to 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 multielectrode 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?

Brett Kagan
Well, that's where the neurocomputational approaches come in, and this is really a question. This is a physics question. How do information systems work at a fundamental level? 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?

Yeah, absolutely. And the answer is, actually, both neurons will spot. It's called spontaneous activity. Okay. 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. So you think. So you think. Who knows what sort of quantum volume is?

Chuck 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. Are you a big man on the hippocampus?

Brett Kagan
No, no, no. Yeah, absolutely. There's a huge amount of complexity that arises there. But what's fascinating is when you shift it and 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. You're saying the neurons intentionally organize themselves, in a way, in response to being laid down on your multi electrode arrays.

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 structural quality to them, they will reorganize their function rapidly. Oh, whoa.

Chuck Nice
Okay. Very Frankenstein here. That's very cool, though, because our brains do the same thing. I guess so, don't they? Exactly.

Our brains do the exact same thing. That's wild. But so you're not growing neurons. You are activating neurons. We have to do both.

Brett Kagan
So we grow them, we plate them, and then we. How can we activate them? With inflammation. Wait, if you can grow neurons, why can't you cure spinal cord, severed nerves, that sort of thing? Well, that's actually what a lot of this technology for the synthetic biology has arisen from.

People looking at Parkinson's. Yep. Spinal cord damage, Alzheimer's, a whole range of things. So people have been trying to develop brain cells for just over a decade from 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. In reading about your project here, Brett, I came across a term called embodied networks. Yes. 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.

Gary O'Reilly
So if we can sort of build. Build some basics as we understand, a little bit more, please. Yeah, sure. So, in the simplest possible terms, embodiment. We all have embodiment, right?

Brett Kagan
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 were 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 neuron. 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 before we. Get to the abilities of these things that you are creating in your Frankenstein lab. Tell me again, convince me that adding neurons to circuits is better than adding circuits to neurons, because you are bucking a trend here.

Neil deGrasse Tyson
You know, Neuralink, Elon Musk and others, they want to put the Internet in our head and enhance our biology. You want to make a head and then put the Internet in it.

So why should I bet on you? 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.

Brett Kagan
So both have their times in place, and there's also the chance they could interact. It's one thing to put a BCI 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.

Chuck Nice
Wow. 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.

Brett Kagan
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 and they can do it quicker in terms of the amount of data they need as well. You don't need to look at too many tigers to learn to know what a tiger is run from the tiger. Right. We have these innate predisposition to learn rapidly. So now let me ask you what you just brought that up.

Chuck Nice
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. Yeah. So how exactly do you transfer that into neurons that are embodied in some circuit other in the 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, our ability to acquire that real world knowledge is what's fascinating.

Brett Kagan
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 prior 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 massive parallelization and flexibility of a neural system. We can make these connections. How long does it take your neurons processors to learn as they play pong?

Gary O'Reilly
Or is this an experiment that's still ongoing? 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 self as, like, anti hype scientists. So we wanted to show people, like, look, here's this work.

Brett Kagan
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 for, 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 reorganizing and actually 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. 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? Yeah. Yeah.

Chuck Nice
Is that what you're talking about when you said the learning aspect? Is that what they say up in the hood? I mean, where'd you get this? No, that's a classic line for something called hebbing plasticity. Neurons that fire together, wire together.

Brett Kagan
It's been a mantra in neuroscience since. Oh, gosh, don't test me on the history of that. But for a long time, maybe it did start up in the hood. Okay. Yeah.

In our hood. In the hood, we share. Yeah. Yeah. It was a dude named Jamal.

Neil deGrasse Tyson
Okay, okay. I know him. Thank you. Jamal the wise. Jamal the wise.

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

Brett Kagan
It's absolutely mind blowing. What you're talking about now that I'm putting all this together is frickin crazy, because what you're talking about right now is a biological computer, basically, that has the ability to do what we do. Because computers right now can't do what we do. They can't do silicon. Thank you.

Chuck 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 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.

Brett Kagan
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. Let me just for one.

Chuck Nice
I'm sorry, guys. Cause I'm freaking out right now. Never. You're freaking me out, man. I'm just saying.

Okay, here's the question, Brett, and I'm not trying to be disrespectful at all. That means he's about to be disrespectful. 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 won't be a computer. It'll be something much more.

That's what I'm getting. You're scared, Chuck. I'm scared, yeah. I'm sorry. I'm scared.

Gary O'Reilly
You're scared. By the way, the terminator had biological tissue affixed to its exoskeleton, but not. And I think this is an important thing, not a biological brain. Right? And I think when you think about the risk, something that can self replicate rapidly be hard to get into the Internet.

Brett Kagan
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 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 bleach. So these things are controllable. Like, they're controllable.

This is one aspect of it. Even if we achieve what you're saying. It'S not going to jump out of petri disk and kick your ass. It's not going to happen. No.

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

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Brett Kagan
I'm Kais from Bangladesh and I support startalk on Patreon. This is startalk with Neil degrasse Tyson.

Neil deGrasse Tyson
So what have you taught it? Pong. Like we did? Yeah, we started out with Pong. Just wait.

No, you can't just. We just started out. No. How the hell you get from some circuits and some neurons to Pong? So that, that was a great question.

Brett Kagan
And we were really interested in not just trying to leverage some basic, you know, reaction response thing. You know, you stimulate here, and it, 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 Karl 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, look, this is great. If true, it could be a generalized intelligence. And if we find evidence against it, well, 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. We built an environment where, when the system missed the ball, right? We gave a 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?

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. Right.

Gary O'Reilly
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've 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, 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.

Brett Kagan
So we basically, and we disconnected them. We disconnected where the information went out in and where the information went out, so that it actually had to be some sort of process going on through the system. And we just looked, would it learn over time? Wow. So mentioning learning.

Gary O'Reilly
If you put synthetic biological intelligence and compared it to a machine learning learning algorithm, what's the effectiveness, the efficiency, the, the speed of how this compares and operates? Yeah, yeah, that's a question that seems to upset a lot of people, because we actually have done that work in depth. It's the answer that upsets them, not the question. Even the question can upset some people. There's been such a narrative of AI ML supremacy for so long.

Brett Kagan
Unfortunately, in science, there can be a lot of gatekeeping in certain areas, but we're 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. 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.

Chuck Nice
So you need a tremendous amount of data to put in in order to get something out and remind us of. What AGI stands for, artificial generalized intelligence, which means the idea of having an AI that's able to solve well, basically with human level capabilities, able to be generalized in their approach with 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. We're used to 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?

Gary O'Reilly
Or is this something that's ultimately going to become scalable and handy to stick in your think back to the telephone. Now we can stick one in a small pocket, whereas before you had a telephone box that couldn't go anywhere. Yeah, great question. And actually, like, bigger isn't always better. You look at elephants.

Brett Kagan
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. Bees are an amazing creature, and they can do so much. If we could harness, that's only 800 to a million, 800,000 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. Wow. That's pretty insane. I just love the fact that elephants die because they don't have a dental plan. Yeah, yeah.

Sorry to bring the tone down, guys. If only elephants had learned to be dentists. What they need are dentures, you know.

Gary O'Reilly
Is this synthetic biological intelligence going to solve problems in a similar way to silicon intelligence? And there's this form of. 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 harking back to the point chuck was making earlier, two very good questions.

Brett Kagan
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, yet between order and disorder. And it's fascinating because that's the exact same sort of thing you'll see as bird flocks respond and change their behavior, 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 the neuron level, at a bird level, perhaps even you can model this in city levels, are actually changing their behavior. 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 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 a morally relevant state is, broad term like consciousness, this tool could 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 of some of these morally relevant states. 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? I'm looking at it like a neural network. If you're looking like, when you look at AI neural networks, if you were to take specifically task 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 intelligence.

Chuck Nice
I'm not intelligence. Consciousness, you know? Exactly. Yeah, you're spot on. And this is something I try to try to communicate with people.

Brett Kagan
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, but in blind sight, 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 it for 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. You say you're not trying to create a brain, but it sounds a lot.

Like you are not a human brain. Not a human brain. 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?

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's 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. Suddenly 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. Where precisely does the ethics concern land in this whole conversation?

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 pipeline or three pathways of ethics that we need to be aware of.

Neil deGrasse Tyson
And you get ethicists, or are neuroscientists also good at that exercise? 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.

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

Chuck Nice
Wait a minute. That's cause you guys are the problem.

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Neil 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? Yeah, sorry.

Brett Kagan
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. For now.

Chuck Nice
For now. For now. But there's so much complexity there. It could just. It could inform not just the ethics of this research area or this application, this technology, but how we interact with the world.

Brett Kagan
And I think that's exciting. But wait a minute, Brett. 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 we're born with it, supposedly.

Chuck Nice
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. And what do you do at that point? Look, it's a brilliant question. It's one we ask. We don't have the answer to it.

Brett Kagan
What we'd like to think, though, that. Was a great answer, by the way. We don't have the answer. And I think 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, and 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 not just blindly going into this and saying, well, maybe we create Frankenstein, maybe we don't.

That's 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 ultimately something that benefits people. 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? Absolutely.

So there's this idea called heterogeneous compute. We already have heterogeneous compute, by the way. I mean, CPU's and GPU's, they process data differently, and they work together really well. What if we could bring about maybe one day, you have your quantum processing unit that does cryptography 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. 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?

Neil deGrasse Tyson
That's interesting. Again, 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. 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.

Brett Kagan
And so you need to figure out, what do we want to do? We want to know how to get from points a, b and c in a way that you can do it with your time, or do you want to figure out the exact optimum pathway that might take you hundreds of thousands 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 people, not always done really well. We haven't always figured out the best approach, the most efficient approach to get things done.

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

Neil deGrasse Tyson
Yeah, I agree. Okay. I'm just looking at. I'm looking at the differences, because those are the differences between how we think and computers think. So I'm looking at those differences.

Chuck Nice
You know, is there any possibility that could serve as a stumbling block in any way? That's all? No. And it could be like, as a scientist, I never like to say a thing is not possible. The possibilities out there are almost endless.

Brett Kagan
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. Yeah, that's super cool, man. Yeah, we go through various versions of that. When 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. Gotcha.

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

Chuck Nice
Right, right. Okay, that makes sense. Okay, so, Brett, as this thing begins to develop and progress, where are you looking at it and thinking, it's really going to do well in this field? Is this field healthcare? Is this field data processing, autonomous, whatever?

Gary O'Reilly
I can't say. Or to first events, driving, flying, or whatever that might be. Yeah. The nice thing about this approach is it's a platform technology. So, yeah, the initial use cases, like just basic science research, is a super interesting question.

Brett Kagan
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, 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?

Well, we have medical. You know, we have medical engineers. 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. That being said, do you ever envision yourself saying these words? It's a lie. It's a lie.

Gary O'Reilly
It took that long. I've been resisting. I've been resisting. I've been resisting. I've been resisting.

I know you caved. We have to say that at least twice a day. It's compulsory. You're not allowed to leave before you've got your maniacal laugh out the way.

Neil deGrasse Tyson
I've seen it twice with Frankenstein and Frankenfooter. These are 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? Yeah, we started with Pong because DeepMind started with that for some of the first RL work.

Brett Kagan
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 or 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 18 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, and we're doing all sorts of things. Some of them are really basic neuro computational questions that just haven't been able to be answered before, you know, trying to understand the music of neurons, the waveforms, and what that means in terms of a computational approach.

Neil 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? Well, I was going to say, why not both? I don't think it would become our overlords, but why not both? Why not? No, no.

Brett Kagan
Yeah. It's gotten to his head, Chuck, you see? 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. I believe that was field of dreams. If I can't understand, I'm pretty sure.

Chuck Nice
I'm sure that was what he said before. He came up with something a little catchier. He came up with something a little catchier. We build it, they will come, right? Okay, yeah, exactly.

Neil deGrasse Tyson
So if we build it, they will kick our ass. You know, there are a lot of ways that could have gone, but before. We get our asses kicked, and I'm not overly keen on that theory Neil mentioned about spinal column damage. Is there a way that this will develop to treat disease neurons and bring a healing process forward? If you've got it playing pong, it's not just sensory, it's motor skills as well.

Gary O'Reilly
There's a lot of complexity in this. Are you able to articulate that forward? Yeah, absolutely. And that's what I was saying. Like neuro clinical trials for psychiatric neurological disease, they fail nearly all of the time.

Brett Kagan
You're looking between seven or 8% down to less than 1%, depending on the area. And part of the reason is our pre screening tools, our pre pre trial testament. Preclinical testing isn't up 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, it's just electrochemical.

Neil deGrasse Tyson
That's it. But 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.

Brett Kagan
Job to do its function. But it's doing it electrochemically, right? 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 in response to drugs, you're going to get a far better understanding of how that drug is affecting the system.

And 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. Oh, wow. Okay. 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.

Gary O'Reilly
You said about criticality, that borderline between organized and chaotic. Isn't that kind of like an epileptic fit? No, no, actually, when you go into. That complete, chaotic state of. Yeah, that's what I'm saying.

Brett Kagan
They don't go there. They balance between the order of chaos. And so that balancing act is actually incredibly important for information transfer, and it's implicated. So 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 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 people and cities and all of this stuff. And you can see the same patterns arising again and again in nature. Yeah, but you can't look at a bird and know that it flocks, can you? Well, no, you need to look at the flock.

Neil deGrasse Tyson
Right. But you don't know that it even has the capacity to do so. So the flocking is itself an emergent element of bird behavior. Right, exactly. Yeah, yeah.

Brett Kagan
And so we were trying to build the system so you can actually look at the emerging properties that happen from the collection. What might be a naive question. We learn in 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 there were. The person loses that ability.

Neil deGrasse Tyson
Does this tell us that if you sample neurons from different parts of the brain, they will behave differently in your circuit or all neurons? Identical, and it's just how they've been trained ever since they were born? Yeah. So, no, there are different types of neurons that do different things. So we mainly work with cortical neurons, which means what?

Brett Kagan
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, very higher order cognition, the good stuff that makes us human. 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.

That's what gives us our humanness. And some people have a much bigger reptilian brain inside. Is there still talk of this? A reptilian brain? Look, it's more of a metaphor or model.

A way about structured. If there were one, I wouldn't want you to use those neurons for me. Yeah, I was going to say give. Me the good neurons, not the reptilian ones. Right.

Chuck Nice
Not the slee stack neurons. Yeah, exactly. But then we can grow other types of cells as well. So we have some hippocampal cells. And really, the limitations we have on this is mostly at the moment due to funding and time.

Brett Kagan
I used to be as an example because I think if we had sort of enough funding and time, we could recreate b brain complexity with the synthetic biology and bioengineering tools we have available. I think it's possible we devoted an. Entire episode of Cosmos to bees. They're fascinating. Just the waggle dance of a bee.

Neil deGrasse Tyson
How they communicate, how they pick up camp and move to another location, and how they scope it out. And their brain is this big, right? I mean, tiny, tiny, tiny. But the complexity. You gotta love any species that communicates through twerking.

Gary O'Reilly
And now I've got that song in my head. That's an earworm, Chuck. That's on you, Chuck. Now we'll never. Chuck.

Chuck Nice
Can't unthink it. Anything they have in men in black, you know, where they. Please take that out of my head. Yeah, that's about it. Well, Brad, 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. Look, I don't think there's any worry about these things becoming our overlord. He doesn't think. You hear that? He doesn't think there is.

Brett Kagan
I don't think that. Hey, look, I'm a scientist. As I said, I have to leave possibility for the unknown always out there. Yes. Helps me get up every day.

I'm sure you feel the same way. It's the unknown that drives us forward. Of course. It's the only driver. Yeah.

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 the fact that this drive to understand the unknown and to optimize it gives this both a chance to understand ourselves and the world better. And also potentially to provide 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 on.

When he approached me, he's like, hey, look, we're starting this. Do you want to come aboard? 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. 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 make an impact on the world for the better. 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.

Gary O'Reilly
Literally, a man from the future. Oh, it's tomorrow. The girl. Ready? Thank you.

Neil deGrasse Tyson
Okay, fine, fine. 14 hours ahead. Yeah. Thank you very much, Chuck, Neil and Gary, it's been a pleasure chatting to you. No, you've opened our eyes to and our minds to a number of things.

Gary O'Reilly
Thank you. You got it. All right, Gary, it was good to have you. Chuck, pleasure. Such a pleasure.

Neil deGrasse Tyson
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.

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Synthetic Biological Intelligence with Brett Kagan

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1: Introduction

2: Exploring SBI

3: Ethical Considerations

4: Future Prospects

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