[00:00:02] Speaker A: There has to be something that is radically different between what our neurons do and what the chimpanzee neurons do, how we encode information and how chimpanzees encode information. I will argue that maybe these concept cells have something to do with that because concept cells encode information in a completely different way compared to place cells in rats.
There's an underlying question that is all over the book, which is basically, what makes us human? If you want to put it like in one sentence. What is it? What makes me different from HAL 9000, from a supercomputer, and what makes me different from other animals? What is it, what is it in our brain that makes that.
[00:00:43] Speaker B: This is brain inspired.
[00:00:57] Speaker C: Stop.
[00:00:57] Speaker B: Dave. I'm afraid.
I'm afraid. Dave. Dave.
[00:01:03] Speaker C: My mind is going.
[00:01:05] Speaker B: I can feel it. I can feel it. My mind is going.
Hey everyone, this is Paul Middlebrooks. Welcome to Brain Inspired.
[00:01:16] Speaker C: Today my guest is neuroscientist Rodrigo Quian Quiroga, who's the director at the center for Systems Neuroscience at the University of Leicester in the uk.
Rodrigo is known best in neuroscience for his discovery of concept cells, or Jennifer Aniston neurons as they're known in the media. But he's also the author of multiple popular science books. And today we talk about his most recent book, Neuroscience Fiction. I won't read the super long subtitle, but it's a really fun book. He uses popular sci fi movies like 2001, A Space Odyssey, the Matrix, Minority Report and a bunch of others. He uses these as introductions into deep topics like free will, identity, mind reading, immortality, consciousness, all the good stuff. And for each of these topics, he takes the reader through their history and philosophy and what modern science and neuroscience has to say about them. So our conversation is wide ranging and we touch on a handful of the topics from the book, but we start with concept cells and their role in intelligence and how they're related to other cell types described in the hippocampus, like the well known place cells and grid cells studied in navigation tasks. And as we talk about his book, concept cells keep coming up as a key to what makes us human and perhaps a key to our general intelligence and awareness.
I link to the book and relevant papers in the show
[email protected]. if you'd like to recommend a guest for the show, as many listeners have, send an email to Paul BrainInspired Co or I'm on Twitter PGMID. Okay, enjoy.
[00:03:09] Speaker A: Rodrigo.
[00:03:11] Speaker B: Rodrigo, welcome and thanks for talking with me today.
[00:03:15] Speaker A: Pleasure.
[00:03:16] Speaker B: So you seem happy. So you wrote this Book Neuroscience Fiction. And I've read your previous works as well.
My judgment is that you're a happy person. Am I right?
[00:03:31] Speaker A: That's great. I mean, it's great to hear that. Well, if I give you this impression, that sounds very good.
[00:03:36] Speaker B: Okay. Well, yeah. So you wrote this book, Neuroscience Fiction. I love the book, by the way. It's actually, it's interesting. I found myself sort of rooting for you because you really weave in so much and sort of effortlessly. It's impressive how you weave in and out of current science, of course, but also historical literature and the philosophy, and you revisit and cycle throughout the entire book. And I found myself rooting for you because there's just so many examples of classic examples of scientific discoveries and stories. You know, as I'm reading about, for instance, a particular movie, and I'm thinking, oh, what experiments is he going to bring up here? And then sometimes you did, and I thought, ah, I got it right. And then other times you'd bring up many others. So anyway, congratulations on the book.
[00:04:29] Speaker A: Thanks. And maybe that's the reason why I look happy, because it's really like a privilege, I think, to be able to do what we like. And. And I really enjoy that. I mean, writing a book for me was not just to get the book out. It was also that I enjoy every single bit. I mean, of all this time that I spent writing it, because I was digging into things that really interest me and I was trying, as you said, to interweave them. I mean, and I don't see why scientists, we have to stick to just scientific facts. I mean, we cannot get out. Reach out to arts and literature and philosophy to. I mean, to try to get a deeper knowledge. And. And I really enjoy that. Yeah.
[00:05:06] Speaker B: But I mean, it's really difficult to string four or five words together and make sense. So to put. To weave all these things together, it's a lot of work. So it's really impressive. I guess you are known for concept cells, Would that be accurate?
[00:05:22] Speaker A: Yeah. Also known as Jennifer Aniston neurons. So I'm known as the Jennifer Aniston neuron guy.
[00:05:28] Speaker B: Yeah. How do you like being known as the Jennifer Aniston neuron guy?
[00:05:32] Speaker A: It's okay. It's fun. I mean, it's. I can't complain. I mean, this somehow made up my career. No, I mean, after finding this, I don't know, I started talking to the big pops in neuroscience. They were interested in my stuff. I got a good job, I got promoted, I got grants. I got very good Papers out. So I cannot regret it. I mean, on the contrary, I'm very happy that this happened. And I'm very lucky, I think, to have found something that people find interesting.
[00:06:02] Speaker B: Should we talk about concept cells and get them out of the way? Are you sick of talking about them after all these years?
[00:06:06] Speaker A: No, no, I love talking about that. I mean, no, no, no. I mean, I love it. What I don't like sometimes is where people will just stick to Jennifer Aniston and they only want to know about the Jennifer Aniston neuron. And because it's so flashy, or it was so flashy, at least for some time, that it really, I mean, it took me a lot of effort when I was giving talks to get people out of that and go into a really deeper connotations of the finding because people were interested in, oh, yeah, but the neuron did not fire to Brad Pitt. How cool. And this and that. And they said, well, forget about that. I mean, that's funny, that's interesting, we can have a laugh about that. But think about it. I mean, these neurons are really telling us a lot. Forget about Jennifer Aniston and all these actors, Luke Skywalker and take a minute to really think about what this really means and then it gets really interesting. And I will never get tired to discuss that. On the contrary.
[00:06:55] Speaker B: Well, you know, I wasn't even going to mention the name Jennifer Aniston. You brought it up, but you can't help it because that's what people go to. I wasn't going to mention it because I think ideas are more interesting and I think a lot of people think people are more interesting. And we have this celebrity geared culture and I don't know, it's disgusting to me.
[00:07:17] Speaker A: But the reason why, actually I look for that. I cannot say, oh, I found this by chance. No, I really look for that because people were doing experiments before. I mean, I should start by explaining the setting. So these are patients that, I mean, they have their candidates for an epilepsy surgery, which means like a resection of the epileptic focus. These are epileptic patients. They cannot be treated with medication. And the surgeon and the team of clinicians to try to localize where the seizures come from and then evaluate the possibility of a surgical resection that we put electrodes inside the brain, intracranial electrodes, and I mean, due to several technical details, we can record single neurons from them. And the first thing I saw is, well, I interacted a lot with the patients, I talked to them and they're Normal people. They're not just patients as, like, people coming from another world. They're very normal people. And they are not scientists. So they don't care about maybe scientific facts. They care about celebrities. And they watch. At the time, this person watched Friends, and she knew Jennifer Aniston. She knew Brad Pitt, she knew Oprah Winfrey. And I said, well, if they're thinking about this more than Albert Einstein or Heisenberg and so on, well, this is the type of things I should use, because these are the things that are familiar to them. And the chance of getting neurons far into these people is much higher. And this is exactly what happened. Now, the interesting twist of this concept, cells of Jennifer Aniston neurons, is the fire. Two concepts. And what do I mean with that? The interesting part of the Jennifer Aniston neuron, as the Luke Skywalker neuron and many others, the Oprah Winfrey neuron, is that it will fire, say to Jennifer Aniston no matter which picture I show of her. So I showed seven different pictures of her from several different views, seven different haircuts and so on. And the neuron fire to all the pictures of her and to no other picture.
And that's very interesting because you say, well, why. Why would I have neurons firing to her no matter how I show her? And this is something that nobody could find in monkeys, and people have been trying that for decades, and nobody could find a neuron that fires to a particular person no matter what.
[00:09:27] Speaker B: No matter what.
[00:09:28] Speaker A: And. And then that was the first interesting puzzle. And that's when I. When things start getting very interesting for me, because, well, why would I have neurons far into specific concepts in an area that is known to be involved in memory? Because it's known as a mainland hippocampus, which we know since decades, that is critical for memory.
And that's when the discussion got very interesting. And that's what somehow sparked or triggered, I would say, my whole scientific career since, I mean, in the last 15 years.
[00:09:58] Speaker B: I'm just curious, where were you pointed before that?
[00:10:02] Speaker A: Oh, I was. I changed a lot. So I studied physics in Argentina. That's my country. And then I did a PhD in Applied Math in Germany, and then I did a postdoc in dynamical systems. So I was working with chaotic system and modeling and so on, and I was doing well. I mean, I think was good stuff. But then I had the chance to get a Sloan fellowship, which was particularly targeting people from hard sciences to switch to neuroscience. And I kind of, like, draw a line, and I say, well, all the stuff that I did in physics and apply math, that's it. And I really want to get into brain. And I confess, at the time I started in Caltech, I was already like a senior postdoc. I really didn't know what a neuron was or how a neuron. But I knew what neurons are, but I didn't know anything about the physiology of neurons, action potentials and so on. So I started learning from scratch, and I really enjoy it. Yeah.
[00:10:55] Speaker B: Yeah. A lot of people seem to come from the physics background and point of view, but, I mean, neuroscience is so young. Everyone comes from somewhere. So hardly anyone started in neuroscience, you know, because it's almost like neuroscience isn't even a thing, but it's becoming a thing, I suppose.
[00:11:11] Speaker A: Yeah, I agree.
[00:11:13] Speaker B: Okay, so you described concept cells a little bit, and I don't know, maybe we should we talk about hippocampus in the hierarchy of brain areas, regional hierarchy of visual processing, and processing in general.
[00:11:26] Speaker A: Yeah. So, I mean, if there's a classic. I mean, neuroscientists will know exactly what I mean when I mention the word Vanessa. So there's a classic diagram from Vanessa and colleagues where you see, like, all these hierarchies of brain areas starting in the retina, hierarchies for visual processing, for processing of visual information. So you start in the retina, then you go to the lateral geniculate nucleus. Then you go to primary visual cortex and areas B1, B2, B4, inferior, temporary cortex and so on. And what is striking, sometimes I like to show this diagram just to show, look, the hippocampus is at the very, very top. So there are dozens of areas, and hippocampus is at the very end. I think this is a bit misleading because the information processing doesn't end in hippocampus. It's just kind of like a hab. And then it goes back, you have feedback, and you can go to prefrontal cortex, and you have many other connections. So it's not ending there. It's not like an homunculus reading all what the other area says. But the key point that I like to make when I show this plot is that, look, I mean, there's nothing magical about hippocampus suddenly having these concept cells. It's just that hippocampus is reading information that has been processed by dozens of areas before it. So you can expect that the coding in this area is something, or could be something quite refined because these areas are reading out the inputs from other areas, which in turn are Reading out the inputs from other areas and so on. So the information has through several stages before reaching hippocampus. And therefore we do find in hippocampus neurons that respond to concepts, that will respond to Jennifer Aniston no matter what, or Oprah Winfrey. And even if I say the name of the person or if I write the name, so I have a neuro firing to Oprah Winfrey will fire to any picture of her. And even if I will write the name Oprah, the neuro will fire. Or if I will say the name, the neuro will fire. So it's very, very high level abstract representation of the particular concept.
[00:13:20] Speaker B: There's been a lot that's been found in hippocampus. Like you said, it's very well known that memory is very important function of the hippocampus. And I guess in the last. Well, I don't know how many years it's been now, but everyone's studying place cells and grid cells in the hippocampus and spatial cognition.
[00:13:39] Speaker A: So since 1971.
[00:13:41] Speaker B: Well, yeah, that's when it started, but it seems like it's just now sort of really exploded because of the Nobel Prize, I think. So how do you think of concept cells in relation to place cells and grid cells? And I haven't even said what those are, so it might be helpful to say what those are as well.
[00:13:58] Speaker A: So, I mean, what you say is a very interesting question, and that's something that really interests me a lot. So there were like two fields that until recently, maybe a few years, except some people that were visionaries, like the late Howard Eichenbaum. They developed in parallel from patient ham in the 1950s study by Brenda Miller and Sue Corkin and other people following them. We know that hippocampus in humans at least is involved in memory. If you don't have the hippocampus for a lesion or something, you just cannot form new memories. You end up like the guy in Memento. Now, on the other hand, since the 1970s, we know for a fact that hippocampus is involved in spatial navigation. And these are exposed experiments that have been done in rats and in mouse, later on and since more recently in bats. So we know that hippocampus kind of like has kind of like the GPS of the brain. So in order to be oriented, to know where you are and how to navigate, for example, to go to the shop or to something, you do need hippocampus.
But then, like, then it's this big puzzle. I mean, you have one field of people studying human hippocampus, and their hippocampus is involved in memory. And then you have the other group of people saying the hippocampus is involved in spatial navigation. It's the same area, and it's not that different. If you see the rat hippocampus and the human hippocampus, the kind of overall structure is very similar.
Now, my starting point is, because I think we will discuss a lot about that, my starting point is, okay, in the rat hippocampus, you do have neurons that fire to specific locations of the environment, to particular places. And this is what we call place cells. Then later on, Edmosser discovered what we call grid cells, which is kind of like a distribution of places, like with a grid type of shape that they tile the whole environment and so on.
My point is that a special location in an environment that is very familiar to the rat, it is a concept. For example, in my office, the place where my sofa is located, it is a concept. I will remember doing things in my sofa. I mean, sitting in my sofa that I didn't do, sitting in front of my computer, which is another place, and it's another concept. And for rats, knowing exactly where they are is crucial because they have to know if they are close to, like, a runaway place or if they are in danger or where food is and so on. So the location for a rat is crucial. And also, rats do not see like us, so we can see at the distance. So we. We don't need to know to have a map. I mean, to go to a particular place close by because we can see it. But a rat cannot see a distance. So therefore it has to have a representation to go from one place to the other. But the point is, we both have somehow a representation of concepts. For a rat is more a representation of places, because this is more important for them. And for a human, it's a representation of specific people or places or salient things because we tend to form memories about these things.
[00:17:05] Speaker B: So place cells are concept cells. So there's a worry that everything will be collapsed into a concept, every single idea, right?
[00:17:16] Speaker A: No, but I guess we will talk about this later. We can talk about it now.
There's a huge difference. They're very, very different. So a few years ago, I will say, yeah, they are similar. Now I will say, no, they're completely different.
[00:17:29] Speaker B: Place cells and concept cells, yes, place.
[00:17:32] Speaker A: Cells, they respond to concepts. But the way place cells encode concepts is exactly the opposite to how concept cells in humans encode Concepts. So they both encode. I mean, if you see a place as a concept, they both encode concepts in a way, either places or particular people. But the way they do encode these concepts is radically different.
And I think this is leading to, I mean, some research that I'm more interested now, which is where my research is going this day, which is trying to find out what are the specific neural principles that are the basics of human memories and human intelligence. And I think it's very different in rats than in humans and even in monkeys.
[00:18:15] Speaker B: Are abstract ideas encoded in the same way as invariant, you know, person representations, let's say, in concept cells.
[00:18:25] Speaker A: If I abstract ideas you made, like abstract concepts, like, for example, I don't know, happiness or gravity and these things. I think these concepts are more encoded in cortex, in neocortex, not in hippocampus. I think hippocampus is critical for episodic memory, memory of our lifetime, of our experiences. Whereas the neocortex, in different areas of neocortex, you have more semantic memory, memories of fact. So knowing that Paris is the capital of France, which is a classic example, is something that is not dependent on hippocampus, I can remove the hippocampus of one person and the patient or the person will still know that Paris is the capital of France. But now if I reprove the hippocampus of one person and I say, what did you do yesterday? The person would not know, I mean, because these memories are hippocampal dependent.
So I think abstract concepts are encoded in neocortex, not in hippocampus. And the coding in your cortex tends to be more distributed. I think in hippocampus is more. I mean, it's more technical, but it's more sparse. So you have. You tend to have more specific neurons dedicated to the concepts, whereas in hippocampus, you will have a big bunch of neurons with a distributed representation that are encoded these abstract semantic concepts.
But the representation is very different.
And the reason is because in hippocampus, you tend to form these memories that you can form on the fly. And for that, it's known through modeling studies that the best you can have is a sparse representation to have specific neurons dedicated to them. Why? Because then you can create associations between different concepts on the fly in one go. But now if you have a representation that is distributed across many neurons and each neuron will let go different details in different ways of the particular concept, then making an association between two concepts takes time. You cannot do it on the go. You cannot do it on the fly because then everything collapses. And I think that's why we have different coding principles for these abstract concepts in cortex, like happiness and so on, or gravity and a different type of representation for specific people or places and so on that you can create quickly associations which are the basics of our episodic memories.
[00:20:40] Speaker B: And is this where the complementary learning system ties in as well?
[00:20:43] Speaker A: Exactly, exactly. I see these as exactly as the complementary learning system. Yeah.
[00:20:50] Speaker B: Which basically is like a quick veritable memory happening in hippocampus. And then things get consolidated and detailed, oriented in the cortex, essentially.
[00:21:00] Speaker A: Exactly, yeah.
[00:21:01] Speaker B: Which came first, language or concept cells? So, and how do you, how do you see them relating? Because language is. Well, so you've mentioned that only humans have these concept cells that we know of anyway so far. Yeah, maybe snails do, who knows?
And only humans have language.
[00:21:19] Speaker A: Yeah, I mean, I think these two things, it's not a coincidence.
And that's why I like, I mean, going back to the start of our conversation, that's why I like digging into other fields. And I got this through a very famous writer from Argentina called Jorge Luis Borges. And Borges has a few stories, short stories, he always wrote short stories where he made very clearly how language impacts our thinking. And for example, he imagined a world called Trollon. Sorry, I said again, clon in a short story called Clon Udvar Orbis Tertius. And this is a world where there's no, there are no nouns and everything is said as a superposition of adjectives.
And then Borges concluded that in this world it was not possible to think or science. He says science was not possible, not even thought, thinking. And why? Because he argued that every noun is an aberration on itself, that to pronounce a noun means to get rid of zillions of details and to focus on the essential.
So if I say horse, I don't mean a particular horse of a particular color, a race horse or a horse to pull it. I mean, to put a chart or so on. I just mean the concept horse. So the fact that I'm using nouns, that I'm using language, is kind of like reinforcing the ability to think in terms of abstractions.
And then. And that, that's the part I enjoy a lot. I started reading a lot about evolution, evolutionary biology. I mean, how comes that suddenly we have language and then how come that we suddenly have concept cells? And I mean, it is not very clear, but maybe language evolved since hundred thousand years or so with Homo sapiens. And I Think we do have concept cells and so far we cannot find them in other species or monkeys because we have evolved with the use of language tens of thousands or maybe even hundred thousand years. And the fact that we have evolved with the use of language created concept cells and the fact we have concept cells reinforce the use of language. I think both things, they co evolve in parallel. So maybe we can use language because we have concept representations and concept cells which at the same time can be, I mean, created because we use language. So I think both things evolved in parallel.
[00:23:47] Speaker B: You need the concept of. You need the concept of an abstract entity for language.
You need nouns, I suppose, for language to really be effective because you need these concepts to associate with other concepts. So, yeah, it's a chicken or egg problem. Which came first, concept cells or language?
[00:24:08] Speaker A: Yeah, I see them. I mean, I don't seem like at some point in time we started speaking English or Germanic or whatever language we spoke in the past. Maybe at first started naming some things, maybe with some sounds or something. And then particular sounds mean specific concepts, like in ancient times.
And due to our vocal cavity, we have the ability to speak, which a monkey doesn't have or a chimpanzee doesn't have. They can do sounds, they can do different type of screens, but they cannot properly speak. And the fact that we can speak give us a huge repertoire of different sounds or words, if you like, that we can pronounce and we can refer to different things. So maybe at first we started naming things, but without having a refined language. And this started creating a different representation in these memory areas. And the fact that we have this representation reinforced, creating a more sophisticated language and so on. So I think it was a virtuous loop that somehow created this phase transition that differentiated us from other species.
[00:25:17] Speaker B: Well, it did allow you to write this book. Let's talk about neuroscience fiction. So, yeah, so the concept of the book, concept. I gotta stop saying concept. The idea in the book is you run through every chapter is sort of framed around a sci fi film. And are you a big sci fi fan? It seems like, yeah, yeah, yeah. And you use the film really just as a starting point to then dive into various concepts in our intelligence and in our minds and brains.
So I mean, just to list off a few, you know, there's Blade Runner, there's Planet of the Apes, there's the Matrix, there's Until the End of the World, which I've never seen, but I had the soundtrack. I think a lot of people had that. It was a really good soundtrack. In that movie, so. And on and on Minority Report. You know, first of all, I'm just curious, like, how did you choose these? You know, what's the. What's the movie that you left out that you wish you could have included?
[00:26:20] Speaker A: Oh, there are a few. I mean, I basically chose my favorite movies. I mean, until the End of the World is not. It's not that known, but it's one of my favorite. One of my overall favorite movies. Not just science fiction movies, movies. Of all the movies I have seen.
I love this movie because I love the soundtrack. And I think Bean Benders, the director did an amazing job of putting together this soundtrack with some beautiful shots, and I love it. And on top of that, there's a very nice scientific story going on, which actually gave me an idea for an experiment that I published 10 years ago.
But I chose movies that I love. I chose movies that I would like to investigate deeper, I mean, further, that I would like to write about. There are some exceptions. There are some movies I do not like that much, but they are there for some reasons, which I can come to in a minute. And there are some movies that are left out. I mean, like, I would have loved to have a chapter on Terminator, but I found out that to make the philosophical point I wanted to make, which is very deep, instead of Terminator, I needed RoboCop. And I love Terminator. I don't like that much RoboCop. I think RoboCop is okay. I mean, it's not horrible, but it's not one of my favorite movies by far. But RoboCop makes very, very clearly the point of what determines identity. Is it our body or is it our brain? Or if you want with the brain, our memories? Whereas in Terminator, this point is not there, because Terminator is a robot. I mean, you wouldn't define identity of a robot. I mean, you would define identity of a person. So if suddenly you change all my body parts and you replace them by, I don't know, some cybernetic robotic parts, will I still be myself? What is it that makes the difference? And I really wanted to get into this discussion. I really love this philosophical discussion. And RoboCop was leading me straight into the discussion, whereas I couldn't.
I couldn't really interweave Terminator in this discussion, as I would have liked to.
[00:28:23] Speaker B: You roll your eyes. Oh, I've got to use robocop.
[00:28:26] Speaker A: Yeah. But there's a point in the movie that I love, which is in the remake of RoboCop. I mean, there's a point where RoboCop sees what is left of his body and the guy freaks out. And it's a very, very dramatic scene. The guy freaks, ah, what am I? I mean, what happened to me? What happened to me? And it's very clear because the guy says, the guy really, I mean, clearly makes a point that I am my body.
And this is a huge philosophical discussion. Are we our body or are we our brain? And I think that's, that's a misleading dichotomy because the brain is also part of the body. But am I my body if you want, or am I my memories, my experiences? So, yeah, that's why I have to choose RoboCop.
[00:29:07] Speaker B: Yeah, I mean, you tie in, just speaking of historical ideas and literature, you tie directly into that the idea of rebuilding. I believe it's directly into that. Rebuilding a ship out at sea, board by board. And then is it the same ship afterward? I don't remember what, what source does.
[00:29:26] Speaker A: The paradox of Theseus. So it's a beautiful story because. Oh, it's a very ancient Greek story.
So the King Minos used to get 20 young, I mean, 10 young men and 10 young women to go to the, to the island of Delos. I'm trying to remember because I haven't read this for a while.
[00:29:49] Speaker B: There's so much in the book. It's.
[00:29:51] Speaker A: Yeah, it's hard to remember everything. But basically, legend says that King Minos used to get 20 young guys and girls from Athens into the island of Delos to go into a laborism where they will be caught and eaten by the Minotaur, half bull, half person.
And then these one time was an Athenian hero will go with this, I mean, with these young guys and girls and will kill the Minotaur and then he will come back. And then he will come back triumphant in his ship. And therefore, for hundreds of years, people from Athens, they were, they will remember the. This big, I mean, this big thing that the SEOs did. And then they will, they will play the sea to the ship and they will sail it to the island of Delos to remember. I mean, this big thing. Now the paradox of the sailor ship is that after hundreds of years, of course the ship start deteriorating. And then they say, well, I mean, let's replace some planks which are getting rotten by new ones. And then the question is, well, is it the same ship or is it a different ship? Because you are replacing part of it. And that's a nice twist. And I think it was Tom and Hobbes, Thomas Hobbes. That did a nice twist and said, well, not only that, there was a very clever guy that was getting every plank from the old ship, was bringing it to another harbor and was rebuilding the original ship. And then the question is, so which one is the ship of Theseus? The one that the guy revealed with the original planks or the one that is going every year to the island of Delos? And it's exactly the same discussion. Is it the material component? Is the material part what makes identity? Or is it the function same as body and memory and function?
[00:31:47] Speaker B: Well, Rodrigo, I mean, there is just so much from the book, it's impossible to know where to start. So I kind of want to ask you. And then we can just use this as a jumping off point, you know, which chapter was the most fun for you to write and why?
[00:32:05] Speaker A: I don't know if there's a chapter that was the most fun, that parts of chapters that I really enjoyed. There were parts of chapters that were difficult for different reasons.
[00:32:16] Speaker B: Well, it's not like they're independent because that's all weaved together. So it's not like.
[00:32:22] Speaker A: Yeah, so for example, I mean, if you ask me, I love writing about until the end of the world. And actually I will mention him because I have a very good friend and colleague in Barcelona and I was visiting him and I remember we were having lunch, looking at the sea, and we both liked this movie. And then we were remembering another movie from Bernard, him, Wings of Desire. And if you watch the German original movie, it's beautiful. If you watch the Hollywood remake, it's not that nice, but. And we're remembering that. And they would say, oh, man, I have to also write about this movie. And this is about two angels that they go around in Berlin and they can read the mind of people. And what they want to know, these two angels is how is it to be human? How is it to live? What are humans thinking about? What are their daily worries and things that they care about and so on? And it's beautiful. It's so poetic. And this got me straight into discussing about the possibility of mind reading. Can we read the mind?
Doing some measurements of brain activity, guess what somebody's thinking about? And they're many experiments that are breathtaking that if you read about, you say, wow, can we really do that? And yes, we can. So I love talking about that, for example, but I don't know. For example, in the chapter that I discussed free will, I really enjoy talking about the determinism because this brought me back to things that I Studied when I was a physicist.
[00:33:50] Speaker B: Chaos theory and dynamics.
[00:33:52] Speaker A: Yeah, chaos. But even before that, I mean, I talk about the 17th century scientific revolution, which I love reading about when, when I was a physics student. So I will talk about, I don't know, Kepler and Galileo and Newton and how suddenly these guys create a revolution which we call classical mechanics and so on. And it was a new paradigm and they changed the way we think in terms of physics. And what I didn't know before when I studied physics, which I learned much later, is that these things, they went together with a whole revolution because at the same time, Descartes was changing philosophy. And Descartes didn't wake up one day and say, okay, I want to build up a new philosophy. Descartes realized, well, our scientific knowledge is changing dramatically because physics is changing. I mean, what we think about how the world works is changing dramatically. So maybe how deep does this go? And then he changed it, philosophy. And then, I mean, he's the Descartes known as the father of modern philosophy. So. And I love talking about that because this leads to determinism and determinism leads to free will. I mean, if everything is determined, I mean, how can we be free?
[00:35:04] Speaker B: Right.
[00:35:05] Speaker A: And I love discussing that. And this brings me to chaos theory at the end because I think chaos theory is the solution and chaos theory is what I studied when I was doing my postgrad in physics.
But on the other hand, I mean, the other side of the coin is that I didn't enjoy much thinking the whole time about free will because it's a very nice. It's a very nice philosophical discussion. But as I say in the acknowledgments, I mean, in the last pages of the book, it left me with this feeling of emptiness. Well, why. I mean, why would I make something if. I mean, if I'm not in control? And you know that you are in control because. But anyway, I mean, you're always thinking about these things and sometimes you get this. This feeling is. I mean, I just want to get out of that.
[00:35:51] Speaker B: Yeah, let's just spell out your conclusion about how free will is saved by, I guess, complexity and chaos.
[00:36:01] Speaker A: So, I mean, the bottom. I mean, the quick and punchy answer is that there's no free will as we think of.
[00:36:10] Speaker B: As we think it. Yeah, yeah.
[00:36:12] Speaker A: And basically all what I'm doing is determined by the activity of my neurons, which is determined by what my neurons did before. What is happening around me. And what is happening around me is also deterministic. And if you think about that what will be the alternative? So if it is not, if it is not deterministic, what is it? Is it random? But if it is random, it's also not free will. That's the way I totally like to think about that. He said, well, I mean, everything is predetermined, but there's some noise in the system. But that, I mean, that's very different from thinking like, oh, I have, I mean, I'm in control of my tough and by some magic process I can decide what I will do. No, you don't decide like this. I mean, is your neurons being activated and then you are conscious of what is happening with your neurons. And this is what we call free will. And actually we know that if this illusion doesn't exist, this can lead to very serious pathologies like schizophrenia. And somehow this feeling of emptiness, knowing that you are not in control in a Descartes way, that you do have a mind that is autonomous and is deciding and then your body's implementing this decision that is just neurons firing in particular way. This is saved by chaos theory because okay, even if we're not, I mean, if there's not something mysterious making decisions, if your decisions are predetermined by brain activity, you can never predict what you will do and nobody can. So even if you record all the neurons in your brain, you cannot predict what the person will do. And this is because of chaos theory, because it's impossible. A complex system like the brain is unpredictable. So, okay, what I'm doing is not, I mean, coming out of some material mind deciding it's predator mind. But since nobody can guess what I will do in principle, I mean, well, who cares, including yourself, it works.
[00:38:02] Speaker B: Yeah, who cares?
[00:38:03] Speaker A: Including myself. Exactly.
[00:38:04] Speaker B: It's not a comforting solution.
[00:38:06] Speaker A: Exactly. Yeah. And that's why I say it's a double edged play. No, because it's nice to be investigating that. But on the other hand, I mean, you're thinking there's something that's still like. I mean, there's something that I still like, I don't like. And it gets worse when in the last chapter I discussed about immortality.
[00:38:26] Speaker B: Yeah.
[00:38:26] Speaker A: And it's a very interesting philosophical and scientific problem. And if you want to put it in one word, I mean, death is not, maybe death is not a metaphysical problem, it's more a biological problem these days. And it's very interesting. What determines death? I mean, when can you say that somebody dies? And it sounds like a trivial question, but it's not that trivial because 100 years ago or I don't Know, a few hundred years ago they would say when the heart stopped, the person is dead. But today you can resuscitate somebody. I say, well, it's not dead, it's come back and it's the same person. So what determines that you die? And are you. If somebody brings you back, are you still the same person?
So what is it in your body, in your brain, in whatever that determines that you're always the same?
And I think this is a fascinating question. And I think, as I discussed in the epilogue of the book, I think philosophies changing. And I say this in a very humble way because I'm not a philosopher, but kind of seeing it from outside, I mean, I have the feeling like, well, it's changing. It's really changing. And I think we're living a revolution more or less like Descartes leave a revolution at his time. Because some of the problems that were puzzling us for centuries, I think they're not that hard to answer anymore. But I think we're facing new problems. And I think immortality and death and the continuity of the selves and what determines our identity is the big, big challenge of the 21st century. Whereas I think until recently the big challenge was consciousness. And I don't see this as a big puzzle anymore. It is puzzling. It's still not solved. But I think the big question that is coming out now is identity.
[00:40:13] Speaker B: Often it seems that we're just not good at asking the right questions or that our questions are formulated in a way that they're unanswerable. And it seems like consciousness might fall under this, classically how we think of consciousness. And I don't know about identity. Maybe you have thoughts on that.
[00:40:34] Speaker A: Yeah, I think you're right. Sometimes when I ask myself a question as a physicist or as a scientist, the first thing I will ask is, okay, let's try to formalize the question in a way that I may approach it. And I'm very egocentric in this sense because I will try to sync it in a way that I can approach it myself with my experiment.
[00:40:56] Speaker B: Sure.
[00:40:56] Speaker A: So sometimes a question, I may not find it that interesting if I'm not. If it's not related to my own stuff. Why is that? I'm egocentric. But it's because I would like to think of things that I could potentially address somehow with very specific experiments. I mean, doing recordings in patients.
So I agree that sometimes we pose the question in a very vague way or general way that are unanswerable. And consciousness is one very good Example, and my boss, my former mentor, Christoph Koch, used to say this for decades. And he said, well, let's try to pin down the question and do experiments and start answering it. And I think one big problem of consciousness is that we talk about it without really defining what we mean by this. And I'm not asking about a definition, but it's like, well, what do we mean with that? I mean, let's not give a precise definition, but. And I have the feeling that if we start, be more precise what we mean about consciousness or qualia, it's not that hard to answer.
[00:41:58] Speaker B: Well, it vanishes. The problem vanishes the more precise you get.
[00:42:01] Speaker A: It seems like, exactly, exactly. And there's some very, very brutal insight that I read it from Dan Dennett in his, in his fantastic book Consciousness Explained like it's a classic night from 20 years ago. And basically Dundee said very, very clearly, let's not try to explain the illusion of consciousness. Let's try to explain actually the underpinnings of consciousness. Let's try to explain the actual processes, process happening in the brain, but not the subjective feeling we have about what we believe it happens. It's kind of like the way I see it is I link it to memory and I link it to vision. The classic example is vision. If you think about vision, if you try to explain how can I see everything that is in front of me right now? Well, it's a hard problem, but if you, if you try to think about the actual neuroscientific process going on, where we're not seeing everything at the same time, what we can see in focus at a particular time is a tiny bit of what is in front of us, which is maybe 1, 2 degrees visual angle. And then we move our eyes, but we don't move it the whole time. We like maybe we jump and we jump the eye position three times a second and from that we carry the construct. So let's not try to explain the construct of what we believe we see in front of us. Let's try to explain the physical process. We see a tiny fraction there and then we extract information from that. Now, from this information and how you process it, you can explain how later we can create this construct of what we call vision seeing. And I think it's the same with consciousness. So if I'm looking at you or if I'm hearing you, and I have, I mean, with very bits of information, I'm kind of like creating this feeling of being, talking to you, talking to a person, but I don't need to explain, or I need to try to find neurons in charge of the whole construct and create around this. The same for memory. I mean, we have the feeling of memory being like a movie that we play back. I mean, we play back in time, but this doesn't exist.
[00:44:04] Speaker B: We're making up those movies.
[00:44:06] Speaker A: Yeah, exactly. We just remember very few facts that we link together. We associate somehow, and we construct a story around the facts. So let's not try to explain the movie. Let's try to explain that, actually, the facts and how we encode these facts and how we can encode these associations, and then we can. I mean, we can construct a movie from that.
[00:44:29] Speaker B: Since we're talking about identity and consciousness, I just want to bring it back to concept cells to touch on that again. Because how might concept cells relate to our sense of identity and consciousness, let's say, without defining it?
[00:44:48] Speaker A: Yeah, so when you talk about consciousness, I like to. Again, I'm pinning it down, and I don't like these big definitions, so I'm pinning it down into different things that we call consciousness. It's very different to be aware of something that I see in front of me. So imagine that I know you. Imagine that we know each other for many years. And I will have many memories related to you. So if I see you, if I'm seeing you in front of me now, there will be a first process linked to consciousness, which is awareness, perceptual awareness. I can recognize you. I know who you are.
I can do this with my visual cortex, even without hippocampus, without concept cells, so I can recognize you. Hm could do that. And HM didn't have his hippocampus. So HM could recognize people he knew from before. So there's a process called perceptual awareness, which is basically recognizing people. But now, since I do have my hippocampus and I do have my concept cells, and if I know you from before, I'm not just recognizing you. I'm bringing memories related to you. And the fact that I recognize you is somehow lightening up my consciousness in hippocampus. And this is bringing together different memories and experiences that I have had with you in the past if I would have known you from before. So then it's like, oh, no, this is not just Paul. Yeah, this is Paul. I mean, I remember meeting him and having a beer with him and this and that. So I start bringing all these memories. I start bringing together all these emotions and so on. And I think this is mediated by concept cells. Now, you put this Thing into context. And I think this second level of consciousness is mediated by concept cells.
[00:46:27] Speaker B: So it's like you said, second level. So it's a richer consciousness, richer experience.
[00:46:34] Speaker A: I think this is qualia.
I think putting together these experiences and these emotions and all this information related to a particular concept, you go to qualia, this brings the experience. I mean, what is the experience of seeing you? Well, it's just recognizing you, plus getting activated all these things that are related to you in my brain. And I think this is mediated by hippocampus. And I will argue that it's mediated by concept cells.
[00:47:02] Speaker B: So qualia requires more than one thing. It requires associations between experiences or concepts.
[00:47:12] Speaker A: Yeah, and I'm not saying that the only way to get qualia is through concept cells because maybe you get somebody with hippocampal lesion and may still like have the feeling of something and so on. But I will see qualia as associations, associations between experiences. Now what hippocampus does facilitates is to have associations between these parrot things.
So episodic memories. For example, if I had a beer with you, I don't know, in California three years ago when I see you, to bring this experience to memory, I do need my hippocampus for that. But other things related that we can call qualia. So for example, to know that you have a postcards or are a scientist or this and that these could be semantic memories that are restoring cortex that are not mediated by concept cells. But I think to get the full richness of everything is to have co activations of different brain areas or things that are related. And I think concept cells have a big role in that. Maybe not an exclusive role, but they do play a big role in that.
[00:48:09] Speaker B: They're almost like the constraints by which these things can become associated. I mean, do you see creativity playing a role being mediated by concept cells?
[00:48:18] Speaker A: Yes, and this is because depends on what I mean. Again, let's not get into the definition of what is creativity. I mean many different things, but I think one aspect of creativity is a key aspect of creativity is to be able to make disparate associations which are meaningful. And I like to give an example that's because I come from physics. So gravity. You mentioned gravity before. And I think that the genius of Newton, I mean when I taught physics to students, I mean I will teach gravity and so on, and then you have this big equation to come up with. The exact formulation of this equation was brilliant. And Newton did that. So the formula of gravity, attraction between two bodies, planets and so on. But I think the brilliance of Newton was not that, was to realize, as he did, that an apple falling from the tree follows the same principle as the moon going around the Earth. And the moon goes around the Earth and doesn't fall because it's in movement, so it circles around the Earth. The Earth is always, like trying to catch it down, but since the moon is moving, continues to orbit around the Earth, whereas the apple is static. And therefore, I mean, the moment it, I mean, gets off the tree, it falls down.
And I think to be able to make an association between an apple, as the legend says, an apple, an object, whatever, and earthy object, and the moon is brilliant because there are two things that you will not in principle link together, like the moon up there and an apple here next to me in the tree. You will not make a connection between that. And in order to make this connection, you need concept representations because you shouldn't be thinking of, oh, this is a red apple with two leaves, which is a little bit big, and this is a moon in its senate. And I don't know, it looks a bit orangey. You get rid of all the details. So you really have to think of the abstract concept. It's an object here, which is an apple, happens to be an apple and an object there in the firmament. And they both follow the same principle. So I think to have concept, I mean, to summarize this big explanation, I think creativity is to be able to make these very disparate associations. And I think to do that, you need to get rid of zillion details and to extract a concept. And I think this is exactly what concept cells represent.
[00:50:35] Speaker B: But you need to keep very key details. You need to get rid of a ton of details, the ones that aren't meaningful to the association. And I guess the key is keeping the correct details.
[00:50:48] Speaker A: Yeah. Otherwise artificial intelligence will be surpassing us right now. So that's very hard. I mean, like, our brain is amazing at destruction. The important information, the relevant information. So how can you teach a computer what is important, what is relevant? Well, this is a very hard problem. And I think computers are fighting against billions of years of evolution.
[00:51:09] Speaker B: Do you think AI is on the right track toward general intelligence?
[00:51:13] Speaker A: So there's something I love. I mean, one of the things that I love about writing the book and that was a surprise. I surprised, I mean, a surprise that I got myself in when writing the first chapter. The first chapter is 2001.
[00:51:26] Speaker B: Yeah.
[00:51:27] Speaker A: One of the big, big movies. Something that, I mean, when I think about, I found amazing. Somebody can Say, I mean, I can tell my kid that 2001 is a movie that just came out and they won't notice it.
[00:51:39] Speaker B: You make that point in the book. That's an interesting point.
[00:51:42] Speaker A: 2001 is 50 years old. It's over 50 years old.
[00:51:45] Speaker B: Yeah.
[00:51:45] Speaker A: And I can tell my son, look at this movie. It's a science fiction that just came out in Netflix, and he won't notice.
[00:51:51] Speaker B: I think that the only real giveaway is that it doesn't cut the scenes. Don't cut every three seconds.
[00:51:58] Speaker A: No, it's not MTV culture. It's like, really. It's more like Tarkovsky. Very, very long since.
But there's also something very interesting. So the filming is amazing. The filming, it could be from yesterday and it's 50 years old.
But the way it treats the topic is also amazing. It's very contemporary. It's very actual. And if you really get into the details of the things that HAL 9000 does, this supercomputer that. It's very actual. I mean, it could be written by somebody doing artificial intelligence today.
And what I learned that I didn't know at the time, that is that the scientific advisor of this movie was Marvin Minsky.
[00:52:38] Speaker B: Yeah.
[00:52:38] Speaker A: And I didn't know it. And Marvin Minsky was the big pope of. I mean, has been the big pope of artificial intelligence from its infancy. And then I said, oh, that makes a lot of sense now. I mean, this guy was behind all these crazy ideas of 2001. Yeah. There was somebody that really knew about it.
[00:52:56] Speaker B: He was a consultant. Right. I'm not sure. You know, one of the. I can't remember what character was named as like an anagram of his name or something.
[00:53:03] Speaker A: Victor K. Minsky. He was one of the astronauts hibernating.
[00:53:07] Speaker B: Yeah.
[00:53:08] Speaker A: Victor K. Minsky. Yeah.
[00:53:10] Speaker B: I'm glad that you didn't pick Ex Machina, you know, or any of the movies these days that play on our. On romance, you know, and the bro dude in his island lab and stuff. So I was very pleased with your 2001 choice there.
[00:53:26] Speaker A: Yeah. But I mean, going back to your question about if artificial intelligence is going the right track. So because of that, I mean. And I read and I was watching many interviews of Binsky, and I was trying to get all the information about the interaction. Actually, there were three key people interacting because Arthur Clark, who was a science fiction writer, was quite involved in the. In the script of 2001 together with Kubrick. And then the third person was Mario Minsky, and they didn't meet that many times. But they met a few times, the three of them.
And then I was reading more and more and more and what Minsky thought about artificial intelligence at the time and what he thought just before he died, like one or two years ago, a couple of years ago, I think. And Minsky, I think he. He just got it right. And he got it right in the 80s when he wrote his big book. And he got it right until the day he died. And he was saying, not me. He was saying, guys, we're going the wrong track. I mean, artificial intelligence is still missing the point. And the point is we're still not tackling the big issue, which is general intelligence, and we are still clueless how to get that. And what he criticized to the advances of artificial intelligence is that now it's so fancy with all these deep neural networks and so on. And I'm not criticizing this because I think it's amazing science. I mean, I love what they do, but we're getting networks, we're getting systems that are very good at solving specific problems. The key challenge still out there, which is how you can get some artificial algorithm to excel at something and then to transfer information to something completely different. And we still don't know how to do that. And you can potentially do that if you train the algorithm with million examples. For example, if you have a chess program to start playing, Go Alpha zero can do both things, but the only way to do that is if you have some artificial intelligence solving a task. And now if you want to train this algorithm to do a different task, well, you need to get serial examples. And we don't work like that. So if I'm facing a new situation in my life, I will not need to face the situation again and again and again to know how to react. I mean, I'm facing a new situation. I know what to do because I somehow develop some common sense. And this is what is still missing in artificial intelligence. And I'm not. It's not me saying that Minsky said that. So what is missing is the ability to do inferences, to do analogies, and to develop common sense. And this is what we're still very, very far to. To know how to do it.
[00:55:59] Speaker B: Well, Minsky, I mean, so back at the birth of AI, everyone essentially was working on artificial general intelligence, because that was the question. And he lamented that there are just very few people who are actually working very few labs that are intentionally working on general intelligence. I don't know. I think that that's changing a little bit. But I think part of the problem is that our conception of what intelligence is has changed. And as we've understood more and I wonder if that has a reason why.
[00:56:29] Speaker A: Well, modern intelligence, I mean, the key issue for me is general intelligence. And actually I saw a talk of one of the big neural networks guy, Joshua Banjo, and there was something very interesting that I really like was the guy was really puzzled now on how to really practice term how to achieve consciousness.
And again, I mean, there's something for me, in artificial intelligence, there are two key challenges that are still unsolved. One is how to make a machine conscious and we are clueless. I mean, we see this in science fiction, but it's still fiction because we have no clue how to do that. And the second is general intelligence. And I'm speculating in the book, well, maybe the two problems are somehow related to each other. Maybe the moment, I don't know how. Well, I think I have some ideas how potentially can do it, but I'm not an expert. Maybe the moment you get general intelligence to a machine, you can have a machine that develops general intelligence. Maybe they become conscious.
[00:57:26] Speaker B: But speaking of definitions, do we know what general intelligence is? Do we have a good definition?
[00:57:32] Speaker A: I will define general intelligence as the ability to do a task you have not been trained to do. And with very little training.
So if you have a machine learning to play chess, that this machine can do visual recognition, but without zillion examples.
[00:57:53] Speaker B: And you speculate in the book that. And I think this is in the epilogue. And by the way, I really did enjoy the book very much, but I think my favorite parts are the epilogue and the acknowledgments.
Oh, really? Because you bring a lot of this together in the epilogue and then the acknowledgments, you talk about how writing the book has changed the direction of your research. And those maybe think, oh, this guy sounds happy.
Okay, so. But you speculate in the book that when we create something that is generally intelligent, has general intelligence, that it will be subjectively aware, maybe because.
[00:58:39] Speaker A: Let's talk about general intelligence. So for me, in order to understand, I mean, I'm not just following some rules. I mean, if this, I do that. If that, I do that. So I have to have more like a grasp of what I'm doing. So I have to go beyond stimulus, response associations. I have to have a deeper understanding. I have to extract the meaning of a task that I'm doing and why I extract the meaning. I can potentially transfer this knowledge into another task. Now, if I'm stick to a Task. If I know whenever this, this, this and it happen, I do that. I can be very good at doing that and I can be very fast and I can excel at this task. Whenever this and this, this happens is this phase. Whenever this and this, this feature come together, it's that phase. But now if, if I get like this, if I'm able to extract a meaning, I'm not sure how to do it. I mean, I think that's a big challenge.
And if I extract a meaning well, I can potentially transfer this meaning to a different task without much training. Now, if I'm able to extract a meaning, well, what's the difference between having a representation of this meaning and being aware of this meaning, of the meaning of this thing, whatever it is. Now, if you don't have this meaning representation, meaning in quotation mark representation, this high level after representation of the meaning of something, maybe you're not aware because A, B and C happens, you do one, C, D and F happens, you do two and so on. So it's always stimulus response, a stimulus response. And you don't even think about what you're doing. You just do it mechanically. But the moment you struck the meaning, well, you are aware of something and then maybe this awareness is not that far from consciousness. And this awareness, this mean representation, which I don't know how to extract, but imagine you can. Well, that's what you need for general intelligence. So in this sense, I seen these two problems which are the big, I think the million dollar question, the $2 million questions of AI, I think they might be related.
[01:00:37] Speaker B: And you think that analogy is one of the factors that ties them together as well?
[01:00:43] Speaker A: Yeah, yeah.
[01:00:46] Speaker B: Just getting back to the book, what was one of the chapters or subjects that you found most challenging to write about?
[01:00:54] Speaker A: Well, I tell you. I tell you a secret that I never saw on the record. I mean, I told a few friends off the record, but I think I can say now because I did it. My previous book was called the Forgetting Machine and it was supposed to be 10 chapters and it ended up in nine because I had to stop. I couldn't write the 10th chapter because it was killing me. It was too hard. And he was talking about death and I couldn't take it. I had to stop. And this time I said, well, I go for it. I mean, I'll do it. I really.
[01:01:27] Speaker B: It was too personal to you or it was just mentally challenging to.
[01:01:32] Speaker A: It's not too personal, but I think, I mean, that's very deep. And I tell you about my own experience I cannot talk for other people, but that's what happens to me. And maybe some people will say, well, actually, I feel the same.
My feeling is, at some time we tend to deny that because too hard. I mean, it's too hard to realize that you will die. Yeah, it's too hard to face death.
However, if you're able to face death, I think you come out much stronger because you say, I'm happy. Why I'm happy? Well, because I know that I'm dying and I don't want to waste the years I have left doing something I'm not enjoying. So being aware of my death make me live my life fully and being a happy person. If I deny that, if I'm not aware of my age, and if I'm not aware that in maybe 20, 30 years, that will be it for me, well, I don't know, maybe I will spend my whole time doing something that I really don't care much, but I just do it out of inertia. But the fact that I know that I'm aware that I can die tomorrow makes me do things that I really enjoy. So on the one hand, it's not fun to be thinking or to be aware that you die, but on the other hand, this pushes you, or it's kind of like being aware of your death makes everything very clear. Sometimes if you think, well, do I really want to do that? Or not? Well, saying, well, fuck it. I mean, if I'm dying next week, will I be doing that? And if the answer is yes, that you should definitely do it. Don't care about. Don't worry about the arrest, don't worry if you should be doing that, if what your colleagues will say, or don't worry about that, just do it.
And that was hard. And I think when I wrote the previous book, I wasn't prepared to face that. And I think it took me a few years of digesting that to be able to have it explicit in a final chapter. So I think that was hard. It was hard to write it, but I think I was more prepared. I couldn't do it in my previous book, so I could do it now, and I could somehow face the issue, but also face a philosophical problem. Because now that I'm okay with my death, somehow, I mean, it still hurts. It's still not easy to talk about that. It's still not easy to think about that, but I can face it as a tackable scientific and philosophical problem, and it's extremely interesting. And it's related to the problem of identity. Which I think is the big puzzle of the 21st century.
[01:03:55] Speaker B: That's one of the. This is the concluding chapter in the book. And you tie it into the movie Abelos ojos. Right. Or open your eyes or the American Vanilla sky, which is not nearly as good. Yeah, you tie the idea. The question is, what's the evolutionary benefit of having a construct of self? And you speculate that it's to ask about our future and to face death. Or in other words, to fear of death.
[01:04:23] Speaker A: Fear of death? Yeah, I'm afraid I don't want to die. The fact that I don't want to die, I mean, I will do what I can to survive. And I think this gives you an evolutionary advantage compared to somebody that doesn't have this fear. I mean, maybe. I don't know, I could take drugs and kill myself. I mean, who cares? If I die, I die. Well, no, I won't do that because I don't want to die. I don't want to kill myself. I don't want to end up frustrated or something. I mean, I'm afraid of that. So I think this contract of the self gives you a fear of that, which is an evolutionary advantage.
[01:04:59] Speaker B: It's crazy how much I personally think about dying, how often it comes across in my head. It's. It's. I mean, I think that half of our function of brain is to.
Is to convince us of the illusion that we're all immortal.
Right. So we don't think about death all the time.
[01:05:19] Speaker A: Yeah, yeah. I mean, it's not. It's a compromise, though, because I'm not every day thinking about death. I mean, it's not that I wake up and say, oh, I will die. I will die. But it's good to have it. I seen the image and I managed to put this image in the book, which, I mean, which is this amazing picture from the Seventh Seal, the movie of Bergman, where this knight, coming from the Crusades, he's playing chess with death and facing death and trying to win the game. And this guy know that it's impossible to be dead. But it's this image of a person facing death. I find it amazing. I love it. And I was so lucky. I mean, I was so happy when I. When I learned that I could really put the image in the book. Because I think this image summarizes a lot. It's not that. I mean, you have to be thinking of death the whole time, but it's good to be aware of it because this really clarifies a lot of uncertainties. And a lot of questions that otherwise, I mean, somehow it gets cloudy and fuzzy in your mind. But when you are aware of this, I think many, many decisions get very clear.
[01:06:24] Speaker B: You have kids, right?
[01:06:26] Speaker A: Yeah.
[01:06:26] Speaker B: So you know very well that the idea of not existing actually is comforting sometimes.
[01:06:36] Speaker A: Yeah. I don't know. Yeah.
No, for me, no. I mean, if I have to choose, I would prefer things to be different. But life is life. I mean, that's the way it is.
[01:06:46] Speaker B: Yeah. I've come to the realization not to harp on death, but it's really just the pain and the experience of dying. It's not death, it's not non existing that I'm afraid of. It's like, oh, it's going to hurt, you know, it's going to be really unpleasant. That's what I'm afraid of, I think.
[01:07:01] Speaker A: No, but also the non existent part for me is hard because I mean I.
I would like to sing that I will meet people that are not here anymore. And I would like to see that I'll be there for the ones I love when I'm not here anymore. So it is more comforting to think that there's something else.
[01:07:19] Speaker B: Yeah. What do you think is one of the most damaging in the book? You write about a lot of historical ideas and research. What do you think is still with us today that's one of the most damaging and influential historical ideas about brains or our minds or intelligence?
[01:07:40] Speaker A: Yeah. So I will tell you something that is extremely controversial, extremely controversial that I mean I have in neuroscience a lot of people that I really admire, that I read and that I really enjoy discussing with them. But I think they.
Okay, I said it straight because it's very controversial and I don't want to be arrogant, I don't want to, I mean sound, oh, I know more than these guys because this guy know hell lot more than me.
But I think we have an inherent bias in neuroscience that we believe that the human brain is just a rat brain scale or a monkey brain scale.
And we are thinking of the processes of the human brain as what we found in animals brain, but just a little bit more complex or a little bit more evolved. And if you think about it, it cannot be. And that's because let's compare a human to a chimpanzee and you say, oh, come on, a chimpanzee, come on, the brain of the chimpanzee is just one third of a human brain. It's not just, I mean our brain is not like a million times larger than a chimpanzee.
[01:08:52] Speaker B: Right.
[01:08:52] Speaker A: It's just three times larger. And actually, okay, I mean, it has a larger set of everyone. So. But an elephant has a bigger brain than us and a whale has a more neurons than us.
So then you start thinking and say, well, why, why, why? If our brain is three times larger, we're so much more intelligent. And I don't mean to say that chimpanzees are not in jelly intelligence. Of course they are very, very intelligent. They can do amazing things, tons of things, but nothing compared to human. So why is it. And then like, then I see that somehow we have a bias to think. I mean, and sorry, and when we say monkeys, we don't mean a chimpanzee, we mean a macaque monkey, which is a species that we study in the lab, which is actually the brain of a macaque monkey is three times smaller than a chimpanzee brain.
So compared to a human brain, the macaque brain is nothing. I mean, and don't worry about a mouse brain or a rat brain. So I think it is true that there are many processes, basic processes that they go all across a species.
[01:09:54] Speaker B: Action potential and it's very.
[01:09:56] Speaker A: Action potential and connection between neurons. And there are many, many bent plasticity, I mean, consolidation, reconsolidation. I mean, even if you stick to memory, there are many things that hold. Visual perception, there are many principles that hold.
But from there, to think that human memory or human intelligence is just an upscale version of what we see in rats or macaque monkeys, it cannot work. And the reason it cannot work is because if you take the brain of a chimpanzee and you compare it to the brain of a human, it's just three times larger. So there has to be something that is radically different.
I mean, you like artificial intelligence. Take artificial intelligence. Imagine that you build up a network to do something and then you build up a network that is three times larger. Would you expect the difference to be as the one you find between a chimpanzee and a human? No. Unless the big network was trained in a completely different way and has a completely different way of working. And I think that's the key. I mean, there has to be something that is radically different between what our neurons do and what the chimpanzee neurons do. How we encode information and how chimpanzees encode information. And I will argue now, and now I'm a bit egocentric because I will talk about my own stuff. I will argue that maybe these concept cells has something to do with that, because concept Cells encode information in a completely different way compared to place cells in rats. And we said at the beginning of our chat that, yeah, place cells can be also seen a concept, but they encode information in a completely different way. And if I can say it very quickly, the difference is that place cells are context dependent. If you change the context a little bit, place cells will remap, they will change the way they fire. Whereas in humans, if you change the context, the neurons do more or less the same. Then Jennifer Aniston, you know, will fire to Jennifer Aniston no matter what, if she is here, if she's there, and so on. So, and I think this context independent representation gives you somehow a tool to have a higher level thinking, a higher level construct, higher degree. Yeah, yeah. Of abstraction. So I think a damaging idea, I mean, it's too hard to say damaging idea because it's not damaging. But I think instead of saying damaging idea because it would be unfair to the people, my heroes in neuroscience, it will be really unfair. I will say somehow. Reading the books and learning from these big guys, we tend to develop unconsciously a bias of trying to find or trying to think that the human brain works in the same way as a monkey brain. But we have to be aware that there has to be many things that are just radically, radically different, otherwise it cannot be.
[01:12:36] Speaker B: Do you think of, with your physics background, is it fair to think of the qualitative difference? So there's quantitative, right? We have a bigger brain and then there's qualitative. Our brains are doing different things. You know, let's say you add the concept cells and, you know, somehow the concept cells come online and we can abstract things.
Is it fair to think of that as sort of a phase change or a, what do I want to say? An emergent phenomenon that allows like a different kind of processing on top of essentially the same hardware?
[01:13:14] Speaker A: I think so. And let's be very precise. So I hope people, I mean, some of your people hearing that, that may work on artificial intelligence. I really hope somebody will one day pay attention to this idea. It may be a useless idea, but it may be interesting. It makes sense because if you have neurons in the right brain that whenever you change the context, they will fire differently. And you know that a rat, I mean a rat, you train the rat in one environment and then you put it in the recording rig and you want to start doing recordings and the rat is completely lost. And you say, come on, I'm asking you to do the same. It's the same task. You have been doing for months. You learn to do the task and now I just put you in the recording rig and you cannot do it well because you change the surrounding. And the rat doesn't understand what she has to do now. But back to general intelligence, the ability to transfer information. It's very hard to transfer information is if the coding of information is attached to a particular context. Because the moment you change the context, you're at chance level again. You have to restart from scratch. And this is what happens with artificial intelligence. You want some AI to do a completely different task? Yeah. Okay, maybe you can train the AI to do that, but you have to train and train and train and train. Now if you will have context independent information, then basically the RAT will say, well, I'm actually doing the same task. So if I'm doing it here, if I'm doing it there, it's the same thing. So I keep doing it. So I think this ability to have a context independent information, as we do find in the human brain, in contrast to what it's found in animals, might be a key component. I mean, a queer requirement for general intelligence. But that's just plain speculation. So I hope somebody doing modeling, AI and so on may pick up this idea one day and say, oh, actually maybe we can try to implement something like that and see what happens.
[01:15:07] Speaker B: Well, I know that we're getting close here to time and I actually want to write about. I want to ask about your writing process because you've written a lot and have you always been a writer?
[01:15:21] Speaker A: I know I love writing since I'm a teenager. I kind of like, I don't know, I'm a frustrated writer. I did science because I couldn't write a novel.
I like misinforms. I mean, what I like about writing is that I feel not constrained. Sometimes science can be constrained because I can only discuss facts that I can prove. I mean, when I write a paper, I don't want to be overly speculative because it's hand waving two lines in.
[01:15:49] Speaker B: A discussion that you can speculate a little bit, right?
[01:15:52] Speaker A: Yeah, but I don't like doing that much. I mean, if I speculate my speculations in science, when I write an article, they're based on facts. We found these neurons and this means this, this and that. So for example, with you, I'm speculating about how concept cells could be the basics of human intelligence. But I'm not just hand waving. I mean, this is based on facts. It's based on the fact that these are context independent abstract neurons. And in Rats, they're not. And this may not be a conscious. But when I write. When I write a book, I just let loose. I mean, I'm not constrained by discussing fact. I can't just speculate about, well, what if, what if this, what if that, what if that? And maybe this is something that cannot be possible now. Anyway, I like to think about that and I like to write about that.
[01:16:33] Speaker B: It's a release, isn't it? Yeah, yeah, but it also informs your research, right? Because then you can make these disparate, otherwise disparate ideas come together. And it informs how you move ahead with your research, Perhaps, yeah.
[01:16:48] Speaker A: For example, I mean, you read in the acknowledgments that writing this book changed my career. There's an underlying question that is all over the book, which is basically, what makes us human? If you want to put it like in one sentence. What is it? What makes me different from HAL 9000, from a supercomputer? And what makes me different from other animals? What is it? What is it in our brain that makes that. At the time I wrote the book was kind of like my hand waving. I mean, my speculative question. I have no clue. I mean. But I like to speculate about that and the fact that I can write a book. I can be speculative, I can think about Planet of the Apes, I can think about 2001, I can think about, I don't know, Terminator and so on. But after playing around with the idea, right now I'm doing experiments, trying to prove the point. So what for me was speculating, like freely speculating in a book? Now it's actually the basis of my research. This is the hypothesis that I want to prove with my experiment.
[01:17:44] Speaker B: That's beautiful.
[01:17:44] Speaker A: So I like playing, like, being in between, like in the border between fiction and science.
[01:17:54] Speaker B: If you had to start your research career over, what would you be doing? Would you be doing AI or how would you go about your trajectory?
[01:18:03] Speaker A: I don't know. It just came this way. Because if.
[01:18:07] Speaker B: Does it matter where you start?
[01:18:10] Speaker A: I guess it does. I mean, but, you know, there's something that happens a lot. I think not. I mean, in life, it's like you ask me. So why do you live in Leicester? Why you're in this city in the middle of England, why you're not, I don't know, in Cambridge or in Oxford? He said, well, actually, it's destiny. I didn't make a conscious choice. This happened somehow. And then I convinced myself there was a good reason for that. And we tend to do that a lot we train to rationalize our decisions that are somehow unconscious and. Well, actually, because there's no free will. We know that already. So.
So back to our issue. So I don't know. I mean, my career has. I mean, what I didn't regret was changing because I changed a lot. And that was at the time you could have said, well, that was suicide. When I was doing physics, I was doing. Well, I mean, I was working with a very, very good physicist. I mean, when I was doing dynamical systems, somebody that I really admire were publishing good papers. But then I really wanted to change and I started neuroscience from scratch with hardly knowing what a neuron was. But it was a good decision because that's exactly what I wanted to do. And so although I may have taken wrong decisions or I have done things that I said, oh, maybe it would have been, I would have been a better neuroscientist now if I wouldn't have spent so much time doing math and physics.
But on the other hand, doing math and physics got me into developing a method for analyzing data that led me to discovering these cells that nobody else could see before.
[01:19:43] Speaker B: So it's hard to say, yeah, no one has. Yeah, everyone. Everyone has an interesting and different path. And I don't know. I just don't know that there's a good way to aim even besides just moving forward. That's like all you can do almost.
[01:19:59] Speaker A: I mean, the only thing, I mean, that's back to this issue of the being aware of your death. I think the key question at any point in time is, am I doing what I really want to do? And as long as the answer is yes, it's fine. I mean, I could be doing cosmology. And if I really enjoy doing cosmology, well, why not?
[01:20:18] Speaker B: I mean, there's a difference. So, you know, the advice used to be to follow your passion, and you hear that a lot these days too, is figure out what your passion is and then follow that. But alternative advice that I think is actually more valid is you work through something and you develop a passion through your work. And so they're not separable.
[01:20:40] Speaker A: I say more than being aware of your passion because I agree with you. I'm passionate about what I do now. I mean, I'm really passionate about trying to understand, let me put it this like, the neural correlates of unique human intelligence. That's in one line, what I want to do now. And I'm really passionate about that. My baby, if I would have follow another path, I will be passionate to discover what's the origin of the universe?
[01:21:04] Speaker B: Yeah.
[01:21:05] Speaker A: Or whatever. I think the key is to more than being aware of your passion because you can be passionate about different things. It's true that if you have a passion, you should. I mean, it's good to be aware of that.
[01:21:16] Speaker B: Right.
[01:21:16] Speaker A: It's also be aware of your strength. Because I recognize. I mean, well, I recognize at the time that I realized, well, maybe to do theoretical cosmology, I don't know. I mean, there are guys that are much more clever for that than me. And I cannot possibly, I mean, parallel these guys. They. I don't see me doing something that it will be useful to somebody in a field that. I mean, it's not my stuff. I mean, I'm not that good at that compared to other guys. So I think it's very good to be aware of your strengths then to exploit them to the maximum.
[01:21:53] Speaker B: What's a setback or a failure that you've experienced and how did you move on from it?
[01:22:00] Speaker A: I mean, there are many things.
I don't have big failures because I have a very. I don't think I have much of an ego. I mean, I say that I'm very egocentric in the fact that I do whatever I care about and I don't care about the other people, but I don't have an ego that if you come tomorrow and say, well, actually what you say about artificial intelligence is wrong because this, this, and that, I will be upset, but I will listen to you. And then tomorrow I say, actually, he's right. Cool. And I will adapt. So my thinking, for example, of concept cells are not the same now compared to when I discovered them. It has evolved.
[01:22:35] Speaker B: Talk about that. Because you didn't really know what you had when you found it, right?
[01:22:39] Speaker A: Yeah. I mean, when I found it, it's like crazy.
Suddenly I got my first Nature paper. It got all over the media and people are talking about this and nobody had a clue what this means. And people were misleading into this discussion about grandmother cells, which I really hated.
But then talking to people and hearing different opinions, I mean, then my. My view of what these neurons do and what. What it means to have this neuron has evolved has really changed a lot. I mean, from the moment I discovered them till today. And maybe it will evolve further. Maybe what I'm thinking now, how these neurons are, I mean, evolving human intelligence will change. So I think an attribute of a good scientist, maybe if I have something that, I mean, I. I think is good, is that I'm. I'm not Stuck with an opinion. So if somebody shows me wrong, I don't have a problem with taking this information. Actually, yeah, I was wrong. So therefore I didn't have any major upsets because I was not holding tight to an opinion and trying to defend it no matter what. For ages I was very malleable. I was very flexible. Now this has evolved and it's very hard now for me for somebody to change my opinion because I heard a lot of opinions and I think it's converging and. Yeah. So I cannot name a major thing. Now in the daily, in the daily practice, I do have major upsets, which is, for example, getting a grant rejected.
[01:24:04] Speaker B: Yeah, right.
[01:24:04] Speaker A: Comes again and again. Or a high profile paper rejected. And I think the recipe of a good scientist is that you just keep trying. I think we're very resilient.
[01:24:14] Speaker B: Yeah, yeah.
[01:24:15] Speaker A: And it's really upsetting. And you want to quit and you don't want. I'm never writing a grant anymore. I'm never sending a paper to this journal ever again. And this and that. But then next day, okay, I go for it again.
[01:24:25] Speaker B: Yeah, you have to start over again.
[01:24:26] Speaker A: We all do the same. Yeah.
[01:24:27] Speaker B: Yeah.
What is something that you've done that you consider a complete waste of your time? Anything.
[01:24:35] Speaker A: Oh, I don't know if.
Well, I can, I can give you an example. Like, for example, when I, when I was finishing physics, I mean, you have to do a final year project which usually lasts one year in Argentina, where I study physics. And mine lasted two years because I really couldn't find the result. And. And that was because I was trying to replicate somebody else's result. And I met this person much later. I won't name the person or the results. And then she confessed that it was all fake.
[01:25:04] Speaker B: Oh, my God.
[01:25:05] Speaker A: And I was like. And I was like, gosh, that was two years of my life.
But I don't regret it because trying to follow something that was wrong got me into studying the brain. Because I was trying to find out. I mean, I was doing chaos theory, a physicist, and I was trying to find out if the brain was a chaotic system as other people claim.
And I wasted my time.
And actually I discovered much later that the question even was that interesting after all. But I wasted my time. I wasted two years. But I learned about chaos tools that I couldn't use in other contexts and so on. And that slowly got me into studying something that I end up been interested about.
[01:25:48] Speaker B: So, yeah, what is something that you feel guilty about not paying more attention to, you know, you should be paying more attention to something, that paper on your desk that you always look at. And then, no, no, it won't be a paper.
[01:26:03] Speaker A: No, it will be a book, maybe. So, for example, when I wrote my first book called Borges and Memory, I wrote a lot about this writer that I mentioned before.
[01:26:12] Speaker B: Is it Borges?
[01:26:14] Speaker A: Yeah, I used to read it as a teenager. I mean, it's a very famous writer in my country, and I love his stories. And to be able to link my research to Borges was like, wow, that's amazing. But reading Borges, I was very interested to get to understand how he came up with these brilliant ideas, not being a scientist. So here, these ideas that nouns are abstractions and that without nouns you won't be able to think. That's fabulous. I said how the guy came up with this idea. And I tried to follow his readings, so I had access. I mean, I met his widow and she allowed me to see his books in his library. So I was checking what he was reading, and I tried to more or less read what he was reading, related to the mind with quotation marks or the brain and so on. And then I realized, oh, I mean, there's a lot there from 19th century maybe. I mean, there's a lot to learn from these guys that wrote about that ages ago. There's a lot to learn about the brain from all philosophers and so on. So I felt guilty at the time of not having read. I mean, being always aware of the latest paper published in our field, but not being aware of what Aristotle said about that 2,000 years ago.
[01:27:29] Speaker B: Yeah, yeah.
[01:27:30] Speaker A: And what the writer has to say about that. And they come from a completely different angle. And I think it's. It's very rewarding to broaden. I mean, somehow our readings. And I think we get a much bigger picture of what we are doing when we start reading a bit more philosophy or literature and these type of things.
[01:27:51] Speaker B: Yeah, that's a rabbit hole to go down, though, too.
[01:27:53] Speaker A: Yeah, it never ends.
[01:27:55] Speaker B: Never, never ends. And that's why I appreciate the way that you bring it up consistently through the book and just keep touching on it. And so we don't have to go just down that road because you're always bringing it up in the context of. Of either a movie or mostly the modern science, which is. Which is great.
If I pinned you down, I'm going to make a scale from 1 to 100, and one is the McCulloch Pitts neuron, artificial neuron. So let's say like the very earliest Artificial intelligence work. And 100 is general intelligence.
Where would you say we are with deep learning?
[01:28:33] Speaker A: It's hard to put it in a scale because you're putting a goal that maybe is not necessarily the goal of deep learning. Because I think somebody could say, look, I mean, I don't care about the brain, and I don't care about general intelligence. I mean, I can have a deep learning network that can recognize faces better than a human. I can translate better than a translator or can recognize, like, handwriting or audio or whatever. And fair enough. I mean, and that's fine. I mean, I don't see why this is. This is wrong. Now, if you want to use deep learning to understand the brain, the human brain, or the monkey brain or whatever, that's a different question. Deep learning can be very interesting on its own. Without addressing this question, I think I have the feeling that the wave is changing, that we have this big boost of deep learning after Hinton's paper, seven, eight years ago. But I think now this big flash of beating somebody at Go or playing all this Atari game is slowly decaying. And the big questions are coming back. And that's back to Minsky. The big question of general intelligence is coming back. And I was very pleased to see Josh Evangel talking about consciousness and also mentioning a bit about artificial intelligence. So I think if the field gets there, who knows? I mean, maybe we'll have super revolution. Maybe we're. I don't know, I'm just making up numbers. Maybe we're 20 right now, but next year will be 80. Maybe it will take decades, who knows? Or maybe we'll never get there. But I think the key point is that we get interested in that.
[01:30:06] Speaker B: Do you think that a deep learning network is closer to a McCulloch Pitts neuron or the brain?
[01:30:14] Speaker A: I mean, my naive version of deep learning, and this might be one of the naive conceptions I have that maybe one year I will say, you talk to me again. Say, well, actually, what I told you, I was too naive, and I was wrong. But a naive feeling I have about deep learning is that as the word says, you have many, many layers. So it's deep because you have many layers. I'm a naive feeling. My gut feeling is that you have cellium parameters to fit. So this can lead to, if you allow me to use the term, I mean, vaguely overfitting. So basically, you get your network to be very, very good at doing a very precise task. But the moment you want the network to do another task is hard because you fit all These variables, all these strengths of the cell layers or whatever, not ceiling layers, but the ceiling nodes in these many, many layers, you fit them very precisely to excel at some particular task. And the fact that you, again, allow me to say, well, you overfitted the network for solving this task may go against the general intelligence task, which is the opposite. You don't want to be that, that good at some tasks. You want it to be good at the task, but to also be able to transfer information. And maybe overfitting is not the answer, but I don't know, maybe I'm too naive in making such like a simplistic, simplistic conclusion.
[01:31:41] Speaker B: Finally, Rodrigo, you are known as the Jennifer Aniston Neuron guy or the concept cell guy. What do you want your legacy. Legacy to be? Do you, do you want to be known as that? Do you want to be known as a writer or as a. Or is there something else?
[01:31:57] Speaker A: Well, the honest answer is back to this issue of the last chapter of the book, which is being aware of that. Do not answer this. I don't care. I mean, or if you want to put it, I mean, if you want me to play with your question, somebody that did, like a happy person, as you say, somebody that did whatever, who enjoyed his career, If I get 20 Nobel prizes for that, if I don't get to publish a good paper for that, who will take the fun out of it from me? If I'm doing something that I don't like and you give me a huge reward in 10 years time, I don't think it's worth it because you get a prize, you get something. And is it really that important? I mean, yeah, you do get the price. Everybody claps and then. But now if you give me a career that I'm enjoying, going to the lab and doing what I do, because I'm really liking it. I think that's the best. I think that that's unbeatable, irrespective of what my legacy will be. I think the legacy will be, well, somebody that really enjoyed what he was doing.
[01:32:58] Speaker B: That's great. Well, again, I just, I really enjoyed the book. It's. You write really well. It's just a joy to read. I was skeptical at first because you. Because I'm not a big movie guy, I think sci fi movies are generally. I don't think I enjoy them nearly as much as you do. I'm a much more critical critic, I think, of movies. But you do it really well in the book. And so I appreciate you writing the book and having read it. So thanks.
[01:33:25] Speaker A: Thank you very much. I really enjoy talking to you. It was great.
[01:33:33] Speaker C: Brain Inspired is a production of me and you. You can support the show through Patreon. For a microscopic 2 or $4 per month. Go to BrainInspired Co and find the red Patreon button there. Your contribution will help sustain and improve the show and prohibit any annoying advertisements like you hear on other shows. To get in touch with me, email paulaininspired. Co. The music you hear is by the New Year. Find
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[01:34:07] Speaker B: The stare of a boundless blank page led me into the snow, the covers.
[01:34:22] Speaker A: Up the paths that take me where I'm.
