Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence | Lex Fridman Podcast #225

Lex Fridman 0:00 The following is a conversation with Jeff Schoenlein, a scientist at NIST interested in optoelectronic intelligence. We have a deep technical dive into computing hardware that will make Jim Keller proud. I urge you to hop on to this roller coaster ride through neuromorphic computing and superconducting electronics, and hold on for dear life. Jeff is a great communicator of technical information, and so it was truly a pleasure to talk to him about some physics and engineering. To support this podcast, please check out our sponsors in the description. This is the Lex Fridman Podcast, and here is my conversation with Jeff Schoenlein.

Lex Fridman 0:44 I got a chance to read a fascinating paper you, um, authored called Optoelectronic Intelligence. So maybe we can start by, uh, talking about this paper, and start with the basic questions. What is optoelectronic intelligence?

Jeffrey Shainline 1:00 Yeah, so in that paper the, the concept I was trying to describe is sort of an architecture for building brain-inspired computing that leverages light for communication in conjunction with electronic circuits for computation. In that particular paper, a lot of the work we're doing right now in our project at NIST is focused on superconducting electronics for computation. I'll go into why that is, but, uh, that might make a little more sense in context if we first describe what that is in contrast to, which is semiconducting electronics.

Lex Fridman 1:37 Mm-hmm.

Jeffrey Shainline 1:38 So is it worth taking a couple minutes to describe semiconducting electronics?

Lex Fridman 1:42 It might even be worthwhile to step back and, uh, talk about electricity and circuits and how circuits work-

Jeffrey Shainline 1:52 Right.

Lex Fridman 1:52 ... uh, before we talk about superconductivity.

Jeffrey Shainline 1:54 Right. Okay.

Lex Fridman 1:56 How does a computer work, Jeff?

Jeffrey Shainline 1:58 Well, I, I won't go into everything-

Lex Fridman 1:59 (laughs)

Jeffrey Shainline 2:00 ... that makes a computer work, but let's talk about the basic building blocks, a transistor, so... And even more basic than that, uh, a semiconductor material, silicon, say. So, uh, in, in silicon, silicon is a semiconductor, and what that means is at low temperature there are no free charges, no free electrons that can move around. So when you talk about electricity, you're talking about predominantly electrons moving to establish electrical currents, and they move under the influence of voltages. So you apply voltages, electrons move around. Those can be measured as currents, and you can represent information in that way. So semiconductors are special in the sense that they are really malleable. So if you have a, a semiconductor material, it, you can change the number of free electrons that can move around by putting different elements, different atoms in lattice sites. So what is a lattice site? Well, a semiconductor is a crystal which means all the atoms that comprise the material are at exact locations that are perfectly periodic in space. So if you start at any one atom and you go along the what are called the lattice vectors, you get to another atom and another atom and another atom, and for high-quality devices it's important that that is a, a perfect crystal with very few defects. But you can intent- intentionally replace a silicon atom with, say, a phosphorus atom, and then you can, you can change the number of free electrons that are in a region of space that has that excess of what are called dopants. So picture a device that has a left terminal and a right terminal, and if you apply a voltage between those two, you can cause electrical current to flow between them. Now we, uh, add a third terminal up on top there, and depending on the voltage between the left and right terminal and that third voltage, you can, you can change that current. So what's commonly done in digital electronic circuits is to leave a fixed voltage from left to right and then change that voltage that's applied at what's called the gate, the gate of the transistor. So, um, what you do is you, you make it to where there's an excess of electrons on the left, excess of electrons on the right, and very few electrons in the middle, and you do this by changing the concentration of different dopants in the lattice spatially. And then when you apply a voltage to that gate, you can either cause current to flow or turn it off, and so that's sort of your zero and one. You, if you apply voltage, current can flow. That current is representing a digital one, and, uh, from that, from that basic element, you can build up all the complexity of digital electronic circuits that have really had a profound influence on our society.

Lex Fridman 4:50 Now you're talking about electrons. Can you give a sense of what scale we're talking about when we're talking about in silicon, uh, being able to mass manufacture these kinds of, uh, gates?

Jeffrey Shainline 5:01 Yeah, so scale in a number of different senses. Well, at the scale of the silicon lattice, the, the distance between two atoms there is half a nanometer, so, um, people often like to compare these things to the, the width of a human hair. I think it's some six orders of magnitude smaller than the width of a human hair, uh, som- something on that order. So remarkably small. We're talking about individual atoms here, and electrons are of that length scale when they're in that environment. But there's another sense that scale matters in digital electronics. This is perhaps the more important sense, although they're related. Scale refers to a number of things. It refers to the size of that transistor. So, for example, I said you have a left contact, a right contact, and some space between 'em where the, the gate electrode sits. That, that's called the, the channel width, uh, or the channel length, and, um, what has enabled what we think of as Moore's law or the continued...... increased performance in silicon microelectronic circuits is the ability to make that size, that feature size, ever smaller, ever smaller at a, at a, uh, r- really remarkable pace. I mean, that h- that feature size has decreased, uh, consistently every couple of years for the l- since the 1960s. And that was, that was what Moore predicted in the 1960s. He thought it would continue for at least two more decades, and it's been much longer than that. And so, um, that is why we've been able to fit evermore devices, evermore transistors, evermore computational power on essentially the same size of chip. So, a user sits back and does essentially nothing. You're running the same computer program, but those devices are getting smaller, so they get faster, they get more energy-efficient. And all of our computing performance just continues to improve. And we don't have to think too hard about what we're, what we're doing as, say, uh, a software designer or someone like that. I- I absolutely don't mean to say that there's no innovation in software, the, or the user side of things. Of course there is. But from, from the hardware perspective, we just have been given this gift of continued performance improvement through this scaling that is ever-smaller feature sizes with very similar, um, say, power consumption. That power consumption is, has not continued to scale in the most recent decades. But, um, nevertheless, we had a really good run there for a while, and now we're down to gates that are seven nanometers, which is state-of-the-art right now, uh, maybe GlobalFoundries is trying to push it even lower than that. I- I can't keep up with where the predictions are that it's gonna end. But seven-nanometer, uh, seven-nanometer transistor has just, just a few tens of atoms along the length of the conduction pathway. So, a naive (laughs) semiconductor device physicist would think you can't go much further than that without some kind of revolution in the way we think about the physics of our devices.

Lex Fridman 8:04 Is there something to be said about the mass manufacture of these devices?

Jeffrey Shainline 8:08 Right, right. So, that's another thing. So, how have we been able to make those transistors smaller and smaller? Well, companies like Intel, GlobalFoundries, they invest a lot of money in the lithography. So, how- how are these, uh, chips actually made? Well, one of the most important steps is this, what's called ion implantation. So, you have, you start with sort of a pristine silicon crystal, and then using photolithography, which is a technique where you can pattern different shapes using light, you can define which regions of space you're going to implant with different, uh, different species of ions that are going to change the local electrical properties right there. So, by using ever-shorther wavelengths of light and different kinds of optical techniques and different kinds of lithographic techniques, things that go far beyond, uh, my knowledge base, you can just simply shrink that feature size down. And you say you're at seven nanometers. Well, the wavelength of light that's being used is over 100 nanometers. That's already deep in the UV, so, um, how, how are those minute features patterned? Well, there's- there's an extraordinary amount of innovation that has gone into that. But nevertheless, it's stayed very consistent in this ever-shrinking feature size, and now the question is, can you make it smaller? And even if you do, do you still continue to get performance improvements? But- but that's another kind of scaling, where these- these companies have been able to... S- so, okay, you- you picture a chip that has a processor on it. Well, that chip is not made as a chip. It's made as a, on a wafer.

Lex Fridman 9:40 Mm-hmm.

Jeffrey Shainline 9:41 And, um, using photolithography, you basically print the same pattern ac- on different dies all across the wafer, multiple layers, tens, probably, probably h- uh, a hundred-some layers in a mature foundry process. And y- and you do this on ever-bigger wafers, too. That's another aspect of scaling that's occurred in the last several decades. So, now you have this 300-millimeter wafer. It's like as big as a pizza, and it has maybe 1,000 processors on it, and then you dice that up using a saw. And now you can sell these things f- so cheap, because the- the manufacturing process was so streamlined. I think a technology as revolutionary as silicon microelectronics has to have that kind of, uh, manufacturing scalability, which I will just emphasize, I believe is enabled by physics. It's not, I mean, the, of course, there's human ingenuity that goes into it. But at least from my, my side, where- where I sit, it sure looks like the physics of our universe allows us to, to produce that, and we've- we've discovered how more so than we've invented it, although, of course, we have invented it. Humans have invented it. But it was, it's almost as if it was there waiting for us to, to discover it.

Lex Fridman 10:53 You mean the entirety of it, or are you specifically talking about the techniques of, uh, photolithography and, like, the optics involved? You mean, the- the entirety of the scaling down to the seven nanometers, that you're able to have electrons not interfere with each other in such a way that y- you could still have gates. Like, that's enabled... To achieve that scale, spatial and temporal, seems to be very special, and is enabled by the physics of our world.

Jeffrey Shainline 11:21 All of the things you just said. So, starting with the- the silicon material itself, silicon is a- a unique semiconductor. It has essentially ideal properties for making a specific kind of transistor that's extraordinarily useful. So, I mentioned that silicon has, uh, th- well, when you make a transistor, you have this gate contact that sits o- on top of the conduction channel, and depending on the voltage you apply there, you pull more carriers into the conduction channel or push them away so it becomes more or less conductive. In order to have that work without just sucking those carriers right into that contact, you need a- a very thin insulator. And- and part of scaling has been to gradually decrease the thickness of that, of that gate insulator so they can use a- a roughly similar voltage and still have the same current-voltage characteristics. So, the material that's used to do that, or I should say was initially used to do that, was, uh, silicon dioxide, which just naturally grows on the silicon surface. So, you expose silicon to the atmosphere that we breathe, and, uh, well, if you're manufacturing, you're gonna...... purify these gases. But nevertheless, that, that, what's called a native oxide will grow there. There are essentially no other materials on the entire periodic table that have as good of a gate insulator as, as that silicon dioxide. And that, that has to do with nothing but the physics of the interaction between silicon and oxygen. And if it wasn't that way, transistors could not, they, they could not perform in nearly the d- the degree of capability that they have. And that, that has to do with the way that the, the oxide grows, the reduced density of defects there. Its, its insulation, meaning essentially its energy gaps, you can apply a very large voltage there without having current leak through it. So, that's physics right there. Um, there, there are other things too. Silicon is a semiconductor in, in an elemental sense. You, you only need silicon atoms. A lot of other semiconductors, you need two different kinds of atoms, like a compound from group three and a compound from group five. That opens you up to lots of defects that can occur, where one atom's not sitting quite at the lattice site it is, and it's switched with another one. That degrades performance. Um, but then also on the side that you mentioned with the, the manufacturing, we have access to light sources that can produce these very short wavelengths of light. Um, how does photolithography occur? Well, you actually put this polymer on top of your wafer, and you expose it to light, and then you use a aqueous chemical processing to dissolve away the regions that were exposed to light and leave the regions that were not. And we are blessed with these polymers that have the right property where they can, um, cause scission events, where the polymer splits where a photon hits. I mean, you know, maybe, maybe that's not too surprising, but I don't know. It all, it all comes together to have this really complex, uh, manufacturable ecosystem where very sophisticated technologies can be devised and, uh, it- it works quite well.

Lex Fridman 14:33 And, and amazingly, like you said, with a wavelength at like 100 nanometers or something like that, you're still able to achieve, on this polymer, precision of whatever, whatever we said, seven nanometers.

Jeffrey Shainline 14:43 Yeah.

Lex Fridman 14:43 I think I've heard like four nanometers being talked about, something like that.

Jeffrey Shainline 14:47 Yes.

Lex Fridman 14:48 All right. If we could just pause on this, and we'll return to s- superconductivity. But in this whole journey, from a history perspective, what, what do you think is the most beautiful, at the intersection of engineering and physics, to you, in this whole process that we talked about with silicon and photolithography, things that people were able to achieve in order to, uh, push, uh, Moore's Law forward? Is it the early days, the, the invention of the transistor itself? Is it, uh, some particular cool little thing that, um, maybe not many people know about? Like, what do you think is most beautiful in this e- in this whole process, journey?

Jeffrey Shainline 15:26 The most beautiful is a little difficult to answer.

Lex Fridman 15:29 (laughs)

Jeffrey Shainline 15:29 Let me, let me try and sidestep it a little bit and just say, what strikes me about looking at the, the history of silicon microelectronics is that, uh, so when, when quantum mechanics was developed, people quickly began applying it to semiconductors. And it was broadly understood that these are fascinating systems, and people cared about 'em for their basic physics, but also their utility as devices. And then the transistor was invented in the late '40s, um, in a (laughs) relatively crude experimental setup, where you just crammed a metal electrode into the semiconductor. And, and that was, that was ingenious. These people were able to, um, make it work, you know? Uh, but so what, what I want to get to that, that really strikes me is that in those early days, there, there were a number of different semiconductors that were being considered. They had different properties, different strengths, different weaknesses. Most people thought germanium was the, the way to go. It, it, it had some, some nice properties, uh, related to things a- about how the electrons move inside the lattice. But other people thought that compound semiconductors, with group three and group five, also had really, really extraordinary, um, properties that might be conducive to, to making the best devices. So there were different groups exploring each of these, and that's great. That's how science works. You have to cast a broad net. But then w- I, what I, what I find striking is why, why is it that silicon won? Because it's not that, it's not that germanium is a useless material and it's not present in technology or compound semiconductors. They're both doing k- doing exciting and important things, slightly more niche applications, whereas silicon is the semiconductor material for microelectronics, which is the platform for digital computing, which has transformed our world.

Lex Fridman 17:22 Mm-hmm.

Jeffrey Shainline 17:22 Why did silicon win? It's because of a remarkable assemblage of qualities that no one of them was the clear winner, but it, it made these sort of compromises between a number of different influences. It had that really excellent, um, gate oxide that allowed it to, uh, that allowed us to make MOSFETs, these high performance transistors, so quickly and cheaply and easily without having to do a lot of materials development. The, the band gap of silicon, um, is actually ... So th- in a semiconductor, there's, there's an important parameter which is called the band gap, which tells you, uh, if you, there are, there are sort of electrons that fill up to one level in, in the energy diagram, and then there's a gap where electrons aren't allowed to have an energy in a certain range, and then there's another energy level above that. And that, that difference between the lower sort of filled level and the unoccupied level, that tells you how much voltage you have to apply in order to induce a current to flow. So with germanium, that's about 0.75 electron volts. Uh, that means you have to apply 0.75 volts to, to get a current moving.And it turns out that if you compare that to the- the- the thermal excitations that are induced just by the temperature of our environment, that gap's not quite big enough. You start to use it to perform computations, it gets a little hot and you get all these accidental carriers that are excited into the- the conduction band, and it- it causes errors in your computation. Silicon's bandgap is just a little higher, 1.1 electron volts, but you have an exponential dependence on the- the nu- the number of carriers that are present that can induce those errors, uh, it decays exponentially with that voltage. So just that- that slight extra energy in that bandgap really puts it in an ideal position to be operated in the- in the conditions of our- of our ambient environment.

Lex Fridman 19:20 It's kind of fascinating that... so you, like, you mentioned errors, um, decrease exponentially, uh, with the voltage. So, it's funny 'cause th- this error thing comes up, you- you know, when you start talking about, uh, quantum computing, and it's kind of amazing that everything we've been talking about, the errors, as we scale down, seems to be extremely low.

Jeffrey Shainline 19:41 Yes.

Lex Fridman 19:42 And, like, all of th- our computation is based on the assumption that it's extremely low.

Jeffrey Shainline 19:47 Yes.

Lex Fridman 19:48 So it's not-

Jeffrey Shainline 19:48 Well, it's digital computation.

Lex Fridman 19:49 Digital.

Jeffrey Shainline 19:50 Yes, yes.

Lex Fridman 19:50 Sorry, digital computation. So- as opposed to our biological computation in our brain, is like the assumption is stuff is gonna fail all over the place, and we somehow have to still be robust to that.

Jeffrey Shainline 20:01 That's

Jeffrey Shainline 20:02 exactly right.

Lex Fridman 20:03 So this also- this is gonna be the most controversial part of our conversation where you're gonna make some enemies. So let me ask, 'cause we've been talking about physics and engineering, wh- which group of people is smarter and more important for this one? (laughs) Let me ask i- the question in a better way. Some of the big innovations, some of the beautiful things that we've been talking about, how much of it is physics? How much of it is engineering? My dad is a physicist, and he talks down to all the amazing engineering that we're doing in, um, the artificial intelligence and the computer science and the robotics and all that space. So, we argue about this all the time. So what do you think? Who gets more credit?

Jeffrey Shainline 20:43 I- I-

Lex Fridman 20:43 Who gets more credit?

Jeffrey Shainline 20:44 I'm genuinely not trying to just be politically correct here.

Lex Fridman 20:46 (laughs)

Jeffrey Shainline 20:46 I don't see how you would have any of the what we consider sort of the great accomplishments of society without both. It-

Lex Fridman 20:54 Okay.

Jeffrey Shainline 20:54 You absolutely need both of those things. Physics tends to play a key role earlier in the development, and then engineering optimization, these things take over, and, uh, I mean, the invention of the transistor or actually even before that, the- the understanding of semiconductor physics that allowed the invention of the transistor, that's all physics. So if you didn't have that physics, you're- you don't even get to get on the- on the- on the field. But once you have understood and demonstrated that this is in principle possible, Moore's Law is engineering. That-

Lex Fridman 21:28 Right.

Jeffrey Shainline 21:28 Wh- why we have, uh, computers more powerful than- than old supercomputers in ou- in each of our phones is- that's all engineering. And I- I think I would be quite foolish to say that, (laughs) that's a f- I mean, that- that's not valuable, that-

Lex Fridman 21:45 Yeah.

Jeffrey Shainline 21:45 ... that's not a great contribution.

Lex Fridman 21:46 Uh, it's a beautiful dance. Would you put, like, silicon, uh, the understanding of the material properties in the space of engineering? Like, how does that whole process work? To understand that it has all these nice properties or even the development of, uh, uh, photolithography, is- is that basically- would you put that in the category of engineering?

Jeffrey Shainline 22:06 No. I would say that it is basic physics. It is applied physics. It's material science. It's, um, x-ray crystallography. It's polymer chemistry. It's- it's everything.

Lex Fridman 22:18 Okay.

Jeffrey Shainline 22:18 I mean-

Lex Fridman 22:18 So chemistry even is thrown in there?

Jeffrey Shainline 22:20 Absolutely. Yes.

Lex Fridman 22:21 Okay. The whole thing.

Jeffrey Shainline 22:21 Yes. Absolutely.

Lex Fridman 22:22 Just no biology.

Jeffrey Shainline 22:23 (laughs)

Lex Fridman 22:25 (laughs)

Jeffrey Shainline 22:25 Oh, except we can get to biology, yeah-

Lex Fridman 22:26 Well, the biology is in the humans that are engineering the systems.

Jeffrey Shainline 22:29 Absolutely. Right.

Lex Fridman 22:29 It's all integrated deeply.

Lex Fridman 22:31 Okay, so let's return. You mentioned this, uh, word superconductivity. So what does that have to do with what we're talking about?

Jeffrey Shainline 22:38 Right. Okay. So in a semiconductor, as I, uh, tried to describe a second ago, you can sort of, uh, in- induce currents by applying voltages, and those have sort of typical properties that you would expect from some kind of a conductor. Those electrons, they don't just flow perfectly without dissipation. If an electron collides with an imperfection in the lattice or another electron, it's gonna slow down. It's gonna lose its momentum. So you have to keep applying that voltage in order to- to keep the current flowing. In a superconductor, something different happens. If you get a current to start flowing, it will continue to flow indefinitely. There's- there's no dissipation. So that's crazy. How does that happen? Well, it happens at low temperature, and this is crucial. It has to- it has to be a- a- a quite low temperature, and what- what I'm talking about there, I... for es- essentially all of our conversation, I'm gonna be talking about conventional superconductors, um, sometimes called low-TC superconductors, low critical temperature superconductors. And so those materials have to be in- at a temperature around- say, around four Kelvin. I mean, their critical temperature might be 10 Kelvin, something like that, but you want to operate them at around four Kelvin, four degrees above absolute zero. And what happens at that temperature, at- at very low temperatures in certain materials, is that the- the noise of atoms moving around, the lattice vibrating, electrons colliding with each other, that becomes sufficiently low that the electrons can settle into this very special state. It's sometimes referred to as a macroscopic quantum state because if I had a- a piece of superconducting material here, let's say niobium is a very typical, um, superconductor, if I- if I had a block of niobium here, and we cooled it below its critical temperature-All of the electrons in that, in that superconducting state would be in one coherent quantum state. They would-

Lex Fridman 24:43 Oh.

Jeffrey Shainline 24:43 ...the, the wave function of that state is described in terms of all of the particles simultaneously. But it extends across macroscopic dimensions. The size of a, whatever material, the size of whatever block of that material I have sitting here. And the way that, the way this occurs is that, you know, we, we ... let's try to be a little bit light on the technical details, but essentially, the electrons coordinate with each other. They, they are able to, in this macroscopic quantum state, they're able to sort of ... one can quickly take the place of the other. You can't tell electrons apart. They're, they're what's known as identical particles, so if this electron runs into a, a defect that would otherwise cause it to scatter, it can just sort of, um, almost miraculously avoid that defect, because it's not really in that location. It's part of a macroscopic quantum state, and the entire quantum state was not scattered by that defect. So, you can get a, a current that flows without dissipation, and that's called a supercurrent.

Lex Fridman 25:42 Mm-hmm.

Jeffrey Shainline 25:43 That's, uh, sort of just very much scratching the surface of, of superconductivity. It ... there, there's very deep and rich physics there, which is probably not the main subject we need to go into right now. But it turns out that when you have this material, you can, you can do usual things, like make wires out of it, so you can get current to flow in a, in a straight line on a chip, but you can also make other devices that perform different kinds of operations. Some of them are kind of logic operations, like you'd, like you'd get in a transistor. The most common or most, um, I would say diverse in its utility, uh, component is a Josephson junction. It's not analogous to a transistor in the sense that if you apply a voltage here, it changes how much current flows from left to right. But it is analogous in sort of a, a sense of ... it's the, it's the go-to component that a, that a circuit engineer is going to use to start to build up more complexity.

Lex Fridman 26:44 So, these are, uh, these junctions serve as gates?

Jeffrey Shainline 26:48 They can, they can serve as gates. They can ... so, I'm not sure how, how s- um, concerned to be with semantics, but let me just briefly say what a Josephson junction is-

Lex Fridman 26:59 Yeah, that would be great.

Jeffrey Shainline 26:59 ...and we can talk about different ways that they can be used. Basically, if you have a, a superconducting wire, and then a, a small gap of, uh, a different material that's not superconducting, an insulator or normal metal, and then another superconducting wire on the other side, that's a Josephson junction. So, it's sometimes referred to as a superconducting weak link. So, you have this superconducting state on one side, and on the other side, and that ... the superconducting wave function actually tunnels across that gap. And when you, when you create such a physical entity, it has very unusual, um, current voltage characteristics.

Lex Fridman 27:41 With a ... in, in that gap. Like, like weird stuff happens.

Jeffrey Shainline 27:44 Through the entire circuit.

Lex Fridman 27:45 Yeah.

Jeffrey Shainline 27:45 So, you can imagine, suppose you had a loop set up that had one of those weak links in, in the loop. Current would flow in that loop, independent ... even if you hadn't applied a voltage to it, and that's called the Josephson effect. So, the fact that there's this phase difference in the quantum wave function from one side of the tunneling barrier to the other induces current to flow.

Lex Fridman 28:05 So, how does you change state in a ... ?

Jeffrey Shainline 28:07 Right, exactly. So, how do you change state? Now, picture, if I have a, uh, current bias coming down this line on my circuit, and then there's a Josephson junction right in, in the middle of it, and now I'd, I make another wire that goes around the Josephson junction, so I have a loop here, a superconducting loop. I can add current to that loop by exceeding the critical current of that Josephson junction. So, like any superconducting material, it can carry this supercurrent that I've described, this current that can propagate without dissipation, up to a certain level. And if you try and pass more current than that through the material, it's going to become, uh, a resistive material, a normal, normal material. So, in the, in the Josephson junction, the same thing happens. I can bias it above its critical current, and then what it's gonna do, it's going to add, uh, a quantized amount of current into that loop. And what I mean by quantized is, it's going to come in discrete packets with a well-defined value of current.

Lex Fridman 29:10 Mm-hmm.

Jeffrey Shainline 29:11 So, in the vernacular of, of some people working in this community, you would say you pop a fluxon into the loop. So, a fluxon-

Lex Fridman 29:21 (laughs) You pop a fluxon into the loop?

Jeffrey Shainline 29:23 Yeah. So, a fluxon-

Lex Fridman 29:24 Sounds like skateboarder talk. I love it.

Jeffrey Shainline 29:26 (laughs)

Lex Fridman 29:26 Okay. Co- uh, sorry. Go ahead. (laughs)

Jeffrey Shainline 29:29 A fluxon is one of these quantized, uh, sort of, um, amounts of current that you can add to a loop. And, and this is a cartoon picture, but I think it's-

Lex Fridman 29:37 Mm-hmm.

Jeffrey Shainline 29:37 ... sufficient for our purposes.

Lex Fridman 29:38 So, which, uh, maybe is useful to say, what is the speed at which these discrete packets of current travel? Because we'll be talking about light a little bit. It seems like the speed is important.

Jeffrey Shainline 29:51 The speed is important. That's an excellent question. Sometimes I wonder where you ... how you became so astute.

Lex Fridman 29:57 (laughs)

Jeffrey Shainline 29:57 But, um, so-

Lex Fridman 29:59 (laughs)

Jeffrey Shainline 30:00 ... this-

Lex Fridman 30:00 Uh, Matrix 4 is coming out, so maybe that's related. I'm not sure.

Jeffrey Shainline 30:05 (laughs)

Lex Fridman 30:05 I'm dressed for the job. I've ... I was trying to get, to become an extra on Matrix 4. It didn't work out. Anyway, uh, so (laughs) what's the speed of these packets?

Jeffrey Shainline 30:13 You'll have to find another gig.

Lex Fridman 30:15 I know. I'm sorry.

Jeffrey Shainline 30:16 Um, so the speed of the pack is actually ... these fluxons, these, these, uh, sort of pulses of, of, um, current that are generated by Josephson junctions, they can actually propagate very close to the speed of light, uh, maybe something like a third of the speed of light. That's quite fast. So, one of the reasons why Josephson junctions are appealing is because their signals can propagate quite fast, and they can, they can also switch very fast. What I mean by switch is perform that operation that I described where you add current to the loop. That can happen within, um, a few tens of picoseconds. So, you can get...You can get devices that operate in the hundreds of gigahertz range, and by comparison, most processors in our, in our conventional computers operate closer to the, the one gigahertz range. Maybe three gigahertz seems to be kind of where, where those speeds have, have leveled out. So-

Lex Fridman 31:12 The gamers listening to this are getting really excited that overclocked their system to like, what is it? Like four gigahertz or something. Hundred is, sounds incredible. All right, can I just as a tiny tangent, is the physics of this understood well, how to do this stably?

Jeffrey Shainline 31:28 Oh, yes. The physics is understood well. The physics of Josephson junctions is understood well. The technology's understood quite well too. The reasons why it hasn't displaced silicon microelectronics in conventional digital computing, I think are more related to what I was alluding to before about the, the myriad practical, almost mundane aspects of silicon that make it so useful. You can make a transistor ever smaller and smaller, and it will still perform its digital function quite well. The same is not true of a Josephson junction. You really... They don't, they just... It's not the same thing that there's this feature that you can keep making smaller and smaller, and it'll keep performing the same operations. This loop I described, any Josephson circuit... Well, I, I'm gonna be careful. I shouldn't say any Josephson circuit. But many Josephson circuits, the way they pr- process information, or the way they perform whatever function it is they're trying to do, maybe it's sensing a weak magnetic field, it, it depends on an interplay between the junction and that loop. And you can't make that loop much smaller, and it's not for practical reasons that have to do with lithography. It's for fundamental physical reasons about the way the magnetic field interacts with that superconducting material. There's, there are physical limits that no matter how good our technology got, those circuits would, I think, would never be able to be scaled down to the, the densities that silicon microelectronics can.

Lex Fridman 32:54 I don't know if we mentioned, is there something interesting about the various superconducting materials involved, or is it all-

Jeffrey Shainline 33:01 There's a lot of stuff that's interesting.

Lex Fridman 33:02 And it's not silicon?

Jeffrey Shainline 33:04 It's not silicon, no.

Lex Fridman 33:05 So, so like it's some materials that also require to be super cold for Kelvin-

Jeffrey Shainline 33:11 Yes, yes, yes.

Lex Fridman 33:12 ... and so on?

Jeffrey Shainline 33:12 So, so let's dissect a couple of those different things. The super-cold part, let me just mention for your gamers out there that are trying to clock it at four gigahertz and would love to go to four kelvin.

Lex Fridman 33:21 Yeah, what kind of cooling system can achieve four Kelvin?

Jeffrey Shainline 33:23 Exact... Four Kelvin, you need liquid helium. And so liquid helium is expensive. It's inconvenient. You need a cryostat that, that sits there, and the energy consumption of that cryostat is impracticable for m... It's not going in your cellphone. You're not... So-

Lex Fridman 33:40 Right.

Jeffrey Shainline 33:40 ... you can picture holding your cellphone like this, and then something the size of, you know, a, a keg of beer or something on your back to cool it.

Lex Fridman 33:47 Hm.

Jeffrey Shainline 33:47 Like that makes no sense.

Lex Fridman 33:49 Yeah.

Jeffrey Shainline 33:49 So, if you, if you're trying to make this in consumer devices, uh, electronics that are ubiquitous across society, superconductors are not in the race for that.

Lex Fridman 33:59 For now. But you're saying, so we're j- just to frame the conversation, maybe the thing we're focused on is computing systems that serve as like, a- as servers. (laughs) Like large systems.

Jeffrey Shainline 34:10 Yes, large systems. So, so then you can contrast what's going on in your cellphone with what's going on at one of the supercomputers. Um, colleague Katy Shuman invited us out to Oak Ridge a few years ago, so we got to see Titan, and that was when they were building Summit. So these are some high-performance supercomputers, uh, out in Tennessee. And those are filling entire rooms the size of warehouses, you know. So once you're at that level, okay, there you're already putting a lot of power into cooling. You need... Cooling is part of your engineering task that you have to deal with. So there, it's not entirely obvious that cooling to four Kelvin is out of the question. It, it's... It has not happened yet, and I can speak to why that is in the digital domain if you're interested.

Lex Fridman 34:55 Yeah.

Jeffrey Shainline 34:55 I think it's not going to happen. I don't think, I don't think superconductors are gonna replace semiconductors for digital computation. Um, there are, there are a lot of reasons for that. But I think ultimately what it comes down to is all things considered, cooling, errors, scaling down to feature sizes, all that stuff, semiconductors work better at the system level.

Lex Fridman 35:19 Is there some aspect of, uh, just cur- curious about the historical momentum of this. Is there some power to the momentum of an industry that's mass manufacturing using a certain material? Is, this is like a Titanic shifting. Like, what's your sense when a good idea comes along, how good does that idea need to be for the Titanic to start shifting?

Jeffrey Shainline 35:42 That's a, that's an excellent question. That's an excellent way to, to frame it. And, you know, I don't know the answer to that. But what I think is... Okay, so the, the history of the superconducting logic goes back to the '70s. IBM made a big push to do superconducting digital computing in the '70s, and they made some choices about their devices and their architectures and things that, in hindsight, were kind of doomed to fail. And I don't mean any disrespect to the people that did it. It was hard to see at the time. But then another generation of superconducting logic was introduced, um, I wanna say the '90s. Uh, someone named Likarev and, uh, Semenov, they propose an entire family of circuits based on Josephson junctions that are doing digital computing based on logic gates and/or not, these kinds of things. Um, and they showed how it could go hundreds of times faster than silicon microelectronics, and it was, it's extremely exciting. I, I wasn't working in the field at that time, but later, when I went back and read the literature, I was just like, "Wow, this is, this is so awesome." Uh, and so it... You might think, "Well...... the reason why it didn't displace silicon is because silicon already had so much momentum at that time. But that was the '90s. Silicon kept that momentum because it had this simple way to keep getting better. You just make features smaller and smaller. So, you know, it would have to be... I don't think it would have to be that much better than silicon to displace it, but the problem is it's just not better than silicon.

Lex Fridman 37:17 Mm-hmm.

Jeffrey Shainline 37:17 It might be better than silicon in one metric, speed of a switching operation or power consumption of a switching operation. But building a digital computer is a lot more than just that elemental operation. It's everything that goes into it, including the manufacturing, including the packaging, including the, um, the, you know, various materials aspects of things. So, the reason why... And even in, even in some of those early papers, I can't remember which one it was, Likarov said something along the lines of, "You can see how we could build an entire family of digital electronic circuits based on these components. They could go 100 or more times faster than semiconductor, uh, logic gates, but I don't think that's the right way to use superconducting electronic circuits." He didn't say what the right way was, but he, he basically said digital logic trying to steal the show from silicon is probably not what these circuits are, are most suited to accomplish.

Lex Fridman 38:16 So, if we can just linger on,

Lex Fridman 38:18 you used the word computation. When you talk about computation, how do you think about it? Do you think purely, uh, on just, um, the g- the switching, or do you think something a little bit larger scale, a circuit taken together performing the basic arithmetic, uh, operations that are then required to do the kind of computation that makes up a computer? 'Cause when we talk about the speed of computation, does it boil down to the basic switching or is there some bigger picture that you're thinking about?

Jeffrey Shainline 38:49 Well, all right, so maybe we should disambiguate. There are a variety of different kinds of computation. I don't pretend to be an expert in the theory of computation or anything like that. I guess it's important to, to differentiate, though, between digital logic, which represents information as a series of bits, binary digits, which, you know, uh, you can think of them as zeros and ones or whatever. Usually, they correspond to a physical system that has two very well separated states. And then other kinds of computation, like we'll get into more the way your brain works, which it is, I, I think, indisputably processing information. But where the computation begins and ends is not anywhere near as well defined. It, it doesn't depend on these two levels, here's a zero, here's a one. It's, there's a lot of gray area that's usually referred to as analog computing. Um, also in, in conventional digital computers or, um, digital computers in, in general, you have a concept of what's called arithmetic depth, which is jargon that basically means how many sequential operations are performed to turn an input into an output. And those kinds of computations in, in digital systems are, are highly serial, meaning that data streams, they don't branch off too far to the side. You, you do, you have to pull some information over there and access memory from here and stuff like that. But by and large, the, the computation proceeds in a, in a serial manner. It's not that way in the brain. In the brain, you're always drawing information from different places. It's much more network-based computing. N- Neurons don't wait for their turn. They fire when they're ready to fire, and so it's, it's asynchronous. So one of the other things about a, a digital system is you're performing these operations on a clock, and that's a, that's a crucial aspect of it. Get rid of a clock in a digital system, nothing makes sense anymore. The brain has no clock. It, it builds its own timescales based on its internal activity. So...

Lex Fridman 40:56 So, you can think of the brain as kind of, um, l- like this, like, network computation where it's actually really trivial, simple computers, uh, just a huge number of them, and they're networked, uh, together.

Jeffrey Shainline 41:09 I would say it is complex, sophisticated little processors, and there's a huge number of them.

Lex Fridman 41:14 Sure, sure. Sorry.

Jeffrey Shainline 41:14 Neurons are not, are not-

Lex Fridman 41:15 No, no offense. I don't mean to offend neurons.

Jeffrey Shainline 41:17 Sure, no, no.

Lex Fridman 41:17 They're very complicated and beautiful, and, yeah. But, w- we often oversimplify them. Yes, they're actually ext- Like, there's computation happening within a neuron, right?

Jeffrey Shainline 41:25 Right. So, I, I would, uh, say, to think of a, a transistor as the building block of a digital computer is accurate. You use a few transistors to make your logic gates. You build up more. You build up processors from logic gates and things like that. So you can think of a transistor as a fundamental building block, or you can think of, as we get into more, uh, highly parallelized architectures, you can think of a processor as a fundamental building block. To make the analogy to the neuro side of things, a neuron is not a transistor. A neuron is a, is a processor.

Lex Fridman 41:57 Yeah.

Jeffrey Shainline 41:57 It has synapses. Even synapses are not transistors, but they are more, um, l- they're lower on the information processing hierarchy in a sense. They do a, a bulk of the computation, but neurons are entire, um, processors in and of themselves that can take in many different kinds of inputs on many different spatial and temporal scales and produce l- many different kinds of outputs so that they can perform different computations in different contexts.

Lex Fridman 42:24 So, this is where enters this distinction between computation and communication. So, you can think of neuron as performing computation, and the inter- the networking, the interconnectivity of neurons is communication between neurons. And you see this with very large server systems. I've been... I mentioned offline, we were talking to Jim Keller, who dreams to build giant computers that, uh, you know... The, the bottleneck there is often the communication between the different-

Jeffrey Shainline 42:53 Yes.

Lex Fridman 42:53 ... pieces of computing. So,

Lex Fridman 42:55 in this paper that we, uh, mentioned, Optoelectronic Intelligence, you say, uh, "Electrons excel at computation."... while light, eh, is excellent for communication. Maybe you can linger and say, in this context, what he mean by computation and communication, what's, what are electrons, what is light, and why do they excel at those two tasks.

Jeffrey Shainline 43:20 Yeah. Just to, to first speak to computation versus communication, I would say computation is essentially taking in some information, performing operations on that information, and producing new, hopefully more useful, information. So, for example, um, imagine you have a, a picture in front of you and there is a, a key in it, and that's what you're looking for, for whatever reason. You wanna, you wanna find the key. We all-

Lex Fridman 43:50 Mm-hmm.

Jeffrey Shainline 43:50 ... wanna find the key. So-

Lex Fridman 43:51 Yeah.

Jeffrey Shainline 43:52 ... um, the input is that, that entire picture, and the output might be the coordinates where the key is. So, you've reduced the total amount of information you have, but you found the useful information-

Lex Fridman 44:02 Mm-hmm.

Jeffrey Shainline 44:03 ... for you in that present moment. That's the useful information.

Lex Fridman 44:05 And you think about this computation a- as, like, controlled synchronous sequential?

Jeffrey Shainline 44:10 Not necessarily. It could be. That could be how w- your system is performing the computation, or it could be asynchronous. It, there are lots of ways to find the key.

Lex Fridman 44:20 Okay.

Jeffrey Shainline 44:21 It depends, it depends on the nature of the data, it depends on, um... That's a very simplified example, a picture with a key in it. What about if you're in the world and you're trying to decide the best way to, um, live your life, you know? That... (laughs)

Lex Fridman 44:36 It might be interactive, it might be, there might be some recurrence or something-

Jeffrey Shainline 44:38 Yeah, sounds-

Lex Fridman 44:39 ... weird. Asynchrony, I got it. So, but, there's an input, there's an output, and you do some stuff in the middle that-

Jeffrey Shainline 44:44 Yeah.

Lex Fridman 44:44 ... actually goes from the input to the output.

Jeffrey Shainline 44:46 You've taken in information and output different information.

Lex Fridman 44:49 Yeah.

Jeffrey Shainline 44:49 Hopefully reducing the total amount of information and extracting what's useful.

Lex Fridman 44:53 Yeah.

Jeffrey Shainline 44:53 Communication is then getting that information from the location in which it's stored, because information is physical, as Landauer de- emphasized. And so, it, it is more, in one place, and you need to get that information to another place so that something else can use it for whatever computation it's working on. Maybe it's part of the same network and you're all trying to solve the same problem, but neuron A over here just deduced something based on its inputs and it's now sending that information across the network to another location. So, that would be the active communication.

Lex Fridman 45:28 Can you linger on Landau on saying information is physical?

Jeffrey Shainline 45:31 Rolf Landauer, not to be confused with Lev Landau. Yeah, and he, he, eh, made huge contributions to our, our understanding of the reversibility of information and, and this concept that energy has to be dissipated in computing when the computation is irreversible. But if you can manage to make it reversible, then you, you don't need to expend energy. But if you, um, if you do expend energy to perform a computation, there's sort of a minimal amount that you have to do, and it's KT log 2.

Lex Fridman 46:04 And it's also how related to the second law of thermodynamics and that the universe is an information pros- and that we're living in a simulation.

Jeffrey Shainline 46:11 (laughs)

Lex Fridman 46:11 So, okay, sorry. Sorry for that tangent. So, informa- so that's the, the defining the, the, the distinction between computation and communication.

Jeffrey Shainline 46:19 Let me say one more thing-

Lex Fridman 46:20 Yes.

Jeffrey Shainline 46:20 ... just to clarify. Communication ideally does not change the information. It moves it from one place to another-

Lex Fridman 46:28 Got it.

Jeffrey Shainline 46:28 ... but it is preserved.

Lex Fridman 46:31 Got it, okay. All right. That is beautiful. So, uh, then the n- electron

Lex Fridman 46:36 versus light distinction, and why are electrons, uh, good at computation and light good at communication?

Jeffrey Shainline 46:44 Yes. This is, um... There's a lot that goes into it, I guess, but just try to speak to the simplest part of it. Electrons interact strongly with one another. They're charged particles, so if I pile a bunch of 'em over here, they're feeling a, a certain amount of force and they wanna, they wanna move somewhere else, they're, they're strongly interactive. You can also get them to sit still. You can... An electron has a mass, so you can, you can cause it to be spatially localized. So, for computation, that's useful, because now I can make these little devices that put a bunch of electrons over here and then I change the, the state of a gate like I've been describing, put a different voltage o- on this gate, and now I move the electrons over here. Now they're sitting somewhere else. I have a physical mechanism with which I can represent information. It's spatially localized and I have knobs that I can adjust to change where those electrons are or what they're doing. Light, by contrast, photons of light, uh, which are the discreet packets of energy that were identified by Einstein, they do not interact with each other, um, especially at, at low light levels. If you're in a medium and you have a, a high, a bright high light level, you, you can get them to interact with each other through the interaction with that medium that they're in.

Lex Fridman 48:05 Mm-hmm.

Jeffrey Shainline 48:05 But that's, that's a little bit more exotic. And for the purposes of this conversation, we can assume that photons don't interact with each other. So, if you have a bunch of them all propagating in the same direction, they don't interfere with each other.

Lex Fridman 48:18 Mm-hmm.

Jeffrey Shainline 48:19 If I wanna send... If I, if I have a, a communication channel and I put one more photon on it, it doesn't screw up what those other ones, it doesn't change what those other ones were doing at all.

Lex Fridman 48:28 Mm-hmm.

Jeffrey Shainline 48:29 So, that's really useful for communication because that means you can sort of allow a lot of these photons to flow, um, with- without disruption of each other, and they can, they can branch really easily and things like that. But it's not good for computation, because it's very hard for this packet of light to change what this packet of light is doing.

Lex Fridman 48:48 Hm.

Jeffrey Shainline 48:48 They, they pass right through each other, so in computation, you want to change information, and if photons don't interact with each other, it's difficult to get them to change the information represented by the others.

Lex Fridman 48:59 So, that, that's the fundamental difference. Is, is there also something about the way they travel through different materials? I- i- or, or is that just, um, a particular engineering...

Jeffrey Shainline 49:10 No, it's not. That's deep physics, I think. So, this gets back to electrons interact with each other, and photons don't. So say, say I'm trying to get a, a packet of information from me to you, and we have a wire going between us. In order for me to send electrons across that wire, I first have to raise the voltage on my end of the wire, and that means putting a bunch of charges on it, and then that, that charge packet has to propagate along the wire. And it has to get all the way over to you. There's, that wire is gonna have something that's called capacitance, which basically tells you how much charge you need to put on the wire in order to, to raise the voltage on it. And the capacitance is gonna be proportional to the length of the wire. So the longer the, the length of the wire is, the more charge I have to put on it, and the energy required to charge up that line and move those electrons to you is also proportional to the capacitance and goes as the voltage squared. So-

Lex Fridman 50:09 Mm-hmm.

Jeffrey Shainline 50:09 ... you get this huge penalty-

Lex Fridman 50:10 Yeah.

Jeffrey Shainline 50:10 ... if you, if you wanna send electrons across a wire over appreciable distances.

Lex Fridman 50:16 So distance is, is an important thing here when you're doing communication.

Jeffrey Shainline 50:20 Distance is an important thing. So is the number of connections I'm trying to make.

Lex Fridman 50:25 Right.

Jeffrey Shainline 50:25 Me to you, okay, one, that's not so bad. If I want to now send it to 10,000 other friends, then I, then all of those wires are adding tons of extra capacitance. Now, not only does it take forever to put the charge on that wire and raise the voltage on all those lines, but it takes a ton of power. And the number 10,000 is not randomly chosen. That's roughly how many connections each neuron in your brain makes. So it... A neuron in your brain needs to send 10,000 messages every time it has something to say. You can't do that if you're trying to drive electrons from here to 10,000 different places. The brain does it in a slightly different way, which we can discuss.

Lex Fridman 51:04 How can light achieve the 10,000 connections, and why is it, um, why is it better in terms of, like, the energy use, uh, required to use light for the communication of the 10,000 connections?

Jeffrey Shainline 51:15 Right, right. So now, instead of trying to send electrons from me to you, I'm trying to send photons. So I can make what's called a wave guide, which is just, uh, a simple piece of, uh, material. It could be glass, like an optical fiber, or silicon, uh, on a, on a chip. And I just have to, I just have to inject photons into that wave guide. And independent of how long it is, independent of how many different connections I'm making, it doesn't change the, the voltage or anything like that that I have to raise up on the, on the wire. So if I have one more connection, if I add additional connections, I need to add more light to the wave guide because those photons need to split and go to different-

Lex Fridman 51:54 Mm-hmm.

Jeffrey Shainline 51:55 ... paths. That makes sense. But I don't have a capacitive penalty, that sometimes these are called wiring parasitics. There are no parasitics associated with light in that same sense. So-

Lex Fridman 52:05 Well, just a... This might be a dumb question, but how do, how do I catch a photon on the other end? Uh, what's... Is it material? Is this the polymer stuff you were talking about for the, for a different application for, uh, photolithography? Like, how do you catch a photon?

Jeffrey Shainline 52:21 There's a lot of ways to catch a photon. It's not a dumb question. It's a, it's a deep and important question that basically defines a lot of the work that goes on in our group at NIST.

Lex Fridman 52:30 Mm-hmm.

Jeffrey Shainline 52:31 Uh, one of my group leaders, Sae Woo Nahm, has built his career around these superconducting single-photon detectors. So if you're going to try to sort of reach a lower limit and detect just one particle of light, superconductors come back into our conversation. And just picture a simple device where you have current flowing through a superconducting wire-

Lex Fridman 52:53 Mm-hmm.

Jeffrey Shainline 52:53 ... and, um-

Lex Fridman 52:54 A loop again, or no?

Jeffrey Shainline 52:56 Let's say, yes, you have a loop. So you have a, a superconducting wire that goes straight down like this. And on, on your loop branch, you have a little ammeter, something that measures current. There's a resistor up there too, huh? Go with me here.

Lex Fridman 53:09 (laughs)

Jeffrey Shainline 53:09 So, um, your, your current biasing this, so there's current flowing through that superconducting branch. Since there's a resistor over here, all the current goes through the superconducting branch. Now, a photon comes in, strikes that superconductor. We talked about this superconducting macroscopic quantum state.

Lex Fridman 53:25 Mm-hmm.

Jeffrey Shainline 53:25 That's gonna be destroyed by the energy of that photon. So now, that branch of the circuit is resistive too, and you've properly designed your circuit so that the resistance on that superconducting branch is much greater than the other resistance. Now all of your current's gonna go that way. Your ammeter says, "Oh, I just got a pulse of current. That must mean I detected a photon." Then where you broke that superconductivity in a matter of a few nanoseconds, it cools back off, dissipates that energy, and the current flows back through that superconducting branch. This is a, a very powerful superconducting device that allows us to understand quantum states of light.

Lex Fridman 54:02 I didn't realize a loop like that could be sensitive to a single photon. I mean, that, um, that seems strange to me because, I mean, so what happens when you just, uh, barrage it with photons?

Jeffrey Shainline 54:16 If you put a bunch of photons in there, essentially the same thing happens. You just drive it into the normal state, it becomes resistive, and it's not particularly interesting.

Lex Fridman 54:25 So you have to be careful how many photons you send. Like, you have to be very precise with your communication.

Jeffrey Shainline 54:30 Well, it depends. So I would say that that's actually... In the, in the application that we're trying to use these detectors for, that's a feature, because what we want is for, uh, if a, if a neuron sends one photon to a synaptic connection-

Lex Fridman 54:46 Mm-hmm.

Jeffrey Shainline 54:46 ... and one of these superconducting detectors is sitting there, you get this pulse of current. And that synapse says, "Event," then I'm gonna do what I do when there's a synapse event. I'm gonna perform computations, that kind of thing.

Lex Fridman 54:58 Mm-hmm.

Jeffrey Shainline 54:58 But if accidentally you send two there or three or five, it does the exact same thing.

Lex Fridman 55:03 Got it.

Jeffrey Shainline 55:04 And so that's... This is, this is how in the system that we're devising here, communication is entirely binary. And that's what I tried to emphasize a second ago. Communication should not change the information. You're not saying...Oh, I got this kind of communication event, four photons. No, we're not keeping track of that. This neuron fired, this synapse says, "That neuron fired." That's it. So that's a, that's a noise filtering property of those detectors. However, there are other applications where you'd rather know the exact number of photons. That can be very useful in quantum computing with light, and our group does a lot of work around another kind of superconducting sensor called a transition edge sensor that, uh, Adrian Alida in our group does a lot of work on that. And that can tell you, based on the amplitude of the current pulse you divert, exactly how many photons-

Lex Fridman 55:58 Hm.

Jeffrey Shainline 55:58 -were in that pulse. So-

Lex Fridman 56:00 What's that useful for? Just...

Jeffrey Shainline 56:02 One way that you can encode information in quantum states of light is in the number of photons. You, you can have what are called number states, and a number state will have a well-defined number of photons and maybe the output of your quantum computation encodes its information in the number of photons that are generated.

Lex Fridman 56:20 Mm-hmm.

Jeffrey Shainline 56:20 So if you have a detector that is sensitive to that, it's extremely useful.

Lex Fridman 56:24 Can you achieve, um, like, a clock with photons? Or is that not important? Is there a asynchronicity here?

Jeffrey Shainline 56:33 In general, it can be important. Uh, clock distribution is a big challenge in, especially large computational systems. And so yes, optical clocks, optical clock distribution i- is a, is a very powerful technology. I don't know the state of that field right now, but I imagine that if you're trying to distribute a clock across any appreciable size computational system, you, you wanna use light.

Lex Fridman 57:00 Yeah, I wonder how these giant systems work, especially, like, uh, super computers. Do they need to do clock distribution or-

Jeffrey Shainline 57:09 Yes.

Lex Fridman 57:09 -are they doing more ad hoc, parallel, like, concurrent programming? Like, there's some kind of locking mechanisms or something? That's a fascinating

Lex Fridman 57:19 question, but the, the, le- let's zoom in at this very particular question of, uh, computation, uh, on a processor and communication between processors. So what does this system look like that you're envisioning? Uh, one of the places you're envisioning it is in the paper on opto-electronic intelligence. So what are we talking about? Are we talking about something that starts to look a lot like the human brain, or does it still look a lot like a computer? What are the size of this thing? Does it go inside a smartphone, or as you said, does it go inside something that's more like a house? Like, uh, wha- what should we be imagining? What are you thinking about when you're thinking about these fundamental, uh, systems?

Jeffrey Shainline 58:05 Let me introduce the word neuromorphic. There's this concept of neuromorphic computing, where what that broadly refers to is, um, computing based on the information processing principles of the brain.

Lex Fridman 58:18 Mm-hmm.

Jeffrey Shainline 58:19 And as digital computing seems to be pushing towards some fundamental performance limits, people are considering architectural advances, drawing inspiration from the brain, more distributed parallel network kind of architectures and stuff. And so there's this continuum of- of neuromorphic from things that are p- pretty similar to digital computers, but maybe there are more cores and the way they send messages is a little bit more like the way brain n- neurons send spikes. But for the most part, it's still digital electronics. And then, you know, you have some things in between where maybe you're- you're using transistors, but now you're starting to use them instead o- of in a digital way in an analog way. And so you're trying to get those circuits to behave more like neurons.

Lex Fridman 59:10 Mm-hmm.

Jeffrey Shainline 59:10 And then that's a little bit, uh, quite, quite a bit more o- on the neuromorphic side of things. You're trying to get your circuits, although they're still based on silicon, you're trying to get them to perform operations that are highly analogous to the operations in the brain. That's where a great deal of work is in neuromorphic computing. People like Iacomo Windiveri and Gerd Kahlenberg, S- Jennifer Hassler, countless others. It's a, it's a rich and exciting field, uh, going back to Carver Mead in the late 1980s. And then all the way on the other extreme of the continuum is where you say, "I'll give up anything related to transistors or semiconductors or anything like that. I'm not, I'm not starting with the assumption that I'm gonna use any kind of conventional computing hardware. And instead what I wanna do is try and understand what makes the brain powerful at the kind of information processing it does. And I wanna think from first principles about what hardware is best going to enable us to capture those information processing principles in an artificial system." And that's where I live. That's where, that's where I'm doing my exploration these days.

Lex Fridman 1:00:21 So, uh, what are the first principles of brain-like computation communication?

Jeffrey Shainline 1:00:30 Right. Yeah, this is, this is so important and I'm glad we booked 14 hours for this because, uh-

Lex Fridman 1:00:35 I only have 13. I'm sorry.

Jeffrey Shainline 1:00:37 (laughs)

Lex Fridman 1:00:37 (laughs)

Jeffrey Shainline 1:00:38 Okay, so th- the brain is notoriously complicated.

Lex Fridman 1:00:41 Yeah.

Jeffrey Shainline 1:00:41 And I think that's a- an important part of why it, why it can do what it does, but okay. Let me, let me try to break it down. Uh, starting with the devices, neurons, w- as I, as I said before, they're, they're sophisticated devices in and of themselves and synapses are too. They, they can, um, change their state based on the activity, so they, they adapt over time. That's crucial to the way the brain works. They don't just adapt on one timescale. They can adapt on m- myriad timescales from the, the spacing between pulses, the spacing between spikes that come from neurons all the way to the age of the organism. Um-Also relevant, perhaps, I think the most important thing that's guided my thinking is the, the network structure of the brain. So-

Lex Fridman 1:01:33 Which can also be adjusted-

Jeffrey Shainline 1:01:35 Yeah. Absolu-

Lex Fridman 1:01:35 ... on different scales.

Jeffrey Shainline 1:01:36 Absolutely, yeah. So- so you're- you're making new cont- you're changing the strength of contacts, you're changing the- the spatial distribution of them. Although, spatial distribution doesn't change that much once you're a- a mature organism, but that network structure is- is really crucial. So, let me dwell on that for a second. Um, you can't talk about the brain without emphasizing that most of the neurons in the- the neocortex or prefrontal cortex, the part of the brain that we think is most responsible for high level reasoning and things like that, those neurons make thousands of connections. So, you have this network that is highly interconnected, and m- I- I think it's safe to say that one of the primary reasons that they make so many different connections is that allows information to be communicated very rapidly from any spot in the network to any other spot in the network. So, that's a, that's a sort of spatial aspect of it. You can quantify this, uh, in terms of concepts that are related to fractals and scale and variance, which I think is a- is a very beautiful concept. So, what I mean by that is, kind of no matter what spatial scale you're looking at in the brain, within certain bounds, you see the same general statistical pattern. So if- if I draw a box around some region of my cortex, most of the connections that those neurons within that box make are gonna be within the box to each other in their local neighborhood, and that's sort of called clustering, loosely speaking. But a non-negligible fraction is gonna go outside of that box, and then if I draw a bigger box, the pattern is gonna be exactly the same. So, you have this scale and variance, and you also have a- a non-vanishing probability of a neuron making con- connection very far away. So, suppose you- you wanna plot the probability of a neuron making a- a connection as a function of distance. If that were an exponential function, it would go E to the minus radius over some characteristic radius, and it would- it would drop off. Up to some certain radius, the probability would be reasonable, clo- close to one, and then a- a beyond that characteristic length, R- R0, it would- it would drop off sharply. And so that would mean that the neurons in your brain are- are really localized-

Lex Fridman 1:03:56 Mm-hmm.

Jeffrey Shainline 1:03:56 ... and that's not what we observe. In- instead what you see is that the probability of making a longer distance connection, it does drop off, but it drops off as a power law. So the probability-

Lex Fridman 1:04:06 Mm-hmm.

Jeffrey Shainline 1:04:06 ... that you're gonna have a connection at some radius R goes as R to the minus sum power, and that's more, that's what we see with- with forces in nature, like the-

Lex Fridman 1:04:17 Mm-hmm.

Jeffrey Shainline 1:04:17 ... electromagnetic force between two particles, or gravity, goes as one over the radius squared. So-

Lex Fridman 1:04:23 You can see this in fractals. I love that there's a- a, like a fractal dynamics of the brain, that if you zoom out, you draw the box and you increase that box by certain step sizes, you're gonna see the same statistics.

Jeffrey Shainline 1:04:36 I think that's probably v- very important to the way the brain processes information. It's not just in the spatial domain. It's also in the temporal domain.

Lex Fridman 1:04:45 Temporal.

Jeffrey Shainline 1:04:45 And what I mean by that is-

Lex Fridman 1:04:48 Pew!

Jeffrey Shainline 1:04:48 (laughs)

Lex Fridman 1:04:48 That's incredible that this emerged through the evolutionary process, that potentially somehow connected to the way the physics of the universe works properly.

Jeffrey Shainline 1:04:57 I- yeah. I- I couldn't agree more, that it's- it's a deep and fascinating subject that I- I hope to be able to spend the rest-

Lex Fridman 1:05:03 So you-

Jeffrey Shainline 1:05:03 ... of my life studying.

Lex Fridman 1:05:04 ... you think you need to solve, understand this- this fractal nature in order to understand intelligence in comp- in competition and communication?

Jeffrey Shainline 1:05:10 I do think so. I think they are deeply intertwined, yes.

Lex Fridman 1:05:13 Okay.

Jeffrey Shainline 1:05:13 I think power laws are right at the heart of it. So l- just to- just to push that one through, the- the same thing happens in the temporal domain. So s- suppose you had, um, suppose your- your neurons in your brain were always oscillating at the same frequency. Then the probability of finding a neuron oscillating as a function of frequency would be this narrowly peaked function around that certain characteristic frequency. That's not at all what we see. The probability of finding neurons oscillating or pulsing, uh, producing spikes at a certain frequency is, again, a power law, which means there's no- there's no defined scale of the temporal activity in the brain. It's-

Lex Fridman 1:05:52 (laughs)

Jeffrey Shainline 1:05:52 You don't- what- at- at what speed do your thoughts occur? Well, there's a- there's a fastest speed they can occur, and that is limited by communication and other- other things. But there's not a characteristic scale. We have thoughts on all temporal scales from, you know, a few tens of milliseconds, which is physiologically limited by our devices, compare that to tens of picoseconds that I talked about in superconductors, all the way up to the lifetime of the organism. You can still think about things that happened to you when you were a kid.

Lex Fridman 1:06:22 Well, if you wanna be really trippy, then across multiple organisms in the entirety of human civilization, you have thoughts that span organisms, right?

Jeffrey Shainline 1:06:29 Yeah, yes. Taking it to that level, yes.

Lex Fridman 1:06:31 If you're willing to see the entirety of the human species as a single organism with a collective intelligence, and that too on a spatial and temporal scale there's thoughts occurring, and then if you look at not just the human species but the entirety of life on Earth as- as- as an organism with thoughts that are occurring that are greater and greater sophisticated thoughts, there's a different spatial and temporal scale there. This is getting very suspicious. (laughs)

Jeffrey Shainline 1:06:57 Well, hold on though. Before we're done, I just wanna just tie the bow-

Lex Fridman 1:07:00 Sure, yes.

Jeffrey Shainline 1:07:01 ... and say that the- the spatial and temporal aspects are intimately interrelated with each other. So, activity between neurons that- that are very close to each other is more likely to happen on this- this faster time scale, and information is gonna propagate and encompass more of the brain, more of your cortices, different modules in the brain are gonna be engaged in information processing on longer time scales. So there's this concept of information integration where most ... Neurons are- neurons are specialized. Any given neuron or any cluster of neuron has its specific purpose, but they're also, um, they're also-... very much integrated, so you ha- you have neurons that specialize but share their information. And so, that happens through these fractal, nested oscillations that occur across spatial and temporal scales. I think capturing those dynamics in, in hardware, to me, that's the goal of, of neuromorphic computing.

Lex Fridman 1:07:57 So, does it need to look... So, first of all, that's fascinating. We stated some clear principles here. Now, does it have to, uh, look like the brain outside of those principles as well? Like, what other characteristics have to look like the human brain? Or can it be something very different?

Jeffrey Shainline 1:08:15 Well, it depends on what you're trying to use it for. And so, uh, I think a lot of the community asks that question a lot, "What are you gonna do with it?" And I, I completely get it. I think that's a very important question, and it's also sometimes not the most helpful question. What if what you wanna do with it is study it? What if you just wanna see, um, what does it, what do you have to build into your hardware in order to observe these dynamical principles? So...

Lex Fridman 1:08:43 And also, I ask sometimes, I ask myself that question every day, and I'm not sure I- I'm able to answer that.

Jeffrey Shainline 1:08:49 (laughs)

Lex Fridman 1:08:49 It's like, what are you, what are you gonna do with this particular neuromorphic machine?

Jeffrey Shainline 1:08:53 Right. So, suppose what we're trying to do with it is build something that thinks.

Lex Fridman 1:08:56 Okay.

Jeffrey Shainline 1:08:57 We're not trying to get it to make us any money or drive a car. Maybe we'll be able to do that, but that's not our goal. Our goal is to see if we can get the same types of behaviors that we observe in our own brain. And by behaviors, in this sense, what I mean, uh, the behaviors of the components, the neurons, the network, that kind of stuff. I think there's another element that I didn't really hit on that, that you also have to build into this, and those are architectural principles. They have to do with the, the hierarchical modular construction of the network. And without getting too lost in jargon, the, the main point that I w- I think is relevant there, let me try and illustrate it with a cartoon picture of...

Lex Fridman 1:09:36 Okay.

Jeffrey Shainline 1:09:36 ... the architecture of the brain. So, in the brain, you have the, the cortex, which is sort of this outer sheet. Um, it's actually a, you can, it's a layered structure. You can, y- if you could take it out of your brain, you could unroll it on the table, and it would be about the size of a, of a pizza sitting there.

Lex Fridman 1:09:54 (laughs)

Jeffrey Shainline 1:09:54 And, uh, that's a module. It, it does certain things. It, it processes, as Gyorgy Buzsaki would say, it processes the what of, of what's going on around you. But you have another really crucial module that's called the hippocampus, and that, that network is structured entirely differently. First of all, this, this cortex that I described, 10 billion neur- neurons in there, so numbers matter here. Uh, and th- they're, they're organized in that sort of power law distribution, where the probability of making a connection drops off as a power law in space. The hippocampus is another module that's important for understanding how, wh- where you are, when you are, um, keeping track of, of your, your position in space and time, and that network is very much random. So, the probability of making a connection, it, it almost doesn't even drop off as a function of distance. It's the same probability that you'll make it here to over there. But there are only about 100 million neurons there, so you can have that huge, densely connected module, because it- it's not so big. And the n- the neocortex, or the cortex and the hippocampus, they talk to each other constantly, and that communication is largely facilitated by what's called the thalamus. I'm not a neuroscientist here.

Lex Fridman 1:11:11 Mm-hmm.

Jeffrey Shainline 1:11:11 I'm trying to do my best to recite things.

Lex Fridman 1:11:13 Cartoon picture of the brain. I gotcha.

Jeffrey Shainline 1:11:15 Yeah, something like that. So, this thalamus is, is coordinating the activity between the neocortex and the hippocampus, and making sure that they, they talk to each other at the right time, and send messages that will be useful to one another. So, this all taken together is called the thalamocortical complex.

Lex Fridman 1:11:31 Mm-hmm.

Jeffrey Shainline 1:11:32 And it seems like building something like that is going to be crucial to capturing the types of activity we're looking for, because tho- those responsibilities, those separate modules, they do different things. That's gotta be central to, um, achieving these states of efficient information integration across space and time.

Lex Fridman 1:11:56 By the way, I am able to achieve this state by watching, uh, simulations, visualizations of the thalamocortical complex. There's a few people, I forget from where, they've created these incredible visual illustrations of, like, visual stimulation from, uh, the eye or something like that. I- it's in this image, like, flowing through the brain.

Jeffrey Shainline 1:12:18 Wow. I, I haven't seen that. I gotta check that out.

Lex Fridman 1:12:20 So, it's one of those things. You, you find this stuff in the world, and you see, like on YouTube, it has like 1,000 views. These like, these visualizations of the human brain processing information. And like, th- 'cause, 'cause there's, um, there's chemistry there. Like it could be, because this is ac- from actual human brains. I don't know how they're doing the coloring, but they're able to actually trace the, uh, like different, the, the, the chemical and the electrical signals throughout the brain, and the visual thing is like, "Whoa." Because it looks kind of like the univ- I mean, it, the, the whole thing is just incred- I recommend it highly. I'll pro- probably post a link to it. But you can just look for, uh, um, one of the things they simulate is the, the, uh, uh, thalamocortical, uh, complex, and just visualization. You can find that yourself on YouTube, but it's, it's beautiful. Um, the, the other question I have for you is, um, m- how does memory play into all of this? Because all the signal sending back and forth, that's kind of like, uh, that's computation and communication, but that's kind of like, uh, you know, processing of inputs and outputs, uh, to produce outputs in the system. That's kind of like maybe reasoning, maybe there's some kind of recurrence. But, uh, like, is there a storage mechanism that you think about in the context in neuromorphic computing?

Jeffrey Shainline 1:13:35 Yeah, absolutely. So, that's gotta be central. You have to have a way that you can store memories and...There are a lot of different kinds of memory in the brain. It, that's, uh, uh, yet another example of how it's, it's not a simple system. So, there's one kind of memory, one way of talking about memory, uh, usually starts in the context of Hopfield networks, you were lucky to talk to John Hopfield on this program, but the, the basic idea there is, um, working memory is stored in the dynamical patterns of activity between neurons. And you can, you can think of a certain pattern of activity as a, an attractor, meaning, if you put in some signal that's similar enough to other previously experienced signals, uh, uh, like that, then you're going to converge to the same network dynamics, and you will see these neurons, um, participate in the same network patterns of activity that they have in the past. So, you can talk about I- th- the probability that different inputs will allow you to converge to different basins of attraction, and you might think of that as, "Oh, I saw this face, and then I excited this network pattern of activity because last time I saw that face, I was at, you know, what, some movie and that, that's a-

Lex Fridman 1:14:56 Mm-hmm.

Jeffrey Shainline 1:14:56 ... famous person that was on the screen," or something like that. So, so that's one memory storage mechanism, but crucial to the ability to imprint those memories in your brain is the ability to change the strength of connection between one neuron and another, that synaptic connection between them. So, synaptic weight update is a massive field of neuroscience and neuromorphic computing as well. So, th- there are two poles to that o- o- on that spectrum. One, a- and, okay, so in, more in the language of machine learning, we would talk about supervised and unsupervised learning.

Lex Fridman 1:15:31 Mm-hmm.

Jeffrey Shainline 1:15:32 And when I'm trying to tie that down to neuromorphic computing, I will use a definition of supervised learning, which basically means the external user, the person who's controlling this hardware, has some knob that they can tune to change each of the synaptic weights depending on whether or not the network's doing what you want it to do. Whereas what I mean in this conversation when I say unsupervised learning is that those synaptic weights are, are dynamically changing in your network based on nothing that the user is doing, nothing that, there's no wire from the outside going into any of those synapses. The network itself is reconfiguring those synaptic weights based on physical properties that you've built into the devices. So, if, if the synapse receives a pulse from here, and that causes the neuron to spike, some circuit built in there, with no help from me or anybody else, adjust the weight in a way that makes it more likely to, um, store the useful information and excite the useful network patterns, and makes it less likely that random noise, useless communication events will have a important, uh, effect on the network activity.

Lex Fridman 1:16:45 So there's memory encoded in the weights, uh, the synaptic weights.

Jeffrey Shainline 1:16:49 Yes.

Lex Fridman 1:16:49 What about the formation of something that's not often done in machine learning, the formation of new synaptic connections?

Jeffrey Shainline 1:16:56 Right, well that seems to, so, again, not, not a neuroscientist here, but my reading of the literature is that that's particularly crucial in early stages of brain development, where a newborn is, uh, b- born with tons of extra synaptic connections, and it's actually pruned over time, so the-

Lex Fridman 1:17:14 Mm-hmm.

Jeffrey Shainline 1:17:14 ... number of synapses decreases, as opposed to growing new long-distance connections. It is possible in the brain to grow new neurons and, um, assign new synaptic connections, but it doesn't seem to be the primary mechanism by which the brain is learning. So for example, like right now, uh, sitting here talking to you, you say lots of interesting things and I learn what, I learn from you, and I can remember things that you just said.

Lex Fridman 1:17:41 Mm.

Jeffrey Shainline 1:17:41 And I didn't grow new axonal connections down to new synapses to, to enable those. It's plasticity mechanisms in the, betwe- in the synaptic connections between neurons that enable me to learn on that time scale.

Lex Fridman 1:17:56 So at the very least, that you can sufficiently approximate that with just weight updates, you don't need to form new connections?

Jeffrey Shainline 1:18:03 I would say weight updates are a big part of it. I also think there's more because broadly speaking, when we're doing machine learning, our networks, say we're talking about feed-forward, deep neural networks, the temporal domain is not really part of it. Okay, you're gonna put in an image and you're gonna get out a classification.

Lex Fridman 1:18:19 Mm-hmm.

Jeffrey Shainline 1:18:20 And you're gonna do that as fast as possible, so you care about time, but time is not part of the essence of this thing, really.

Lex Fridman 1:18:27 Hm.

Jeffrey Shainline 1:18:27 Um, whereas in spiking neural networks, what we see in the brain, time is as crucial as space and they're intimately intertwined as I've tried to say. And so adaptation on, on different time scales is important not, not just in memory for- formation, although it plays a key role there, but also in just keeping the activity in a useful dynamic range. So you have other plasticity mechanisms, not just weight update, or at least not on the time scale of m- many action potentials, but even on the shorter time scale. So, a synapse can become much less efficacious, it can, it can transmit a, a weaker signal after the second, third, fourth, it, that can, uh, second, third, fourth action potential to occur in a sequence. So that's what's called short-term synaptic plasticity, which is a form of learning. You're learning that I'm getting too much stimulus from looking

Lex Fridman 1:19:20 Mm-hmm.

Jeffrey Shainline 1:19:20 ... at something bright right now, so I need to tone that down, you know? Um, there's also another really important mechanism in learning, it's called metaplasticity. What that seems to be is a, a way that you change not the weights themselves but the rate at which the weights change. So, when I am in-... say, a, a lecture hall, and my... This is a, a potentially terrible cartoon example.

Lex Fridman 1:19:48 (laughs)

Jeffrey Shainline 1:19:48 But let's say I'm in a lecture hall and, uh, it's time to learn, right? So-

Lex Fridman 1:19:52 Right.

Jeffrey Shainline 1:19:52 ... my brain will release more, perhaps, dopamine or some neuromodulator that's gonna change wh- the, the rate at which synaptic plasticity occurs. So, that can make me more sensitive to learning at certain times, more sensitive to overriding previous information, and less sensitive at other times. And finally, as long as I'm rattling off the list, I think another concept that falls in the category of learning or memory adaptation is homeostasis or homeostatic adaptation, where neurons have the ability to control their firing rates. So, if, if one neuron is just, like, blasting way too much, it will naturally tone itself down. Its, its threshold will adjust so that it's, it stays in a useful dynamical range. And we see that, uh, that's, that's captured in, in deep neural networks, where you don't just change the synaptic weights, but you can also move the thresholds of-

Lex Fridman 1:20:45 Mm-hmm.

Jeffrey Shainline 1:20:45 ... of simple neurons in those models.

Lex Fridman 1:20:47 And so to, um, to achieve the spi- uh, spike in neural networks, you want to use, like... You want to implement the first principles that you mentioned, of the temporal and the spatial fractal dynamics here, so you can, you can communicate locally, you can communicate, uh, uh, across much greater distances and do the same thing in space and do the same thing in time. Now, you have, like, a chapter called Superconducting Hardware for Neuromorphic Computing. So, what are some ideas, uh, that integrate some of the things we've been talking about in terms of the first principles of neuromorphic computing and the ideas that you outline in, uh, uh, Optoelectronic Intelligence?

Jeffrey Shainline 1:21:38 Yeah. So, let me start, I guess, on the communication side of things, 'cause that's wh- what led us down this track in the first place. By "us," I'm talking about my, my team of colleagues at NIST. Uh, you know, Sayid Khan, Bryce Primavera, Sonia Buckley, Geoff Chiles, Adam McKone, to name... Alex Tate, to name a few. Our group leader, Sae Woo Nam, and Rich Miron. We've all contributed to this, so this is not, this is not me saying necessarily just the things that, that I've proposed, but sort of where our team's thinking has evolved over the years.

Lex Fridman 1:22:11 Can I, can I quickly ask, what is NIST and where is this amazing group of people located?

Jeffrey Shainline 1:22:18 NIST is the National Institute of Standards and Technology. They, uh... Th- the larger facility is out in Gaithersburg, Maryland. Our team is located in Boulder, Colorado. Um, we, NIST, is a, is a, a federal agency under the Department of Commerce. We do a lot with... By "we," I mean other people at NIST, but they'll do a lot with standards, you know, um, making sure that we understand the system of units, international system of units, uh, precision measurements. There's a lot going on in, uh, electrical engineering, material science.

Lex Fridman 1:22:54 And it's historic. I mea- I mean, it's like... It's one of those. It's like MIT or something like that. It has a reputation over many decades of just being this really, um, uh, a place where there's a lot of brilliant people have done a lot of amazing things. But in terms of the people in your team, in this team of people involved in the concept we're talking about now, I'm just curious, what kind of disciplines are we talking about? What... Is it-

Jeffrey Shainline 1:23:17 Mostly physicists and electrical engineers. Some material scientists, um, but I would say, yeah, I think physicists and electrical engineers. My background is in photonics, the use of light for technology, so coming from there, I, I tend to have found colleagues that are more from that background. Although, uh, Adam McKone, more of a superconducting electronics background. We need a diversity of folks. This project is s- sort of cross-discipline. I would love to be working more with neuroscientists and things, um, but w- we haven't, we haven't reached that scale yet, but yeah.

Lex Fridman 1:23:54 But you- you're, you're focused on the hardware side, which requires all, uh, all the disciplines that you mentioned?

Jeffrey Shainline 1:23:59 Yes.

Lex Fridman 1:23:59 And then, of course, you know, science may be a source of inspiration-

Jeffrey Shainline 1:24:02 Yes.

Lex Fridman 1:24:02 ... for some of the, the, the, the long-term vision.

Jeffrey Shainline 1:24:04 I would actually call it more than inspiration. I would call it sort of, um, a roadmap, you know? I, we're, we're not trying to, to build exactly the brain, but I don't think it's enough to just say, "Oh, neurons kinda work like that. Let's kinda do that thing."

Lex Fridman 1:24:20 Right.

Jeffrey Shainline 1:24:20 I- I mean, we're, we're, we're very much following the concepts that the cognitive scientists have laid out for us, which I believe is a, is a really robust roadmap. I mean, just o- on a little bit of a tangent, it's often stated that we just don't understand the brain, and so it's really hard to replicate it because we j- we just don't know what's going on there.

Lex Fridman 1:24:40 Yeah.

Jeffrey Shainline 1:24:40 And I... Maybe five or seven years ago, I would, I would have said that, but as I got more interested in the subject, I ha- read more of the neuroscience literature, and I was just taken by the exact opposite sense. I can't believe how much they know about this. I can't believe how mathematically rigorous and, um, sort of theoretically complete a lot of the concepts are. That's not to say we understand consciousness or we understand the self or anything like that. But why is the brain... What is the brain doing and why is it doing those things? We have a... Neuroscientists have a lot of answers to those questions, so there's