How long have you been at Dartmouth now? Three years as of July 1st. So I always have felt like when you go from one job to another, that it's an opportunity to sort of abandon strategies that hadn't worked and adopt some new ones. Did you do much of that or was it? Yeah, because I have one, I had to do an evaluation of Dartmouth culture. You have to fit in as the CIO, right? You can't just go in and think that you're going to be walking out of an EVP job at a small liberal arts college and be in an EVP position when you're taking a VP position and you have people above you in a different hierarchical relationship and how those people integrate and work with each other is something you have to take into perspective. So I wanted to be myself, but I also recognized that I was taking a job and they had a goal for me. What a great tension built into what you've already said, which is you want to make sure it's a fit, but you also want to be a change agent. And that's a tension I imagine you've lived with for a while and found peace with it. Because I've been alive. You've taken me back to my imagining Mitch in third grade and I'm not sure how that went. I was the same kid. Well, we've been talking about change, you and I over the years, and always been interested in your comfort with working at the edge of things. I thought we might take some time just to probe that a little bit and think about it. And I'll start here that to me, one of the most complicating factors in the COVID pandemic has been the tendency to bring out deficit thinking, focusing on all the things that the pandemic has taken away or changed in a way that's uncomfortable. And I get a sense that you're seeing it a little differently at Dartmouth. So I'm going to step aside and hear from you on that. People get so wound up in the past that all their sort of intellectual ability is focused on thinking about what they could have been rather than what could be. And for me, I don't have all those free cycles. So I try to strip out anything that I can actually do and focus on something I can get done. And it's what I do every morning is I wake up in the morning and I think this is my first day in the job, my first day alive. What is it today that I can do to make tomorrow better? I've been doing some community conversations where I've been talking to folks and I've questioned, I've asked everybody, what was the thing you did best? Like what was your, that happened during the spring that you're particularly proud of? Almost everybody said some version of the same thing, which is I used to believe the narrative that higher ed can only move at glacial speed. And yet we discovered that we can do things way faster than we ever had before and do it, you know, better than we thought we could faster. So I imagine that's been part of the, what's happening at Dartmouth. All the promises of change in higher ed that people used when they were talking about MOOCs all came to true with COVID. It just took a few years. It just took eight years. All the change MOOCs into a thing, but COVID in a very short period of time has changed higher ed in actually a good way. Operationally, it has them focused and they started to learn that, hey, we can move fast. And actually in the future, we have to move fast. So if you think about this, this is like a primer for the future. It's preparing higher ed to actually operate in a way that it needs to be successful. It just needed a kick in the pants to do so. Are things going to go back to the way they were? I don't think so. I think there's permanent change. That permanent change is one just operationally as people are not all coming back to campus. We were already hiring people and not even having them move. That's a permanent change. How we, now we're able to recruit across the United States and maybe around the world were before that would have been highly resistant. Now it's just another option. And then how we work digitally is if you think of higher ed and sort of online education has been pretty stave for some time.  And it's not much different than what they're doing today. So you think in 20 years there'd be a lot of innovation but what you're seeing now with people, everyone being involved and there's a lot of energy and thought going into it. There's everything from VR, there's different ways of integrating with Canvas. We need tools. We're actually working on a project with Slack to build an integration of Slack into Canvas and they're kicking your money. We're kicking your money. It's going to be a service that's going to be available to anybody that's using Slack. That sort of business academic partnership is something that I've always fostered but it's becoming much more apparent and much more easier to do to the point where now we're actually having to say no to various projects because we have to weigh how much can we put in to all these sort of business partnerships that are out there to build tools that actually make education a better space in the future. So the environment's changing. The potential partnerships outside of higher ed are changing. What about the sort of the, I think in an email you referred to it as bureaucratic tendencies of higher education. Are those, is this a temporary change or is this going to continue past pandemic? I think it depends on the institution. The ones that are actually cataloging and sort of finding the attributes of the things that were successful and they want to continue forward and they're actually thinking about the future that's fine. A lot of people are again are back in that survival mode. How do we get through this rather than how do we thrive into the future? How do we take everything that we've learned and come out of this running rather than walking?


Debo Samuel addressed him playing running back and wide receiver because this was a talking point during the off-season for why Debo was frustrated with his contract situation with the San Francisco 49ers And I said from the beginning if they were to pay him more money to play running back as well as play wide receiver He would probably go on to do so he has agreed to do that and he has basically come out and said to everybody else Is full of good run this Debo clip shall we Evan Foxy talking about him taking some reps at running back as well One of the narratives this off-season was that you were upset with how you were gonna be used That's false There's a lot of things that came out that I want to speak on but at the end of the day I wasn't allowed to so I mean you can turn on the tape or go back to the Cowboys game It kind of shows what kind of player I am And also I mean you can go turn on the Pro Bowl taping like what I said about being a wideback I don't mind. You know I'm saying do whatever it takes for this team to win


So, in other words, you have this vast associative web inside your head that's embodying information about all your previous experiences and knowledge. And information that you learn newly will have to make sense and be referenced or grounded by this associational web. That's how it enters it. So, again, this is just an entirely different paradigm of how we learn, right, and how we think than how a computer does it. Boom! What's up, everyone? Welcome to Simulation. I'm your host, Alan Sakyan. We are at the Brain and Cognitive Sciences Building at the Massachusetts Institute of Technology in the beautiful Cambridge, Massachusetts. We are now going to be talking about the differences between brains and computers. We have Dr. Robert Ajemyan joining us on the show. Hello. How are you doing? Thank you so much for coming on the show. My pleasure. I'm super excited. Robert's Backgrounds Epic. He's a research scientist at MIT's McGovern Institute for Brain Research, a professor of brain and cognitive sciences at MIT, and he teaches a computational neuroscience class titled Emerging Computation in Distributed Neural Circuits and studies the science of mnemonic techniques. And you can find his link below. He's also a fellow Armenian. And check out his link, super interesting stuff up there. Let's jump into this question that we love starting with. We find ourselves as stewards of Earth. What is your current take on the state of our world? Well, obviously, I think the state of our world is failing in numerous respects. And there's a geopolitical answer and then a more personal answer. So the geopolitical answer is that as humans are transforming the planet, the recent UN report on all the species vanishing, etc., we're not necessarily taking action to counter that. So that's a big problem, but that's one that I can't do too much about. I am concerned that the scientists know what we're doing is going too far and pushing the planet to its limits, but the policymakers aren't necessarily listening properly and taking corrective actions. So that's on the big geopolitical level. But again, that doesn't necessarily affect me on the personal level. What concerns me more on the personal level is that as our society has become more technologically driven with social mediation of the entire planet, I'm concerned that the way that humans think has been transformed in a not propitious way. In particular, it strikes me that with all of these electronic devices and contraptions that a lot of our thinking and a lot of our daily behavior is all stimulus-driven. We see a beep, we read a text, we see these things, and we respond. It's all short bursts of attention and quick thinking. And I don't think that's necessarily conducive to, say, academic scientific problem solving. Some of my colleagues ridicule me for being what they call a technophobe because I don't have a smartphone. And I'm not a technophobe. I think technology has a lot of good uses. But I think one of the downsides of technology is that it interferes with people to sustain their attention for a long time on hard problems because you're always being perturbed by the electronic influx of information from a variety of sources. And you kind of see this in what's happened to the current scientists. The current scientist nowadays is not sitting in his desk thinking for four or five hours a day. I try to do that. I don't maintain that ideal, but I think a reasonable amount of time. They're constantly managing. They become big project managers as if they're in industry because you've got lots of grants, you've got to write these reports, you've got lots of students, you've got to handle this administrative aspect, you've got to handle this administrative aspect. You're always on your smartphone answering mail, texting with students. I don't think that's the way thoughtful science can be done, which is you really need to maintain a lot of internal focus to solve hard problems. And God knows the brain presents lots of hard problems being one of the last undiscovered regions of scientific inquiry. We know very little about the brain relative to almost every other natural phenomenon. Michelangelo probably would not have been able to spend over three years sculpting the statue of David if there was social media. Oh, I completely agree.   And if that's done nowadays, that's considered a sign of unproductivity, right? Because everything is stimulus driven and response driven. And there's a sense the time scale has increased for everything, right? Everything is rapid. Everything is instant gratification. There's no sense of delayed gratification. Now what people want to do when they write a paper is get it out on the worldwide web, get lots of hits, get lots of immediate citations. They're not writing the paper for what people used to write the paper for, which is, will it be around in 50 years? What's the indication of whether it's good work? It's not how many hits you get through some kind of advertising or branding. It's about how enduring is that work over time? Because history will get it right in terms of science. There's right and there's wrong, unlike in say art, where it's a matter of taste and people pass through different phases. So work that's enduring will be known. There's an interesting article by two guys, Giman and Giman. I think I sent it to you about what's it called, science in the age of selfies. Yes. And it talks about some of these trends where scientists are basically interested in photographing themselves, right? Instead of using your example, slowly doing a landscape of a far away horizon. And that's unfortunate. Another famous one is the, we wanted flying cars and we got 140 characters. That's absolutely great. I mean, I don't even understand, I don't see how this can help students write, read or think when they're operating with Twitter. You just don't understand that. So just one of the quick arguments on behalf of it is that you can get a piece of news from around the world disseminated effectively to lots of people. I agree. And that's one of the core arguments for it. And we both agree. And then, but at the same time, a lot of the profound thinkers and executors, scientists, artists of the days before us, were able to be at complete silence and peace by themselves. And you also highlighted the, can you create something that has a lindy effect? Can it withstand the test of time? And I do think that whatever Satoshi Nakamoto, whatever blockchain, whoever that is, whatever it is, ended up potentially making something that will last several decades. And so I do think that creating things like that and focusing on creating things like that is way more important than what some of the perverse scientific incentives are in our systems today. I agree completely. And again, all technology has advantages. For example, the internet, I use the internet. I mean, think about, I can get a paper from any journal, you know, as long as we have subscriptions at the university, that I'd have to go to the library. Maybe my library wouldn't happen. So information flow is a good thing. The problem is, do people have the discipline to police themselves internally? Or can this thing kind of escalate out of control? And from my point of view, it's kind of escalated out of control. Some people have the discipline to police themselves, some don't. But the younger generation being thrust in it is not going to know the difference and won't even be able to police themselves. Especially because the news feed has become a slot machine. And that's one of the addictive strategies of a reward system that's varied. Yeah. And one of the ironies of the fact that information is so accessible is that now no one reads.  My boss, Emilio Bicci, tremendous individual, he still reads to this day. Most people don't read journal articles. Why? There's no time. You've got to get out. You've got to get out. You've got to go on Twitter. You've got to promote yourself. You've got to promote your brand. You've got to do all these things. And just like people say, Facebook has made people lonelier, ironically. Same thing. Great access to information, which, if used properly, is undeniably a great thing, has somehow led to people reading less. Yeah. So that's not a good outcome. And we've got to be very careful. And I think one of the phrases that can help us get there is signal to noise. We have to really be able to identify what is true knowledge. How can we get that into these feeds more effectively? All right, let's do the journey. Sure. How did you, as a child, get inspired by neuroscience and computation and then find yourself here? Well, actually, I did not intend ever on being a neuroscientist. What I intended on being was a mathematician physicist. And in fact, when I went to college, I studied math and physics. And I enjoyed math and physics, but I realized that my area of facility and expertise is in theory. And in terms of physics in particular, the amount of jobs or even the need for theorists is relatively small. There aren't that many theorists necessary. Whereas, and I looked towards various medical problems, including neuroscience, I'm like, boy, we know very little about this. So maybe a theorist can make a big difference there. As a result, a few years after I graduated, I enrolled in an excellent program at Boston University called the Department of Cognitive Neurosystems, which was basically a theoretical neuroscience program. So the idea was, you're not going to do experiments. You're going to figure out how to come up with frameworks that will allow us to interpret the results in these experiments, maybe codify all these experiments to develop theories of various motor, sensory, cognitive behaviors, circuits in the brain. And that's what I became impassioned about. And ever since, I have not regretted that decision. And then one more bit is how did you even pre-physics and math get hooked into it? Would you have mentors or any family? What was the big thing for you that sparked the interest? I don't know. I always liked math and physics ever since I was a kid, because I was amazed at the ability to predict and control how things happen in nature. It's almost like magical disability to make some calculations on a piece of paper and wow. That's true understanding of the system, right? And so when I realized how far to jump forward from the brain, we aren't actually making any predictions about this stuff, I'm like, wouldn't it be great if the brain could just be nudged a step towards these remarkable predictions you can make in physics? Yeah, with a few strokes on a piece of paper, you can say what's going to happen with great certainty? That just always amazed me. That's beautiful. OK, and then the PhD itself, the analysis of movement representation in motor cortical cells at Boston University. Sure. So what I got very interested at the time, and I was personally interested in motor control, because I actually am a fairly avid tennis player. I've played tennis, and so how do you learn a skill? How do you maintain a skill? Why is it that you never forget how to ride a bike? A person comes back from playing tennis after not playing for five years, and they seem to be able to hit the stroke pretty well. These were problems that all fascinated me in systems neuroscience and motor control. So what I specifically studied for my dissertation was something called the encoding problem. And the encoding problem is how is information about upcoming movements represented as a series of commands in the brain? In other words, Roger Federer wants to go hit a forehand. That must trigger a cascade of events from intention at the higher levels of the frontal cortex to the motor cortical output to the spinal cord, thereby telling his muscles what steps to take to hit his forehand cross-court. And that's kind of a fascinating problem. I focused on the motor cortex, which is the region that projects to the spinal cord. And that got me interested actually in the differences in brains and computers, which is something that, as you know, I've been pursuing since. Because it turns out that I came out, I wanted to set out to come up with this beautiful mathematical formalism to explain if information is represented in this nice coordinate system, this is how it's encoded in the brain. If it's represented like this, this is how it's encoded in the brain. At the end of the day, I realized early during my post doc that that's great. But that's kind of a computer perspective. Unfortunately, the brain doesn't obey any of these rules. The question is, do these concepts that we impose from an anthropomorphic perspective even make sense? So my thesis, which I'm still very proud of, kind of changed my direction in the sense that if the assumptions were correct, the work was nice and beautiful. The question is, are those assumptions even correct for the brain? And if not, where do you go next? And so I've been trying to follow those secondary directions like maybe it's not such a nice system like a computer program we would write to implement these things. It's a different type of system obeying a different set of rules. And we need to focus on those. Even the example of being able to take a thought from an executive function state down to a motor cortex, down to the spinal cord, down to the muscle to hit back the ball is a very profound way of analyzing something that seems to be quite, at the surface level, just a simple tennis. Oh, people think it's simple. The complexities are remarkable. And this is why, while there have been successes in AI, there is no AI device even near Roger Federer in his ability to play tennis. And we can get into maybe some of that later. But the point is motor control is an incredibly complicated problem of an appreciated difficulty. Compared to something like an image recognition. Object recognition. These problems are simpler and smaller dimensional. And it's very interesting that the time scales to do something like an object recognition seems to almost be here. It is. We are ready to do that. There are other problems we are not ready for. And God knows when we'll be ready for them. Yeah, yeah. Quick on that point, where does the thought of, in the milliseconds of time, of calculating where the ball got hit back, where does it originate in the executive functioning? Well, that's a part of the brain that makes it difficult to study. Even that question is, to some extent, ill-posed because it's a highly distributed representation that's represented in parallel in multiple places. So I could give you some canonical answer with the frontal cortex has the goals, and then you do this stream back. But it's really everywhere. It's also in the cerebellum. It's also in the basal ganglia. These circuits are all interacting in parallel. So our whole serial understanding, based on the computer metaphor for how these things work, is inadequate for understanding how the brain works. It's just more complicated than that. And we don't know all the answers. Yeah, yeah. This is going to be a reoccurring theme. Unfortunately, regarding the brain, not knowing the answers is more the norm than the exception. It's extremely important to be humble and admit that that that is the case. So let's talk about these differences between computers and brains. You really gave us a really good one right there, which is that there's just so much parallel circuitry that understanding the stimuli of the environment and making decision in return. So keep going on these things. You gave us transmission speed, clock rate, clock rate, parallelism, yeah, all these different things. So brains are composed of neurons and synapses, right? Those are the fundamental units. And computers are, loosely speaking, constructive chips. So let's just take simply operating characteristics of these smaller units, neurons and chips, before we even get into higher level systems differences, like parallelism, et cetera. What is the clock rate for a computer? That's how many gigahertz you have, right? That's how many computations can be performed per second. Roughly on the order of, say, 10 to the ninth. It's probably higher for some of the faster computers, and that just keeps going up. That's 10 to the ninth. What's the speed with which, say, neurons can compute things? Well, a neuron can only spike. That's the way information is transmitted from one neuron to another with an electrical signal sent down its axon, where it stimulates the dendrite of the neighboring neuron. I would say about 200 spikes per second would be really high. So let's say, best case scenario, 10 to the third is this clock rate. 10 to the third being about order of one spike per millisecond, which is probably faster than we can do. So you have 10 to the ninth for the computer, 10 to the third for a neuron. What about signal transmission? How fast does that signal move along the axon? Well, in computers, electric signals obviously travel the speed of light, which is, if I recall correctly, roughly 10 to the 8 meters per second. How fast do signals move along axons? Well, roughly 10 to the second meters per second. So 10 to the ninth, 10 to the third for clock rate, 10 to the eighth, 10 to the second for signal transmission speed. What about noise? Signal to noise ratio. Computers, basically the problem of noise is solved. If you run a program the same way, you always get the same answer, right? There's very little noise in these circuits. Some thermal noise is estimated around 10 to the sixth, which is 10 to the sixth signal ratio to the noise ratio. In the neurons, it's hard to estimate signal to noise ratio because we don't even really know what the signal is. But it's on the ballpark of 1, charitably 10. So the noise is very, very significant. So if you look at these, just these three operating characteristics, and we can discuss some of the systems level differences as well, you have 10 to the sixth. That's the order of magnitude difference between these operating characteristics. So then why is the computer used as a metaphor? And there's a good reason why the computers use as a metaphor, right? I mean, there have been all kinds of metaphors about the brain throughout history. Hydraulic engines, watches, right? Electrical telephone signals, right? The prevailing technology at the time, and many articles and books have been written about this, is generally the leading metaphor for the brain because the brain obviously is capable of the most remarkable functions possible. So I want to match that with whatever technology is capable. And this dates back since the ancient Greece, which is the time of ancient Greece, basically. These different metaphors have evolved over time. So now we are able to get computers to do very, very remarkable things. Because we can do remarkable things with computers, because we can do remarkable things with our brains, there's a natural tendency to say brains and computers are alike. They're not. How we're doing those things is entirely different. And then you mentioned the others, parallelism, right? Computers do not work in parallel. There's some efforts at doing parallel processing, neuromorphic engineering. None of those have panned out. Standard computers operate basically in series and very rapidly. So the motif for computing in a computer is lots of serial computations. In the brain, you've got the slow, sloppy, and precise computing, but it's highly parallelized. And that's because each neuron in the brain connects to 10 to the fourth other neurons, roughly 10,000 other neurons. That's a rule of thumb. Some are more and some are less. But the whole architecture is different. Maybe you've heard of that game, Six Ways to Kevin Bacon or Six Connections to Kevin Bacon. Six Degrees of Separation. Six Degrees of Separation, right, Kevin? And so you start with one name, and then you make these associated jumps. Well, something like that actually existed in the brain.  And the idea is that no synapse anywhere in the brain is more than three or four synapses removed from affecting some other synapse. So these are just testaments to the degree to which there's this parallel architecture where everything is talking to everything simultaneously. But somehow, the system is organized in a way that it's able to make sense of it all and perform these remarkable functions. But this doesn't mean you're doing it any way like the brain is doing it. In fact, again, the differences are striking. How did these object recognition algorithms manage to all of a sudden perform at this human level of performance? Was it because there are architectural changes? No, architecture's been the same since the early 80s. Is it because there are any algorithmic changes? No, algorithms have been the same since the 70s. What's changed is processing speed and the amount of data. Particularly, you've heard of Moore's Law, processing speed doubling, what is it, every two years or something? That has been maintained for a long time. This is what's going on. We are good. We have developed a technology to enslave electronic circuits for performing trillions and trillions of calculations in series to perform tasks amenable to those operations. And that's why object recognition has been a problem where we've made a lot of progress. It's not going to be the same for all the problems. As I said, the problem of motor control with Roger Federer, for a variety of reasons, you have all these interaction torques between all the degrees of freedom of your body. It's a continuous problem as opposed to a discrete categorization problem. Those are hard problems. When there's a computer or a robot that can play tennis like Roger Federer, then we've solved those problems. It's going to be a long time before we ever get to the stage because it's just a different type of intelligence. Artificial intelligence will work better for emulating some types of natural intelligence than others. Now, you talked about differences between the computers and the brains. Let me give you another strong one which also harkens to an area where I do research as I was discussing mnemonic techniques. And so the question is, how is information stored and processed in the brain? In the brain, information is stored in bits in these registers. I have my one, zero, one, zero, one, zero, one, zero, everything's reduced to a binary code. And I have an address by which I can connect to that register. So if I want information somewhere, I need the address, okay, then I go to the information. So one point about that information is that it's segregated, it's localized, it's only there, okay? And the address is what connects the user to that information. The brain is entirely different, okay? All information is stored relationally, associationally in terms of its content and it's all in a distributed fashion, meaning that information is encoded in distributed activation patterns that share neurons for different pieces of information. It's not segregated in one register. It's spread everywhere, okay? And I don't have someone reading it with an address. It's organized by content. So in other words, you have this vast associative web inside your head that's embodying information about all your previous experiences and knowledge. And information that you learn newly will have to make sense and be referenced or grounded by this associational web. That's how it enters it. So again, this is just an entirely different paradigm of how we learn, right? And how we think than how a computer does it. And one way that you can see this is in the use of mnemonic techniques to improve people's memory. And I don't know how much your audience is familiar with it, lots of people aren't familiar with it, but are these ancient techniques dating back to Greek civilization, gentlemen named Simonides, and even actually further back than that. And if you apply these techniques, which I'll just describe very cursorily, you can remember huge pieces of information. Now, why was this important? Well, in the old days, Papyrus was expensive. They didn't have computers. There was no printing press. So you'd want to learn vast pieces of information. You want to memorize your speeches, et cetera. People practiced at this, okay? And all of these techniques were based on association. You would take some new information and you'd relate it to some old information.  And voila, the magic of these techniques was that it became really easy to remember lots of things. This is now a lost art. We completely ignore memory because we have these electronic devices. Kids are not even learning the multiplication tables in school systems. Or handwriting. Or handwriting, right? But think about what an incredible mistake this could be if these memory techniques embody a more general associate of computing faculty that is being ignored. In other words, if association is a fundamental paradigm of how we process information, i.e. relating new information to old information and experience and putting it in context. And that helps us not only memorize things, but it helps us with pattern formation, concept formation, even creativity, linking different things to each other, right? It's all part of general neural computing, which we don't really understand. And if we all of a sudden omit one major component of it, memory, because the way memory's done in computers is so easy that we have infinite amount of memory, it would be like not running on the treadmill, not staying in shape because I can drive a car in a train to wherever I wanna go, right? That wouldn't make sense. You gotta exercise the fundamental faculties of your brain. And if association in memory's only one instantiation of it is an important faculty and you're ignoring it, that's a problem. But you only realize that if you try to think carefully in a nuanced fashion, what are the differences between how the brain works and how a computer works? And they are profound. The differences are far greater than similarities. Only similarities is that they're both capable of remarkable feats. Yeah, that was very eloquently explained. And I think that there's potentially some ways that we can break this down to make it even, continue to make it really relatable. So when a child is first recognizing and being taught what an object is and what we do with that object, like these chairs, what we do is we sit in these chairs and what they are called is a chair and then that practice happens and then they're set in different languages as well. Sure. Okay, so we're building an associative web. Exactly. Yes, of what we do with the object, what the object's called. What it means to us, how it feels to us, the purpose it serves for us. This is basically the grounding problem, right? I mean, the object doesn't exist in a vacuum, it exists relative to our lives and how we use it, right? And that grounding problem has to be solved for biological organisms, but you don't need to ground anything in a computer. It's not. So could it potentially be that the amount of time that it takes for a child to learn what the chair, the associative web of information about the chair is just a lot longer than what it could be for a computer and then is there a way to ground the computer into the meaningful reality? For various technical reasons, I don't think it's possible. It's a little bit of a theoretical discussion to possible to ground the computer, but this grounding happens naturally in humans for a variety of reasons I said, or any biological organism. One is you've got this small world's architecture in your brain. So the sensory areas are speaking to the goal areas, they're speaking to the motor areas. They are all in communication with these cyclic redundant loops. So everything is seeing everything on multiple levels and scales. So as a result of that, you are naturally learning the object at multiple levels. As of now, like a computer, how are you gonna give it drive? How are you gonna give it motivation? Well, you could program it to do this, et cetera. That's fine, but it's not really solving the grounding problem in the sense that it doesn't have an innate intrinsic complicated purpose in the world to which it's able to naturally relate all phenomena to it on multiple levels and scales. And that's the unique feature about animal intelligence, a particular human intelligence. You do these things naturally. Yeah. And to be able to build an associative web for a robot that is trying to compete against the world's best tennis players is much harder right now than it is to have the perception systems of an autonomous vehicle that already has so many cameras that we don't have. We only get to look forward and we have to turn around versus the slurping of data that is able to be done. So we're already seeing sophistication. Yeah, absolutely. Yeah, speak about this for us. Well, the case of object recognition, and again, I don't wanna get too technical on it, but this is a problem that essentially can be overcome with big data and Moore's law. All right, so in order to solve, and I don't think self-driving cars are as near our future as people say, because I just think they don't really generalize, so all of a sudden it's a cloudy rainy day, something bad's gonna happen, all right? You can train it on all the data you want. When you put it out in the real world, something bad's gonna happen, something different's gonna happen, and that's gonna cause consequences. But that being said, certainly the performance of object recognition systems clearly rivals human performance, and that just happens to be a problem where big data and computing power can overcome it. And for a variety of reasons, motor control is a harder problem, in part because I have many degrees of freedom to my body, and they all interact in highly complex, non-linear ways. And so if you're gonna hit a ground stroke and move your arm, the degrees of freedom and the continuous signal necessary is in such a high-dimensional space that I can't really overcome it with a lot of training data and Moore's law. That's computing power. I can't throw training data and computing power at every problem. Some problems are more amenable than to others, and this unfortunately is an aspect of artificial intelligence that's completely neglected by the AI community, that they just wanna keep throwing these success stories, that the algorithms behind current success stories to future problems without thinking, well, maybe I need to match the problem to the architecture. I wanna see what your thoughts are on this to see if we can continue on this relatable example. We are working on, when a child's born, we're working on effectively helping that child build a mental map, an associative web of its reality that sees to, for it to be able to find a meaning or purpose in the world, make its basic physiological needs be met. Absolutely. So that is kind of this general way that we're trying to help with this mental model, okay. All right, and then with, and it seems like everything right now with the artificial intelligences are very narrow intelligences. Highly constrained. Highly constrained intelligences. And I really enjoyed all of the ways that you, that you really, you're really helping out with this associative web versus these narrow and also just neurons versus computer chips. These are, there's gonna be some sort of a weird potentially Venn diagram plus new things that we have yet to really think about that. Yeah. But no one's in that space right now because everyone's dominated by what's called deep nets, which is the current architecture that's successful in a few problems. And no one's actually thinking logically about, well, how far can this go? As you probably know, there's, this is the third AI revolution. First one started in 57, 58 by a guy named Rosenblatt. Did excellent work, then Minsky and Parpe, both of them were at MIT, showed that these networks can solve certain problems.  They had back prop current algorithms and architecture behind today's AI. That work, they went fairly far and then boom, support vector machines, which was a different simpler algorithm, overcame them. So everyone forgot about them. But Moore's Law kept cranking along and then eventually, the guy you mentioned, the Russian guy in Hinton, I can't remember his name, came out with that paper in 2012. Oh, Ilya. Yeah, eventually the computing power and the data got big enough so that all of a sudden, wow, you can do object recognition in a level that rivals humans and support vector machines fell by the wayside. What's going to happen now, right? I mean, these cycles repeat. If you don't understand history, you're doomed to repeat it. There are gonna be some limitations to these problems that are discovered. It's better that we discover that sooner than later. So great, in some domains, they work, we use them, we enrich our society and our community through their use. And you were literally just teaching a class where I got the privilege to sit down and see your students debating about what the limitations are on deep nets. And that was so interesting that you're already trying to push with different tools, push the edge, yeah. Because what I want is I want people to think about different paradigms for understanding the brain. This is still the unexplored scientific country. We do not know how it works. If we just relax our guard and say, well, deep nets do certain things well, we're not gonna explore sloppy, slow modes of parallel computing that somehow miraculously give rise to the, it works, our brains work, animal brains work. We don't know why they work so well, but there's something about them that is truly exceptional. That's what we need to discover. And to just use the algorithm and architecture that's most relatable to our current computing device, the computer, and to say, well, maybe that's how the brain works, that's naive and ignorant and it's not true. Full circling back to the constant age of stimulus versus the longer, slower, focused periods of creative endeavoring, creative exploration. So what is that process that's occurring in the mind? And it feels like it's a hijacking of the short term. I agree. So essentially, if you buy into this framework of association being an important part of our computing, anything we can do to enhance our associative infrastructure will help our brains do their thing, right? For, as I said, for mnemonic techniques, people put a lot of their old information in order and then when they learn new information, they mesh the old information and the new information so they kind of expand the web. And sleep is so good. Well, sleep is very important for everything, right?  Absolutely, it helps consolidate, it helps everything. Now, if someone is just basically stimulus-driven, they're not gonna be forming certain high order associations. They're forming low level peripheral associations, kind of stimulus response type behavior. We wanna go deeper than that. We wanna create associative infrastructure from radically different parts of our brain. This one mnemonic expert, this guy, Jim Carroll, that is a good friend of mine. The man who has the greatest long-term memory in the world, as far as we know, and he just knows a ridiculous amount of information. They did an FMRI on his brain and he's got these really thick tracks communicating information from one part to the other. I mean, it's not a surprise, right? If you spend a lot of time building associations, you're gonna build your associative infrastructure. It's hard to do that if you're basically doing stimulus response, this text, this thing, this button, right? I mean, you gotta think deeply to create new, novel, meaningful associations. Yes. Yes, and so these meaningful associations, I think are potentially some of the most important ways to both push the edge of knowledge with different fields, connecting different fields, but also to figure out the associations between the existing fields and figuring out how to make it relatable, more relatable for children to be able to rocket them out faster to the edge of neuroscience or to the edge of a different field because it's both this interplay between pushing the edge further, connecting different vertices on the edge, and then I think a really strong synthesizer and a person that knows metaphors and analogies. Absolutely. And the more information you put in your brain, the more likely you are to create the right connections for new creative insights and discoveries, right? This is why you can't defer all of your memory to Google and to electronic devices.  If you wanna be a scientist, you shouldn't just know your science, you should know related scientists, are you saying so that then you can go with the interdisciplinary boundaries, make connections, what principles carry over from here to there? One of the best aspects of my training for computational neuroscience was the fact that I took classes not just in physics and math, but biology and chemistry. So when I had a broad scientific background that prepared me to think about the brain in scientific terms, as opposed to a lot of people who just think of it in terms of engineering and computer science terms, which is in my opinion obviously not the app metaphor. And now I wonder on these techniques that are really old that help with mnemonics, I wanna know is where you sit on memory palaces, on meditation, on long periods of focus. So teach us about where you sit on these ancient techniques and how they can help us. So the way the ancient memory techniques work is very simple. It's method of loci, memory palace, et cetera, and I'll just do it very quickly. You have some part of your personal experience, some, for example, set of locations on your route to school, your journey to school, or the rooms of the house you grew up in, or anything like that, where you have a serial ordering of information in the form of a spatial map. That's kind of your old skeleton structure of information. Now I wanna learn, say, all the capitals of the world. Then what you do is you take a capital, the first capital, you put it in the first room, you tell some story that helps you remember it, that's an associative technique, and then you go to the second one, the third one, et cetera. And the techniques are obviously much more elaborate than that and they involve all kinds of different types of association, but that's the basic structure. You have old information, you have new information, you merge the two using the faculty of association.  There's this old paper about the magic number seven, and you may have heard of it, it's one of the most cited papers in all of sociology. It was written a little bit tongue in cheek by a Harvard psychologist, what? The working memory. Yeah, the working memory seven, right? Seven plus or minus two. It's actually, if you do these mnemonic techniques, you'll be up to 50 in three months with 20 minutes a day. I mean, and this is documented, this is not, I mean, this is why this was so popular in ancient Greece and ancient Rome and in societies before there was a means to record information. So one perspective would be, well, now we can record information, so I don't need to do this stuff anymore. And that's true, I mean, in a certain sense, why do I go to the gym if I got a bike or if I have a car? But the way that I look at it is if these associations, forming these associations, exercises a more general associate of faculty that it's useful for pattern recognition, concept formation, creativity, et cetera, it's good exercise for your mind. It's exercise, not that there aren't other types, not the only type of exercise. I think this is one of multiple types of exercise. And another thing it does, which I think is also important, focus obviously is a big problem. We live in an ADD society, everything stimulus-driven, you're responding, the use of these mnemonic techniques requires internal introspection to an extent that other forms of memorization or just accessing it online don't. And it's kind of important, at least I believe, for proper intellectual development that you sometimes turn your focus inward and concentrate. And I think meditation does the same thing. I'm no way an expert on meditation at all, but my understanding is that it allows you kind of to focus longer and to be in your mind and not of the external sensory rush that's constantly fluctuating. And I think that's good for thinking and creating and ideating in general. Yeah, this really relatable example I think is when we prepare for a presentation. Because we so often take our computer or our phone or we write notes on our hand or whatever we do, versus when we have, I remember for the TEDx San Francisco talk that I gave last year, I remember building out one of the most beautiful imaginative structures for me to be able to go and remember the pillars of the talk that would then cascade all of the sub points that I would already know. And so to be able to associate senses is one of the big things. And I like how you gave the example of childhood because those are such early formed memories, vivid memories that if you put the points on your walk from the bedroom to the living room. Exactly. And when you add in these massive exaggerations and senses that they're on that stop is where the smell of the apple pie comes in and that's where the massive purple elephants were. And so these are those ways to be able to retain. And I just love helping with memory. I myself want to, and I'm going to continue working on it at a deeper level because it just seems to be so critical. But I wonder on- Oh, by the way. Yeah, yeah. This is not new. I mean, Francis Yates in the 60s wrote the book The Art of Memory and you heard of Moonwalking with Einstein by Joshua Four. I mean, this stuff is literally 2,500 years old at least and it probably dates back since the dawn of civilization. There's nothing new about doing this. Especially when the resources that we wanted to put the information on were so limited. Exactly. We didn't have the ability to etch into stone like holy cow, now it's just pressing keys on a keyboard or speaking into the device or recording it. Exactly, yeah. Completely, hugely different. I'm curious as to the trade-off that occurs, the opportunity cost that occurs between, it depends on the weight. What is the weight of the information that you want to store? Because sometimes the weight of the information maybe I want to put into the appendage of the technological device. And maybe that's one of those reasons because it would probably be more difficult for every single one of these shows if I went and passed my time building a memory palace on every single point that we were gonna hit versus having it on a whiteboard or a computer that we could hit. I got you. I mean, I think the point is you want the most relevant key points to be put in memory. You can't do it all. I'm not saying we shouldn't have books or diaries or pieces of paper because there's so much information you can't retain vertically everything. So this is not a diatribe against all storage media. In fact, I gave this interview for, what was it, the German Der Spiegel, I think. The Der Spiegel. So the whole article was in German. And at the end, I wasn't sure what it said. So I typed it in Google and it said something like, I am fighting against two of the greatest adventures of all time, the computer and the printing press. And I'm not fighting against the computer or the printing press. I'm acknowledging that they're good and they're important. I'm just saying, just as you go to the gym in order to exercise, there are certain mental exercises involving memory that are worthwhile. That's it. Exactly. That's it.   So the question is where is the balance and how far has society tipped? Not for us, our generation, I'm older than you, but I'm really concerned for the younger generation that doesn't have anything to memorize when they go to school. The ones that are growing up with the smartphones, it's just the example of really, you're giving a presentation just, or you're telling a story, like let's say, you just literally just want to tell one person a story. Build a memory palace of that story and then you'll see how much better you can retain it and speak about it. Similar with meditation, just go and try it, right? Just go and try it. Just try it. And those are old things too, right? That's thousands of years old. These are not novel, risky techniques of unknown benefit. What's happening to the neural architecture with the children that are born with smartphones? I mean, that's a complicated question. I don't know. I just think behaviorally, there's a bad trend towards attention deficits when everything is stimulus driven, right? When kids don't have time to think, to create by themselves and everything is a video game or television show or text or whatever. At some level, your associative infrastructure isn't as deep. It's more peripheral. It's at the periphery where it's being built, but you're not making all the kinds of deep cross connections that you want to make. And I'm not going to pretend I understand how this all works at the system neuroscience level because nobody does, but it does seem to me intuitive that the denser you can build that web in a meaningful way, the better you'll be. And I guess I'd put it like this. Is a child gonna become a great mathematician if they didn't memorize their times tables when they're six? And I would say, no, I'd rather have the kid learn the times table at the age of six. I think he's more likely to go on to be a great mathematician because that's in his head in memory. And then he can put other things in memory. And eventually there's enough stuff in memory he can start doing hypothesis testing. You gotta put a certain amount of stuff in there to get some good results out. And my concern is we're not putting enough in. And an educator's response might be, well, we need to teach critical thinking skills instead. Well, first of all, how well is that working? But second of all, that assumes that association is not an important part of critical thinking, which of course it is. What is critical thinking, right? How would they define it? So the idea, remember, this all comes from the computer architecture. Computing and memory are separate. They're not in our brains, okay? This is not the von Neumann architecture. Computing and memory are not separate. They're integrated, they're enmeshed.  And that perplexes them a little because they're not used to thinking of memory as a dynamical entity because they're not used to thinking of content addressable associational memory, even though we kinda know that's the way it is from a theoretical standpoint. So if memory is a compute and you want a lot of associations, you don't want your kid growing up without any of them. And what would you say right now on the conversation you were having with your students, what are people trying to figure out that is not the deep nets? Where can we take our flashlight and look? What does it seem like is gonna be pushing the bounds for AI computation? So there are a lot of areas. One is unsupervised learning. And the idea is that these are very unrealistic systems where you have a very specific error signal that's completely biologically implausible. But we manage to self-organize information kinda naturally. A kid doesn't necessarily need a teacher. He figures things out by touching, right? By going around feeling, looking at things from different perspectives, sitting on it, all these different things. So unsupervised learning and there are a variety of techniques that are out there and they're starting to come more into prominence, self-organizing feature maps, et cetera. There's an old literature on this that's very important and that people need to pay attention because ultimately we can't have this teacher, this tight supervised learning system be the model for how we learn because kids don't need that tight supervision in order to learn. They learn on their own from exploration and that's a self-organized type of learning. Also I think the dynamical systems approach which is ignored, the dynamical systems approach is essentially more in line with other types of sciences, less along the line of touring computation where a whole bunch of serial computations are made. Instead let's think about a dynamical system that's largely connected in parallel but does something useful in a short time even under noisy conditions. So there are a couple of other areas, all of which are challenging and also complex systems theory, right? The brain is the ultimate complex system, a complex system being defined as the whole being more than some of the parts that exist at multiple spatial temporal scales, et cetera, et cetera. It's hard math to figure out how these things work but that's another area of math that I encourage my class to look into. The whole point though is that these are all unexplored areas and none of them as of yet have proven that useful technologically. So what dominates is deep nets because right now hundreds of billions of dollars is being spent on them for a few technological applications. So that's a great imbalance. That will ultimately be corrected. Again, these boom and bust cycles and it will self-correct but the question is how much financial and human capital will be kind of misspent in the process? And it also helps so much that when you are with your students that you enable this nuanced discourse to actually occur. You even said there's a lack of comedy with people being sometimes challenged. We have a sage on the stage and we don't feel comfortable raising our hand and interjecting, giving a little bit of pushback, arguing a little bit in a friendly, nuanced way with love and compassion but wanting to get to what are the best ideas. And so I want you to speak about what that's been like as a research scientist and professor in academia. How do you feel about that? Well, I feel science has changed for the worse along those lines but for understandable reasons there's a lot more funding pressure on science. I mean, you're probably well aware of this. I'm sure all the scientists you speak to tell you about this. So when funding gets tight, obviously that can lead to certain negative tendencies including the fact that scientists then have to have a stronger will to brand themselves in order to kind of stand out above the fray. So I mentioned that article science in the age of selfies and that's really what goes on a little bit. Again, understandable reasons. You gotta survive. You gotta make a living. And so there's a lot less, again, interconnection. Okay, reaching out to others, questioning others. I mean, just take, this is not a good example in the sense of how extreme it is but in the old days like Boer and Einstein they had these debates on quantum mechanics and they had them all the time. They fought, and they're great friends. They fought, they fought, they fought, they discussed. Some people could say those debates, you know, what's the productivity? Nowadays it'd be like, well, how many papers are resulting from the debates? But back, of course, those were great science. They didn't need to worry about that because they already had plenty of papers. But the point is is intellectual rumination, ideation, there's not enough time for it because that will by definition cut your short-term productivity as far as publishable items. And if it's a dog eat dog world where you're competing for grants that have a success rate of five to 10%, what are you gonna do? So scientists is in a very difficult situation these days because the incentive structure is completely out of whack. Yeah, that's so critical that every time that we try and have this intellectual nuance, rumination, reflection, discourses that are not being productive during that time, not building your brand and not competing in the attention economy at that moment. Exactly. And this is one of the benefits of being able to do things like this, I think, is that we get to have this nuanced discourse potentially inspire and engage other people to care about it and that kind of brings those two things a little bit together. So hopefully there's maybe more of these, here we are, right, eye to eye, human to human while other people can watch from around the world that maybe don't get the opportunity to sit down with an MIT research scientist. I encourage my class, I mean I deliberately tell my class I want you to do 10% of the speaking. And I don't want it to be, and they're like shocked by this because it's just not the way the system works. They're supposed to go, retain information, do things, no, I don't want them to do that.  I want them to argue with me. Yeah, yeah, exactly. I love that. And that way you can get the young 20, 30 year old minds, even younger 15 sometimes to just, sometimes throw you a good interesting curveball and then that can be how we can, they help teach me. Exactly. If they really want to think about these things I'm sure they have plenty of things and I have been taught things by my students and I will continue to be taught things by the students and I hope the more they teach me, better off we both are. And on the way out, this question about geopolitics, you mentioned at the beginning in your state of the world answer, it seems as though there's a period of deep spiritual actualization and transcendence that we need to go through to understand more of the sheer miracle it is that we all share this rock orbiting a star together and that we were so privileged that 100 billion humans before us got all of these ubiquities and the basic physiological needs that we have. So on that geopolitical level is there a thought that comes to mind to assist with that spiritual growth that we all need? I think the hard part is this concept that is very non-intuitive that in molding our environment to shape say our short term needs, we can actually do long term structure to the whole planet. And it's understandable why we don't think this way because for the entirety of human civilization we couldn't affect our planet adversely, right? When we were just hunters or gatherers there's nothing we could really do to adversely affect our planet. So that encourages unbridled ingenuity where you do whatever it takes to make the quality of your life better. And that's a great system and that's worked. But now there are these externalities rising. Even economists acknowledge their externalities, pollution, global warming, all these things. These are hard to wrap our head around unless we understand them. I am hopeful that the next generation, the younger generation will understand them much better because it's being inculcated in the school curricula. My only concern is, will it be too late or how late, how much damage will be done? But ultimately we have to get our heads wrapped around the idea that we can actually adversely affect the planet doing something trying to be ingenious at a local scale that never caused any harm before. But it does now. That's just the way it's worked. We have to adjust, we have to adapt. And if we don't adapt, we're gonna pay a big price. But that is a very, very difficult concept. Yeah. And on the way out, last two questions that we like asking our guests. First question is, are we in a simulation? No, to me that's kind of a ridiculous idea. One of the other neuro myths that I like to dispel, I have a little neuro-mislecture in my class and this is like this is that, you can download your brain to a computer. I don't know where this stuff comes from, but it's really hard to understand why people would say these things. And for the downloading the brain to your computer, which surprisingly I actually gave that question to my class last year. I haven't yet asked this year's class. And more than half of them thought that they could download in the future. It's in theory possible to do this. And this is just a fundamental misunderstanding of the kind of the grounding problem, right? I mean, I can do computer simulation of a lot of different things, right? And what I'm doing is I'm focusing on one or two features of that thing. It doesn't make it isomorphic to the thing. If I simulate, right, a water molecule on my computer, is it wet? Am I gonna drink it? No, but what you're doing is you're simulating various features of it, various aspects of it, right? But it's funny, you know, people don't ask that question, but they're like, maybe we can put our brain, well you can't put a molecule of water onto a computer, right? And then there are all kinds of grounding issues and parallel systems versus discrete systems and Turing machines and stuff like that. So I mean, it's wrong on multiple levels, but I mean, I guess the problem there is that sci-fi culture is so prevalent that the people don't necessarily go to the scientist to ask them to think about these things carefully. They just say, oh, isn't this a cool move? And it's the same with chips in the brain, right? Everyone's putting a chip in the brain. Chips in the brain don't really work very well. It's gonna be a long time, if ever, before chips in the brain's work. So you gotta think about these things carefully, right? And not just jump to conclusions because it's cool. And it's in that scientific fiction movie, science fiction movie that I enjoyed. Yeah, yeah. And really think deeply about ethical considerations, the democratization of the technology, the accessibility of it for many other people. Absolutely, all that's important. But that people know about. They think about those things, actually, which is good. Well, the big imagination's very important too because maybe there are potentially ways in a very long time from now to be able to do this. Oh, I'm all for that. But people need to think about these things carefully instead of just kind of going off the handle and saying, wow, this is possible. I'm sure I can do it. No, this is very deep, very nuanced, very subtle question. You need to think carefully about it. Yeah, so if you were to simulate a water molecule in a world, how could you make that molecule feel wet to the animals in that world? So that's a great question. And how about the last questions? What is the most beautiful thing in the world? Well, I mean, I think there's a certain elegance and symmetry in the laws that govern the universe and at multiple scales, not just the scale of physics, but chemistry and biology. Somehow, elegant, codifying principles filter down at almost all levels and scales that allow us to comprehend the world. You know, the old quote, the most incomprehensible thing about the world is that it's so comprehensible. I believe it is attributed to Einstein. I mean, it's an amazing aspect of existence that there's so much of it that we can understand. Even though there's lots we don't understand, we certainly understand more than we almost have a right to think we could ever understand. So that's always boggled my mind and probably will continue to boggle it. Yeah, this has been such an honor. Thank you, Robert, for coming on to the show and teaching us. Thank you very much. Really, we really appreciate it as well. I want everyone to share more ideas around the differences between computers and brains around neuroscience and consciousness. Definitely get talking more about the nuance of these concepts with your friends, your family, online, your coworkers. Get chatting about it and sharing about it. Check out Robert's link below in the bio as well. And also support the artists, the entrepreneurs, the organizations around the world that you believe in. Simulations, links are below. Support us, help us continue doing cool things like coming to MIT and doing interviews. And go and build the future, everyone. Manifest your dreams into the world. Thank you so much for tuning in and we will see you soon. Peace.


My name's Regan. I work for Anthony J. Lyon, International Detective Bureau. They call me The Lion's Eye. Wednesday at nine and CBS brings you Jeff Regan, investigator starring Frank Graham as Regan with Frank Nelson as Anthony J. Lyon. So stand by for mystery and suspense and adventure in tonight's story of... The 500 Santa Clauses from Mexico. Like my boss, the lion said, it got you into the Christmas spirit early, all those Santa Clauses. But what it got Judd Brown into was the morgue. Judd Brown was the six-foot-tall part-time Santa Claus from Jensen's department store. But got him into the morgue was one of those six-inch-tall Santa Clauses from Mexico. Well, the thing teed off on me when I walked into the lion's office. He was looking at somebody's picture. And the way he was luring, I could tell it wasn't gorgeous George. Oh, hello, Jeffrey. You're just in time to meet our new client, Lorna Lee Lovely. Yeah? Yes, here in this picture. Lorna Lee Lovely. See what you mean? Yes. She gave me to understand that wasn't a real name. Professional name, of course. She's a dancer. I figured. So naturally, she needs a professional name. Something suitable. And I must say her name is suitable, Jeffrey. Lovely. Lovely. Liquid eyes, rosy cheeks. Beautiful. And that reminds me, Jeffrey. She works at the Rose Ballroom. She's a taxi dancer. She wants you to get right over there. It's something about finding her husband. Well, those looks, she can't find her husband? Yes. That's what I thought, too. But you see it, well, it happens that he's dead. He lets that stop him? Well, she thinks he's dead anyhow. It's something about collecting the insurance, proof of death, that sort of thing. I got it. An opportunity to help out a poor girl in distress, Jeffrey. Get her the insurance money before the holiday. Christmas is less than four weeks away, you know. OK, Fatso. You check the insurance companies. And give me that photo. Maybe Christmas is going to come early this year. Lornerly Lovely was what you wanted to wake up and find under your Christmas tree all right. Only not too close to the branches. It'd be a fire hazard. I drove across town and parked a couple of blocks away from the Rose Ballroom. Walked back and went in. I shoved through a cover of seagulls waiting around to get themselves danced with. That was when a wave of taboo came busting through. Rumble, Mr. You Lornerly Lovely? That's right. Tickets go on my expense account. Then you're Regan. You picked it. Rumble, Regan. You hired me. You don't look like the kind, Regan. Kind? To rumble by some good dream boy. Well, I'll do that a little bit. The kind? You know. Yeah.  You dance all right. Thanks. Maybe I'll get a night off sometime. Maybe. You talk brief. I like that. You gave the lion a retainment. That wind tunnel with a cigar in the middle? That's the lion. Who said he'd send you over? He did. Yeah, he did. What do I do, lady, for the 50 bucks? I'm looking for a husband. The 50 bucks? There's a hundred more in it if you find him dead. Dead? Dance, Regan. I used my ticket. Oh, no. No thanks. I'll see you around. Oh, wait a minute, Regan. I'm not hiring a gun. He's dead already. Then we can talk. Over here. Manager's office. Won't he object? He's not here. He hates the joy. Better. Now he can have privacy. That's what I thought. How do you know your husband's dead? He's married to me, Regan. And he hasn't been home for seven weeks. He's dead. See what you mean? And he was insured for 10,000 bucks. So I prove he's dead. You collect the insurance.  Your husband's name? Dodd Brown. Brown? My real name's Lorna Brown. Description? He's six feet tall, dark hair. Scar on the right hand, he got fishing. Got a hook in his hand. He's nuts about sports, baseball, football. If I find a corpse in the stand at the Rose Ballroom, I'll let you know. What else?  Oh, yes, one thing. At Christmas, he sometimes works as a department store stand-up car. That's nice work. When you can get it. Where does he get it? Around where they want him. I'll check. One question, Mrs. Brown. Yeah? What if I turn your husband up alive? Tell him, Regan. Find out where he lives and what he does. Call me. Anytime, day or night. Live at the Sunrise Motel Grindale Boulevard. Don't lose him, Regan. From the way the green ice lit up in her eyes when you said that, it figured her husband wasn't so dead. It also figured he wouldn't be easy to find. I turned her back over to the diamond struggle crowd and headed for my car. There was an alley led down behind some Chinese chop suey joints. It was a shortcut. My mistake was I turned into it. Because the guy turned right after me. I ducked around a corner and waited. Pretty soon his steps came up. Then a shadow. And that was when I grabbed you. What? What do you think you're tailing? Let go of me. Let go of me. I'll get some answers. No, no, no.  Let go.  Hold still. Are you just a kid? I wasn't doing anything. You were tailing me? Yes, yes, I wasn't. Why? Well, I... Why? I saw you with that girl. Girl? The taxi dancer in the Rose Ballroom. I watched you through the glass doors. I didn't dare go in. Wait a minute. Let me see your right hand. Why? Let me see it. What do you want with me? Fish Hook's car. What? Six feet tall, dark hair. You're Judd Brown. Yes. What have I done?  Why? Lorna Lee Brown, alias Lorna Lee Lovely. I'm not married to her. I never saw her till this week.  I haven't time for them. I'm studying to be a corruptist. Sure. At Joshua Addison College of Pedal, Caron Cure, and Eagle Rock. I haven't time for anything but people's feet. And right now, being Santa Claus. Give me that again. Being Santa Claus. The department store Santa Claus at Jensen's downtown store. Well, that checks anyway. Checks? Look, I don't understand this. You aren't one of them, are you? I'm Regan, international detective girl. A detective? Oh, then maybe you can help me. I think they meant to kill me. They? Yes. You better give it to me from the top. Well, last Friday I went to work at Jensen's weekend. At Santa Claus? I needed the extra money. Go on. I worked Friday and Saturday, and then Sunday I studied at the college library on the tibial tarsal articulation in the jitter-grade animals. Get to Monday. Well, Monday, that was the first time I saw her following me. Lorna Lee Lovely? Yes. And that same night when I was leaving the Metatarsal Foundation, somebody grabbed me in the dark. Who? No, but I fought and I got loose. At first I thought it might just be a holo. Something changed your mind? Yes. Tuesday somebody tried to break into my room at the boarding house, so I moved out Wednesday. And hid out? Yes, Mr. Regan. I moved into the New Canton Hotel and I didn't come out till tonight. Now, but you knew exactly where to find Lorna Lee Lovely. Well, yes. I saw her picture in the Rose Ballroom advertisement in the examiner. I thought that if I hung around and she didn't see me, I might find out why she was following. Mr. Regan, behind you, look out. I turned too late and got one alongside of the head. I thought I was seeing light from the naked bulb at the end of the alley, but the light began to spread like an orange bruise. Then a fist the size of a pillow drifted up. I couldn't see anything else. I rested my chin on the pillow. I shouldn't have done that. When I opened my eyes, I was looking up at some writing on a plaster wall. It was upside down. When I got to my feet, the writing still looked upside down. That was because it was Chinese. I was in the same alley behind the chop suey joint, only Judd Brown wasn't around anymore. He was the guy who sapped me. I knew what answer I'd get when I went back to Rose Ballroom. Lorna Lee Lovely wasn't around either.  Sure she had. I decided to waste a nickel on a phone call to the Sunrise Motel where she said she'd lived. That was when I got surprised. She wasn't in, but she did live there. That made it worth the drive out. Help you, mister? You work here? Yeah. And her speaking to do. My place. I own it. I'm looking for Lorna Lee Lovely. Left. Over that way? Gone, I mean. How long? You ain't lucky. Maybe three minutes? Checked out. Left no address? Left no address. She alone? You ask questions, mister. I like answers.  Yeah. Here's my identity. Huh? Never seen a private detective except in the movies. Well, what can I do you for? Let's have a look where Lorna Lee Lovely lived. Oh, that cottage over there. This way. Mind the grass there. Don't grow good out here in California. No rain. Never no rain. Just small. Gets in your eyes. All that small commissioners. Yeah, yeah. Anybody ever come over to see a thorn? Oh, one guy. Walrus. Walrus, me and the old lady called him. Big guy. Black guys. Oh, Walrus Mustache. Dark skin. Maybe a Mexican. You know his right name? Felipe. She called him. Felipe, huh? Yeah. Here's where she lived. Hmm. Kind of left in a hurry, didn't she? Oh, the old lady ain't gonna like it. This is a mess. All these paper cartons around? Oh, them. Them's what them Santa Clauses come packed in. Santa Clauses? Christmas sidelines. She sold them to department stores. Walrus would bring them. The guy she called, Felipe. Yeah. Hey, here's one of them right here. Down in these flavors. A busted Santa Claus. Somehow when I say it, I don't like the way it sounds. All of a sudden I was awful anxious to visit Toyland at Jensen's department store. I did. It was Friday night, so they were open. But Judd Brown, part-time Santa Claus, hadn't showed up. But there were some other Santa Clauses there that sent me looking for a white carnation. Hey. Yes, sir. You the floor walker? Yes, sir. May I assist you, sir? What about these things? Oh, yes, the little pottery Santa Clauses. You'd like one, sir, to brighten heart and home for the coming yield season? Maybe I saw one a little while ago. You know where they came from? Why? Well, yes, Mexico. La Paz in Lower California. You got any broken ones? Well, now it's a very queer thing that you should ask that. Why? Because somebody else came here and asked exactly the same question. Don't tell me. Tall, good-looking, green eyes. About 30. Why, yes. How could you know that? What figures? Name? Lorna Lee Lovely. Well, I don't know about that. The Santa Clauses are imported by the La Paz importing company, Small Importers. We took 500 on consignment. She merely said she was a representative of the La Paz Company. Sure. Just a moment, sir. I don't know that I should discuss this with you. Oh, here's my identification. What was the connection between Judd Brown and these Santa Clauses from Mexico? The broken Santa Claus. Go on. Well, last Friday, a week ago today, Brown noticed that one of these little Santa Claus sets changed. Naturally, Jensen's does not sell damaged goods. So you gave it to Judd Brown. As a matter of fact, I did. Going through the proper channels, of course. He said he wanted to give it to his nephew for Christmas. You gave the broken Santa Claus to Brown. Yes. Then Lorna Lee Lovely shows up. On Monday. And asks about the broken Santa Claus. You tell her you give it to Judd Brown. Yes, that's what I did. Yeah, it's an inside bet that the next question she asked was, how do I find Judd Brown? Exactly. I didn't tell her. So she went to the personnel department after she left me and asked questions about Mr. Brown. Now Brown has disappeared. Jensen's toyland is without a Santa Claus. If there's anything you can do to help us. I'll see what I can dig up. But dig up was too true. I got down to the street in front of Jensen's store in the late editions of the papers that hit the stands. They had a big story. Front page. Colorful. A broken Santa Claus had been found on the railroad tracks near Union Station. Only this Santa Claus was real. Or it used to be. You guessed it. Judd Brown. Well, I checked in at the international detective bureau office to fill my boss, the lion, in on some angles. Like, for instance, a female of the blowtorch species named Lorne Lee-Lovely wants to find Judd Brown. So she makes up a phony story about him being her husband and hires me to do the trace job. But Brown finds me. Then some muscle finds us. He tags me out and when I wake up, Brown's gone. The muscle's gone and when I check the Rose Ballroom, Lorne Lee-Lovely's gone. I took care of my 50-buck retrainer. But the lion doesn't like to lose a client in the middle of a case. And you say Miss Lorne Lee-Lovely checked out of the sunrise motel just before you got there? I'm so thorn-said. He's the owner of the joint. Oh, pity, Jeffrey. Our client. Our client gave us a sour pitch. So it would seem. I checked the insurance companies as you suggested, Jeffrey. No Judd Brown insured with any of them. He could use some insurance now. So I've just been reading in the paper. Dead on the railroad tracks. The poor boy got the damaged pottery Santa Claus at the department store. Then, quite obviously, Lorne Lee-Lovely and that man Felipe set out to hunt him down. But why, Jeffrey? Maybe there's something in the Santa Claus. You know that? It figures. Yes, I see. Smuggling, you mean, over the Mexican border. Could be. Of course, of course, the man Felipe or a smuggler perhaps. Marijuana, Jeffrey, in the Santa Claus. Who knows? That's one of the things I'm going to find out. It figured that tracing the broken Santa Claus that got Judd Brown killed would give me my answers. So I decided to start at the beginning. I went to the New Canton Hotel where Brown had been hiding out. A Chinese kid with glasses the size of portholes was sitting behind the hotel desk. You want something? Judd Brown lived here. Why you want him? I want to see him. He's not here. Who are you? Your name, please. Never mind reaching in the drawer. What? You won't be needing that gun. Oh, why you want Judd Brown? He murdered. Check. What do you know about it? The gongse. The kooloi. Go away. Go see. Sure, I got figured myself. Anything else? I know nothing. He come. He go. He dead. Maybe I could take a look at his room. No, no, no. Police say no. They've been here already? Oh, here room. All seal up by police. They take everything. Police station. Everything? Oh, yes. Clean up whole room. Thanks. But that didn't turn out to be any good either. The Santa Claus wasn't in the stuff the cops picked up in Judd Brown's room. I phoned the Chinese desk clerk at the New Canton Hotel descriptions of Lorna Lee Lovely and Felipe. Figured they might have beat the cops of the hotel and grabbed the Santa. But he'd been on duty since 6 p.m. and you had to pass him to get upstairs. And he'd never seen or heard of either one of them. That made the score at the end of the quarter nothing than nothing. But when I dropped by the office half an hour later the lion's blood pressure had hit the gong and won the Cupid doll. And it looked like one side or the other was lining up to score a touchdown. Jeffrey, Jeffrey, here you are. I've been trying to locate you everywhere. You're lonesome. Jeffrey, don't joke me serious. Something has happened. Like what? She's telephoned, Jeffrey. She's telephoned here. You mean Lovely Phones? It's ten minutes ago. What did she say? What was she? She wants to see you, Jeffrey. Jeffrey, I didn't know what to do. Or I'd have notified the police or since she's our client. Okay, so what did you do? Well, I did do... Well, Jeffrey, I... did nothing. It figures. It was only ten minutes ago. Sure. Oh, what was she? Lonely Lovely. Where was she? Yes, Echo Park, Jeffrey, on Glendale Boulevard, the little lake where the boats are. She said she'd be on the boat platform. She said if I could find you... Jeffrey, where are you going? Echo Park, Lake. Do you need both hands to paddle, dream boy? Maybe I do. Maybe you don't. Dream boy, you do everything so good. Thanks. Why are we here, baby? On the lake. To hop. Nobody can see it. Like the law? Sure, that's right. You want to talk business?  So I lied to you. You killed Judd Brown? Felipe. Paired a minute of it. Alley passed the Rose Ballroom. Got scared maybe Brown could identify him. So... Yeah. Dream boy? Yeah. Who's got the Santa Claus? That turns out to be the six feet under question. Maybe you got it. So that's why you want to talk business. I'm the next Judd Brown. Oh, not you, Dream. Don't be a waste. You're too good to go. I was thinking, partner, I got a lot. Yeah. What have I got? Regan, you've got the Santa Claus. Sorry, baby, not my line of work.  You see the guy who doesn't? Okay, bring me the Santa Claus. Sorry, baby. Sorry, Regan. It could have been nice.  Where's the Santa Claus, Regan? I want it, I want it real bad. Sure. Those pearls would look good around your neck. But there were pearls in the Santa Claus? You just told me. You're a wise guy, Regan. Thanks. So there are pearls in the Santa Claus. The lion figured it was marijuana, but it was just one Santa Claus you were after, sister. But there wouldn't be enough marijuana and one to be worth the trouble. What comes from La Paz that's worth a lot of dough? Pearls, baby. I figured pearls. Now, do you want to go, partners? There were 12 Santa Clauses that had pearls in that ship and a 500. A match string with 60,000 bucks, Regan. The broken Santa Clauses had the pearls. They weren't ever supposed to get into the store, but one got away. And Judd Brown got it. And now you've got it. Maybe not. Who else? Think fast, Regan. I'm not scared to shoot. Nobody'd hear the shot backfire from the traffic. That's what they'd think. Sure. Sure they would.  It was dark. Nobody sees that. We're going back to the dock. Shoot, Regan. You won't get me. I'm in too deep. I'll shoot. That wouldn't get you your Santa Claus, even if I had it. I'll shoot, Regan. Okay, shoot. Regan, you stinkin' rat. Okay, give me that gun. No, you... Give me that gun. Oh, the kind of canoe. Give me... I'm drowning. What's the matter, baby? I'm drowning. In four feet of water, put your feet on the bottom. You're not in as deep as you think you are. But you're in deep enough. Felipe's accomplice at the murder of Judd Brown. Come on, baby.  So I dropped Lonely Lovely off at the nearest precinct station and went to my place and changed my clothes. I still had two things to turn up. Felipe and the toy Santa Claus with the pearls in its belly. They figured this way. If one thing can't have happened, but nothing else could have happened, then the thing that can happen must have happened. See what I mean? So I went to where the missing Santa Claus had to be. Hey. Hey, wake up. Yeah, ma. No, ma. Don't sleep on the job, pilgrim.  You'll come back. They work you long hours, don't they? Oh, oh, yes, long hours. You've been on duty here at the desk in 6 p.m. Oh, yes, yes, true. Nobody can go up to the rooms without walking past you. No, no, cannot do it. I got figured. Okay, where's the Santa Claus? Oh, no, no, no, no, Santa Claus. Don't have it. You got it. You took it out of Judd Brown's room before the cops got here. Yeah, but, but, but broken. Santa Claus, all broken, nothing. Yes. You saw Brown's picture in the paper and read he'd been murdered. So you went up to his room for a look around, but you saw the Santa Claus figured Brown wouldn't be wanting it. The Santa Claus, all broken. Nothing. I went for only a small sister. You arrest me? Nobody's going to arrest you, because you're right. It is broken. When you say it's nothing, nope, that doesn't figure. The Chinese desk clerk brought me the Santa Claus he'd borrowed out of Judd Brown's room. The pearls were in it. They were nice. Sleek, fat, pure pearl white. Well, lonely, lovely was in the lock up, so that left just Felipe. I didn't know where Felipe was, but that didn't worry me. I told the Chinese desk clerk that if anybody came around asking questions, it was okay to say I had the Santa Claus. Then I bought a book and some sandwiches and beer and spent a nice, quiet weekend at home. I hadn't been out of my apartment when the lion showed up Monday night. Regen! Now I know you're in there. Regen, open this door. Okay, okay. Jeffrey, this is insufferable. Why haven't you come to the office? What have you been doing? Why haven't you answered your telephone? Run along, Fatso. I want to be alone. Jeffrey, our agency is working on a murder case. Felipe, Cal is cold-hearted killer of that boy Judd Brown. And you, Jeffrey, what are you doing? Staying home and catching up on my reading. Then it's true. You quit. Abandon ship. Betrayed the flag. And eaten 11 liverwurst sandwiches in three days. I'll beat it, will you? I'm expecting company. Company? Jeffrey, what happened to the lights? They've gone out. There's a fuse box down the hall. Looks like the company had been expecting to come. Jeffrey, Jeffrey, it's not that, that Felipe. Could be, lion. Then you found him. Didn't have to. Figured if I had the Santa Claus, he'd find me. So that's why you stayed home. Clever, very clever. Hold it, lion. Stay still, lion. Who's there? Look out, Jeffrey! I got him. He was a blacker, safe on the black blackness of the room. He shot twice at where he thought I was that he was wild in the dark. And I closed on him. The gun went off again and I got his arm. Again. And then I had him around the shoulders and he went down. This is for that staff you gave me in the alley, Buster. And this was one's on the house. Jeffrey, Jeffrey, Jeffrey, that's enough. Yeah. Hey, lion, there's a flashlight on the table in front of the window. Get it, will you? And you. Don't hit me again. Don't hit me again, will you? Now, let's take a look at this Felipe, Jeffrey. Aha! Not Felipe, lion. Thorn of the Sunrise Motel. Have a glass, Jeffrey. Oh, thanks. You know, ever since I've had this mountain spring water dispenser put into the office, I've been feeling better and better. Ah! Oh, splendid for the help. Oh, perhaps I feel good because of the way we solved the Santa Claus case. The way we sound it? Thanks, Fatso. So that Sunrise Motel man, Thorn, was Lorna Lee, lovely's partner and the murderer of Judd Brown after all. That's right. Felipe was just a name he made up and then she followed his lead. The non-existent suspect. The police, the gentlemen of the press searching three days for an inhabitant of the Kingdom of McComicom. Give me that again. In the Kingdom of McComicom. What does it mean? I don't know, Jeffrey. I don't know. I believe I heard the expression somewhere. Wind tunnel with a cigar in the middle. You won't hurt me. I don't know, Fatso. Just an expression I heard somewhere. Jeff Regan investigator stars Frank Graham as Regan with Frank Nelson as Anthony J. Lion. Original music is by Dick Arant. Jeff Regan investigator has heard each week at the same time over CBS. Bob Stephenson speaking and inviting you to be with us again next week at this same time for Jeff Regan investigator. This is CBS, The Columbia Broadcasting System.