Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI | Lex Fridman Podcast #61
 The following is a conversation with Melanie Mitchell. She's a professor of computer science at Portland State University and an external professor at Santa Fe Institute. She has worked on and written about artificial intelligence from fascinating perspectives, including adaptive complex systems, genetic algorithms, and the copycat cognitive architecture, which places the process of analogy making at the core of human cognition. From her doctoral work with her advisors, Douglas Hufstadter and John Holland, to today, she has contributed a lot of important ideas to the field of AI, including her recent book, simply called Artificial Intelligence, a Guide for Thinking Humans. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcast, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N. I recently started doing ads at the end of the introduction. 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I think it has a few problems because it means so many different things to different people. And intelligence is one of those words that isn't very clearly defined either. There's so many different kinds of intelligence, degrees of intelligence, approaches to intelligence. John McCarthy was the one who came up with the term artificial intelligence. And from what I read, he called it that to differentiate it from cybernetics, which was another related movement at the time. And he later regretted calling it artificial intelligence. Herbert Simon was pushing for calling it complex information processing, which got mixed, but probably is equally vague, I guess. Is it the intelligence or the artificial in terms of words that is most problematic, would you say? Yeah, I think it's a little of both. But it has some good size because I personally was attracted to the field because I was interested in phenomenon of intelligence. And if it was called complex information processing, maybe I'd be doing something wholly different now. What do you think of, I've heard that term used, cognitive systems, for example, so using cognitive. Yeah, I mean, cognitive has certain associations with it. And people like to separate things like cognition and perception, which I don't actually think are separate, but often people talk about cognition as being different from sort of other aspects of intelligence. It's sort of higher level. So to you, cognition is this broad, beautiful mess of things that encompasses the whole thing. Memory, perception. I think it's hard to draw lines like that. When I was coming out of grad school in 1990, which is when I graduated, that was during one of the AI winters. And I was advised to not put AI, artificial intelligence, on my CV, but instead call it intelligence systems. So that was kind of a euphemism, I guess. What about to stick briefly on terms and words, the idea of artificial general intelligence, or like Jan LeCun prefers human level intelligence, sort of starting to talk about ideas that achieve higher and higher levels of intelligence and somehow artificial intelligence seems to be a term used more for the narrow, very specific applications of AI and sort of what set of terms appeal to you to describe the thing that perhaps we strive to create. People have been struggling with this for the whole history of the field and defining exactly what it is that we're talking about. John Searle had this distinction between strong AI and weak AI. And weak AI could be general AI, but his idea was strong AI was the view that a machine is actually thinking, that as opposed to simulating thinking or carrying out intelligent processes that we would call intelligent. At a high level, if you look at the founding of the field, McCarthy and Searle and so on, are we closer to having a better sense of that line between narrow, weak AI and strong AI? Yes, I think we're closer to having a better idea of what that line is. Early on, for example, a lot of people thought that playing chess would be, you couldn't play chess if you didn't have sort of general human level intelligence. And of course, once computers were able to play chess better than humans, that revised that view. And people said, okay, well, maybe now we have to revise what we think of intelligence as. And so that's kind of been a theme throughout the history of the field is that once a machine can do some task, we then have to look back and say, oh, well, that changes my understanding of what intelligence is because I don't think that machine is intelligent. At least that's not what I wanna call intelligence. Do you think that line moves forever or will we eventually really feel as a civilization like we crossed the line if it's possible? It's hard to predict, but I don't see any reason why we couldn't in principle create something that we would consider intelligent. I don't know how we will know for sure. Maybe our own view of what intelligence is will be refined more and more until we finally figure out what we mean when we talk about it. But I think eventually we will create machines in a sense that have intelligence. They may not be the kinds of machines we have now. And one of the things that that's going to produce is making us sort of understand our own machine-like qualities that we, in a sense, are mechanical in the sense that like cells, cells are kind of mechanical. They have algorithms, they process information by. And somehow out of this mass of cells, we get this emergent property that we call intelligence. But underlying it is really just cellular processing and lots and lots and lots of it. Do you think we'll be able to, do you think it's possible to create intelligence without understanding our own mind? You said sort of in that process, we'll understand more and more, but do you think it's possible to sort of create without really fully understanding from a mechanistic perspective, sort of from a functional perspective, how our mysterious mind works? If I had to bet on it, I would say, no, we do have to understand our own minds, at least to some significant extent. But I think that's a really big open question. I've been very surprised at how far kind of brute force approaches based on, say, big data and huge networks can take us. I wouldn't have expected that. And they have nothing to do with the way our minds work. So that's been surprising to me, so it could be wrong. To explore the psychological and the philosophical, do you think we're okay as a species with something that's more intelligent than us? Do you think perhaps the reason we're pushing that line further and further is we're afraid of acknowledging that there's something stronger, better, smarter than us humans? Well, I'm not sure we can define intelligence that way because smarter than is with respect to what, computers are already smarter than us in some areas. They can multiply much better than we can. They can figure out driving routes to take much faster and better than we can. They have a lot more information to draw on. They know about traffic conditions and all that stuff. So for any given particular task, sometimes computers are much better than we are. And we're totally happy with that, right? I'm totally happy with that. It doesn't bother me at all. I guess the question is, which things about our intelligence would we feel very sad or upset that machines had been able to recreate? So in the book, I talk about my former PhD advisor, Douglas Hofstadter, who encountered a music generation program. And that was really the line for him. If a machine could create beautiful music, that would be terrifying for him because that is something he feels is really at the core of what it is to be human, creating beautiful music, art, literature. He doesn't like the fact that machines can recognize spoken language really well. He personally doesn't like using speech recognition, but I don't think it bothers him to his core because it's like, okay, that's not at the core of humanity. But it may be different for every person. What really they feel would usurp their humanity. And I think maybe it's a generational thing also. Maybe our children or our children's children will be adapted, they'll adapt to these new devices that can do all these tasks and say, yes, this thing is smarter than me in all these areas, but that's great because it helps me. Looking at the broad history of our species, why do you think so many humans have dreamed of creating artificial life and artificial intelligence throughout the history of our civilization? So not just this century or the 20th century, but really throughout many centuries that preceded it. That's a really good question. And I have wondered about that because I myself was driven by curiosity about my own thought processes and thought it would be fantastic to be able to get a computer to mimic some of my thought processes. And I'm not sure why we're so driven. I think we want to understand ourselves better and we also want machines to do things for us. But I don't know, there's something more to it because it's so deep in the kind of mythology or the ethos of our species. And I don't think other species have this drive. So I don't know. If you were to sort of psychoanalyze yourself in your own interest in AI, are you, I don't know, in your own interest in AI, are you, what excites you about creating intelligence? You said understanding our own selves? Yeah, I think that's what drives me particularly. I'm really interested in human intelligence, but I'm also interested in the sort of the phenomenon of intelligence more generally. And I don't think humans are the only thing with intelligence or even animals, but I think intelligence is a concept that encompasses a lot of complex systems. And if you think of things like insect colonies or cellular processes or the immune system or all kinds of different biological or even societal processes have as an emergent property, some aspects of what we would call intelligence. They have memory, they can process information, they have goals, they accomplish their goals, et cetera. And to me, the question of what is this thing we're talking about here was really fascinating to me and exploring it using computers seemed to be a good way to approach the question. So do you think kind of of intelligence, do you think of our universe as a kind of hierarchy of complex systems and then intelligence is just the property of any, you can look at any level and every level has some aspect of intelligence. So we're just like one little speck in that giant hierarchy of complex systems. I don't know if I would say any system like that has intelligence, but I guess what I wanna, I don't have a good enough definition of intelligence to say that. So let me do sort of multiple choice, I guess. So you said ant colonies. So are ant colonies intelligent? Are the bacteria in our body intelligent? And then going to the physics world, molecules and the behavior at the quantum level of electrons and so on, are those kinds of systems, do they possess intelligence? Like where's the line that feels compelling to you? I don't know. I mean, I think intelligence is a continuum and I think that the ability to in some sense have intention, have a goal, have some kind of self-awareness is part of it. So I'm not sure if, it's hard to know where to draw that line. I think that's kind of a mystery, but I wouldn't say that say that, the planets orbiting the sun is an intelligent system. I mean, I would find that maybe not the right term to describe that. And this is, there's all this debate in the field of like, what's the right way to define intelligence? What's the right way to model intelligence? Should we think about computation? Should we think about dynamics? And should we think about free energy and all of that stuff? And I think that it's a fantastic time to be in the field because there's so many questions and so much we don't understand. There's so much work to do. So are we the most special kind of intelligence in this kind of, you said there's a bunch of different elements and characteristics of intelligence systems and colonies, are his human intelligence the thing in our brain? Is that the most interesting kind of intelligence in this continuum? Well, it's interesting to us, because it is us. I mean, interesting to me, yes. And because I'm part of the human. But to understanding the fundamentals of intelligence, what I'm getting at, is studying the human, if everything we've talked about, what you talked about, in your book, what just the AI field, this notion, yes, it's hard to define, but it's usually talking about something that's very akin to human intelligence. Yeah, to me it is the most interesting because it's the most complex, I think. It's the most self-aware. It's the only system, at least that I know of, that reflects on its own intelligence. And you talk about the history of AI and us, in terms of creating artificial intelligence, being terrible at predicting the future with AI, with tech in general. So, why do you think we're so bad at predicting the future? Are we hopelessly bad? So, no matter what, whether it's this decade or the next few decades, every time we make a prediction, there's just no way of doing it well, or as the field matures, we'll be better and better at it. I believe as the field matures, we will be better. And I think the reason that we've had so much trouble is that we have so little understanding of our own intelligence. So, there's the famous story about Marvin Minsky assigning computer vision as a summer project to his undergrad students. And I believe that's actually a true story. Yeah, there's a write-up on it that everyone should read. I think it's like a proposal that describes everything that should be done in that project. It's hilarious because it, I mean, you can explain it, but for my sort of recollection, it describes basically all the fundamental problems of computer vision, many of which that still haven't been solved. Yeah, and I don't know how far they really expect it to get, but I think that, and they're really, you know, Marvin Minsky is a super smart guy and very sophisticated thinker. But I think that no one really understands or understood, still doesn't understand how complicated, how complex the things that we do are because they're so invisible to us. You know, to us, vision, being able to look out at the world and describe what we see, that's just immediate. It feels like it's no work at all. So, it didn't seem like it would be that hard, but there's so much going on. Unconsciously sort of invisible to us that I think we overestimate how easy it will be to get computers to do it. And sort of for me to ask an unfair question, you've done research, you have thought about many different branches of AI through this book, widespread looking at where AI has been, where it is today. If you were to make a prediction, how many years from now would we as a society create something that you would say achieved human level intelligence or superhuman level intelligence? That is an unfair question. A prediction that will most likely be wrong. So, but it's just your notion because- Okay, I'll say more than a hundred years. More than a hundred years. More than a hundred years. And I quoted somebody in my book who said that human level intelligence is a hundred Nobel prizes away. Which I like because it's a nice way to sort of, it's a nice unit for prediction. And it's like that many fantastic discoveries have to be made. And of course there's no Nobel prize in AI. Not yet at least. If we look at that hundred years, your sense is really the journey to intelligence has to go through something more complicated that's akin to our own cognitive systems. Understanding them, being able to create them in the artificial systems as opposed to sort of taking the machine learning approaches of today and really scaling them and scaling them exponentially with both compute and hardware and data. That would be my guess. I think that in the sort of going along in the narrow AI that these current approaches will get better. I think there's some fundamental limits to how far they're gonna get. I might be wrong, but that's what I think. And there's some fundamental weaknesses that they have that I talk about in the book that just comes from this approach of supervised learning, requiring sort of feed forward networks and so on. It's just, I don't think it's a sustainable approach to understanding the world. Yeah, I'm personally torn on it. Sort of everything you read about in the book instead of what we're talking about now, but I agree with you, but I'm more and more, depending on the day, first of all, I'm deeply surprised by the success of machine learning and deep learning in general. From the very beginning, when it's really been my main focus of work, I'm just surprised how far it gets. And I'm also think we're really early on in these efforts of these narrow AI. So I think there'll be a lot of surprise of how far it gets. I think we'll be extremely impressed. Like, in my sense, everything I've seen so far, and we'll talk about autonomous driving and so on, I think we can get really far. But I also have a sense that we will discover, just like you said, is that even though we'll get really far, in order to create something like our own intelligence is actually much farther than we realize. Right. I think these methods are a lot more powerful than people give them credit for, actually. So that, of course, there's the media hype, but I think there's a lot of researchers in the community, especially, like, not undergrads, right? But people who have been in AI, they're skeptical about how far deep learning can get. And I'm more and more thinking that it can actually get farther than they'll realize. It's certainly possible. One thing that surprised me when I was writing the book is how far apart different people are in the field are on their opinion of how far the field has come and what is accomplished and what's gonna happen next. What's your sense of the different, who are the different people, groups, mindsets, thoughts in the community about where AI is today? Yeah, they're all over the place. So there's kind of the singularity transhumanism group. I don't know exactly how to characterize that approach, which is just so- It occurs while you- Yeah, the sort of exponential progress where we're on the sort of almost at the hugely accelerating part of the exponential. And by in the next 30 years, we're going to see super intelligent AI and all that, and we'll be able to upload our brains and that. So there's that kind of extreme view that most, I think most people who work in AI don't have. They disagree with that. But there are people who are maybe aren't singularity people, but they do think that the current approach of deep learning is going to scale and is going to kind of go all the way basically and take us to true AI or human level AI or whatever you wanna call it. And there's quite a few of them. And a lot of them, like a lot of the people I met who work at big tech companies in AI groups kind of have this view that we're really not that far. Just to linger on that point sort of, if I can take as an example, like Jan LeCun, I don't know if you know about his work and so her viewpoints on this. I do. He believes that there's a bunch of breakthroughs like fundamental, like Nobel prizes that are needed still. But I think he thinks those breakthroughs will be built on top of deep learning. And then there's some people who think we need to kind of put deep learning to the side a little bit as just one module that's helpful in the bigger cognitive framework. Right, so I think somewhat I understand Jan LeCun is rightly saying supervised learning is not sustainable. We have to figure out how to do unsupervised learning that that's gonna be the key. And I think that's probably true. I think unsupervised learning is gonna be harder than people think. I mean, the way that we humans do it. Then there's the opposing view, there's the Gary Marcus kind of hybrid view where deep learning is one part, but we need to bring back kind of these symbolic approaches and combine them. Of course, no one knows how to do that very well. Which is the more important part to emphasize and how do they fit together? What's the foundation? What's the thing that's on top? What's the cake? What's the icing? Right. Then there's people pushing different things. There's the people, the causality people who say, deep learning as it's formulated today completely lacks any notion of causality. And that's dooms it. And therefore we have to somehow give it some kind of notion of causality. There's a lot of push from the more cognitive science crowd saying, we have to look at developmental learning. We have to look at how babies learn. We have to look at intuitive physics, all these things we know about physics. And as somebody kind of quipped, we also have to teach machines intuitive metaphysics, which means like objects exist. Causality exists. These things that maybe we're born with, I don't know. That they don't have the, machines don't have any of that. They look at a group of pixels and they maybe they get 10 million examples, but they can't necessarily learn that there are objects in the world. So there's just a lot of pieces of the puzzle that people are promoting and with different opinions of like how important they are and how close we are to being able to put them all together to create general intelligence. Looking at this broad field, what do you take away from it? Who is the most impressive? Is it the cognitive folks, the Gary Marcus camp, the Jan camp, unsupervised, self-supervised, there's the supervised, and then there's the engineers who are actually building systems. You have sort of the Andre Karpathy at Tesla building actual, it's not philosophy, it's real systems that operate in the real world. What do you take away from all this beautiful variety? I don't know if these different views are not necessarily mutually exclusive. And I think people like Jan LeCun agrees with the developmental psychology, causality, intuitive, physics, et cetera. But he still thinks that it's learning, like end to end learning is the way to go. Will take us perhaps all the way. Yeah, and that we don't need, there's no sort of innate stuff that has to get built in. This is, it's because it's a hard problem. I personally, I'm very sympathetic to the cognitive science side, cause that's kind of where I came in to the field. I've become more and more sort of an embodiment adherent saying that without having a body, it's gonna be very hard to learn what we need to learn about the world. That's definitely something I'd love to talk about in a little bit. To step into the cognitive world, then if you don't mind, cause you've done so many interesting things. If you look to copycat, taking a couple of decades, step back, you, Douglas Hofstadter and others have created and developed copycat more than 30 years ago. That's painful to hear. What is it? What is copycat? It's a program that makes analogies in an idealized domain, idealized world of letter strings. So as you say, 30 years ago, wow. So I started working on it when I started grad school in 1984. Wow. Dates me. And it's based on Doug Hofstadter's ideas that analogy is really a core aspect of thinking. I remember he has a really nice quote in the book by himself and Emmanuel Sander called Surfaces and Essences. I don't know if you've seen that book, but it's about analogy. And he says, without concepts, there can be no thought and without analogies, there can be no concepts. So the view is that analogy is not just this kind of reasoning technique where we go, shoe is to foot, as glove is to what? These kinds of things that we have on IQ tests or whatever, but that it's much deeper, it's much more pervasive in everything we do, in our language, our thinking, our perception. So he had a view that was a very active perception idea. So the idea was that instead of having kind of a passive network in which you have input that's being processed through these feed forward layers, and then there's an output at the end, that perception is really a dynamic process, where our eyes are moving around and they're getting information. And that information is feeding back to what we look at next influences what we look at next and how we look at it. And so CopyCat was trying to do that, kind of simulate that kind of idea where you have these agents, it was kind of an agent-based system, and you have these agents that are picking things to look at and deciding whether they were interesting or not, whether they should be looked at more. And that would influence other agents. And how do they interact? So they interacted through this global, kind of what we call the workspace. So it was actually inspired by the old blackboard systems, where you would have agents that post information on a blackboard, a common blackboard. This is like very old fashioned AI. Is that, are we talking about like in physical space? Is this a computer program? It's a computer program. So agents posting concepts on a blackboard? Yeah, we called it a workspace. And the workspace is a data structure. The agents are little pieces of code that you could think of them as little detectors or little filters that say, I'm gonna pick this place to look, and I'm gonna look for a certain thing. And is this the thing I think is important? Is it there? So it's almost like a convolution in a way, except a little bit more general. And saying, and then highlighting it in the workspace. Well, once it's in the workspace, how do the things that are highlighted relate to each other? Like what's, is it? So there's different kinds of agents that can build connections between different things. So just to give you a concrete example, what CopyCat did was it made analogies between strings of letters. So here's an example, ABC changes to ABD. What does IJK change to? And the program had some prior knowledge about the alphabet, knew the sequence of the alphabet. Had a concept of letter, successor of letter, it had concepts of sameness. So it has some innate things programmed in. But then it could do things like, say, discover that ABC is a group of letters in succession. And then an agent can mark that. So the idea that there could be a sequence of letters, is that a new concept that's formed or that's a concept that's innate? That's a concept that's innate. Sort of, can you form new concepts? Or are all concepts innate? No, so in this program, all the concepts of the program were innate. So, cause we weren't, I mean, we were not able to do that. We were trying to say, suppose you have some innate concepts, how do you flexibly apply them to new situations? And how do you make analogies? Let's step back for a second. So I really liked that quote that you said, without concepts, there can be no thought. And without analogies, there can be no concepts. In a Santa Fe presentation, you said that it should be a concept of a new concept. In a Santa Fe presentation, you said that it should be one of the mantras of AI. Yes. And that you also yourself said, how to form and fluidly use concepts is the most important open problem in AI. Yes. How to form and fluidly use concepts is the most important open problem in AI. So let's, what is a concept and what is an analogy? A concept is in some sense, a fundamental unit of thought. So say we have a concept of a dog, okay? And a concept is embedded in a whole space of concepts, so that there's certain concepts that are closer to it or farther away from it. Are these concepts, are they really like fundamental, like we mentioned innate, almost like axiomatic, like very basic and then there's other stuff built on top of it? Yeah. Or does this include everything? Are there complicated, like? You can certainly form new concepts. Right, I guess that's the question I'm asking. Yeah. Can you form new concepts that are complex combinations of other concepts? Yes, absolutely. And that's kind of what we do in learning. And then what's the role of analogies in that? So analogy is when you recognize that one situation is essentially the same as another situation. And essentially is kind of the key word there because it's not the same. So if I say, last week I did a podcast interview in actually like three days ago in Washington, DC. And that situation was very similar to this situation, although it wasn't exactly the same. It was a different person sitting across from me. We had different kinds of microphones. The questions were different. The building was different. There's all kinds of different things, but really it was analogous. Or I can say, so doing a podcast interview, that's kind of a concept, it's a new concept. I never had that concept before this year essentially. I mean, and I can make an analogy with it like being interviewed for a news article in a newspaper. And I can say, well, you kind of play the same role that the newspaper reporter played. It's not exactly the same. It's not exactly the same because maybe they actually emailed me some written questions rather than talking. And the written questions are analogous to your spoken questions. There's just all kinds of similarities. And this somehow probably connects to conversations you have over Thanksgiving dinner, just general conversations. There's like a thread you can probably take that just stretches out in all aspects of life that connect to this podcast. I mean, conversations between humans. Sure, and if I go and tell a friend of mine about this podcast interview, my friend might say, oh, the same thing happened to me. Let's say, you ask me some really hard question and I have trouble answering it. My friend could say, the same thing happened to me, but it wasn't a podcast interview. It was a completely different situation. And yet my friend is seeing essentially the same thing. We say that very fluidly, the same thing happened to me. Essentially the same thing. But we don't even say that, right? We just say the same thing. You imply it, yes. Yeah, and the view that kind of went into say, copycat, that whole thing is that that act of saying the same thing happened to me is making an analogy. And in some sense, that's what underlies all of our concepts. Why do you think analogy making that you're describing is so fundamental to cognition? Like it seems like it's the main element action of what we think of as cognition. Yeah, so it can be argued that all of this generalization we do of concepts and recognizing concepts in different situations is done by analogy. Every time I'm recognizing that say, you're a person, that's by analogy, because I have this concept of what person is and I'm applying it to you. And every time I recognize a new situation, like one of the things I talked about in the book was the concept of walking a dog. That that's actually making an analogy, because all of the details are very different. So reasoning could be reduced down to sensory analogy making. So all the things we think of as like, yeah, like you said, perception. So what's perception is taking raw sensory input and it's somehow integrating into our understanding of the world, updating the understanding. And all of that has just this giant mess of analogies that are being made. I think so, yeah. If you just linger on it a little bit, like what do you think it takes to engineer a process like that for us in our artificial systems? We need to understand better, I think, how we do it, how humans do it. And it comes down to internal models, I think. People talk a lot about mental models, that concepts are mental models, that I can, in my head, I can do a simulation of a situation like walking a dog. And that there's some work in psychology that promotes this idea that all of concepts are really mental simulations. That whenever you encounter a concept or situation in the world, or you read about it, or whatever, you do some kind of mental simulation that allows you to predict what's gonna happen, to develop expectations of what's gonna happen. So that's the kind of structure I think we need, is that kind of mental model that, and in our brains, somehow these mental models are very much interconnected. Again, so a lot of stuff we're talking about are essentially open problems, right? So if I ask a question, I don't mean that you would know the answer, I'm really just hypothesizing. But how big do you think is the network graph, data structure of concepts that's in our head? Like if we're trying to build that ourselves, like we take it, that's one of the things we take for granted, we think, I mean, that's why we take common sense for granted, we think common sense is trivial. But how big of a thing of concepts is on that, is that underlies what we think of as common sense, for example? Yeah, I don't know. And I'm not, I don't even know what units to measure it in. You say how big is it? That's beautifully put, right? What? But we have, it's really hard to know, we have what, 100 billion neurons or something, I don't know, and they're connected via trillions of synapses. And there's all this chemical processing going on. There's just a lot of capacity for stuff. And their information's encoded in different ways in the brain. It's encoded in chemical interactions, it's encoded in electric, like firing and firing rates. And nobody really knows how it's encoded, but it just seems like there's a huge amount of capacity. So I think it's huge, it's just enormous. And it's amazing how much stuff we know. Yeah. And if we're, but we know, and not just know, like facts, but it's all integrated into this thing that we can make analogies with. Yes. There's a dream of semantic web, and there's a lot of dreams from expert systems of building giant knowledge bases. Do you see a hope for these kinds of approaches of building, of converting Wikipedia into something that could be used in analogy making? Sure. And I think people have made some progress along those lines. I mean, people have been working on this for a long time. But the problem is, and this I think is the problem of common sense. Like people have been trying to get these common sense networks. Here at MIT, there's this concept net project, right? But the problem is that, as I said, most of the knowledge that we have is invisible to us. It's not in Wikipedia. It's very basic things about intuitive physics, intuitive psychology, intuitive metaphysics, all that stuff. If you were to create a website that described intuitive physics, intuitive psychology, would it be bigger or smaller than Wikipedia? What do you think? I guess describe to whom? I'm sorry, but. No, that's really good. I think you're exactly right, yeah. That's a hard question because, you know, how do you represent that knowledge is the question, right? I can certainly write down F equals MA and Newton's laws and a lot of physics can be deduced from that. But that's probably not the best representation of that knowledge for doing the kinds of reasoning we want a machine to do. So I don't know, it's impossible to say now. And people, you know, the projects like there's the famous Psych project, right? That Douglas Linat did that was trying. I think it's still going. I think it's still going. And the idea was to try and encode all of common sense knowledge, including all this invisible knowledge in some kind of logical representation. And it just never, I think, could do any of the things that he was hoping it could do because that's just the wrong approach. Of course, that's what they always say, you know, and then the history books will say, well, the Psych project finally found a breakthrough in 2058 or something. You know, so much progress has been made in just a few decades that who knows what the next breakthroughs will be. It could be. It's certainly a compelling notion what the Psych project stands for. I think Linat was one of the earliest people to say, common sense is what we need. That's what we need. All this like expert system stuff that is not gonna get you to AI. You need common sense. And he basically gave up his whole academic career to go pursue that. And I totally admire that, but I think that the approach itself will not in 2020 or 2040 or whatever. What do you think is wrong with approach? What kind of approach might be successful? Well, if I knew that. Again, nobody knows the answer, right? If I knew that, you know, one of my talks, one of the people in the audience, this is a public lecture, one of the people in the audience said, what AI companies are you investing in? I'm like, well, I'm- Investment advice, okay. I'm a college professor for one thing. So I don't have a lot of extra funds to invest, but also like no one knows what's gonna work in AI, right? That's the problem. Let me ask another impossible question in case you have a sense. In terms of data structures that will store this kind of information, do you think they've been invented yet, both in hardware and software? Or is something else needs to be, are we totally, you know- I think something else has to be invented. That's my guess. Is the breakthroughs that's most promising, would that be in hardware or software? Do you think we can get far with the current computers? Or do we need to do something that you- I see what you're saying. I don't know if Turing computation is gonna be sufficient. Probably, I would guess it will. I don't see any reason why we need anything else. But so in that sense, we have invented the hardware we need, but we just need to make it faster and bigger. And we need to figure out the right algorithms and the right sort of architecture. Turing, that's a very mathematical notion. When we have to build intelligence, it's now an engineering notion where you throw all that stuff. Well, I guess it is a question. People have brought up this question, you know, and when you asked about like, is our current hardware, will our current hardware work? Well, Turing computation says that like, our current hardware is in principle a Turing machine, right? So all we have to do is make it faster and bigger. But there have been people like Roger Penrose, if you might remember that he said Turing machines cannot produce intelligence because intelligence requires continuous valued numbers. I mean, that was sort of my reading of his argument and quantum mechanics and what else, whatever, you know. But I don't see any evidence for that, that we need new computation paradigms. But I don't know if we're, you know, I don't think we're gonna be able to scale up our current approaches to programming these computers. What is your hope for approaches like CopyCAD or other cognitive architectures? I've talked to the creator of SOAR, for example, I've used Actar myself, I don't know if you're familiar. Yeah, I am. What do you think is, what's your hope of approaches like that in helping develop systems of greater and greater intelligence in the coming decades? Well, that's what I'm working on now is trying to take some of those ideas and extending it. So I think there's some really promising approaches that are going on now that have to do with more active generative models. So this is the idea of this simulation in your head, of a concept when you, if you wanna, when you're perceiving a new situation, you have some simulations in your head. Those are generative models, they're generating your expectations, they're generating predictions. So that's part of a perception, you have a metamodel that generates a prediction and you compare it with, and then the difference. And you also, that generative model is telling you where to look and what to look at and what to pay attention to. And it, I think it affects your perception. It's not that just you compare it with your perception. It becomes your perception in a way. It's kind of a mixture of the bottom up information coming from the world and your top down model being imposed on the world is what becomes your perception. So your hope is something like that can improve perception systems and that they can understand things better. Yes. Understand things. Yes. What's the, what's the step, what's the analogy making step there? Well, there, the idea is that you have this pretty complicated conceptual space. You can talk about a semantic network or something like that with these different kinds of concept models in your brain that are connected. So let's take the example of walking a dog. So we were talking about that. Okay. Let's say I see someone out on the street walking a cat. Some people walk their cats, I guess. It seems like a bad idea, but. Yeah. So my model, my, you know, there's connections between my model of a dog and model of a cat. And I can immediately see the analogy of, that those are analogous situations, but I can also see the differences and that tells me what to expect. So also, you know, I have a new situation. So another example with the walking the dog thing is sometimes people, I see people riding their bikes with a leash, holding a leash and the dogs running alongside. Okay, so I know that the, I recognize that as kind of a dog walking situation, even though the person's not walking, right? And the dog's not walking. Because I have these models that say, okay, riding a bike is sort of similar to walking or it's connected, it's a means of transportation, but I, because they have their dog there, I assume they're not going to work, but they're going out for exercise. You know, these analogies help me to figure out kind of what's going on, what's likely. But sort of these analogies are very human interpretable. Mm-hmm. So that's that kind of space. And then you look at something like the current deep learning approaches, they kind of help you to take raw sensory information and to sort of automatically build up hierarchies of, well, you can even call them concepts. They're just not human interpretable concepts. What's the link here? Do you hope, it's sort of the hybrid system question, how do you think that two can start to meet each other? What's the value of learning in this systems of forming, of analogy making? The goal of, you know, the original goal of deep learning in at least visual perception was that you would get the system to learn to extract features that at these different levels of complexity. So maybe edge detection, and that would lead into learning, you know, simple combinations of edges and then more complex shapes and then whole objects or faces. And this was based on the ideas of the neuroscientists, Hubel and Weasel, who had seen, laid out this kind of structure in brain. And I think that's right to some extent. Of course, people have found that the whole story is a little more complex than that. And the brain of course always is, and there's a lot of feedback. So I see that as absolutely a good brain inspired approach to some aspects of perception. But one thing that it's lacking, for example, is all of that feedback, which is extremely important. The interactive element that you mentioned. The expectation, right? The conceptual level. The conceptual level, go back and forth with the expectation, the perception and just go back and forth. So, right. So that is extremely important. And, you know, one thing about deep neural networks is that in a given situation, like, you know, they're trained, right? They get these weights and everything, but then now I give them a new image, let's say. Yes. They treat every part of the image in the same way. You know, they apply the same filters at each layer to all parts of the image. There's no feedback to say like, oh, this part of the image is irrelevant. Right. I shouldn't care about this part of the image or this part of the image is the most important part. And that's kind of what we humans are able to do because we have these conceptual expectations. There's, by the way, a little bit of work in that. There's certainly a lot more in what's under the, called attention in natural language processing knowledge. It's an, it's a, and that's exceptionally powerful. And it's a very, just as you say, it's a really powerful idea. But again, in sort of machine learning, it all kind of operates in an automated way. That's not human nature. Well, it's not, it's not also, okay. So that, right, it's not dynamic. I mean, in the sense that as a perception of a new example is being processed, those attention's weights don't change. Right. So, I mean, there's a, there's a kind of notion that there's not a memory. So you're not aggregating the idea of like this mental model. Yes. I mean, that seems to be a fundamental idea. There's not a really powerful, I mean, there's some stuff with memory, but there's not a powerful way to represent the world in some sort of way that's deeper than, I mean, it's so difficult because, you know, neural networks do represent the world. They do have a mental model, right? But it just seems to be shallow. Like, it's hard to criticize them at the fundamental level, to me at least. It's easy to criticize them. Well, look, like exactly what you're saying, mental models sort of almost from a psycho, put a psychology hat on, say, look, these networks are clearly not able to achieve what we humans do with forming mental models, but the analogy making so on. But that doesn't mean that they fundamentally cannot do that. It's very difficult to say that, I mean, at least to me. Do you have a notion that the learning approach is really, I mean, they're going to, not only are they limited today, but they will forever be limited in being able to construct such mental models. I think the idea of the dynamic perception is key here. The idea that moving your eyes around and getting feedback, and that's something that, there's been some models like that. There's certainly recurrent neural networks that operate over several time steps. And, but the problem is that the actual, the recurrence is, basically the feedback is to the brain, and the next time step is the entire hidden state of the network, which is, and it turns out that that doesn't work very well. But see, the thing I'm saying is, mathematically speaking, it has the information in that recurrence to capture everything. It just doesn't seem to work. Yeah, right. So, it's like, it's the same touring machine question, right? Yeah, maybe theoretically, computers, anything that's touring a universal touring machine can be intelligent, but practically, the architecture might be very specific kind of architecture to be able to create it. So it's just, I guess to sort of ask almost the same question again is, how big of a role do you think deep learning needs, will play or needs to play in this, in perception? I think that deep learning as it's currently, as it currently exists, will place, that kind of thing will play some role. But I think that there's a lot more going on in perception. But who knows, the definition of deep learning, I mean, it's pretty broad. It's kind of an umbrella for a lot of different things. So what I mean is purely sort of neural networks. Yeah, and feed forward neural networks. Essentially, or there could be recurrence, but. Yeah. Sometimes it feels like, for example, I talked to Gary Marcus, it feels like the criticism of deep learning is kind of like us birds criticizing airplanes for not flying well, or that they're not really flying. Do you think deep learning, do you think it could go all the way, like Yalda Lipton thinks? Do you think that, yeah, the brute force learning approach can go all the way? I don't think so, no. I mean, I think it's an open question, but I tend to be on the innateness side that there's some things that we've been evolved to be able to learn. And that learning just can't happen without them. So one example, here's an example I had in the book that I think is useful to me, at least, in thinking about this. So this has to do with the Deep Minds Atari game-playing program, okay? And it learned to play these Atari video games just by getting input from the pixels of the screen. And it learned to play the game Breakout thousand percent better than humans, okay? That was one of their results, and it was great. And it learned this thing where it tunneled through the side of the bricks in the Breakout game, and the ball could bounce off the ceiling, and then just wipe out bricks. Okay, so there was a group who did an experiment where they took the paddle that you move with the joystick and moved it up two pixels or something like that. And then they looked at a deep Q learning system that had been trained on Breakout and said, could it now transfer its learning to this new version of the game? Of course, a human could, and it couldn't. Maybe that's not surprising, but I guess the point is it hadn't learned the concept of a paddle. It hadn't learned the concept of a ball or the concept of tunneling. It was learning something. You know, we, looking at it, kind of anthropomorphized it and said, oh, here's what it's doing and the way we describe it. But it actually didn't learn those concepts. And so because it didn't learn those concepts, it couldn't make this transfer. Yeah, so that's a beautiful statement. But at the same time, by moving the paddle, we also anthropomorphize flaws to inject into the system that will then flip how impressed we are by it. What I mean by that is, to me, the Atari games were, to me, deeply impressive that that was possible at all. So like, I have to first pause on that, and people should look at that just like the game of Go, which is fundamentally different to me than what Deep Blue did. Even though there's still Monte Carlo, there's still a tree search. It's just everything a deep mind has done in terms of learning, however limited it is, is still deeply surprising to me. Yeah, I'm not trying to say that what they did wasn't impressive. I think it was incredibly impressive. To me, it's interesting. Is moving the board just another thing that needs to be learned? So like, we've been able to, maybe, maybe, been able to, through the current neural networks, learn very basic concepts that are not enough to do this general reasoning. And maybe with more data, I mean, the data, you know, the interesting thing about the examples that you talk about, and beautifully, is it's often flaws of the data. Well, that's the question. I mean, I think that is the key question, whether it's a flaw of the data or not. Or the mechanism, right, beautifully, yeah. The reason I brought up this example was because you were asking, do I think that, you know, learning from data could go all the way? Yes. And this was why I brought up the example, because I think, and this is not at all to take away from the impressive work that they did, but it's to say that when we look at what these systems learn, do they learn the human, the things that we humans consider to be the relevant concepts? And in that example, it didn't. Sure, if you train it on, you know, the paddle being in different places, maybe it could deal with, maybe it would learn that concept. I'm not totally sure. But the question is, you know, scaling that up to more complicated worlds, to what extent could a machine that only gets this very raw data learn to divide up the world into relevant concepts? And I don't know the answer, but I would bet that without some innate notion, that it can't do it. Yeah, 10 years ago, I 100% agree with you as the most experts in AI system, but now I have a glimmer of hope. OK, that's fair enough. And I think that's what deep learning did in the community is, no, no, if I had to bet all my money, 100% deep learning will not take us all the way. But there's still, I was so personally sort of surprised by the Atari games, by Go, by the power of self-play, of just game playing against each other, that I was, like many other times, just humbled of how little I know about what's possible. Yeah, I think fair enough. Self-play is amazingly powerful. And, you know, that goes way back to Arthur Samuel, right, with his checker playing program, and that which was brilliant and surprising that it did so well. So just for fun, let me ask you on the topic of autonomous vehicles. It's the area that I work at least these days most closely on, and it's also area that I think is a good example that you use as sort of an example of things we as humans don't always realize how hard it is to do. It's like the constant trend in AI, but the different problems that we think are easy when we first try them and then realize how hard it is. Okay, so why, you've talked about this autonomous driving being a difficult problem, more difficult than we realize, humans give it credit for. Why is it so difficult? What are the most difficult parts in your view? I think it's difficult because of the world is so open-ended as to what kinds of things can happen. So you have sort of what normally happens, which is just you drive along and nothing surprising happens. And autonomous vehicles can do, the ones we have now evidently can do really well on most normal situations, as long as the weather is reasonably good and everything. But if some, we have this notion of edge cases or things in the tail of the distribution, we call it the long tail problem, which says that there's so many possible things that can happen that was not in the training data of the machine that it won't be able to handle it because it doesn't have common sense. It's the old paddle moved problem. It's the paddle moved problem. Right. And so my understanding, and you probably are more of an expert than I am on this, is that current self-driving car vision systems have problems with obstacles, meaning that they don't know which quote unquote obstacles they should stop for and which ones they shouldn't stop for. And so a lot of times I read that they tend to slam on the brakes quite a bit. And the most common accidents with self-driving cars are people rear-ending them because they were surprised. They weren't expecting the car to stop. Yeah. So there's a lot of interesting questions there. Because you mentioned two things. So one is the problem of perception, of understanding, of interpreting the objects that are detected correctly. And the other one is more like the policy, the action that you take, how you respond to it. So a lot of the cars breaking is a kind of notion of, to clarify, there's a lot of different kinds of things that are people calling autonomous vehicles. But the L4 vehicles with a safety driver are the ones like Waymo and Cruise and those companies. They tend to be very conservative and cautious. So they tend to be very, very afraid of hurting anything or anyone and getting in any kind of accidents. So their policy is very kind of... That results in being exceptionally responsive to anything that could possibly be an obstacle, right? Right. Which the human drivers around it, it behaves unpredictably. That's not a very human thing to do, caution. That's not the thing we're good at, especially in driving. We're in a hurry, often angry and etc., especially in Boston. So and then there's another... And a lot of times that's machine learning is not a huge part of that. It's becoming more and more unclear to me how much, you know, sort of speaking to public information because a lot of companies say they're doing deep learning and machine learning just to attract good candidates. The reality is, in many cases, it's still not a huge part of the perception. There's lidar and there's other sensors that are much more reliable for optical detection. And then there's Tesla approach, which is vision only. And I think a few companies doing that, but Tesla most sort of famously pushing that forward. And that's because the lidar is too expensive, right? Well, I mean, yes, but I would say if you were to for free give to every Tesla vehicle, Elon Musk fundamentally believes that lidar is a crutch, right? Fantasy said that that if you want to solve the problem of machine learning, lidar is not, should not be the primary sensor is the belief. The camera contains a lot more information. So if you want to learn, you want that information. But if you want not to hit obstacles, you want lidar. It's sort of this weird trade off because yeah, so what Tesla vehicles have a lot of, which is really the thing, the primary fallback sensor is radar, which is a very crude version of lidar. It's a good detector of obstacles except when those things are standing, right? The stopped vehicle. Right. So they had problems with crashing into stop fire trucks. Stop fire trucks. That's right. So the hope there is that the vision sensor would somehow catch that. There's a lot of problems with perception. They are doing actually some incredible stuff in the almost like an active learning space where it's constantly taking edge cases and pulling back in. There's this data pipeline. Another aspect that is really important that people are studying now is called multitask learning, which is sort of breaking apart this problem, whatever the problem is, in this case driving into dozens or hundreds of little problems that you can turn into learning problems. So this giant pipeline, it's kind of interesting. I've been skeptical from the very beginning, but become less and less skeptical over time how much of driving can be learned. I still think it's much farther than the CEO of that particular company thinks it will be, but it's constantly surprising that through good engineering and data collection and active selection of data, how you can attack that long tail. It's an interesting open question that you're absolutely right. There's a much longer tail and all these edge cases that we don't think about, but it's a fascinating question that applies to natural language in all spaces. How big is that long tail? Not to linger on the point, but what's your sense in driving in these practical problems of the human experience? Can it be learned? So the current, what are your thoughts of sort of Elon Musk thought, let's forget the thing that he says it would be solved in a year, but can it be solved in a reasonable timeline or do fundamentally other methods need to be invented? So I think that ultimately driving, so it's a trade off in a way, being able to drive and deal with any situation that comes up does require kind of full human intelligence and even in humans aren't intelligent enough to do it. Cause humans, I mean, most human accidents are because the human wasn't paying attention or the humans drunk or whatever. And not because they weren't intelligent enough. Not because they weren't intelligent enough. Right. Whereas the accidents with autonomous vehicles is because they weren't intelligent enough. They're always paying attention. Yeah, they're always paying attention. So it's a trade off and I think that it's a very fair thing to say that autonomous vehicles will be ultimately safer than humans because humans are very unsafe. It's kind of a low bar. But just like you said, I think humans got a better rap, right? Cause we're really good at the common sense thing. Yeah, we're great at the common sense thing. We're bad at the paying attention thing. Paying attention thing. Especially when we're driving is kind of boring and we have these phones to play with and everything. But I think what's going to happen is that for many reasons, not just AI reasons, but also like legal and other reasons that the definition of self-driving is going to change or autonomous is going to change. It's not going to be just, I'm going to go to sleep in the back and you just drive me anywhere. It's going to be more certain areas are going to be instrumented to have the sensors and the mapping and all of the stuff you need for that the autonomous cars won't have to have full common sense. And they'll do just fine in those areas as long as pedestrians don't mess with them too much. That's another question. But I don't think we will have fully autonomous self-driving in the way that like most the average person thinks of it for a very long time. And just to reiterate, this is the interesting open question that I think I agree with you on is to solve fully autonomous driving, you have to be able to engineer in common sense. I think it's an important thing to hear and think about. I hope that's wrong, but I currently agree with you that unfortunately you do have to have to be more specific sort of these deep understandings of physics and of the way this world works and also human dynamics. I can mention pedestrians and cyclists actually that's whatever that nonverbal communication is some people call it, there's that dynamic that is also part of this common sense. Right. And we humans are pretty good at predicting what other humans are going to do. And how our actions impact the behaviors of this weird game theoretic dance that we're good at somehow. And the funny thing is, because I've watched countless hours of pedestrian video and talked to people, we humans are also really bad at articulating the knowledge we have. Right. Which has been a huge challenge. Yes. So you've mentioned embodied intelligence. What do you think it takes to build a system of human level intelligence? Does it need to have a body? I'm not sure, but I'm coming around to that more and more. And what does it mean to be, I don't mean to keep bringing up Yala Kuhn. He looms very large. Well, he certainly has a large personality. Yes. He thinks that the system needs to be grounded, meaning it needs to sort of be able to interact with reality, but doesn't think it necessarily needs to have a body. So when you think of- So what's the difference? I guess I want to ask, when you mean body, do you mean you have to be able to play with the world? Or do you also mean like there's a body that you have to preserve? That's a good question. I haven't really thought about that, but I think both, I would guess. I think intelligence, it's so hard to separate it from our desire for self-preservation, our emotions, all that non-rational stuff that kind of gets in the way of logical thinking. But because the way, if we're talking about human intelligence or human level intelligence, whatever that means, a huge part of it is social. We were evolved to be social and to deal with other people, and that's just so ingrained in us that it's hard to separate intelligence from that. I think AI for the last 70 years, or however long it's been around, it has largely been separated. There's this idea that there's like, it's kind of very Cartesian. There's this thinking thing that we're trying to create, but we don't care about all this other stuff. I think the other stuff is very fundamental. There's idea that things like emotion get in the way of intelligence. As opposed to being an integral part of it. Integral part of it. I'm Russian, so romanticize the notions of emotion and suffering and all that kind of fear of mortality, those kinds of things. So in AI, especially- By the way, did you see that there was this recent thing going around the internet of this? Some, I think he's a Russian or some Slavic, had written this thing, sort of anti the idea of super intelligence. I forgot, maybe he's Polish. Anyway, so he had all these arguments and one was the argument from Slavic pessimism. My favorite. Do you remember what the argument is? It's like nothing ever works. Everything sucks. So what do you think is the role? That's such a fascinating idea that what we perceive as sort of the limits of the human mind, which is emotion and fear and all those kinds of things are integral to intelligence. Could you elaborate on that? Why is that important, do you think, for human level intelligence? At least the way the humans work, it's a big part of how it affects how we perceive the world. It affects how we make decisions about the world. It affects how we interact with other people. It affects our understanding of other people, for me to understand what you're likely to do. I need to have kind of a theory of mind and that's very much a theory of emotions and motivations and goals. To understand that, we have this whole system of mirror neurons. I sort of understand your motivations through sort of simulating it myself. So it's not something that I can prove that's necessary, but it seems very likely. So okay, you've written the op-ed in the New York Times titled, We Shouldn't Be Scared by Superintelligent AI. And it criticized a little bit Stuart Russell and Nick Bostrom. Can you try to summarize that article's key ideas? So it was spurred by an earlier New York Times op-ed by Stuart Russell, which was summarizing his book called Human Compatible. The article was saying, if we have superintelligent AI, we need to have its values aligned with our values and it has to learn about what we really want. He gave this example, what if we have a superintelligent AI and we give it the problem of solving climate change and it decides that the best way to lower the carbon in the atmosphere is to kill all the humans. Okay. So to me, that just made no sense at all because a superintelligent AI, first of all, trying to figure out what superintelligence means. And it seems that something that's superintelligent can't just be intelligent along this one dimension of, okay, we're going to figure out all the steps, the best optimal path to solving climate change and not be intelligent enough to figure out that humans don't want to be killed, that you could get to one without having the other. And you know, Bostrom in his book talks about the orthogonality hypothesis where he says he thinks that a system's, I can't remember exactly what it is, but like a system's goals and its values don't have to be aligned. There's some orthogonality there, which didn't make any sense to me. So you're saying in any system that's sufficiently, not even superintelligent, but as it opposed greater and greater intelligence, there's a holistic nature that will sort of attention that will naturally emerge that prevents it from sort of any one dimension running away. Yeah, yeah, exactly. So you know, Bostrom had this example of the superintelligent AI that turns the world into paper clips because its job is to make paper clips or something. And that just as a thought experiment didn't make any sense to me. Well as a thought experiment or as a thing that could possibly be realized? Either. So I think that, you know, what my op-ed was trying to do was say that intelligence is more complex than these people are presenting it. That it's not so separable, the rationality, the values, the emotions, all of that. The view that you could separate all these dimensions and build a machine that has one of these dimensions and it's superintelligent in one dimension, but it doesn't have any of the other dimensions. That's what I was trying to criticize that I don't believe that. So can I read a few sentences from Yoshio Benjy who is always super eloquent. So he writes, I have the same impression as Melanie that our cognitive biases are linked with our ability to learn to solve many problems. There may also be a limiting factor for AI. However, this is a may in quotes. Things may also turn out differently and there's a lot of uncertainty about the capabilities of future machines. But more importantly for me, the value alignment problem is a problem well before we reach some hypothetical superintelligence. It is already posing a problem in the form of super powerful companies whose objective function may not be sufficiently aligned with humanity's general wellbeing, creating all kinds of harmful side effects. He goes on to argue that the orthogonality and those kinds of things, the concerns of just aligning values with the capabilities of the system is something that might come long before we reach anything like superintelligence. So your criticism is kind of really nice to saying this idea of superintelligence systems seem to be dismissing fundamental parts of what intelligence would take. And then Yoshio kind of says, yes, but if we look at systems that are much less intelligent, there might be these same kinds of problems that emerge. Sure. But I guess the example that he gives there of these corporations, that's people, right? Those are people's values. I mean, we're talking about people, the corporations are their values are the values of the people who run those corporations. But the idea is the algorithm, that's right. So the fundamental person, the fundamental element of what does the bad thing is a human being. Yeah. But the algorithm kind of controls the behavior of this mass of human beings. Which algorithm? For a company that's the, for example, if it's an advertisement driven company that recommends certain things and encourages engagement, so it gets money by encouraging engagement and therefore the company more and more, it's like the cycle that builds an algorithm that enforces more engagement and maybe perhaps more division in the culture and so on, so on. I guess the question here is sort of who has the agency? So you might say, for instance, we don't want our algorithms to be racist. And facial recognition, some people have criticized some facial recognition systems as being racist because they're not as good on darker skin and lighter skin. That's right. But the agency there, the actual facial recognition algorithm isn't what has the agency. It's not the racist thing, right? It's the, I don't know, the combination of the training data, the cameras being used, whatever. But my understanding of, and I agree with Benjio there that I think there are these value issues with our use of algorithms. But my understanding of what Russell's argument was is more that the machine itself has the agency now. It's the thing that's making the decisions and it's the thing that has what we would call values. Yes. So whether that's just a matter of degree, it's hard to say, right? But I would say that's sort of qualitatively different than a face recognition neural network. And to broadly linger on that point, if you look at Elon Musk, or Russell, or Bostrom, people who are worried about existential risks of AI, however far into the future, their argument goes is it eventually happens. We don't know how far, but it eventually happens. Do you share any of those concerns and what kind of concerns in general do you have about AI that approach anything like existential threat to humanity? So I would say, yes, it's possible, but I think there's a lot more closer in existential threats to humanity. Because you said like a hundred years for, so your time- It's more than a hundred years. More than a hundred years. Maybe even more than 500 years. I don't know. I mean, it's- So the existential threats are so far out that the future is, I mean, there'll be a million different technologies that we can't even predict now that will fundamentally change the nature of our behavior, reality, society, and so on before then. Yeah, I think so. I think so. And we have so many other pressing existential threats going on right now. Nuclear weapons even. Nuclear weapons, climate problems, poverty, possible pandemics. You can go on and on. And I think though worrying about existential threat from AI is not the best priority for what we should be worrying about. That's kind of my view. Because we're so far away. I'm not necessarily criticizing Russell or Bostrom or whoever for worrying about that. And I think some people should be worried about it. It's certainly fine. But I was more sort of getting at their view of what intelligence is. So I was more focusing on their view of superintelligence than just the fact of them worrying. And the title of the article was written by the New York Times editors. I wouldn't have called it that. We shouldn't be scared by superintelligence. If you wrote it, it'd be like, we should redefine what you mean by superintelligence. I actually said something like superintelligence is not a sort of coherent idea. That's not something the New York Times would put in. And the follow-up argument that Yoshio makes also, not argument but a statement, and I've heard him say it before, and I think I agree. He kind of has a very friendly way of phrasing it. It's good for a lot of people to believe different things. He's such a nice guy. It's also practically speaking, we shouldn't be, while your article stands, like, Stuart Russell does amazing work, Bostrom does amazing work, you do amazing work. And even when you disagree about the definition of superintelligence or the usefulness of even the term, it's still useful to have people that use that term and then argue. I absolutely agree with Benjo there. I think it's great that the New York Times will publish all this stuff. It's an exciting time to be here. What do you think is a good test of intelligence? Is natural language ultimately a test that you find the most compelling, like the original, or the higher levels of the Turing test? Yeah, I still think the original idea of the Turing test is a good test for intelligence. I mean, I can't think of anything better. The Turing test, the way that it's been carried out so far has been very impoverished, if you will. But I think a real Turing test that really goes into depth. Like the one that I mentioned, I talk about in the book, I talk about Ray Kurzweil and Mitchell Kapoor have this bet that in 2029, I think is the date there, a machine will pass the Turing test. And they have a very specific, many hours, expert judges and all of that. And Kurzweil says yes, Kapoor says no. We only have like nine more years to go to see. If something, a machine could pass that, I would be willing to call it intelligent. Of course, nobody will. They will say that's just a language model, if it does. So you would be comfortable. So language, a long conversation that's, well, yeah, I mean, you're right, because I think probably to carry out that long conversation, you would literally need to have deep common sense understanding of the world. I think so. And conversation is enough to reveal that. I think so. So another super fun topic of complexity that you have worked on, written about. Let me ask the basic question. What is complexity? So complexity is another one of those terms like intelligence that's perhaps overused. But my book about complexity was about this wide area of complex systems, studying different systems in nature, in technology, in society, in which you have emergence, kind of like I was talking about with intelligence. We have the brain, which has billions of neurons. And each neuron individually could be said to be not very complex compared to the system as a whole. But the system, the interactions of those neurons and the dynamics creates these phenomena that we call intelligence or consciousness that we consider to be very complex. So the field of complexity is trying to find general principles that underlie all these systems that have these kinds of emergent properties. And the emergence occurs from like underlying the complex system is usually simple fundamental interactions. Yes. And the emergence happens when there's just a lot of these things interacting. Yes. And then most of science to date, can you talk about what is reductionism? Well, reductionism is when you try and take a system and divide it up into its elements, whether those be cells or atoms or subatomic particles, whatever your field is, and then try and understand those elements and then try and build up an understanding of the whole system by looking at sort of the sum of all the elements. So what's your sense, whether we're talking about intelligence or these kinds of interesting complex systems, is it possible to understand them in a reductionist way, which is probably the approach of most of science today, right? I don't think it's always possible to understand the things we want to understand the most. So I don't think it's possible to look at single neurons and understand what we call intelligence, to look at sort of summing up. And sort of the summing up is the issue here that we're... One example is that the human genome, right? So there was a lot of work on excitement about sequencing the human genome because the idea would be that we'd be able to find genes that underlies diseases. But it turns out that... And it was a very reductionist idea. We figure out what all the parts are and then we would be able to figure out which parts cause which things. But it turns out that the parts don't cause the things that we're interested in. It's like the interactions, it's the networks of these parts. And so that kind of reductionist approach didn't yield the explanation that we wanted. What do you use the most beautiful complex system that you've encountered? The most beautiful. That you've been captivated by? Is it sort of... I mean, for me, it's the simplest to be cellular automata. Oh, yeah. So I was very captivated by cellular automata and worked on cellular automata for several years. Do you find it amazing or is it surprising that such simple systems, such simple rules in cellular automata can create sort of seemingly unlimited complexity? Yeah, that was very surprising to me. How do you make sense of it? How does that make you feel? Is it just ultimately humbling or is there a hope to somehow leverage this into a deeper understanding and even able to engineer things like intelligence? It's definitely humbling. How humbling in that... Also kind of awe-inspiring that it's that awe-inspiring part of mathematics that these incredibly simple rules can produce this very beautiful, complex, hard to understand behavior. And it's mysterious and surprising still. But exciting because it does give you kind of the hope that you might be able to engineer complexity just from... From simple rules from the beginnings. Can you briefly say what is the Santa Fe Institute, its history, its culture, its ideas, its future? So I've never... As I mentioned to you, I've never been, but it's always been this, in my mind, this mystical place where brilliant people study the edge of chaos. Yeah, exactly. So the Santa Fe Institute was started in 1984 and it was created by a group of scientists, a lot of them from Los Alamos National Lab, which is about a 40-minute drive from the Santa Fe Institute. They were mostly physicists and chemists. But they were frustrated in their field because they felt that their field wasn't approaching kind of big interdisciplinary questions like the kinds we've been talking about. And they wanted to have a place where people from different disciplines could work on these big questions without sort of being siloed into physics, chemistry, biology, whatever. So they started this Institute and this was people like George Cowan, who was a chemist in the Manhattan Project, and Nicholas Metropolis, a mathematician, physicist, Marie-Gail Mann, a physicist, and there's some really big names here. Ken Arrow, a Nobel Prize-winning economist. And they started having these workshops. And this whole enterprise kind of grew into this research institute that itself has been kind of on the edge of chaos its whole life because it doesn't have a significant endowment. And it's just been kind of living on whatever funding it can raise through donations and grants and however it can, business associates and so on. But it's a great place. It's a really fun place to go think about ideas that you wouldn't normally encounter. So Sean Carroll, so physicists. Yeah, he's on the external faculty. And you mentioned that there's, so there's some external faculty and there's people that are- A very small group of resident faculty, maybe about 10 who are there on five-year terms that can sometimes get renewed. And then they have some post-docs and then they have this much larger, on the order of 100, external faculty or people like me who come and visit for various periods of time. So what do you think is the future of the Satterfield Institute? And if people are interested, what's there in terms of the public interaction or students or so on that could be a possible interaction with the Satterfield Institute or its ideas? Yeah, so there's a few different things they do. They have a complex system summer school for graduate students and post-docs and sometimes faculty attend too. And that's a four-week, very intensive residential program where you go and you listen to lectures and you do projects and people really like that. I mean, it's a lot of fun. They also have some specialty summer schools. There's one on computational social science. There's one on climate and sustainability, I think it's called. There's a few. And then they have short courses where just a few days on different topics. They also have an online education platform that offers a lot of different courses and tutorials from SFI faculty, including an introduction to complexity course that I taught. Awesome. And there's a bunch of talks too online from those guest speakers and so on. They host a lot of... Yeah, they have sort of technical seminars and colloquia and they have a community lecture series like public lectures and they put everything on their YouTube channel so you can see it all. Watching. Douglas Haustader, author of Ghetto Escherbach was your PhD advisor. He mentioned a couple of times and collaborator. Do you have any favorite lessons or memories from your time working with him that continues to this day, I guess, but just even looking back throughout your time working with him? So one of the things he taught me was that when you're looking at a complex problem, to idealize it as much as possible, to try and figure out what is the essence of this problem. And this is how the copycat program came into being was by taking analogy making and saying, how can we make this as idealized as possible but still retain really the important things we want to study? And that's really been a core theme of my research, I think. And I continue to try and do that. And it's really very much kind of physics inspired. Haustader was a PhD in physics. That was his background. This is like first principles kind of thinking like you're reduced to the most fundamental aspect of the problem so that you can focus on solving that fundamental aspect. And in AI, people used to work in these micro worlds, right? Like the blocks world was very early important area in AI. And then that got criticized because they said, oh, you can't scale that to the real world. And so people started working on much more real world like problems. But now there's been kind of a return even to the blocks world itself. We've seen a lot of people who are trying to work on more of these very idealized problems for things like natural language and common sense. So that's an interesting evolution of those ideas. So perhaps the blocks world represents the fundamental challenges of the problem of intelligence more than people realize. It might, yeah. Is there sort of when you look back at your body of work and your life, you've worked in so many different fields. Is there something that you're just really proud of in terms of ideas that you've gotten a chance to explore, create yourself? So I am really proud of my work on the CopyCat project. I think it's really different from what almost everyone has done in AI. I think there's a lot of ideas there to be explored. And I guess one of the happiest days of my life, aside from the births of my children, was the birth of CopyCat when it actually started to be able to make really interesting analogies. And I remember that very clearly. That was a very exciting time. Well you kind of gave life an artificial system. That's right. In terms of what people can interact, I saw there's like a, I think it's called MetaCat. And there's a Python 3 implementation. If people actually wanted to play around with it and actually get into it and study it and maybe integrate into whether it's with deep learning or any other kind of work they're doing, what would you suggest they do to learn more about it and to take it forward in different kinds of directions? Yeah, so there's Douglas Hofstadter's book called Fluid Concepts and Creative Analogies talks in great detail about CopyCat. I have a book called Analogy Making as Perception, which is a version of my PhD thesis on it. There's also code that's available that you can get it to run. I have some links on my webpage to where people can get the code for it. And I think that would really be the best way to get into it. Yeah, I've been, yeah. Well, Melanie, it was an honor talking to you. I really enjoyed it. Thank you so much for your time today. Thanks. It's been really great. Thanks for listening to this conversation with Melanie Mitchell. And thank you to our presenting sponsor, Cash App. Download it, use code LexPodcast. You'll get $10 and $10 will go to FIRST, a STEM education nonprofit that inspires hundreds of thousands of young minds to learn and to dream of engineering our future. If you enjoy this podcast, subscribe on YouTube, give it five stars on Apple Podcast, support it on Patreon, or connect with me on Twitter. And now let me leave you with some words of wisdom from Douglas Huffstader and Melanie Mitchell. Without concepts, there can be no thought. And without analogies, there can be no concepts. And Melanie adds, how to form and fluidly use concepts is the most important open problem in AI. Thank you for listening and hope to see you next time.
 The following is a conversation with Melanie Mitchell.
 She's a professor of computer science
 at Portland State University
 and an external professor at Santa Fe Institute.
 She has worked on and written about artificial intelligence
 from fascinating perspectives,
 including adaptive complex systems, genetic algorithms,
 and the copycat cognitive architecture,
 which places the process of analogy making
 at the core of human cognition.
 From her doctoral work with her advisors,
 Douglas Hufstadter and John Holland, to today,
 she has contributed a lot of important ideas
 to the field of AI,
 including her recent book,
 simply called Artificial Intelligence,
 a Guide for Thinking Humans.
 This is the Artificial Intelligence Podcast.
 If you enjoy it, subscribe on YouTube,
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 And now here's my conversation with Melanie Mitchell.
 The name of your new book is Artificial Intelligence,
 subtitle, A Guide for Thinking Humans.
 The name of this podcast is Artificial Intelligence.
 So let me take a step back
 and ask the old Shakespeare question about roses.
 And what do you think of the term artificial intelligence
 for our big and complicated and interesting field?
 I'm not crazy about the term.
 I think it has a few problems
 because it means so many different things
 to different people.
 And intelligence is one of those words
 that isn't very clearly defined either.
 There's so many different kinds of intelligence,
 degrees of intelligence, approaches to intelligence.
 John McCarthy was the one who came up with the term
 artificial intelligence.
 And from what I read, he called it that
 to differentiate it from cybernetics,
 which was another related movement at the time.
 And he later regretted calling it artificial intelligence.
 Herbert Simon was pushing for calling it
 complex information processing,
 which got mixed, but probably is equally vague, I guess.
 Is it the intelligence or the artificial
 in terms of words that is most problematic, would you say?
 Yeah, I think it's a little of both.
 But it has some good size because I personally
 was attracted to the field because I was interested
 in phenomenon of intelligence.
 And if it was called complex information processing,
 maybe I'd be doing something wholly different now.
 What do you think of, I've heard that term used,
 cognitive systems, for example, so using cognitive.
 Yeah, I mean, cognitive has certain associations with it.
 And people like to separate things like cognition
 and perception, which I don't actually think are separate,
 but often people talk about cognition as being different
 from sort of other aspects of intelligence.
 It's sort of higher level.
 So to you, cognition is this broad, beautiful mess
 of things that encompasses the whole thing.
 Memory, perception.
 I think it's hard to draw lines like that.
 When I was coming out of grad school in 1990,
 which is when I graduated,
 that was during one of the AI winters.
 And I was advised to not put AI, artificial intelligence,
 on my CV, but instead call it intelligence systems.
 So that was kind of a euphemism, I guess.
 What about to stick briefly on terms and words,
 the idea of artificial general intelligence,
 or like Jan LeCun prefers human level intelligence,
 sort of starting to talk about ideas
 that achieve higher and higher levels of intelligence
 and somehow artificial intelligence seems to be a term
 used more for the narrow, very specific applications of AI
 and sort of what set of terms appeal to you
 to describe the thing that perhaps we strive to create.
 People have been struggling with this
 for the whole history of the field
 and defining exactly what it is that we're talking about.
 John Searle had this distinction between strong AI
 and weak AI.
 And weak AI could be general AI,
 but his idea was strong AI was the view
 that a machine is actually thinking,
 that as opposed to simulating thinking
 or carrying out intelligent processes
 that we would call intelligent.
 At a high level, if you look at the founding
 of the field, McCarthy and Searle and so on,
 are we closer to having a better sense of that line
 between narrow, weak AI and strong AI?
 Yes, I think we're closer to having a better idea
 of what that line is.
 Early on, for example, a lot of people thought
 that playing chess would be, you couldn't play chess
 if you didn't have sort of general human level intelligence.
 And of course, once computers were able to play chess
 better than humans, that revised that view.
 And people said, okay, well, maybe now we have to revise
 what we think of intelligence as.
 And so that's kind of been a theme
 throughout the history of the field is that
 once a machine can do some task,
 we then have to look back and say, oh, well,
 that changes my understanding of what intelligence is
 because I don't think that machine is intelligent.
 At least that's not what I wanna call intelligence.
 Do you think that line moves forever
 or will we eventually really feel as a civilization
 like we crossed the line if it's possible?
 It's hard to predict, but I don't see any reason
 why we couldn't in principle create something
 that we would consider intelligent.
 I don't know how we will know for sure.
 Maybe our own view of what intelligence is
 will be refined more and more until we finally figure out
 what we mean when we talk about it.
 But I think eventually we will create machines
 in a sense that have intelligence.
 They may not be the kinds of machines we have now.
 And one of the things that that's going to produce
 is making us sort of understand our own machine-like
 qualities that we, in a sense, are mechanical
 in the sense that like cells, cells are kind of mechanical.
 They have algorithms, they process information by.
 And somehow out of this mass of cells,
 we get this emergent property that we call intelligence.
 But underlying it is really just cellular processing
 and lots and lots and lots of it.
 Do you think we'll be able to,
 do you think it's possible to create intelligence
 without understanding our own mind?
 You said sort of in that process,
 we'll understand more and more,
 but do you think it's possible to sort of create
 without really fully understanding
 from a mechanistic perspective,
 sort of from a functional perspective,
 how our mysterious mind works?
 If I had to bet on it, I would say, no,
 we do have to understand our own minds,
 at least to some significant extent.
 But I think that's a really big open question.
 I've been very surprised at how far
 kind of brute force approaches based on, say, big data
 and huge networks can take us.
 I wouldn't have expected that.
 And they have nothing to do with the way our minds work.
 So that's been surprising to me, so it could be wrong.
 To explore the psychological and the philosophical,
 do you think we're okay as a species
 with something that's more intelligent than us?
 Do you think perhaps the reason we're pushing that line
 further and further is we're afraid of acknowledging
 that there's something stronger, better,
 smarter than us humans?
 Well, I'm not sure we can define intelligence that way
 because smarter than is with respect to what,
 computers are already smarter than us in some areas.
 They can multiply much better than we can.
 They can figure out driving routes to take
 much faster and better than we can.
 They have a lot more information to draw on.
 They know about traffic conditions and all that stuff.
 So for any given particular task,
 sometimes computers are much better than we are.
 And we're totally happy with that, right?
 I'm totally happy with that.
 It doesn't bother me at all.
 I guess the question is, which things about our intelligence
 would we feel very sad or upset
 that machines had been able to recreate?
 So in the book, I talk about my former PhD advisor,
 Douglas Hofstadter, who encountered
 a music generation program.
 And that was really the line for him.
 If a machine could create beautiful music,
 that would be terrifying for him
 because that is something he feels is really at the core
 of what it is to be human, creating beautiful music,
 art, literature.
 He doesn't like the fact that machines can recognize
 spoken language really well.
 He personally doesn't like using speech recognition,
 but I don't think it bothers him to his core
 because it's like, okay, that's not at the core of humanity.
 But it may be different for every person.
 What really they feel would usurp their humanity.
 And I think maybe it's a generational thing also.
 Maybe our children or our children's children
 will be adapted, they'll adapt to these new devices
 that can do all these tasks and say,
 yes, this thing is smarter than me in all these areas,
 but that's great because it helps me.
 Looking at the broad history of our species,
 why do you think so many humans have dreamed
 of creating artificial life and artificial intelligence
 throughout the history of our civilization?
 So not just this century or the 20th century,
 but really throughout many centuries that preceded it.
 That's a really good question.
 And I have wondered about that
 because I myself was driven by curiosity
 about my own thought processes
 and thought it would be fantastic
 to be able to get a computer
 to mimic some of my thought processes.
 And I'm not sure why we're so driven.
 I think we want to understand ourselves better
 and we also want machines to do things for us.
 But I don't know, there's something more to it
 because it's so deep in the kind of mythology
 or the ethos of our species.
 And I don't think other species have this drive.
 So I don't know.
 If you were to sort of psychoanalyze yourself
 in your own interest in AI,
 are you, I don't know,
 in your own interest in AI, are you,
 what excites you about creating intelligence?
 You said understanding our own selves?
 Yeah, I think that's what drives me particularly.
 I'm really interested in human intelligence,
 but I'm also interested in the sort of the phenomenon
 of intelligence more generally.
 And I don't think humans are the only thing with intelligence
 or even animals,
 but I think intelligence is a concept
 that encompasses a lot of complex systems.
 And if you think of things like insect colonies
 or cellular processes or the immune system
 or all kinds of different biological
 or even societal processes have as an emergent property,
 some aspects of what we would call intelligence.
 They have memory, they can process information,
 they have goals, they accomplish their goals, et cetera.
 And to me, the question of what is this thing
 we're talking about here was really fascinating to me
 and exploring it using computers
 seemed to be a good way to approach the question.
 So do you think kind of of intelligence,
 do you think of our universe
 as a kind of hierarchy of complex systems
 and then intelligence is just the property of any,
 you can look at any level
 and every level has some aspect of intelligence.
 So we're just like one little speck
 in that giant hierarchy of complex systems.
 I don't know if I would say any system like that
 has intelligence, but I guess what I wanna,
 I don't have a good enough definition of intelligence
 to say that.
 So let me do sort of multiple choice, I guess.
 So you said ant colonies.
 So are ant colonies intelligent?
 Are the bacteria in our body intelligent?
 And then going to the physics world, molecules
 and the behavior at the quantum level of electrons
 and so on, are those kinds of systems,
 do they possess intelligence?
 Like where's the line that feels compelling to you?
 I don't know.
 I mean, I think intelligence is a continuum
 and I think that the ability to in some sense
 have intention, have a goal,
 have some kind of self-awareness is part of it.
 So I'm not sure if,
 it's hard to know where to draw that line.
 I think that's kind of a mystery,
 but I wouldn't say that say that,
 the planets orbiting the sun is an intelligent system.
 I mean, I would find that maybe not the right term
 to describe that.
 And this is, there's all this debate in the field of like,
 what's the right way to define intelligence?
 What's the right way to model intelligence?
 Should we think about computation?
 Should we think about dynamics?
 And should we think about free energy
 and all of that stuff?
 And I think that it's a fantastic time to be in the field
 because there's so many questions
 and so much we don't understand.
 There's so much work to do.
 So are we the most special kind of intelligence
 in this kind of, you said there's a bunch
 of different elements and characteristics
 of intelligence systems and colonies, are his human
 intelligence the thing in our brain?
 Is that the most interesting kind of intelligence
 in this continuum?
 Well, it's interesting to us, because it is us.
 I mean, interesting to me, yes.
 And because I'm part of the human.
 But to understanding the fundamentals of intelligence,
 what I'm getting at, is studying the human,
 if everything we've talked about, what you talked about,
 in your book, what just the AI field, this notion,
 yes, it's hard to define, but it's usually talking
 about something that's very akin to human intelligence.
 Yeah, to me it is the most interesting
 because it's the most complex, I think.
 It's the most self-aware.
 It's the only system, at least that I know of,
 that reflects on its own intelligence.
 And you talk about the history of AI and us,
 in terms of creating artificial intelligence,
 being terrible at predicting the future with AI,
 with tech in general.
 So, why do you think we're so bad at predicting the future?
 Are we hopelessly bad?
 So, no matter what, whether it's this decade
 or the next few decades, every time we make a prediction,
 there's just no way of doing it well,
 or as the field matures, we'll be better and better at it.
 I believe as the field matures, we will be better.
 And I think the reason that we've had so much trouble
 is that we have so little understanding
 of our own intelligence.
 So, there's the famous story about Marvin Minsky
 assigning computer vision as a summer project
 to his undergrad students.
 And I believe that's actually a true story.
 Yeah, there's a write-up on it that everyone should read.
 I think it's like a proposal
 that describes everything that should be done
 in that project.
 It's hilarious because it, I mean, you can explain it,
 but for my sort of recollection,
 it describes basically all the fundamental problems
 of computer vision, many of which
 that still haven't been solved.
 Yeah, and I don't know how far they really expect it to get,
 but I think that, and they're really, you know,
 Marvin Minsky is a super smart guy
 and very sophisticated thinker.
 But I think that no one really understands or understood,
 still doesn't understand how complicated, how complex
 the things that we do are
 because they're so invisible to us.
 You know, to us, vision, being able to look out at the world
 and describe what we see, that's just immediate.
 It feels like it's no work at all.
 So, it didn't seem like it would be that hard,
 but there's so much going on.
 Unconsciously sort of invisible to us
 that I think we overestimate how easy it will be
 to get computers to do it.
 And sort of for me to ask an unfair question,
 you've done research, you have thought about
 many different branches of AI through this book,
 widespread looking at where AI has been,
 where it is today.
 If you were to make a prediction,
 how many years from now would we as a society
 create something that you would say
 achieved human level intelligence
 or superhuman level intelligence?
 That is an unfair question.
 A prediction that will most likely be wrong.
 So, but it's just your notion because-
 Okay, I'll say more than a hundred years.
 More than a hundred years.
 More than a hundred years.
 And I quoted somebody in my book who said that
 human level intelligence is a hundred Nobel prizes away.
 Which I like because it's a nice way to sort of,
 it's a nice unit for prediction.
 And it's like that many fantastic discoveries
 have to be made.
 And of course there's no Nobel prize in AI.
 Not yet at least.
 If we look at that hundred years,
 your sense is really the journey to intelligence
 has to go through something more complicated
 that's akin to our own cognitive systems.
 Understanding them, being able to create them
 in the artificial systems as opposed to sort of
 taking the machine learning approaches of today
 and really scaling them and scaling them exponentially
 with both compute and hardware and data.
 That would be my guess.
 I think that in the sort of going along in the narrow AI
 that these current approaches will get better.
 I think there's some fundamental limits
 to how far they're gonna get.
 I might be wrong, but that's what I think.
 And there's some fundamental weaknesses that they have
 that I talk about in the book that just comes
 from this approach of supervised learning,
 requiring sort of feed forward networks and so on.
 It's just, I don't think it's a sustainable approach
 to understanding the world.
 Yeah, I'm personally torn on it.
 Sort of everything you read about in the book
 instead of what we're talking about now,
 but I agree with you, but I'm more and more,
 depending on the day, first of all,
 I'm deeply surprised by the success of machine learning
 and deep learning in general.
 From the very beginning,
 when it's really been my main focus of work,
 I'm just surprised how far it gets.
 And I'm also think we're really early on
 in these efforts of these narrow AI.
 So I think there'll be a lot of surprise
 of how far it gets.
 I think we'll be extremely impressed.
 Like, in my sense, everything I've seen so far,
 and we'll talk about autonomous driving and so on,
 I think we can get really far.
 But I also have a sense that we will discover,
 just like you said, is that even though we'll get really far,
 in order to create something like our own intelligence
 is actually much farther than we realize.
 Right.
 I think these methods are a lot more powerful
 than people give them credit for, actually.
 So that, of course, there's the media hype,
 but I think there's a lot of researchers in the community,
 especially, like, not undergrads, right?
 But people who have been in AI,
 they're skeptical about how far deep learning can get.
 And I'm more and more thinking
 that it can actually get farther than they'll realize.
 It's certainly possible.
 One thing that surprised me when I was writing the book
 is how far apart different people are in the field are
 on their opinion of how far the field has come
 and what is accomplished and what's gonna happen next.
 What's your sense of the different,
 who are the different people, groups,
 mindsets, thoughts in the community
 about where AI is today?
 Yeah, they're all over the place.
 So there's kind of the singularity transhumanism group.
 I don't know exactly how to characterize that approach,
 which is just so-
 It occurs while you-
 Yeah, the sort of exponential progress
 where we're on the sort of almost at the hugely accelerating
 part of the exponential.
 And by in the next 30 years,
 we're going to see super intelligent AI and all that,
 and we'll be able to upload our brains and that.
 So there's that kind of extreme view
 that most, I think most people who work in AI don't have.
 They disagree with that.
 But there are people who are maybe aren't singularity people,
 but they do think that the current approach
 of deep learning is going to scale
 and is going to kind of go all the way basically
 and take us to true AI or human level AI
 or whatever you wanna call it.
 And there's quite a few of them.
 And a lot of them, like a lot of the people I met
 who work at big tech companies in AI groups
 kind of have this view that we're really not that far.
 Just to linger on that point sort of,
 if I can take as an example, like Jan LeCun,
 I don't know if you know about his work
 and so her viewpoints on this.
 I do.
 He believes that there's a bunch of breakthroughs
 like fundamental, like Nobel prizes that are needed still.
 But I think he thinks those breakthroughs
 will be built on top of deep learning.
 And then there's some people who think we need to kind of
 put deep learning to the side a little bit
 as just one module that's helpful
 in the bigger cognitive framework.
 Right, so I think somewhat I understand Jan LeCun
 is rightly saying supervised learning is not sustainable.
 We have to figure out how to do unsupervised learning
 that that's gonna be the key.
 And I think that's probably true.
 I think unsupervised learning is gonna be harder
 than people think.
 I mean, the way that we humans do it.
 Then there's the opposing view,
 there's the Gary Marcus kind of hybrid view
 where deep learning is one part,
 but we need to bring back kind of these symbolic approaches
 and combine them.
 Of course, no one knows how to do that very well.
 Which is the more important part to emphasize
 and how do they fit together?
 What's the foundation?
 What's the thing that's on top?
 What's the cake?
 What's the icing?
 Right.
 Then there's people pushing different things.
 There's the people, the causality people who say,
 deep learning as it's formulated today
 completely lacks any notion of causality.
 And that's dooms it.
 And therefore we have to somehow give it
 some kind of notion of causality.
 There's a lot of push from
 the more cognitive science crowd saying,
 we have to look at developmental learning.
 We have to look at how babies learn.
 We have to look at intuitive physics,
 all these things we know about physics.
 And as somebody kind of quipped,
 we also have to teach machines intuitive metaphysics,
 which means like objects exist.
 Causality exists.
 These things that maybe we're born with, I don't know.
 That they don't have the,
 machines don't have any of that.
 They look at a group of pixels
 and they maybe they get 10 million examples,
 but they can't necessarily learn
 that there are objects in the world.
 So there's just a lot of pieces of the puzzle
 that people are promoting and with different opinions
 of like how important they are and how close we are
 to being able to put them all together
 to create general intelligence.
 Looking at this broad field,
 what do you take away from it?
 Who is the most impressive?
 Is it the cognitive folks, the Gary Marcus camp,
 the Jan camp, unsupervised, self-supervised,
 there's the supervised,
 and then there's the engineers
 who are actually building systems.
 You have sort of the Andre Karpathy at Tesla
 building actual, it's not philosophy,
 it's real systems that operate in the real world.
 What do you take away from all this beautiful variety?
 I don't know if these different views
 are not necessarily mutually exclusive.
 And I think people like Jan LeCun
 agrees with the developmental psychology,
 causality, intuitive, physics, et cetera.
 But he still thinks that it's learning,
 like end to end learning is the way to go.
 Will take us perhaps all the way.
 Yeah, and that we don't need,
 there's no sort of innate stuff that has to get built in.
 This is, it's because it's a hard problem.
 I personally, I'm very sympathetic
 to the cognitive science side,
 cause that's kind of where I came in to the field.
 I've become more and more sort of an embodiment adherent
 saying that without having a body,
 it's gonna be very hard to learn
 what we need to learn about the world.
 That's definitely something I'd love to talk about
 in a little bit.
 To step into the cognitive world,
 then if you don't mind,
 cause you've done so many interesting things.
 If you look to copycat,
 taking a couple of decades, step back,
 you, Douglas Hofstadter and others have created
 and developed copycat more than 30 years ago.
 That's painful to hear.
 What is it?
 What is copycat?
 It's a program that makes analogies
 in an idealized domain, idealized world of letter strings.
 So as you say, 30 years ago, wow.
 So I started working on it when I started grad school
 in 1984.
 Wow.
 Dates me.
 And it's based on Doug Hofstadter's ideas
 that analogy is really a core aspect of thinking.
 I remember he has a really nice quote in the book
 by himself and Emmanuel Sander called
 Surfaces and Essences.
 I don't know if you've seen that book,
 but it's about analogy.
 And he says, without concepts,
 there can be no thought and without analogies,
 there can be no concepts.
 So the view is that analogy is not just
 this kind of reasoning technique where we go,
 shoe is to foot, as glove is to what?
 These kinds of things that we have on IQ tests or whatever,
 but that it's much deeper, it's much more pervasive
 in everything we do, in our language,
 our thinking, our perception.
 So he had a view that was a very active perception idea.
 So the idea was that instead of having kind of
 a passive network in which you have input
 that's being processed through these feed forward layers,
 and then there's an output at the end,
 that perception is really a dynamic process,
 where our eyes are moving around
 and they're getting information.
 And that information is feeding back to
 what we look at next influences what we look at next
 and how we look at it.
 And so CopyCat was trying to do that,
 kind of simulate that kind of idea
 where you have these agents,
 it was kind of an agent-based system,
 and you have these agents that are picking things
 to look at and deciding whether they were interesting or not,
 whether they should be looked at more.
 And that would influence other agents.
 And how do they interact?
 So they interacted through this global,
 kind of what we call the workspace.
 So it was actually inspired by the old blackboard systems,
 where you would have agents that post information
 on a blackboard, a common blackboard.
 This is like very old fashioned AI.
 Is that, are we talking about like in physical space?
 Is this a computer program?
 It's a computer program.
 So agents posting concepts on a blackboard?
 Yeah, we called it a workspace.
 And the workspace is a data structure.
 The agents are little pieces of code
 that you could think of them as little detectors
 or little filters that say,
 I'm gonna pick this place to look,
 and I'm gonna look for a certain thing.
 And is this the thing I think is important?
 Is it there?
 So it's almost like a convolution in a way,
 except a little bit more general.
 And saying, and then highlighting it in the workspace.
 Well, once it's in the workspace,
 how do the things that are highlighted
 relate to each other?
 Like what's, is it?
 So there's different kinds of agents
 that can build connections between different things.
 So just to give you a concrete example,
 what CopyCat did was it made analogies
 between strings of letters.
 So here's an example, ABC changes to ABD.
 What does IJK change to?
 And the program had some prior knowledge about the alphabet,
 knew the sequence of the alphabet.
 Had a concept of letter, successor of letter,
 it had concepts of sameness.
 So it has some innate things programmed in.
 But then it could do things like,
 say, discover that ABC is a group of letters in succession.
 And then an agent can mark that.
 So the idea that there could be a sequence of letters,
 is that a new concept that's formed
 or that's a concept that's innate?
 That's a concept that's innate.
 Sort of, can you form new concepts?
 Or are all concepts innate?
 No, so in this program,
 all the concepts of the program were innate.
 So, cause we weren't, I mean,
 we were not able to do that.
 We were trying to say, suppose you have some innate concepts,
 how do you flexibly apply them to new situations?
 And how do you make analogies?
 Let's step back for a second.
 So I really liked that quote that you said,
 without concepts, there can be no thought.
 And without analogies, there can be no concepts.
 In a Santa Fe presentation,
 you said that it should be a concept of a new concept.
 In a Santa Fe presentation, you said that it should be
 one of the mantras of AI.
 Yes.
 And that you also yourself said,
 how to form and fluidly use concepts
 is the most important open problem in AI.
 Yes.
 How to form and fluidly use concepts
 is the most important open problem in AI.
 So let's, what is a concept and what is an analogy?
 A concept is in some sense, a fundamental unit of thought.
 So say we have a concept of a dog, okay?
 And a concept is embedded in a whole space of concepts,
 so that there's certain concepts that are closer to it
 or farther away from it.
 Are these concepts, are they really like fundamental,
 like we mentioned innate, almost like axiomatic,
 like very basic and then there's other stuff
 built on top of it?
 Yeah.
 Or does this include everything?
 Are there complicated, like?
 You can certainly form new concepts.
 Right, I guess that's the question I'm asking.
 Yeah.
 Can you form new concepts that are
 complex combinations of other concepts?
 Yes, absolutely.
 And that's kind of what we do in learning.
 And then what's the role of analogies in that?
 So analogy is when you recognize that one situation
 is essentially the same as another situation.
 And essentially is kind of the key word there
 because it's not the same.
 So if I say, last week I did a podcast interview
 in actually like three days ago in Washington, DC.
 And that situation was very similar to this situation,
 although it wasn't exactly the same.
 It was a different person sitting across from me.
 We had different kinds of microphones.
 The questions were different.
 The building was different.
 There's all kinds of different things,
 but really it was analogous.
 Or I can say, so doing a podcast interview,
 that's kind of a concept, it's a new concept.
 I never had that concept before this year essentially.
 I mean, and I can make an analogy with it
 like being interviewed for a news article in a newspaper.
 And I can say, well, you kind of play the same role
 that the newspaper reporter played.
 It's not exactly the same.
 It's not exactly the same because maybe they actually
 emailed me some written questions rather than talking.
 And the written questions are analogous
 to your spoken questions.
 There's just all kinds of similarities.
 And this somehow probably connects to conversations
 you have over Thanksgiving dinner,
 just general conversations.
 There's like a thread you can probably take
 that just stretches out in all aspects of life
 that connect to this podcast.
 I mean, conversations between humans.
 Sure, and if I go and tell a friend of mine
 about this podcast interview, my friend might say,
 oh, the same thing happened to me.
 Let's say, you ask me some really hard question
 and I have trouble answering it.
 My friend could say, the same thing happened to me,
 but it wasn't a podcast interview.
 It was a completely different situation.
 And yet my friend is seeing essentially the same thing.
 We say that very fluidly, the same thing happened to me.
 Essentially the same thing.
 But we don't even say that, right?
 We just say the same thing.
 You imply it, yes.
 Yeah, and the view that kind of went into say,
 copycat, that whole thing is that that act of saying
 the same thing happened to me is making an analogy.
 And in some sense, that's what underlies
 all of our concepts.
 Why do you think analogy making that you're describing
 is so fundamental to cognition?
 Like it seems like it's the main element action
 of what we think of as cognition.
 Yeah, so it can be argued that all of this
 generalization we do of concepts
 and recognizing concepts in different situations
 is done by analogy.
 Every time I'm recognizing that say, you're a person,
 that's by analogy, because I have this concept
 of what person is and I'm applying it to you.
 And every time I recognize a new situation,
 like one of the things I talked about in the book
 was the concept of walking a dog.
 That that's actually making an analogy,
 because all of the details are very different.
 So reasoning could be reduced down
 to sensory analogy making.
 So all the things we think of as like, yeah,
 like you said, perception.
 So what's perception is taking raw sensory input
 and it's somehow integrating into our understanding
 of the world, updating the understanding.
 And all of that has just this giant mess of analogies
 that are being made.
 I think so, yeah.
 If you just linger on it a little bit,
 like what do you think it takes to engineer a process
 like that for us in our artificial systems?
 We need to understand better, I think,
 how we do it, how humans do it.
 And it comes down to internal models, I think.
 People talk a lot about mental models,
 that concepts are mental models, that I can,
 in my head, I can do a simulation of a situation
 like walking a dog.
 And that there's some work in psychology
 that promotes this idea that all of concepts
 are really mental simulations.
 That whenever you encounter a concept
 or situation in the world, or you read about it,
 or whatever, you do some kind of mental simulation
 that allows you to predict what's gonna happen,
 to develop expectations of what's gonna happen.
 So that's the kind of structure I think we need,
 is that kind of mental model that,
 and in our brains, somehow these mental models
 are very much interconnected.
 Again, so a lot of stuff we're talking about
 are essentially open problems, right?
 So if I ask a question, I don't mean
 that you would know the answer,
 I'm really just hypothesizing.
 But how big do you think is the network graph,
 data structure of concepts that's in our head?
 Like if we're trying to build that ourselves,
 like we take it, that's one of the things
 we take for granted, we think, I mean,
 that's why we take common sense for granted,
 we think common sense is trivial.
 But how big of a thing of concepts is on that,
 is that underlies what we think of as common sense,
 for example?
 Yeah, I don't know.
 And I'm not, I don't even know what units to measure it in.
 You say how big is it?
 That's beautifully put, right?
 What?
 But we have, it's really hard to know,
 we have what, 100 billion neurons or something,
 I don't know, and they're connected
 via trillions of synapses.
 And there's all this chemical processing going on.
 There's just a lot of capacity for stuff.
 And their information's encoded
 in different ways in the brain.
 It's encoded in chemical interactions,
 it's encoded in electric, like firing and firing rates.
 And nobody really knows how it's encoded,
 but it just seems like there's a huge amount of capacity.
 So I think it's huge, it's just enormous.
 And it's amazing how much stuff we know.
 Yeah.
 And if we're, but we know, and not just know, like facts,
 but it's all integrated into this thing
 that we can make analogies with.
 Yes.
 There's a dream of semantic web,
 and there's a lot of dreams from expert systems
 of building giant knowledge bases.
 Do you see a hope for these kinds of approaches
 of building, of converting Wikipedia
 into something that could be used in analogy making?
 Sure.
 And I think people have made some progress
 along those lines.
 I mean, people have been working on this for a long time.
 But the problem is, and this I think
 is the problem of common sense.
 Like people have been trying to get
 these common sense networks.
 Here at MIT, there's this concept net project, right?
 But the problem is that, as I said,
 most of the knowledge that we have is invisible to us.
 It's not in Wikipedia.
 It's very basic things about intuitive physics,
 intuitive psychology, intuitive metaphysics, all that stuff.
 If you were to create a website
 that described intuitive physics, intuitive psychology,
 would it be bigger or smaller than Wikipedia?
 What do you think?
 I guess describe to whom?
 I'm sorry, but.
 No, that's really good.
 I think you're exactly right, yeah.
 That's a hard question because, you know,
 how do you represent that knowledge is the question, right?
 I can certainly write down F equals MA
 and Newton's laws and a lot of physics
 can be deduced from that.
 But that's probably not the best representation
 of that knowledge for doing the kinds of reasoning
 we want a machine to do.
 So I don't know, it's impossible to say now.
 And people, you know, the projects like
 there's the famous Psych project, right?
 That Douglas Linat did that was trying.
 I think it's still going.
 I think it's still going.
 And the idea was to try and encode
 all of common sense knowledge,
 including all this invisible knowledge
 in some kind of logical representation.
 And it just never, I think,
 could do any of the things that he was hoping it could do
 because that's just the wrong approach.
 Of course, that's what they always say, you know,
 and then the history books will say,
 well, the Psych project finally found a breakthrough
 in 2058 or something.
 You know, so much progress has been made
 in just a few decades that who knows
 what the next breakthroughs will be.
 It could be.
 It's certainly a compelling notion
 what the Psych project stands for.
 I think Linat was one of the earliest people to say,
 common sense is what we need.
 That's what we need.
 All this like expert system stuff
 that is not gonna get you to AI.
 You need common sense.
 And he basically gave up his whole academic career
 to go pursue that.
 And I totally admire that,
 but I think that the approach itself will not
 in 2020 or 2040 or whatever.
 What do you think is wrong with approach?
 What kind of approach might be successful?
 Well, if I knew that.
 Again, nobody knows the answer, right?
 If I knew that, you know, one of my talks,
 one of the people in the audience,
 this is a public lecture,
 one of the people in the audience said,
 what AI companies are you investing in?
 I'm like, well, I'm-
 Investment advice, okay.
 I'm a college professor for one thing.
 So I don't have a lot of extra funds to invest,
 but also like no one knows what's gonna work in AI, right?
 That's the problem.
 Let me ask another impossible question
 in case you have a sense.
 In terms of data structures
 that will store this kind of information,
 do you think they've been invented yet,
 both in hardware and software?
 Or is something else needs to be, are we totally, you know-
 I think something else has to be invented.
 That's my guess.
 Is the breakthroughs that's most promising,
 would that be in hardware or software?
 Do you think we can get far with the current computers?
 Or do we need to do something that you-
 I see what you're saying.
 I don't know if Turing computation
 is gonna be sufficient.
 Probably, I would guess it will.
 I don't see any reason why we need anything else.
 But so in that sense, we have invented the hardware we need,
 but we just need to make it faster and bigger.
 And we need to figure out the right algorithms
 and the right sort of architecture.
 Turing, that's a very mathematical notion.
 When we have to build intelligence,
 it's now an engineering notion
 where you throw all that stuff.
 Well, I guess it is a question.
 People have brought up this question, you know,
 and when you asked about like, is our current hardware,
 will our current hardware work?
 Well, Turing computation says that like,
 our current hardware is in principle a Turing machine, right?
 So all we have to do is make it faster and bigger.
 But there have been people like Roger Penrose,
 if you might remember that he said Turing machines
 cannot produce intelligence
 because intelligence requires continuous valued numbers.
 I mean, that was sort of my reading of his argument
 and quantum mechanics and what else, whatever, you know.
 But I don't see any evidence for that,
 that we need new computation paradigms.
 But I don't know if we're, you know,
 I don't think we're gonna be able to scale up
 our current approaches to programming these computers.
 What is your hope for approaches like CopyCAD
 or other cognitive architectures?
 I've talked to the creator of SOAR, for example,
 I've used Actar myself, I don't know if you're familiar.
 Yeah, I am.
 What do you think is,
 what's your hope of approaches like that
 in helping develop systems of greater
 and greater intelligence in the coming decades?
 Well, that's what I'm working on now
 is trying to take some of those ideas and extending it.
 So I think there's some really promising approaches
 that are going on now that have to do with
 more active generative models.
 So this is the idea of this simulation in your head,
 of a concept when you, if you wanna,
 when you're perceiving a new situation,
 you have some simulations in your head.
 Those are generative models,
 they're generating your expectations,
 they're generating predictions.
 So that's part of a perception,
 you have a metamodel that generates a prediction
 and you compare it with, and then the difference.
 And you also, that generative model is telling you
 where to look and what to look at
 and what to pay attention to.
 And it, I think it affects your perception.
 It's not that just you compare it with your perception.
 It becomes your perception in a way.
 It's kind of a mixture of the bottom up information
 coming from the world and your top down model
 being imposed on the world is what becomes your perception.
 So your hope is something like that can improve
 perception systems and that they can understand things better.
 Yes. Understand things.
 Yes.
 What's the, what's the step,
 what's the analogy making step there?
 Well, there, the idea is that you have this
 pretty complicated conceptual space.
 You can talk about a semantic network or something like that
 with these different kinds of concept models in your brain
 that are connected.
 So let's take the example of walking a dog.
 So we were talking about that.
 Okay.
 Let's say I see someone out on the street walking a cat.
 Some people walk their cats, I guess.
 It seems like a bad idea, but.
 Yeah.
 So my model, my, you know, there's connections
 between my model of a dog and model of a cat.
 And I can immediately see the analogy of,
 that those are analogous situations,
 but I can also see the differences
 and that tells me what to expect.
 So also, you know, I have a new situation.
 So another example with the walking the dog thing is
 sometimes people, I see people riding their bikes
 with a leash, holding a leash and the dogs running alongside.
 Okay, so I know that the,
 I recognize that as kind of a dog walking situation,
 even though the person's not walking, right?
 And the dog's not walking.
 Because I have these models that say,
 okay, riding a bike is sort of similar to walking
 or it's connected, it's a means of transportation,
 but I, because they have their dog there,
 I assume they're not going to work,
 but they're going out for exercise.
 You know, these analogies help me to figure out
 kind of what's going on, what's likely.
 But sort of these analogies are very human interpretable.
 Mm-hmm.
 So that's that kind of space.
 And then you look at something like
 the current deep learning approaches,
 they kind of help you to take raw sensory information
 and to sort of automatically build up hierarchies of,
 well, you can even call them concepts.
 They're just not human interpretable concepts.
 What's the link here?
 Do you hope, it's sort of the hybrid system question,
 how do you think that two can start to meet each other?
 What's the value of learning in this systems of forming,
 of analogy making?
 The goal of, you know, the original goal of deep learning
 in at least visual perception was that
 you would get the system to learn to extract features
 that at these different levels of complexity.
 So maybe edge detection,
 and that would lead into learning, you know,
 simple combinations of edges and then more complex shapes
 and then whole objects or faces.
 And this was based on the ideas of the neuroscientists,
 Hubel and Weasel, who had seen,
 laid out this kind of structure in brain.
 And I think that's right to some extent.
 Of course, people have found that the whole story
 is a little more complex than that.
 And the brain of course always is,
 and there's a lot of feedback.
 So I see that as absolutely a good brain inspired approach
 to some aspects of perception.
 But one thing that it's lacking, for example,
 is all of that feedback, which is extremely important.
 The interactive element that you mentioned.
 The expectation, right?
 The conceptual level.
 The conceptual level, go back and forth with the expectation,
 the perception and just go back and forth.
 So, right.
 So that is extremely important.
 And, you know, one thing about deep neural networks
 is that in a given situation, like, you know,
 they're trained, right?
 They get these weights and everything,
 but then now I give them a new image, let's say.
 Yes.
 They treat every part of the image in the same way.
 You know, they apply the same filters at each layer
 to all parts of the image.
 There's no feedback to say like,
 oh, this part of the image is irrelevant.
 Right.
 I shouldn't care about this part of the image
 or this part of the image is the most important part.
 And that's kind of what we humans are able to do
 because we have these conceptual expectations.
 There's, by the way, a little bit of work in that.
 There's certainly a lot more in what's under the,
 called attention in natural language processing knowledge.
 It's an, it's a, and that's exceptionally powerful.
 And it's a very, just as you say,
 it's a really powerful idea.
 But again, in sort of machine learning,
 it all kind of operates in an automated way.
 That's not human nature.
 Well, it's not, it's not also, okay.
 So that, right, it's not dynamic.
 I mean, in the sense that as a perception
 of a new example is being processed,
 those attention's weights don't change.
 Right. So, I mean, there's a,
 there's a kind of notion that there's not a memory.
 So you're not aggregating the idea of like
 this mental model.
 Yes.
 I mean, that seems to be a fundamental idea.
 There's not a really powerful,
 I mean, there's some stuff with memory,
 but there's not a powerful way to represent the world
 in some sort of way that's deeper than,
 I mean, it's so difficult because, you know,
 neural networks do represent the world.
 They do have a mental model, right?
 But it just seems to be shallow.
 Like, it's hard to criticize them at the fundamental level,
 to me at least.
 It's easy to criticize them.
 Well, look, like exactly what you're saying,
 mental models sort of almost from a psycho,
 put a psychology hat on, say,
 look, these networks are clearly not able to achieve
 what we humans do with forming mental models,
 but the analogy making so on.
 But that doesn't mean that they fundamentally
 cannot do that.
 It's very difficult to say that, I mean, at least to me.
 Do you have a notion that the learning approach is really,
 I mean, they're going to, not only are they limited today,
 but they will forever be limited in being able to construct
 such mental models.
 I think the idea of the dynamic perception is key here.
 The idea that moving your eyes around and getting feedback,
 and that's something that,
 there's been some models like that.
 There's certainly recurrent neural networks
 that operate over several time steps.
 And, but the problem is that the actual, the recurrence is,
 basically the feedback is to the brain,
 and the next time step is the entire hidden state
 of the network, which is,
 and it turns out that that doesn't work very well.
 But see, the thing I'm saying is, mathematically speaking,
 it has the information in that recurrence
 to capture everything.
 It just doesn't seem to work.
 Yeah, right.
 So, it's like, it's the same touring machine question, right?
 Yeah, maybe theoretically, computers,
 anything that's touring a universal touring machine
 can be intelligent, but practically,
 the architecture might be very specific kind of architecture
 to be able to create it.
 So it's just, I guess to sort of ask
 almost the same question again is,
 how big of a role do you think deep learning needs,
 will play or needs to play in this, in perception?
 I think that deep learning as it's currently,
 as it currently exists,
 will place, that kind of thing will play some role.
 But I think that there's a lot more going on in perception.
 But who knows, the definition of deep learning,
 I mean, it's pretty broad.
 It's kind of an umbrella for a lot of different things.
 So what I mean is purely sort of neural networks.
 Yeah, and feed forward neural networks.
 Essentially, or there could be recurrence, but.
 Yeah.
 Sometimes it feels like,
 for example, I talked to Gary Marcus,
 it feels like the criticism of deep learning
 is kind of like us birds criticizing airplanes
 for not flying well, or that they're not really flying.
 Do you think deep learning,
 do you think it could go all the way,
 like Yalda Lipton thinks?
 Do you think that, yeah,
 the brute force learning approach can go all the way?
 I don't think so, no.
 I mean, I think it's an open question,
 but I tend to be on the innateness side
 that there's some things that we've been evolved to be able to learn.
 And that learning just can't happen without them.
 So one example, here's an example I had in the book
 that I think is useful to me, at least, in thinking about this.
 So this has to do with the Deep Minds Atari game-playing program, okay?
 And it learned to play these Atari video games
 just by getting input from the pixels of the screen.
 And it learned to play the game Breakout
 thousand percent better than humans, okay?
 That was one of their results, and it was great.
 And it learned this thing where it tunneled
 through the side of the bricks in the Breakout game,
 and the ball could bounce off the ceiling,
 and then just wipe out bricks.
 Okay, so there was a group who did an experiment
 where they took the paddle that you move with the joystick
 and moved it up two pixels or something like that.
 And then they looked at a deep Q learning system
 that had been trained on Breakout and said,
 could it now transfer its learning to this new version of the game?
 Of course, a human could,
 and it couldn't.
 Maybe that's not surprising, but I guess the point is
 it hadn't learned the concept of a paddle.
 It hadn't learned the concept of a ball or the concept of tunneling.
 It was learning something.
 You know, we, looking at it, kind of anthropomorphized it and said,
 oh, here's what it's doing and the way we describe it.
 But it actually didn't learn those concepts.
 And so because it didn't learn those concepts,
 it couldn't make this transfer.
 Yeah, so that's a beautiful statement.
 But at the same time, by moving the paddle,
 we also anthropomorphize flaws to inject into the system
 that will then flip how impressed we are by it.
 What I mean by that is, to me, the Atari games were, to me,
 deeply impressive that that was possible at all.
 So like, I have to first pause on that,
 and people should look at that just like the game of Go,
 which is fundamentally different to me than what Deep Blue did.
 Even though there's still Monte Carlo, there's still a tree search.
 It's just everything a deep mind has done in terms of learning,
 however limited it is, is still deeply surprising to me.
 Yeah, I'm not trying to say that what they did wasn't impressive.
 I think it was incredibly impressive.
 To me, it's interesting.
 Is moving the board just another thing that needs to be learned?
 So like, we've been able to, maybe, maybe,
 been able to, through the current neural networks,
 learn very basic concepts that are not enough
 to do this general reasoning.
 And maybe with more data, I mean, the data, you know,
 the interesting thing about the examples that you talk about,
 and beautifully, is it's often flaws of the data.
 Well, that's the question.
 I mean, I think that is the key question,
 whether it's a flaw of the data or not.
 Or the mechanism, right, beautifully, yeah.
 The reason I brought up this example was because you were asking,
 do I think that, you know, learning from data could go all the way?
 Yes.
 And this was why I brought up the example, because I think,
 and this is not at all to take away from the impressive work that they did,
 but it's to say that when we look at what these systems learn,
 do they learn the human, the things that we humans consider
 to be the relevant concepts?
 And in that example, it didn't.
 Sure, if you train it on, you know, the paddle being in different places,
 maybe it could deal with, maybe it would learn that concept.
 I'm not totally sure.
 But the question is, you know, scaling that up to more complicated worlds,
 to what extent could a machine that only gets this very raw data
 learn to divide up the world into relevant concepts?
 And I don't know the answer, but I would bet that without some innate notion,
 that it can't do it.
 Yeah, 10 years ago, I 100% agree with you as the most experts in AI system,
 but now I have a glimmer of hope.
 OK, that's fair enough.
 And I think that's what deep learning did in the community is,
 no, no, if I had to bet all my money, 100% deep learning will not take us all the way.
 But there's still, I was so personally sort of surprised by the Atari games,
 by Go, by the power of self-play, of just game playing against each other,
 that I was, like many other times, just humbled of how little I know about what's possible.
 Yeah, I think fair enough.
 Self-play is amazingly powerful.
 And, you know, that goes way back to Arthur Samuel, right, with his checker playing program,
 and that which was brilliant and surprising that it did so well.
 So just for fun, let me ask you on the topic of autonomous vehicles.
 It's the area that I work at least these days most closely on,
 and it's also area that I think is a good example that you use as sort of an example of things
 we as humans don't always realize how hard it is to do.
 It's like the constant trend in AI, but the different problems that we think are easy
 when we first try them and then realize how hard it is.
 Okay, so why, you've talked about this autonomous driving being a difficult problem,
 more difficult than we realize, humans give it credit for.
 Why is it so difficult?
 What are the most difficult parts in your view?
 I think it's difficult because of the world is so open-ended as to what kinds of things can happen.
 So you have sort of what normally happens, which is just you drive along and nothing surprising happens.
 And autonomous vehicles can do, the ones we have now evidently can do really well on most normal situations,
 as long as the weather is reasonably good and everything.
 But if some, we have this notion of edge cases or things in the tail of the distribution,
 we call it the long tail problem, which says that there's so many possible things that can happen
 that was not in the training data of the machine that it won't be able to handle it
 because it doesn't have common sense.
 It's the old paddle moved problem.
 It's the paddle moved problem.
 Right. And so my understanding, and you probably are more of an expert than I am on this,
 is that current self-driving car vision systems have problems with obstacles,
 meaning that they don't know which quote unquote obstacles they should stop for and which ones they shouldn't stop for.
 And so a lot of times I read that they tend to slam on the brakes quite a bit.
 And the most common accidents with self-driving cars are people rear-ending them because they were surprised.
 They weren't expecting the car to stop.
 Yeah. So there's a lot of interesting questions there.
 Because you mentioned two things.
 So one is the problem of perception, of understanding, of interpreting the objects that are detected correctly.
 And the other one is more like the policy, the action that you take, how you respond to it.
 So a lot of the cars breaking is a kind of notion of, to clarify,
 there's a lot of different kinds of things that are people calling autonomous vehicles.
 But the L4 vehicles with a safety driver are the ones like Waymo and Cruise and those companies.
 They tend to be very conservative and cautious.
 So they tend to be very, very afraid of hurting anything or anyone and getting in any kind of accidents.
 So their policy is very kind of...
 That results in being exceptionally responsive to anything that could possibly be an obstacle, right?
 Right. Which the human drivers around it, it behaves unpredictably.
 That's not a very human thing to do, caution.
 That's not the thing we're good at, especially in driving.
 We're in a hurry, often angry and etc., especially in Boston.
 So and then there's another...
 And a lot of times that's machine learning is not a huge part of that.
 It's becoming more and more unclear to me how much, you know,
 sort of speaking to public information because a lot of companies say they're doing deep learning
 and machine learning just to attract good candidates.
 The reality is, in many cases, it's still not a huge part of the perception.
 There's lidar and there's other sensors that are much more reliable for optical detection.
 And then there's Tesla approach, which is vision only.
 And I think a few companies doing that, but Tesla most sort of famously pushing that forward.
 And that's because the lidar is too expensive, right?
 Well, I mean, yes, but I would say if you were to for free give to every Tesla vehicle,
 Elon Musk fundamentally believes that lidar is a crutch, right?
 Fantasy said that that if you want to solve the problem of machine learning, lidar is not,
 should not be the primary sensor is the belief.
 The camera contains a lot more information.
 So if you want to learn, you want that information.
 But if you want not to hit obstacles, you want lidar.
 It's sort of this weird trade off because yeah, so what Tesla vehicles have a lot of,
 which is really the thing, the primary fallback sensor is radar, which is a very crude version
 of lidar.
 It's a good detector of obstacles except when those things are standing, right?
 The stopped vehicle.
 Right.
 So they had problems with crashing into stop fire trucks.
 Stop fire trucks.
 That's right.
 So the hope there is that the vision sensor would somehow catch that.
 There's a lot of problems with perception.
 They are doing actually some incredible stuff in the almost like an active learning space
 where it's constantly taking edge cases and pulling back in.
 There's this data pipeline.
 Another aspect that is really important that people are studying now is called multitask
 learning, which is sort of breaking apart this problem, whatever the problem is, in
 this case driving into dozens or hundreds of little problems that you can turn into
 learning problems.
 So this giant pipeline, it's kind of interesting.
 I've been skeptical from the very beginning, but become less and less skeptical over time
 how much of driving can be learned.
 I still think it's much farther than the CEO of that particular company thinks it will
 be, but it's constantly surprising that through good engineering and data collection and active
 selection of data, how you can attack that long tail.
 It's an interesting open question that you're absolutely right.
 There's a much longer tail and all these edge cases that we don't think about, but it's
 a fascinating question that applies to natural language in all spaces.
 How big is that long tail?
 Not to linger on the point, but what's your sense in driving in these practical problems
 of the human experience?
 Can it be learned?
 So the current, what are your thoughts of sort of Elon Musk thought, let's forget the
 thing that he says it would be solved in a year, but can it be solved in a reasonable
 timeline or do fundamentally other methods need to be invented?
 So I think that ultimately driving, so it's a trade off in a way, being able to drive
 and deal with any situation that comes up does require kind of full human intelligence
 and even in humans aren't intelligent enough to do it.
 Cause humans, I mean, most human accidents are because the human wasn't paying attention
 or the humans drunk or whatever.
 And not because they weren't intelligent enough.
 Not because they weren't intelligent enough.
 Right.
 Whereas the accidents with autonomous vehicles is because they weren't intelligent enough.
 They're always paying attention.
 Yeah, they're always paying attention.
 So it's a trade off and I think that it's a very fair thing to say that autonomous vehicles
 will be ultimately safer than humans because humans are very unsafe.
 It's kind of a low bar.
 But just like you said, I think humans got a better rap, right?
 Cause we're really good at the common sense thing.
 Yeah, we're great at the common sense thing.
 We're bad at the paying attention thing.
 Paying attention thing.
 Especially when we're driving is kind of boring and we have these phones to play with and
 everything.
 But I think what's going to happen is that for many reasons, not just AI reasons, but
 also like legal and other reasons that the definition of self-driving is going to change
 or autonomous is going to change.
 It's not going to be just, I'm going to go to sleep in the back and you just drive me
 anywhere.
 It's going to be more certain areas are going to be instrumented to have the sensors and
 the mapping and all of the stuff you need for that the autonomous cars won't have to
 have full common sense.
 And they'll do just fine in those areas as long as pedestrians don't mess with them too
 much.
 That's another question.
 But I don't think we will have fully autonomous self-driving in the way that like most the
 average person thinks of it for a very long time.
 And just to reiterate, this is the interesting open question that I think I agree with you
 on is to solve fully autonomous driving, you have to be able to engineer in common sense.
 I think it's an important thing to hear and think about.
 I hope that's wrong, but I currently agree with you that unfortunately you do have to
 have to be more specific sort of these deep understandings of physics and of the way this
 world works and also human dynamics.
 I can mention pedestrians and cyclists actually that's whatever that nonverbal communication
 is some people call it, there's that dynamic that is also part of this common sense.
 Right.
 And we humans are pretty good at predicting what other humans are going to do.
 And how our actions impact the behaviors of this weird game theoretic dance that we're
 good at somehow.
 And the funny thing is, because I've watched countless hours of pedestrian video and talked
 to people, we humans are also really bad at articulating the knowledge we have.
 Right.
 Which has been a huge challenge.
 Yes.
 So you've mentioned embodied intelligence.
 What do you think it takes to build a system of human level intelligence?
 Does it need to have a body?
 I'm not sure, but I'm coming around to that more and more.
 And what does it mean to be, I don't mean to keep bringing up Yala Kuhn.
 He looms very large.
 Well, he certainly has a large personality.
 Yes.
 He thinks that the system needs to be grounded, meaning it needs to sort of be able to interact
 with reality, but doesn't think it necessarily needs to have a body.
 So when you think of-
 So what's the difference?
 I guess I want to ask, when you mean body, do you mean you have to be able to play with
 the world?
 Or do you also mean like there's a body that you have to preserve?
 That's a good question.
 I haven't really thought about that, but I think both, I would guess.
 I think intelligence, it's so hard to separate it from our desire for self-preservation,
 our emotions, all that non-rational stuff that kind of gets in the way of logical thinking.
 But because the way, if we're talking about human intelligence or human level intelligence,
 whatever that means, a huge part of it is social.
 We were evolved to be social and to deal with other people, and that's just so ingrained
 in us that it's hard to separate intelligence from that.
 I think AI for the last 70 years, or however long it's been around, it has largely been
 separated.
 There's this idea that there's like, it's kind of very Cartesian.
 There's this thinking thing that we're trying to create, but we don't care about all this
 other stuff.
 I think the other stuff is very fundamental.
 There's idea that things like emotion get in the way of intelligence.
 As opposed to being an integral part of it.
 Integral part of it.
 I'm Russian, so romanticize the notions of emotion and suffering and all that kind of
 fear of mortality, those kinds of things.
 So in AI, especially- By the way, did you see that there was this
 recent thing going around the internet of this?
 Some, I think he's a Russian or some Slavic, had written this thing, sort of anti the idea
 of super intelligence.
 I forgot, maybe he's Polish.
 Anyway, so he had all these arguments and one was the argument from Slavic pessimism.
 My favorite.
 Do you remember what the argument is?
 It's like nothing ever works.
 Everything sucks.
 So what do you think is the role?
 That's such a fascinating idea that what we perceive as sort of the limits of the human
 mind, which is emotion and fear and all those kinds of things are integral to intelligence.
 Could you elaborate on that?
 Why is that important, do you think, for human level intelligence?
 At least the way the humans work, it's a big part of how it affects how we perceive the
 world.
 It affects how we make decisions about the world.
 It affects how we interact with other people.
 It affects our understanding of other people, for me to understand what you're likely to
 do.
 I need to have kind of a theory of mind and that's very much a theory of emotions and
 motivations and goals.
 To understand that, we have this whole system of mirror neurons.
 I sort of understand your motivations through sort of simulating it myself.
 So it's not something that I can prove that's necessary, but it seems very likely.
 So okay, you've written the op-ed in the New York Times titled, We Shouldn't Be Scared
 by Superintelligent AI.
 And it criticized a little bit Stuart Russell and Nick Bostrom.
 Can you try to summarize that article's key ideas?
 So it was spurred by an earlier New York Times op-ed by Stuart Russell, which was summarizing
 his book called Human Compatible.
 The article was saying, if we have superintelligent AI, we need to have its values aligned with
 our values and it has to learn about what we really want.
 He gave this example, what if we have a superintelligent AI and we give it the problem of solving
 climate change and it decides that the best way to lower the carbon in the atmosphere
 is to kill all the humans.
 Okay.
 So to me, that just made no sense at all because a superintelligent AI, first of all, trying
 to figure out what superintelligence means.
 And it seems that something that's superintelligent can't just be intelligent along this one
 dimension of, okay, we're going to figure out all the steps, the best optimal path to
 solving climate change and not be intelligent enough to figure out that humans don't want
 to be killed, that you could get to one without having the other.
 And you know, Bostrom in his book talks about the orthogonality hypothesis where he says
 he thinks that a system's, I can't remember exactly what it is, but like a system's goals
 and its values don't have to be aligned.
 There's some orthogonality there, which didn't make any sense to me.
 So you're saying in any system that's sufficiently, not even superintelligent, but as it opposed
 greater and greater intelligence, there's a holistic nature that will sort of attention
 that will naturally emerge that prevents it from sort of any one dimension running away.
 Yeah, yeah, exactly.
 So you know, Bostrom had this example of the superintelligent AI that turns the world into
 paper clips because its job is to make paper clips or something.
 And that just as a thought experiment didn't make any sense to me.
 Well as a thought experiment or as a thing that could possibly be realized?
 Either.
 So I think that, you know, what my op-ed was trying to do was say that intelligence is
 more complex than these people are presenting it.
 That it's not so separable, the rationality, the values, the emotions, all of that.
 The view that you could separate all these dimensions and build a machine that has one
 of these dimensions and it's superintelligent in one dimension, but it doesn't have any
 of the other dimensions.
 That's what I was trying to criticize that I don't believe that.
 So can I read a few sentences from Yoshio Benjy who is always super eloquent.
 So he writes, I have the same impression as Melanie that our cognitive biases are linked
 with our ability to learn to solve many problems.
 There may also be a limiting factor for AI.
 However, this is a may in quotes.
 Things may also turn out differently and there's a lot of uncertainty about the capabilities
 of future machines.
 But more importantly for me, the value alignment problem is a problem well before we reach
 some hypothetical superintelligence.
 It is already posing a problem in the form of super powerful companies whose objective
 function may not be sufficiently aligned with humanity's general wellbeing, creating all
 kinds of harmful side effects.
 He goes on to argue that the orthogonality and those kinds of things, the concerns of
 just aligning values with the capabilities of the system is something that might come
 long before we reach anything like superintelligence.
 So your criticism is kind of really nice to saying this idea of superintelligence systems
 seem to be dismissing fundamental parts of what intelligence would take.
 And then Yoshio kind of says, yes, but if we look at systems that are much less intelligent,
 there might be these same kinds of problems that emerge.
 Sure.
 But I guess the example that he gives there of these corporations, that's people, right?
 Those are people's values.
 I mean, we're talking about people, the corporations are their values are the values of the people
 who run those corporations.
 But the idea is the algorithm, that's right.
 So the fundamental person, the fundamental element of what does the bad thing is a human
 being.
 Yeah.
 But the algorithm kind of controls the behavior of this mass of human beings.
 Which algorithm?
 For a company that's the, for example, if it's an advertisement driven company that
 recommends certain things and encourages engagement, so it gets money by encouraging engagement
 and therefore the company more and more, it's like the cycle that builds an algorithm that
 enforces more engagement and maybe perhaps more division in the culture and so on, so
 on.
 I guess the question here is sort of who has the agency?
 So you might say, for instance, we don't want our algorithms to be racist.
 And facial recognition, some people have criticized some facial recognition systems as being racist
 because they're not as good on darker skin and lighter skin.
 That's right.
 But the agency there, the actual facial recognition algorithm isn't what has the agency.
 It's not the racist thing, right?
 It's the, I don't know, the combination of the training data, the cameras being used,
 whatever.
 But my understanding of, and I agree with Benjio there that I think there are these
 value issues with our use of algorithms.
 But my understanding of what Russell's argument was is more that the machine itself has the
 agency now.
 It's the thing that's making the decisions and it's the thing that has what we would
 call values.
 Yes.
 So whether that's just a matter of degree, it's hard to say, right?
 But I would say that's sort of qualitatively different than a face recognition neural network.
 And to broadly linger on that point, if you look at Elon Musk, or Russell, or Bostrom,
 people who are worried about existential risks of AI, however far into the future, their
 argument goes is it eventually happens.
 We don't know how far, but it eventually happens.
 Do you share any of those concerns and what kind of concerns in general do you have about
 AI that approach anything like existential threat to humanity?
 So I would say, yes, it's possible, but I think there's a lot more closer in existential
 threats to humanity.
 Because you said like a hundred years for, so your time-
 It's more than a hundred years.
 More than a hundred years.
 Maybe even more than 500 years.
 I don't know.
 I mean, it's-
 So the existential threats are so far out that the future is, I mean, there'll be a
 million different technologies that we can't even predict now that will fundamentally
 change the nature of our behavior, reality, society, and so on before then.
 Yeah, I think so.
 I think so.
 And we have so many other pressing existential threats going on right now.
 Nuclear weapons even.
 Nuclear weapons, climate problems, poverty, possible pandemics.
 You can go on and on.
 And I think though worrying about existential threat from AI is not the best priority for
 what we should be worrying about.
 That's kind of my view.
 Because we're so far away.
 I'm not necessarily criticizing Russell or Bostrom or whoever for worrying about that.
 And I think some people should be worried about it.
 It's certainly fine.
 But I was more sort of getting at their view of what intelligence is.
 So I was more focusing on their view of superintelligence than just the fact of them worrying.
 And the title of the article was written by the New York Times editors.
 I wouldn't have called it that.
 We shouldn't be scared by superintelligence.
 If you wrote it, it'd be like, we should redefine what you mean by superintelligence.
 I actually said something like superintelligence is not a sort of coherent idea.
 That's not something the New York Times would put in.
 And the follow-up argument that Yoshio makes also, not argument but a statement, and I've
 heard him say it before, and I think I agree.
 He kind of has a very friendly way of phrasing it.
 It's good for a lot of people to believe different things.
 He's such a nice guy.
 It's also practically speaking, we shouldn't be, while your article stands, like, Stuart
 Russell does amazing work, Bostrom does amazing work, you do amazing work.
 And even when you disagree about the definition of superintelligence or the usefulness of
 even the term, it's still useful to have people that use that term and then argue.
 I absolutely agree with Benjo there.
 I think it's great that the New York Times will publish all this stuff.
 It's an exciting time to be here.
 What do you think is a good test of intelligence?
 Is natural language ultimately a test that you find the most compelling, like the original,
 or the higher levels of the Turing test?
 Yeah, I still think the original idea of the Turing test is a good test for intelligence.
 I mean, I can't think of anything better.
 The Turing test, the way that it's been carried out so far has been very impoverished, if
 you will.
 But I think a real Turing test that really goes into depth.
 Like the one that I mentioned, I talk about in the book, I talk about Ray Kurzweil and
 Mitchell Kapoor have this bet that in 2029, I think is the date there, a machine will
 pass the Turing test.
 And they have a very specific, many hours, expert judges and all of that.
 And Kurzweil says yes, Kapoor says no.
 We only have like nine more years to go to see.
 If something, a machine could pass that, I would be willing to call it intelligent.
 Of course, nobody will.
 They will say that's just a language model, if it does.
 So you would be comfortable.
 So language, a long conversation that's, well, yeah, I mean, you're right, because I think
 probably to carry out that long conversation, you would literally need to have deep common
 sense understanding of the world.
 I think so.
 And conversation is enough to reveal that.
 I think so.
 So another super fun topic of complexity that you have worked on, written about.
 Let me ask the basic question.
 What is complexity?
 So complexity is another one of those terms like intelligence that's perhaps overused.
 But my book about complexity was about this wide area of complex systems, studying different
 systems in nature, in technology, in society, in which you have emergence, kind of like
 I was talking about with intelligence.
 We have the brain, which has billions of neurons.
 And each neuron individually could be said to be not very complex compared to the system
 as a whole.
 But the system, the interactions of those neurons and the dynamics creates these phenomena
 that we call intelligence or consciousness that we consider to be very complex.
 So the field of complexity is trying to find general principles that underlie all these
 systems that have these kinds of emergent properties.
 And the emergence occurs from like underlying the complex system is usually simple fundamental
 interactions.
 Yes.
 And the emergence happens when there's just a lot of these things interacting.
 Yes.
 And then most of science to date, can you talk about what is reductionism?
 Well, reductionism is when you try and take a system and divide it up into its elements,
 whether those be cells or atoms or subatomic particles, whatever your field is, and then
 try and understand those elements and then try and build up an understanding of the whole
 system by looking at sort of the sum of all the elements.
 So what's your sense, whether we're talking about intelligence or these kinds of interesting
 complex systems, is it possible to understand them in a reductionist way, which is probably
 the approach of most of science today, right?
 I don't think it's always possible to understand the things we want to understand the most.
 So I don't think it's possible to look at single neurons and understand what we call
 intelligence, to look at sort of summing up.
 And sort of the summing up is the issue here that we're...
 One example is that the human genome, right?
 So there was a lot of work on excitement about sequencing the human genome because the idea
 would be that we'd be able to find genes that underlies diseases.
 But it turns out that...
 And it was a very reductionist idea.
 We figure out what all the parts are and then we would be able to figure out which parts
 cause which things.
 But it turns out that the parts don't cause the things that we're interested in.
 It's like the interactions, it's the networks of these parts.
 And so that kind of reductionist approach didn't yield the explanation that we wanted.
 What do you use the most beautiful complex system that you've encountered?
 The most beautiful.
 That you've been captivated by?
 Is it sort of...
 I mean, for me, it's the simplest to be cellular automata.
 Oh, yeah.
 So I was very captivated by cellular automata and worked on cellular automata for several
 years.
 Do you find it amazing or is it surprising that such simple systems, such simple rules
 in cellular automata can create sort of seemingly unlimited complexity?
 Yeah, that was very surprising to me.
 How do you make sense of it?
 How does that make you feel?
 Is it just ultimately humbling or is there a hope to somehow leverage this into a deeper
 understanding and even able to engineer things like intelligence?
 It's definitely humbling.
 How humbling in that...
 Also kind of awe-inspiring that it's that awe-inspiring part of mathematics that these
 incredibly simple rules can produce this very beautiful, complex, hard to understand behavior.
 And it's mysterious and surprising still.
 But exciting because it does give you kind of the hope that you might be able to engineer
 complexity just from...
 From simple rules from the beginnings.
 Can you briefly say what is the Santa Fe Institute, its history, its culture, its ideas, its
 future?
 So I've never...
 As I mentioned to you, I've never been, but it's always been this, in my mind, this mystical
 place where brilliant people study the edge of chaos.
 Yeah, exactly.
 So the Santa Fe Institute was started in 1984 and it was created by a group of scientists,
 a lot of them from Los Alamos National Lab, which is about a 40-minute drive from the
 Santa Fe Institute.
 They were mostly physicists and chemists.
 But they were frustrated in their field because they felt that their field wasn't approaching
 kind of big interdisciplinary questions like the kinds we've been talking about.
 And they wanted to have a place where people from different disciplines could work on these
 big questions without sort of being siloed into physics, chemistry, biology, whatever.
 So they started this Institute and this was people like George Cowan, who was a chemist
 in the Manhattan Project, and Nicholas Metropolis, a mathematician, physicist, Marie-Gail Mann,
 a physicist, and there's some really big names here.
 Ken Arrow, a Nobel Prize-winning economist.
 And they started having these workshops.
 And this whole enterprise kind of grew into this research institute that itself has been
 kind of on the edge of chaos its whole life because it doesn't have a significant endowment.
 And it's just been kind of living on whatever funding it can raise through donations and
 grants and however it can, business associates and so on.
 But it's a great place.
 It's a really fun place to go think about ideas that you wouldn't normally encounter.
 So Sean Carroll, so physicists.
 Yeah, he's on the external faculty.
 And you mentioned that there's, so there's some external faculty and there's people that
 are-
 A very small group of resident faculty, maybe about 10 who are there on five-year terms
 that can sometimes get renewed.
 And then they have some post-docs and then they have this much larger, on the order of
 100, external faculty or people like me who come and visit for various periods of time.
 So what do you think is the future of the Satterfield Institute?
 And if people are interested, what's there in terms of the public interaction or students
 or so on that could be a possible interaction with the Satterfield Institute or its ideas?
 Yeah, so there's a few different things they do.
 They have a complex system summer school for graduate students and post-docs and sometimes
 faculty attend too.
 And that's a four-week, very intensive residential program where you go and you listen to lectures
 and you do projects and people really like that.
 I mean, it's a lot of fun.
 They also have some specialty summer schools.
 There's one on computational social science.
 There's one on climate and sustainability, I think it's called.
 There's a few.
 And then they have short courses where just a few days on different topics.
 They also have an online education platform that offers a lot of different courses and
 tutorials from SFI faculty, including an introduction to complexity course that I taught.
 Awesome.
 And there's a bunch of talks too online from those guest speakers and so on.
 They host a lot of...
 Yeah, they have sort of technical seminars and colloquia and they have a community lecture
 series like public lectures and they put everything on their YouTube channel so you can see it
 all.
 Watching.
 Douglas Haustader, author of Ghetto Escherbach was your PhD advisor.
 He mentioned a couple of times and collaborator.
 Do you have any favorite lessons or memories from your time working with him that continues
 to this day, I guess, but just even looking back throughout your time working with him?
 So one of the things he taught me was that when you're looking at a complex problem,
 to idealize it as much as possible, to try and figure out what is the essence of this
 problem.
 And this is how the copycat program came into being was by taking analogy making and saying,
 how can we make this as idealized as possible but still retain really the important things
 we want to study?
 And that's really been a core theme of my research, I think.
 And I continue to try and do that.
 And it's really very much kind of physics inspired.
 Haustader was a PhD in physics.
 That was his background.
 This is like first principles kind of thinking like you're reduced to the most fundamental
 aspect of the problem so that you can focus on solving that fundamental aspect.
 And in AI, people used to work in these micro worlds, right?
 Like the blocks world was very early important area in AI.
 And then that got criticized because they said, oh, you can't scale that to the real
 world.
 And so people started working on much more real world like problems.
 But now there's been kind of a return even to the blocks world itself.
 We've seen a lot of people who are trying to work on more of these very idealized problems
 for things like natural language and common sense.
 So that's an interesting evolution of those ideas.
 So perhaps the blocks world represents the fundamental challenges of the problem of intelligence
 more than people realize.
 It might, yeah.
 Is there sort of when you look back at your body of work and your life, you've worked
 in so many different fields.
 Is there something that you're just really proud of in terms of ideas that you've gotten
 a chance to explore, create yourself?
 So I am really proud of my work on the CopyCat project.
 I think it's really different from what almost everyone has done in AI.
 I think there's a lot of ideas there to be explored.
 And I guess one of the happiest days of my life, aside from the births of my children,
 was the birth of CopyCat when it actually started to be able to make really interesting
 analogies.
 And I remember that very clearly.
 That was a very exciting time.
 Well you kind of gave life an artificial system.
 That's right.
 In terms of what people can interact, I saw there's like a, I think it's called MetaCat.
 And there's a Python 3 implementation.
 If people actually wanted to play around with it and actually get into it and study it and
 maybe integrate into whether it's with deep learning or any other kind of work they're
 doing, what would you suggest they do to learn more about it and to take it forward in different
 kinds of directions?
 Yeah, so there's Douglas Hofstadter's book called Fluid Concepts and Creative Analogies
 talks in great detail about CopyCat.
 I have a book called Analogy Making as Perception, which is a version of my PhD thesis on it.
 There's also code that's available that you can get it to run.
 I have some links on my webpage to where people can get the code for it.
 And I think that would really be the best way to get into it.
 Yeah, I've been, yeah.
 Well, Melanie, it was an honor talking to you.
 I really enjoyed it.
 Thank you so much for your time today.
 Thanks.
 It's been really great.
 Thanks for listening to this conversation with Melanie Mitchell.
 And thank you to our presenting sponsor, Cash App.
 Download it, use code LexPodcast.
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 If you enjoy this podcast, subscribe on YouTube, give it five stars on Apple Podcast, support
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 And now let me leave you with some words of wisdom from Douglas Huffstader and Melanie
 Mitchell.
 Without concepts, there can be no thought.
 And without analogies, there can be no concepts.
 And Melanie adds, how to form and fluidly use concepts is the most important open problem
 in AI.
 Thank you for listening and hope to see you next time.