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- [00:03]
Podcast Announcer
You're listening to A Book With Legs, a podcast presented by Smead Capital Management. At Smead Capital Management, we advise investors who play the long game. You can learn more@smeedcap.com or by calling your financial advisor.
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Cole Smead
Welcome to A Book with Legs podcast. I'm Cole Smead, CEO and Portfolio Manager here at Smead Capital Management. At our firm, we are readers and we believe in the power of books
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Interviewer/Host Assistant
to help shape informed investors.
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Cole Smead
In this podcast, we speak to great authors about their writings the late, great Charlie Munger prescribed. Using multiple mental models and analysis, we analyze their work through the lens of business markets and people. Welcome to this episode of A Book with Legs. We're doing something different than we've done typically. Instead of one author and one book, which is our typical style, over 20 times in the year I wanted to step back, we've had some questions and conversations that have flourished around similar topics across the episodes, across different professions. And so I want to open those up to kind of ask what I think are some deeper questions we're going to look across. Through a computer scientist, a geneticist, a neuroscientist, and a cognitive scientist. I keep asking a version of this same question. Can a human being be fully explained by the machine, by code, by chemistry, biology, by data? Or is there something left over? Today, I want to play you the best answers I've gotten from people who would never share a stage, frankly. We'll start with the first question and then look at the sharpest place to show up. A small child learning to speak. And then turn and look straight at the machine of our moment of artificial intelligence. Three conversations, but all in the same thread. First, Tom Griffiths. He wrote a history of the quest to build a mathematical theory of the mind, Basically the story of how we got to what we now know as modern AI. I asked him about the one thing these machines still can't do, the way a child does. Learn a language from nothing. Here's where he took it.
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Interviewer/Host Assistant
You know, late in the book, you're really talking about the difference between, you know, machine learning or AI and the human. You're kind of pointing out small differences, or big differences for that matter. So if we could put up slide 3. You quote Chomsky here, the child who learns a language has in some sense constructed the grammar for himself on the basis of observation of sentences and non sentences, I.e. corrections by the verbal community. Study of the actual observability of a speaker to distinguish sentences from non sentences, detect ambiguities, et cetera, apparently forces us to the conclusion that this grammar is, is of an extremely complex and abstract character and that the young child has succeeded in carrying out what, from the formal point of view at least, seems to be a remarkable type of theory construction. Furthermore, this task is accomplished in an astonishingly short time, to a large extent independently of intelligence in a comparable way by all children. Any theory of learning must cope with these facts. So I thought this was incredible because
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Cole Smead
he's really saying, you know, if you
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Interviewer/Host Assistant
think of how we do, you know, machine learning or AI, there's a vast amount of data or intelligence that's provided to it. Right back to the idea of there's a store of information, as we've learned through computing, and the child doesn't have really much, if any, data or store of information to go and execute on. And yet, you know, as Hume might say innately, I, and others like Chomsky might say more divinely, are able to do that. Doesn't that show you how incredibly unique the human is? Even while they will use AI to function and learn going forward?
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Tom Griffiths
This is really the biggest gap that I think remains between AI and human minds, right? Which is that humans are able to make these inferences from relatively small amounts of data. Sure. And so if you think about that, it's really a consequence of the fact that the way that neural networks work, they're really good at just learning anything, right? So you can take your neural network, you can use the same kinds of neural networks to learn all sorts of different kinds of things, right? The same ones that we use for learning language, you can use to learn things about vision and so on. And so that means that they're sort of like these general purpose learning machines. And so they have very weak, what we call inductive bias. They don't really bring much to the problem. They can learn all sorts of things, but they need a lot of data to learn it. So if you want to go from zero to wherever you end up, that whole space is sort of filled up with data. That's how we're going to get a neural network to that point for the human. We know that just that end point, we only have this much data. And so all of the rest of that needs to be what we call inductive bias. And so the humans are bringing to the learning problem some set of sort of innate expectations, but also other kinds of experiences. Right? The experiences that we have as embodied entities that live in the world, that get to have visual experiences, that get to interact with other humans, that have all of the other Things going on that are part of being a human being. All of that adds to the information that we're getting from that limited amount of data that we're learning from. And so that's important in terms of being able to understand why we're able to learn so much from so little.
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Tim Wu
Right.
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Tom Griffiths
There's an evolutionary process that has produced our minds that's filling in that gap that in the AI model is really just being filled in with data. So you can kind of think about that big chunk of data that's really. That's standing in for evolution. Right. That's standing in for other forces that have shaped human minds and the other kinds of experiences that humans have.
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
The other thing about it is that that's something which influences the kinds of solutions that human minds find relative to the kinds of solutions that the AI systems find.
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
Right. So part of the reason why AIs act in this way, which is often counterintuitive to us, and do weird things and have these weird failure modes, and they succeed at one thing and then fail at something that's right next to it, what's called jagged intelligence. That is a consequence of not having the same inductive biases as us. Right. They're good at the things that are in their training data. They're less good at things that aren't in the training data. We don't know what's in their training data. They learn weird idiosyncratic patterns because they're just sort of learning to mimic behavior that they see in their training data.
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Interviewer/Host Assistant
Yeah.
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Tom Griffiths
And so when we learn something, we're guided by those inductive biases. And you can make pretty good inferences about what another human being will be able to know or do based on things that they know or do. But we can't make those kinds of inferences for AI systems because they're operating with just like a completely different set of inductive biases.
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Cole Smead
Yeah.
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Interviewer/Host Assistant
And I mentioned you before, it reminds me of, like a new intern to an industry. Right. It's got jagged, jagged intelligence or information. It can be refined over time, but they might do something that's helpful. And then you're thinking like, well, what did you just do? I don't understand what you've given me. And makes it very human, like, in that respect, in many ways.
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Cole Smead
I want to go, oh, one other
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Interviewer/Host Assistant
thing I want to mention on this.
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Cole Smead
Totally.
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Interviewer/Host Assistant
I mentioned, you know, this technology conference kosm, and there was a gentleman when I was thinking about, you know, Chomsky's idea I was at this conference, and this gentleman made a presentation on brains where they have epileptic attacks that are regular, and how do you solve for that? And so he mentioned that what they do, what neurologists do, is they sever the hemispheres of the brain because it's the misfiring between the two hemispheres that's causing these epileptic issues. And so we know that physically, once the two hemispheres are separated, they can't talk to each other. And you obviously control one part of your body with one hemisphere and another part of your body with the other hemisphere. And we know also that, you know, each hemisphere has different things that they take data in and process based on. And his interesting presentation was based on this. If you're thinking in your left brain, you should not be using your left hand to respond, because your. Your left brain would correspond with your right side of your body or your right hand. And what they found in studies is when people have severed brains, they. They're just as likely to use their left or their right hand to respond. And so the question is, like, how is this going on? Kind of like, how is the child able to develop this, you know, innate ability? And he argues that there is something divine going on. He said it's a soul. That's the divine nature. And when Chomsky said that, it played along that idea of there's something more divine. And, you know, who knows how you define, define, but. But there's something more incredible or awesome that is fairly unexplainable and to your point, tough to replicate and compute.
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Tom Griffiths
Yeah, I mean, I think that's an interesting way of framing it in terms of. It kind of goes back past Chomsky, back to Plato. So Plato had the same answer to this question. Right. So Plato was trying to explain how it is that we come to know the things that we know from limited experience in the world. And the way that he answered that question was that we're not learning things, we're remembering things. Sure. And we're remembering things that are, like, stored in our souls that come from the experience that we had in some Platonic realm.
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Interviewer/Host Assistant
Right.
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Tom Griffiths
And comes through. And so I think. I think Chomsky would think about this in much more materialistic terms that he really thinks that it is biology. Right. It's. It's, you know, the, the 21st century scientific analog of the soul is the genes or something like that. Right. And it's, like, built into our genetic heritage.
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Charles Murray
Yeah.
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Cole Smead
So Griffiths looks at the child and sees something almost divine. Now Here's a neuroscientist who builds a neural network for a living. Gaurav Suri. Coming at the same leftover thing from a completely different direction.
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Gaurav Suri
I think that human behavior is a beautiful emergent product of many things. It's not this reasoned self interest alone. It's not we are capable of empathy. We are capable of well spirited gambles. We're capable of proceeding without knowledge. We're capable of happiness even around suffering. Machines are not having these aspects of our experience. And we are capable of, even based on our experience, we're capable of persisting in adversity. We have many aspects of intelligence. The LLM is based on one of those aspects, one of many. And the LLM, meantime, it's based on language pattern prediction. And that is one basis of our intelligence. But by far it's not the only basis. Right? We're not limited to words. Thoughts exist without words. Thoughts can exist in images. So many times I'm listening to somebody and I already know I think they're wrong without having any words for why I think they're wrong. Or I'm watching Jeopardy and I know I know the answer without the answer coming to mind. So this happens a lot. That's because language is not thought. And we have other modes of thought. And the large language model deals in the modality of language. We have goals. The large language model doesn't have any of its goals. We have efficient learning from a few instances. We have the capability to learn very quickly. Large language models have to be trained billions and billions of times. There are many aspects of the human experience that are outside the large language models. I do want to say, and this goes to an initial conversation, I see, in principle there isn't some reason why we can't understand our working. Our workings are beautiful, profound, sublime and understandable. And it is in this understanding that I find my awe.
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Cole Smead
Faith gambles love the parts the machine doesn't have. You know, the really human part, that we think about our own families or our spouse or our kids. Now listen to a geneticist, Katherine Page Hardin, who spent her days reading people's DNA. And really the coding of that DNA, you expect the hardest determinist in the room. Instead, she found something that sounds a lot like Grace.
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Katherine Page Hardin
You know, I think the thread that you're picking up on here is that the line between the individual and their community is much fuzzier than we'd like to imagine. None of us is an island, none of us is a fortress. And that means our individual choices reverberate and have implications for other people. And they're structured by other people's choices in this way where the inside of our body and the outside of our body is a lot less clearly demarked than we would imagine. I write about this when I talk about, you know, there's the so called nature nurture debate. And if you ask any scientists, they'll be like the nature nurture debate is dead. Like it's always nature and nurture in combination. And that also, even the division between nature and nurture is really artificial because your nurture changes your body. It changes the expression of your genes and your nature, by shaping your behavior is then providing that nurture to other people.
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Gaurav Suri
Right?
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Katherine Page Hardin
And I think that example you just gave of if you have a man who is predisposed towards antisocial behavior because of his genes, but then he's passing on both those genes and the environment that he is shaping to his children, it's nature nurture, it's one combination. I think the other thing is just as a geneticist, I see that all of us are just a few genetic mutations away from being profoundly different people with different capacities and different. It is, it is just by the luck or the grace of whatever that you didn't have that experience. And also that DNA strand that runs through them is 99, 99 and a half percent the same as mine. And so I really think genetics has given me this profound appreciation for no one is a monster. People can behave monstrously, but they're doing that in part because of genes and environments of natures and nurtures that run through all of our experience. Like I have mostly the same DNA as someone who's, who has committed the most atrocious act that you can think of. And that goes a long way to undercut that. Both the separation between the individual and the community, but also the separation we might feel from someone who's behaving in this, you know, really abhorrent way. We are connected in the same tree of life to that person.
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Cole Smead
And last, Charles Murray, a social scientist who I had a ton of fun visiting with, spent most of his life as a contented agnostic. A happy agnostic is what he termed himself and came to faith, this idea of faith almost against his will and really late in life, alongside of his wife, he has a name for the leftover thing. You make the point. And I, you know, I've heard this
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Interviewer/Host Assistant
term used a lot. You know, people talk about God sized holes.
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Cole Smead
You'll hear people explain that in their own personal experience.
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Interviewer/Host Assistant
But you use it differently in your book. You say that in your eyes, modernity has hidden God sized holes. In other words, it's, it's the thought, it's the cultural view that's covering that up.
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Cole Smead
Can you explain that?
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Charles Murray
Well, I think it has to do with Bill, your father, of not getting serious about religion until he had serious cancer. We in the 21st century and basically in the 20th century as well, sort of assume we've got 80, 90 years coming. I mean, odds are we're going to live that long. We're probably going to live pretty healthy lives. Yeah, we know people who don't do that. But, but there is kind of an assumption we're going to be okay. Just the same as in wartime. Soldiers assume I'm going to be okay even though everybody else around me dies.
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Bill Smead
Sure.
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Charles Murray
And in doing that, you are enabled to put off all sorts of things that you're more comfortable putting off, such as the prospect of death, such as taking the serious questions about human existence and the human condition seriously. And in addition to being protected from a lot of the miseries that people had before the 19th, 20th century, their children dying, very commonly, a spouse dying, very commonly, terrible diseases that left you helpless and couldn't be cured, we've also developed in the 20th century the ability to amuse ourselves constantly. And that's grown by leaps and bounds in the 21st century where you can entertain yourself 247 with no problem at all.
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Cole Smead
Correct.
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Charles Murray
Right. So in all of those situations you can kind of shove the big questions out of your mind. And a crisis, such as a health crisis, such as the death of a loved one, is one of the few ways remaining in which you are driven to the depths of despair. And, and in that depth of despair you discover you do have a God sized hole after all. You're not making it up. The hole was there all along, but you were never forced to recognize it before. I didn't hear it until the mid-1990s and I heard it from Charles Krauthammer, the late columnist.
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Cole Smead
Sure.
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Charles Murray
And it just hit me. Why is there something rather than nothing? And I had no ready answer to that because I am a good Aristotelian, or I was, when, even when I wasn't. Religion, religious. And things have to have a creator. This had to come from somewhere. Well, where did it come from? And one way out of that, of course, is that you could say, oh, if you try to answer that question, you just enter an infinite regress. You know, it's God created it. Okay, who created God, etc. Etc.
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Cole Smead
Sure.
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Charles Murray
Well, that doesn't deny that there's a mystery here. It just means that maybe it's a mystery with a capital M instead of a small M. Sure. And that's what I came to believe. And that was one nudge that kind of pushed me off dead center in my attitude toward religion. And it said to me, you've got to take the idea of a creator seriously. That was a big step forward. At the same time, something else crossed my mind that wasn't as powerful, but it's the regularity of mathematics, the simplicity of mathematics, where you have very profound physical phenomena that can be explained by laws that give you exact results from from very simple equations. E equals MC squared is the most famous.
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Interviewer/Host Assistant
Sure.
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Charles Murray
But Newton's law of motion, they're also very simple. And same is true of many others. Mathematics is way too elegant to have been produced by chance. And that leads you to say, well, if it wasn't produced by chance, what produced it?
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Cole Smead
Four people, four completely different fields, code neurons, DNA statistics. And every one of them pointed past the machine to the same missing piece. They called it different things. A soul, emergence, grace, a God sized hole. You can call it whatever you want, but not one of them could make the human being add up without it. Keep that thread in mind because we're about to watch it show up in the single sharpest place it appears anywhere on this show. Hi, I'm Cole Smead, CEO and Portfolio Manager here at Smead Capital Management and host of this podcast. If you enjoy this podcast, I'd like to invite you to check out smeedcap.com at our firm. We are stock market investors. We advise investors who play the long game with a discipline that has proven
- [21:02]
Interviewer/Host Assistant
success over long periods of time.
- [21:04]
Cole Smead
Learn more about our funds at smeatcap. Past performance is not indicative of future results. Investing involves risks, including loss of principal. Please refer to the prospectus for important information about the investment company, including objectives, risks, charges and expenses. Read and consider it carefully before investing. Smead Funds Distributed by Smead Funds Distributors llc. Not affiliated alright, let's slow it down and let's look at the one place that question gets really concrete. The place that stopped being philosophy and starts being something you can almost touch. It's a small child learning to speak. I have four kids myself, so I think about this as they're growing up and their brains are learning to learn. In effect, nobody programs the child. It's working from almost nothing. A few years of messy half heard conversations. We think of babbling we think of the weird words that kids use in lieu of the words that we would. And what they pull off is something, you know, maybe our largest, most expensive machines have trouble to even match. We think of all the progress of technology to get where we've been, and they have matched that linguistically. But yet it took data and energy and time and money. Our children do require some of that, but not to the same level. One man built an entire career insisting that that proving something is wired into us from birth. Here's that argument and the rebuttal. I'm really blown away that when Noam Chomsky asked a question 70 years ago, it's still reverberating, I would argue, and it's something that's really affected me personally. A small child learns to speak from a tiny, messy sample of language effectively. They have a couple parents framing that and others around them and in a stunningly short window. And every child does it the same way, regardless of how smart they are. Intelligence is not the main factor, Chomsky said no system that just learns word by word could pull that off. In other words, it's not just the words themselves, it is the phrase, and it's the formatting of language and how we communicate with each other. Something that has to be built in. Today, our biggest AI models learn language from something like 50,000 years worth of speech. A toddler does it on a rounding error of that. So who's right about what language and the mind actually is? Here's the debate. In three voices, Tom Griffith lays out Chomsky's actual argument, why a simple word association model of language was never going to work.
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Interviewer/Host Assistant
We're kind of back to the Wilkins idea, if you will, which Chomsky tried to tackle. What was novel about Chomsky's work at that time?
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Tom Griffiths
Chomsky was a young linguist who had a very different view of what linguistics was from all of the other linguistics at the time. Linguists at the time. So for a lot of the other linguists at the time, what it meant to be a linguist was you were going out and you were collecting information about what languages are like, and then maybe thinking about what some of the correspondences were across these languages, what some of the general principles were that characterized human languages.
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Cole Smead
Sure.
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Tom Griffiths
And Chomsky wanted something which was different from that. He wanted to have a way of characterizing. If you had a single language, could you come up with something like a mathematical system that would allow you to characterize all of the sentences that were grammatical statements in that language? So trying to create a formal system that gives you a way of producing all the sentences that are in that language and nothing else. And so this approach, which is called generative grammar, led him to then ask this question, what kinds of formal systems do you need in order to capture the structure of human languages? And so he was able to show that some simple kinds of systems that were like things that the behaviorists and the information theorists had assumed would be sufficient to capture language weren't going to work. And in fact, you needed something much richer that had a sort of internal structure, like ideas like noun phrases and verb phrases and so on, that then could capture the structure that sentences have in order to get even close to the structure of English.
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Interviewer/Host Assistant
Sure.
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Cole Smead
Explain what's a finite state language versus
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Interviewer/Host Assistant
something that's not a finite state language.
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Tom Griffiths
So this is the distinction that Chomsky was focused on in terms of ruling out a sort of simple model. So a finite state language. You could think about it again, going back to our board game analogy as a language that you could generate just by creating a board game where a bunch of positions that you're playing, pieces can be in, and you have moves that can take you from one position to another. And then as you move into each position, you produce a word. Right. So I go from the start position to the next position, I produce the word the I go to another position, I produce the word dog, and so on. And then maybe I go from that position to the end of the game, and I produce the word runs. And now I produced a sentence the dog runs.
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Interviewer/Host Assistant
So.
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Tom Griffiths
Sure. That structure allows you to capture certain things about language. Right. You can imagine now you can capture the fact that you can have sentences like the dog runs and the dogs run, and you're reusing parts of the board. Right. When you're producing those sentences, there's not a unique trajectory that you have to follow for every sentence. And so it allows you to capture some of the structure that exists in language. And for the behaviorists, that was nice because it suggested that you could learn language just by learning associations between words.
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
So you could learn that after dog, then you're more likely to produce the word runs, Right?
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
And after dogs, you're more likely to produce the word run, and you could sort of learn these associations between things. And it was good for the information theorists because they wanted to be able to estimate the probability of different pairs of words occurring next to one another. And so that's something which, again, is like sort of forming these associations between words. And so A finite state language is a language which can be captured just by creating a board game like that.
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Interviewer/Host Assistant
Teach us about induction. And who tried to prove and test the needs of induction.
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Tom Griffiths
So the challenge that Chomsky ran into. So he showed that English was not a finite state language. There are sort of structures in English which are richer than the kinds of things that you can capture using those simple board games. And as he increased the complexity of. Of his understanding of what language was, you start to run into a problem which is if language is something nice and simple, then there's a story about how you learn language, which is you just learn the associations between successive words, and you can kind of string them together, and you've learned how to produce sentences. But the more complex language turns out to be, the harder it's going to be for you to learn language.
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
And Chomsky was a sort of skeptic about the possibility that children could learn language from the data that they saw and said that you needed to have some sort of strong constraints, what he called universal grammar, on the kinds of things that you could learn. But part of that was that he was trying to think about learning a language in a way that looks a lot like logic. So his kind of intuitive idea of what it means to learn a language is you hear enough sentences that you get to the point where you know exactly what the language is. And that was the way that sort of early on, people were thinking about formalizing learning. And that misses the point that, in fact, many of the things that we do are things that we do without certainty.
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Interviewer/Host Assistant
Sure.
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Tom Griffiths
So logic is the math of how you go from things that you know to be true to things that you know can conclude are also true. Right. So you're sort of doing that with certainty. And we call that deductive problems that have that kind of structure. But a lot of the things that we do are things where we go from some data that we see to some uncertain, underdetermined hypothesis. And that's a problem of induction. And that's something that philosophers have sort of grappled with for hundreds of years in terms of trying to characterize how would you describe that process of inductive inference mathematically? And can you give it the same kind of precision that we give to deductive inference?
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Cole Smead
Now it's time for the rebuttal. Gaurav Suri represents the other camp entirely. The people who think Chomsky got it backwards, that language isn't the. This magic grammar engine at all or something. Incredible.
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Gaurav Suri
This goes to a debate about what language is, Right, So syntax means following grammar, right? Syntax means that the language is determined by the rules of grammar. And then if you believe that that's how language is created, then one has to sort of answer the question, where do these rules come from? And if you ask people, philosophers and linguists, they will say something like, they came about in human evolutionary heritage. There's a different way to think about language, which is language is for the communication of ideas. That, yes, syntax emerges. But syntax, just like consciousness, need not always drive intelligence. Syntax does not need to drive language. We care about the meaning of things, right? So let me give you an example. Cole put the wallpaper on the table, then he put his coffee cup on it. And what does it refer to? Well, it obviously refers to the table because you put the wallpaper on it, then you put the. Your coffee cup on the table. But if I say Cole put the wallpaper on the wall, then he put his coffee on it. Now all of a sudden the grammar is exactly the same, but the second produces a mismatch because of we put meaning in things like we are embodied creatures. We know that wallpaper is on horizontal things. And you can't put coffee cups on horizontal things because. Because. And that's semantics, right? Semantic means having to do with meaning, and syntax mean having to do with grammar and structure. And the point of this debate about syntax and semantics is that, you know, many of us, myself included in the neural network tradition of the mind, which we explain in the emergent mind, believe that its meaning, language is invented for the communication of ideas rather than the output of this magical seeming grammatical syntax engine. Sure, that's the key distinction.
- [31:40]
Cole Smead
Griffiths hears something almost divine in what that child does. A structure we couldn't have earned from the data alone. Siri says no, it's simpler than that. Language is just meaning and the grammar takes care of itself. Same child, two completely different universes of thought. I'm not going to tell you who's right, but notice that whichever way you lean, you just said something about what you think a human being fundamentally is. We hope you're enjoying the podcast. You know, we work hard putting together this show, but we work even harder for our investors at Smead Capital Management. At smead, we believe in disciplined investing, which is why the SMEAD funds have a proven track record of long term outperformance. If you're an investor who plays the long game and want to invest in wonderful companies to build wealth, we invite you to visit smeedcap.com Past performance is not indicative of future results. Investing involves risks, including loss of principal. Please refer to the prospectus for important information about the investment company, including objectives, risks, charges and expenses. Read and consider it carefully before investing. Smead funds distributed by Smead Funds Distributors, llc. Not affiliated. Notice what's been sitting underneath that whole argument. The machine, the compute power, the AI. We keep using it as a measuring stick. The child does what the machine can't. So let's stop talking about what it proves about us and turn and look straight at the thing itself. What actually is this? A new kind of mine. A new neural network. A very good tool. Maybe like the hammer was at one point, or just the latest thing the whole world has agreed to lose its head over. Okay, let's strip away the hype for a second. What is artificial intelligence really? A new kind of mind, A very good tool, or the latest in a long line of medias? I've asked a law professor, a neuroscientist, and a cognitive scientist what kind of thing it actually is. And then I want to put it next to something my own father, Bill Smead, our chief investment officer, has been saying for years about every boom that ever was. One of these guests calls the money behind AI a religion. One says the machine has no soul. One says it even isn't the same kind of mind. And then history gets a vote. First, Tim Wu. He coined the term net neutrality and has studied tech platforms his whole career. I asked him about the hundreds of billions of dollars pouring into AI right now, and he reached for a word I didn't expect.
- [34:24]
Tim Wu
I just want to. Folks, pick up on your godhead comment right now for a second because, you know, the amount of capex going into AI right now is everyone knows here is massive. You know, it's like, you know, 400, I think 400 billion last year or something like that. The weird part about those investments is when you get deep into them and you listen carefully, there is a lot of what I think cannot be described differently than as religious faith in it. That this.
- [34:51]
Interviewer/Host Assistant
Oh, we agree.
- [34:51]
Tim Wu
Yeah. That this thing is going to come, you know, there's going to be this thing. I mean, some of this I was reading, I was listening to one of the chief scientists and he was talking about in artificial general intelligence and what's, you know. And it started like we're gonna do. And then it started. And then it started going into. Well, in 10 years we're gonna, you know, cure all these diseases and then we're gonna, you know, prolong life. And he said then the final Step is we need to go out into the galaxy and colonize the rest of it. And I was like, wow. I mean, we're pretty far from that. When chatgpt is, you know, give me advice on my, like, relationships.
- [35:25]
Cole Smead
Wu says it's religious faith. His words, not ours. Now, a neuroscientist, Gaurav suri on the mistake he thinks almost everyone is making about what these machines actually are.
- [35:41]
Gaurav Suri
So right now, if you go to Gemini or Claude or ChatGPT and ask them, hey, are you sentient? It'll say, no, no, I'm just a large language model. But at that time, that's because somebody's given it that rule when it's in the model, in the model that's already there. But when Blake was working with these models, there was no rule, right? So he would ask the thing, are you sentient? And say, of course I am. And he'll say, what are you afraid of? I'm afraid of being turned off. So he had hours of conversations with these machines, and he came up with the view that these things were sentient. Like a sweet little child, he called them, who wants to help everybody? And he, of course, Google removed him from his job and they said that, no, these models are not sentient. They're word associating machines. I have a lot of sympathy for Blake because I understand the mistake. The mistake is to conflate intelligence with consciousness. So Blake found a machine that was intelligent. No doubt about it. He would give it puzzles and it'll give it beautiful. So it was intelligent. And so we think that our intelligence comes from our consciousness. So Blake's assumption was that something this intelligent must be conscious. It's not conscious, it's intelligent. Large language models are far from human cognition in many ways, they exceed human cognition in some ways, but in some ways, they're not even as intelligent as a mosquito. And in some ways, they exceed what humans can do. So they are intelligence because of that. Their intelligence comes from their neural network, just as our intelligence does. But our intelligence is a much more flexible and different neural network than the neural network of a large language model.
- [37:26]
Cole Smead
Let me ask you kind of a weird question. Have we built machines before that we didn't necessarily understand?
- [37:33]
Gaurav Suri
Okay, so it's true that we don't understand the details of what a large language model is doing, meaning, because it has billions of these units. So we can't say, now this, now that unit. It's, in this sense, it's emergent units are activating each other through their connections. Connections are Getting built with learning. We are surprised by many of the things that a large language model does. But in principle, we fully understand the large language model. We trained it on a lot of data and it was able to capture these properties. So what we don't understand is the specifics of how it was able to generalize from its training data to be able to make sensible proclamations in data that it wasn't trained on. We don't precisely understand that. We understand it in principle, but not precisely. Just as we understand some medicines in principle. This is your question, which is, have we ever made machines that we don't fully understand? Yeah, medicine, like medicine, sometimes work and we, we don't necessarily know the full details of why they work. We, we try the medicine, it, it happens to work. Many medicinal discoveries are with trial and
- [38:53]
Cole Smead
error or side indications where we find something out from the compound that wasn't in the original indication.
- [39:01]
Gaurav Suri
Yeah, like Viagra was like that, right? Viagra was for heart.
- [39:04]
Interviewer/Host Assistant
Heart medicine. Yeah.
- [39:05]
Gaurav Suri
Hard medicine. And all of a sudden people are, hey, doc, can I have some more of this?
- [39:09]
Cole Smead
Right, yeah, that actually happened in the 80s. My dad had a stock brokerage client who was a friend of my grandfather's and he's like, hey, you wouldn't believe what this medicine does.
- [39:18]
Gaurav Suri
Exactly. So I think the human experience is like that. Like many times we'll come up with things that have behavior that we can't fully pin down the way we can pin down a geometric theorem. But nevertheless, we do understand them in principle at a high level, but not necessarily at the granular level?
- [39:38]
Cole Smead
Sure it is. So let me ask a little bit
- [39:41]
Interviewer/Host Assistant
different to this kind of question we're
- [39:43]
Cole Smead
around is one of my better tasks, or maybe my spouse is, my wife,
- [39:49]
Interviewer/Host Assistant
we're working together on something.
- [39:51]
Cole Smead
Is one of her better tasks to guess what I'm about to say, or is her better task is to complement what I'm thinking about?
- [39:59]
Gaurav Suri
Yeah. Interesting. So now you're getting into the nature of what's desirable in human relationships. Right.
- [40:09]
Cole Smead
Because the large language model might do best at guessing what I'm about to say.
- [40:13]
Gaurav Suri
Exactly. So here's another version of your question. Can one fall in love with a large language model? Well, some people can. I can't. I'll tell you why I can't. Because I want the shared experience of having a body, feeling emotion, feeling one with nature, feeling awe, feeling grace. I want that. That's a big part of what makes me me. And if I knew that the thing was just simulating me or simulating Empathy, even if it had a body like a woman's body. Right. Even if I knew that it's not real in some sense, it didn't share my feelings for me. That's a non starter and it may be unreasonable. But hey, I'm a human. I get to. I'm a human chauvinist, by the way. I like humans and I'm going to
- [41:10]
Interviewer/Host Assistant
use that, by the way. That's a great term. I'm a human chauvinist.
- [41:12]
Gaurav Suri
I'm a humanist, totally. I mean, you know, some people talk about rights for machines and all that. I'm like, no, no, no, these things are tools and let's keep them as tools. I'm a human chauvinist and I love other humans and it's because I understand that we have a shared experience. So I'm not, I'm not that impressed by. Well, at some levels it's very impressive about pattern extraction and, and it's a key aspect of intelligence. But if it's about love and empathy and relationships, no, that doesn't cut it for me.
- [41:45]
Cole Smead
You drive your car often getting to a place and you don't know how you got there. It proves that intelligence is, is not consciousness. And finally, Tom Griffiths, who tell you the whole framing of the machine is getting smarter than us is wrong from the start.
- [42:03]
Tom Griffiths
So where I would start with this, I mean, so when people talk about the Singularity, they're talking about this idea that you're going to make an AI system that's then smart enough to modify its own code to make itself smarter and sort of like zoom off right into super intelligence. The way that I normally talk about this is in terms of, I think there are assumptions that we make when we talk about things like superhuman AI, that intelligence is like this one dimensional axis, right. And AI is getting smarter and smarter and then at some point it's going to get past us and then it's going to be smarter than us.
- [42:32]
Cole Smead
Yeah.
- [42:33]
Tom Griffiths
And I don't think that's a constructive way of thinking about what intelligence is. Just for the reasons that I told you about inductive bias. Right. The kinds of solutions that AI finds are different from the kinds of solutions that humans find. And so I would think about this as these are really two different kinds of intelligence. In a way that means that they can be complementary to one another and in a way that we might expect that there would still be some meaningful differences even as we make AI sort of smarter in the way that it's smart at the moment. One way of thinking about that that maybe engages with some of the points that you were bringing up earlier is, I think, about the fundamental thing that characterizes human intelligence in this realm, as we're making smarter and smarter machines, is being more about our limitations. Sure. So as biological organisms with limited lifetimes, with only a certain amount of compute that we can carry around inside our heads, and with a constraint that we can only share data and ideas with one another by making noises with our mouths or wiggling fingers, those are all constraints on how human minds work, and they're things that have shaped human minds. The reason we're able to learn from small amounts of data is that that's all we're going to get. Right. And the reason why we are able to be efficient in the way that we use our computational resources is that we have to be, because we're going to use those same computational resources to do everything that we're going to do.
- [43:50]
Interviewer/Host Assistant
Sure.
- [43:50]
Tom Griffiths
And the reason we're good at coming up with ways of communicating with one another and sharing information and building societies and doing all these kinds of things, is that we have to if we want to achieve anything, which is more than what we can do as individuals. And so those are sort of a. A humanistic perspective, but it's one which is emphasising the constraints that shape humanity and AI systems, to the extent that they're not subject to those same constraints, we're going to find different solutions that are not going to be like the ones that we find. And so it might be that they're able to keep iterating and keep sort of getting smarter. But even as they do that, I think they're going to be sort of doing it in a way that's maybe becoming increasingly alien, increasingly different from us, rather than more like us.
- [44:35]
Cole Smead
So there's the mine versus tool question, but there's a third possibility we shouldn't skip that. Whatever AI turns out to be, the way we're investing in it is just the oldest story in markets. Here's my dad and business partner, Bill, smead on what every mania in history
- [44:50]
Interviewer/Host Assistant
has had in common.
- [44:53]
Bill Smead
You know, I hope everybody knows that in 1636 you could trade one fine tulip board bulb for a house, a fine carriage and two good horses. About 700 or $800,000 in today's dollars. So as we watch in the markets right now, bitcoin rollover and various manias, it's just great to step back into the history and just make sure you understand the timelines and you understand the.
- [45:26]
Gaurav Suri
The
- [45:28]
Bill Smead
Difference. There are differences, but there are rhymes. And we have to know those rhymes. Our job as we look after other people's money.
- [45:39]
Cole Smead
What's the name of the book, though?
- [45:40]
Charles Murray
Oh.
- [45:40]
Bill Smead
Boom and Bust A Global History of Financial Bubbles by William Quinn and John D. Turner.
- [45:45]
Cole Smead
And we've had William Quinn and John
- [45:47]
Interviewer/Host Assistant
D. Turner on the podcast.
- [45:49]
Cole Smead
They use like a triangle.
- [45:51]
Interviewer/Host Assistant
They explain it kind of like a fire.
- [45:53]
Cole Smead
Their view was that you have the initial spark, you have to have oxygen.
- [45:59]
Interviewer/Host Assistant
And so what provides the oxygen?
- [46:00]
Cole Smead
Historically, they argued leverage was a way to provide oxygen. Borrowed money, Tulips, Florida land chip stocks. The same rhyme every time. So let's land it right on AI. Justin Baer wrote the history of Fidelity, and he told me about the morning a stock called Netscape went public back in 1996, and then said the part that should make every one of us sit up and lean forward.
- [46:28]
Justin Baer
This company, this was still, you know, it was only at that point, maybe 2 years old, run by this guy who was, you know, still in his 20s called Netscape, which was for that moment, you know, as it turns out, sort of a relatively brief moment in time, but was the most popular web browser that everyone was just starting to use. Here comes this opportunity to invest not only in this company that you never heard of three months earlier, but in the Internet itself. So it was a moment and people were extremely excited about it. And so the scene that I wanted to work in and I just ended up taking maybe a little bit too long to get to it was,
- [47:12]
Cole Smead
you
- [47:12]
Justin Baer
know, at that point, Fidelity has branches, retail branches in most big cities around the country. And there's this one in, in Austin, in this Austin, Texas, in this, just this, you know, this strip mall. Not, you know, high traffic area at all. And this, this young associate of Fidelity shows up to unlock the door this, this one day. And when he gets there like 8 in the morning, the. There's already a line, you know, waiting outside the office. And he's like, you know, he just sort of looks at it and he's, you know, doesn't know what was going on. And so he unlocks the door, you know, and with any, any business, you know, they sort things out and everyone kind of gets in place. And he's sitting there at the front desk and when the door opens at 9 or 8:30 and the first customer races up to the front desk and it's like, okay, is it too late? I really want to buy stock in Netscape. It's IPOs today. And he looks at the guy and he just Sort of says Annette. What? He had never heard of this company and didn't really expect this to happen. It turns out all those people waiting outside were just eager to get their hands on this stock. Right? And to me, it just really. Exemplified that sort of moment in time and the enthusiasm that everyday investors had about the Internet and what was coming. And I've been thinking a lot about that, that period and that scene because, you know, we will probably get, you know, this shades of that some point, maybe as early as later this year, right, when you start to see all the AI companies go public. And you know, the AI is not the Internet. Right there, you know, people are maybe not as excited about what AI is going to bring to their lives personally, but. But they know it's a big deal, right? And here is this opportunity once again to own a piece of it.
- [49:39]
Charles Murray
Right.
- [49:39]
Justin Baer
And I know, I just thought it was really fun, the story and like God, you know, knowing obviously from my vantage point what happens after that, the Netscape IPO day. In the years and decades to come, it was just a fun, fun thing to relive.
- [49:56]
Cole Smead
A law professor calls the money a religion. A neuroscientist says it has no soul. A cognitive scientist says it's not even the same kind of mind, that as it improves, it gets more alien, not more human. I don't know if that's what we're looking for. History says we felt this exact certainty before about tulips, railroads, dot coms, and the line out the door looked just like this. I'm not telling you AI is nothing. It seems to be something. I'm saying the hold the mind, the tool and the mania has on us all at the same time. Might be the conspicuous part. Well, there you have it. Three big conversations across many authors and one thread running through all of it. Whether we were talking about a child learning to speak, a geneticist reading our DNA, or a neuroscientist building artificial minds, the same question kept servicing, can you explain a human like you can a machine? Or is there something always left over? Every guest bumped into that leftover thing and reached for a different word for it. A soul. Emergence, grace, A God sized soul. They all had different words and I don't think they all have a tidy answer. I think this is a great question that people have to put their neck out there and answer themselves. I will leave that up to the listeners. But what I do love about this discussion is that this is a liberal arts podcast. We're learning to learn. And I think these questions really prove that we have to do that individually. If you enjoyed this episode, go to Apple, Spotify, YouTube, wherever you listen to A Book With Legs, give us review and tell others about the books and the great authors that we have the opportunity to understand and study the world with and through for our tribe. If you have a great book that you like to recommend, email podcastmeedcap.com that's podcastmeedcap.com you can also send your suggestions to us on X. Our handle is meedcap. Thank you for joining us for A Book with Legs podcast. We look forward to the next episode.
- [51:55]
Podcast Announcer
Thank you for listening to A Book With Legs, a podcast brought to you by Smead Capital Management. The material provided in this podcast is for informational use only and should not be construed as investment advice. You can learn more about Smead Capital Management and its products@smead meadcap.com or by calling your financial advisor.
- [52:36]
Sam
Sam.