YouSaid · the spoken record

Jeffrey Shainline

lines on the record: 208
first: 2021-09-26
most recent: 2021-09-26
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“It's physics. So, okay, so it's physics, I think. So, and what I mean by that is, as we discussed, silicon is the material of choice for transistors, and it's very difficult to imagine that that's going to change anytime soon. Silicon is notoriously bad at emitting light, and that has to do with the immutable properties of silicon itself, the way that the energy bands are structured in silicon, you're never going to make silicon efficient as a light source at room temperature without doing very exotic things that degrade its ability to interface nicely with those transistors in the first place. So that's like one of these things where it's why is nature dealing us that blow? You give us these beautiful transistors and you give us all the motivation to use light for communication, but then you don't give us a light source. So, well, okay, you do give us a light source.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So the point I was making there is that if it was easy to manufacture light sources along with transistors on a silicon chip, they would be everywhere. And it's not easy. People have been trying for decades, and it's actually extremely difficult. I think an important part of our research is dwelling right at that spot there.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Be yes, yes. Sometimes it's helpful to me to say in this hardware, a neuron is that entity which has a light source. And I can.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Communication is not a stretch. It makes perfect sense. So you might ask, well, why don't you use Light for Communication in a conventional microchip? And the answer to that is, I believe physical. If we had a light source on a silicon chip that was as simple as a transistor, there would not be a processor in the world that didn't use light for communication, at least above some distance.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, to me, it starts with thinking about the communication network. You know for sure that the ability of each neuron to communicate to many thousands of colleagues across the network is indispensable. I take that as a core principle of my architecture, my thinking on the subject. So coming from a background in photonics, it was very natural to say, okay, we're going to use light for communication just in case listeners may not know. Light is often used in communication. I mean, if you think about radio, that's light. It's long wavelengths, but it's electromagnetic radiation. It's the same physical phenomenon obeying exactly the same Maxwell's equations. And then all the way down to fiber, fiber optics, now you're using visible or near infrared wavelengths of light, but the way you send messages across the ocean is now contemporary over optical fibers. So using light.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“As I got more interested in the subject, I 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 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? Neuroscientists have a lot of answers to those questions. So there's a lot if you're a hardware designer.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I would actually call it more than inspiration. I would call it sort of. Roadmap, you know, we're not trying to build exactly the brain, but I don't think it's enough to just say, oh, neurons kind of work like that. Let's kind of do that thing. I mean, we're Very much following the concepts that the cognitive sciences have laid out for us, which I believe is a really robust roadmap. I mean, just 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 just don't know what's going on there. And maybe five or seven years ago, I would have said that, but”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Mostly physicists and electrical engineers, some material scientists, 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 tend to have found colleagues that are more from that background, although Adam McConne, more of a superconducting electronics background, we need a diversity of folks. This project is sort of cross-disciplinary. I would love to be working more with neuroscientists and things, but we haven't reached that scale yet.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“NIST is the National Institute of Standards and Technology. The larger facility is out in Gathersburg, Maryland. Our team is located in Boulder, Colorado. NIST is a federal agency under the Department of Commerce. We do a lot with by we, I mean other people at NIST, do a lot with standards, making sure that we understand the system of units, international system of units, precision measurements. There's a lot going on in electrical engineering, material science.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. So let me start, I guess, on the communication side of things because that's what led us down this track in the first place. By us, I'm talking about my team of colleagues at NIST, you know, Saeed Han, Bryce Primavera, Sonia Buckley, Jeff Chiles, Adam McCon, to name Alex Tate, to name a few. Our group leaders Sabu Nam and Rich Mirin. We've all contributed to this. So this is not me saying necessarily just the things that I've proposed, but sort of where our team's thinking has evolved over the years.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's time to learn, right? So, my brain will release more perhaps dopamine or some neuromodulator that's going to change the rate at which synaptic plasticity occurs. So that can make me more sensitive to learning at certain times, more sensitive to overwriting 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 rate. So if one neuron is just like blasting way too much, it will naturally tone itself down. Its threshold will adjust so that it stays in a useful dynamical range. And we see that that's captured in deep neural networks where you don't just change the synaptic weights, but you can also move the thresholds of.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Potentials, but even on the shorter time scale, so a synapse can become much less efficacious. It can transmit a weaker signal after the second, third, fourth, that can 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 at something bright right now. So I need to tone that down, you know. There's also another really important mechanism in learning that's called metaplasticity. What that seems to be is a Way that you change not the weights themselves, but the rate at which the weights change. So when I am in, say, a lecture hall and my, this is a potentially terrible cartoon example, but let's say I'm in a lecture hall and”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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 going to put in an image and you're going to get out a classification and you're going to do that as fast as possible. So you care about time, but time is not part of the essence of this thing, really. 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 different timescales is important, not just in memory 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 many.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, well, that seems to so again, not a neuroscientist here, but my reading of the literature is that that's particularly crucial in early stages of brain development where newborn is born with tons of extra synaptic connections and it's actually pruned over time. So the number of synapses decreases as opposed to growing new long distance connections. It is possible in the brain to grow new neurons and 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, 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. And I didn't grow new. Axonal connections down to new synapses to enable those.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Or anybody else adjust the weight in a way that makes it more likely to store the useful information and excite the useful network patterns and makes it less likely that random noise useless communication events will have an important effect on the network activity.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Talk about supervised and unsupervised learning. 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 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 the synapse receives a pulse from here and that causes the neuron to spike, some circuit built in there with no help from me.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Participate in the same network patterns of activity that they have in the past. So you can talk about 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, some movie and that's a famous person that's on the screen or something like that. So that's one memory storage mechanism. 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 there are two poles to that spectrum. Okay, so more in the language of machine learning.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. So that's got to 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. Yet another example of how it's not a simple system. So there's one kind of memory, one way of talking about memory usually starts in the context of hop field networks. You were lucky to talk to John Hopfield on this program. But the basic idea there is working memory is stored in the dynamical patterns of activity between neurons and you can think of a certain pattern of activity as an attractor meaning if you put in some signal that similar enough to other previously experienced signals alike like that then you're going to converge to the same network dynamics and you will see These neurons.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, something like that. So, this thalamus is coordinating the activity between the neocortex and the hippocampus and making sure that 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. And it seems like building something like that is going to be crucial to capturing the types of activity we're looking for because those responsibilities, those separate modules, they do different things, that's got to be central to achieving these states of efficient information integration across space and time.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, you can have that huge, densely connected module because it's not so big. And 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. I'm trying to do my best to recite things.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a module. It does certain things. It processes as Yorgi Buzaki would say, it processes the what of what's going on around you. But you have another really crucial module that's called the hippocampus. And that network is structured entirely differently. First of all, this cortex that I described, 10 billion neurons in there. So numbers matter here. And 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 where you are, when you are keeping track of your position in space and time. And that network is very much random. So the probability of making a connection, 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Architecture of the brain. So, in the brain, you have the core text, which is sort of this outer sheet. It's actually, it's a layered structure. 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 pizza sitting there.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So suppose what we're trying to do with it is build something that thinks 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 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 you also have to build into this. And those are architectural principles. They have to do with the hierarchical modular construction of the network without getting too lost in jargon. The main point that I think is relevant there. Let me try and illustrate it with a cartoon picture of.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, it depends on what you're trying to use it for. And so I think a lot of the community. Asks that question a lot. What are you going to do with it? And 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 want to do with it is study it? What if you just want to see What do you have to build into your hardware in order to observe these dynamical principles?”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Hold on though. Before we're done, I just want to just tie the boat and say that the The spatial and temporal aspects are intimately interrelated with each other. So activity between neurons that are very close to each other is more likely to happen on this faster time scale and information is going to propagate and encompass more of the brain, more of your cortices, different modules in the brain are going to 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 very much integrated. So 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 hardware, to me, that's the goal of neuromorphic computing.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I do think so. I think they're deeply intertwined. Yes, I think power laws are right at the heart of it. So just to push that one through, the same thing happens in the temporal domain. So suppose you had 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 producing spikes at a certain frequency is again a power law, which means there's no defined scale of the temporal activity in the brain. At what speed do your thoughts occur? Well, there's a fastest speed they can occur, and that is limited by communication and other things, but there's not a characteristic scale. We have thoughts on all.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I couldn't agree more. That's a deep and fascinating subject that I hope to be able to spend the rest of my life studying.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that's probably very important to the way the brain processes information. It's not just in the spatial domain, it's also in the temporal domain. And what I mean by that is incredible.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Localized That's not what we observe. 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 that you're going to 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 forces in nature, like the electromagnetic force between two particles or gravity goes as one over the radius squared.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Most of the connections that those neurons within that box make are going to 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 going to go outside of that box. And then if I draw a bigger box, the pattern is going to be exactly the same. So you have this scale invariance, and you also have a non-vanishing probability of a neuron making connection very far away. Suppose you want to plot the probability of a neuron making 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 drop off to some certain radius, the probability would be reasonable close to one, and then beyond that characteristic length R0, it would drop off sharply. And so that would mean that the neurons in your brain are really”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a sort of spatial aspect of it. You can quantify this in terms of. Concepts that are related to fractals and scale invariance, which I think is a very beautiful concept. What I mean by that is kind of no matter what spatial Within certain bounds, you see the same general statistical pattern. So if I draw a box around some region of my cortex.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“On different scales. Absolutely. Yes. So you're making new content, you're changing the strength of contacts. You're changing the spatial distribution of them. Although spatial distribution doesn't change that much once you're a mature organism, but that network structure is really crucial. So let me dwell on that for a second. You can't talk about the brain without emphasizing that most of the neurons in the neocortex or the 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 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, so the brain is notoriously complicated, and I think that's an important part of why it can do what it does. But okay, let me try to break it down. Starting with the devices, neurons, as I said before, they're sophisticated devices in and of themselves, and synapses are too. They can change their state based on the activity. So they adapt over time. That's crucial to the way the brain works. They don't just adapt on one time scale. They can adapt on myriad time scales from the spacing between pulses, the spacing between spikes that come from neurons, all the way to the age of the organism. Also relevant, perhaps, I think the most important thing that's guided my thinking is the network structure of the brain.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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 I'm doing my exploration these days.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And then that's a little bit more 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 Yakimo Indoveri and Gerkauenberg, Jennifer Hazler, countless others. It's a rich and exciting field 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 starting with the assumption that I'm going to use any kind of conventional computing hardware. And instead, what I want to do is try and understand what makes the brain powerful at the kind of information processing it does. And I want to think from first principle.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Introduce the word neuromorphic. There's this concept of neuromorphic computing where what that broadly refers to is Computing based on the information processing principles of the brain. 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 neuromorphic from things that are 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 neuron sends 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 using transistors, but now you're starting to use them instead of in a digital way, in an analog way. And so you're trying to get those circuits to behave more like neurons.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“In general, it can be important. Clock distribution is a big challenge in especially large computational systems. And so, yes, optical clocks, optical clock distribution 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 want to use light.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“One way that you can encode information in quantum states of light is in the number of photons. 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. So if you have a detector that is sensitive to that, it's extremely useful.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, I got this kind of communication event for 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 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 Adriana Lita 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 In that pulse. So”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, it depends. So I would say that that's actually in the application that we're trying to use these detectors for, that's a feature because what we want is for If a neuron sends one photon to a synaptic connection and one of these superconducting detectors is sitting there. Get this pulse of current and that synapse says event, then I'm going to do what I do when there's a synapse event. I'm going to perform computations, that kind of thing. But if accidentally you send two there or three or five, it does the exact same thing. And so 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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. That's going to 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 going to 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”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Let's say yes, you have a loop. So you have a superconducting wire that goes straight down like this, and on your loop branch, you have a little ammeter, something that measures current. There's a resistor up there too. Go with me here.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“There's a lot of ways to catch a photon. It's not a dumb question. It's a deep and important question that basically defines a lot of the work that goes on in our group at NISP. One of my group leaders Seywam has built his career around these superconducting single photon detectors. So if you're going to try to sort of reach a lower limit in 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, right. So now instead of trying to send electrons or me to you, I'm trying to send photons. So I can make what's called a waveguide, which is just a simple piece of a material. It could be glass like an optical fiber or silicon on a chip. And I just have to inject photons into that waveguide. And independent of how long it is, independent of how many different connections I'm making, it doesn't change the voltage or anything like that that I have to raise up on the wire. So if I have one more connection, if I add additional connections, I need to add more light to the waveguide because those photons need to split and go to different That makes sense, but I don't have a capacitive penalty. Sometimes these are called wiring parasitics. There are no parasitics associated with light in that same sense.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Distance is an important thing, so is the number of connections I'm trying to make. Me to you, okay, one, that's not so bad. If I want to now send it to 10,000 other friends 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 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Portion of the capacitance and goes as the voltage squared. So you get this huge penalty if you want to send Electrons across a wire over appreciable distances.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“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 I'm trying to get 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 charge packet has to propagate along the wire and it has to get all the way over to you. That wire is going to have something that's called capacitance, which basically tells you how much charge you need to put on the wire in order to raise the voltage on it and the capacitance is going to be proportional to the length of the wire. So the longer 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”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“If I have a communication channel and I put one more photon on it, it doesn't screw up what those other, it doesn't change what those other ones were doing at all. So that's really useful for communication because that means you can sort of allow a lot of these photons to flow without disruption of each other and 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. 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.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source