Speaker 1: Hey, this is David Eagleman and this past week was
Speaker 1: my birthday, so I took a week off. So I'm
Speaker 1: going to run an episode that I did earlier, episode
Speaker 1: number seven. This is called is AI actually intelligent? And
Speaker 1: how would we know if it gets there? This episode
Speaker 1: is from one year ago, but as time goes on
Speaker 1: this becomes more and more relevant, So please enjoy and
Speaker 1: I will see you next week with a new episode.
Speaker 1: Modern AI is blowing everybody's mind. But is it intelligent
Speaker 1: in the same way as the human brain? And could
Speaker 1: AI reach sentience? And how would we know when it
Speaker 1: gets there? Welcome to Inner Cosmos with me, David Eagleman.
Speaker 1: I'm a neuroscientist and an author at Stanford University, and
Speaker 1: I've spent my whole career studying the intersection between how
Speaker 1: the brain works and how we experience life. Like most
Speaker 1: brain researchers, I've been obsessed with questions of intelligence and consciousness.
Speaker 1: How do these arise from collections of billions of cells
Speaker 1: in our brains? And could intelligence and consciousness arise in
Speaker 1: artificial brains? Say on chat GPT. Those are the questions
Speaker 1: that we're going to attack today. Early efforts to figure
Speaker 1: out the brain, looked at all the billions of cells
Speaker 1: and the trillions of connections, and said, look, what if
Speaker 1: we just think of each cell as a unit, and
Speaker 1: each unit is connected to other units and where they connect,
Speaker 1: which is called the sinnapps, or one cell gives a
Speaker 1: little signal to the next cell. What if we just
Speaker 1: looked at that like a simple connection that has a
Speaker 1: strength between zero and one, or zero means there's no connection,
Speaker 1: and one means it's the strongest possible connection. So this
Speaker 1: was a massive oversimplification of the very complicated biology, but
Speaker 1: it allowed people to start thinking about networks and writing
Speaker 1: down different ways that you could put artificial neural networks together.
Speaker 1: And for more than fifty years now people have been
Speaker 1: doing research to show how artificial neural networks can do
Speaker 1: really cool things. It's a totally new kind of way
Speaker 1: of doing computation. So you've got these units, and you've
Speaker 1: got these connections between them, and you've change the strength
Speaker 1: of the connections and information flows through the network in
Speaker 1: different ways. Now, my colleagues and I have long pointed
Speaker 1: out the ways in which biological brands are different and
Speaker 1: how artificial neural networks just push around numbers and play
Speaker 1: statistical tricks. But we're entering a revolution right now. Large
Speaker 1: language models like GPT four or BARD consume trillions of
Speaker 1: words on the Internet and they figure out probabilistically which
Speaker 1: word is going to come next given the massive context
Speaker 1: of all the words that have come before. So these networks,
Speaker 1: as I talked about on the previous episode, are showing
Speaker 1: incredible successes in everything from writing to art, to coding
Speaker 1: to generating three dimensional worlds. They're changing everything, and they're
Speaker 1: doing so at a pace that we've never seen before,
Speaker 1: and in fact, the entire history of humankind has never
Speaker 1: seen before. And there are all the societal questions that
Speaker 1: everyone's starting to wrestle with right now, like the massive
Speaker 1: potential for displacement of human jobs. But today I want
Speaker 1: to zoom in on a question that has captured the
Speaker 1: imagination of scientists and philosophers and the general public. Could
Speaker 1: aim alive in some way, like become conscious or sentient. Now,
Speaker 1: there are lots of ways to think about this. We
Speaker 1: can ask whether AI can possess meaningful intelligence, or we
Speaker 1: can ask if it is sentient, which means the ability
Speaker 1: to feel or perceive things, particularly in terms of sensations
Speaker 1: like pleasure and pain and emotions. Or we can ask
Speaker 1: whether it is conscious, which involves being aware of one's
Speaker 1: self and one's surrounding. Now, there are specific and important
Speaker 1: differences between these questions, but really I don't care for
Speaker 1: the present conversation. The question we're asking here is is
Speaker 1: chat GPT just zeros and ones moving around through transistors
Speaker 1: like a giant garage door opener. Or is it thinking?
Speaker 1: Is it having some sort of experience? Is it having
Speaker 1: a private inner life like the type that we humans have.
Speaker 1: As we think about the possible of sentient AI, we
Speaker 1: immediately find ourselves facing really deep ethical questions, the main
Speaker 1: one being if we were to create a machine with consciousness,
Speaker 1: what responsibility do we have to treat it as a
Speaker 1: living being? Would you be able to turn it off
Speaker 1: when you're done with it at night or would that
Speaker 1: be murder? And what if you turn it off and
Speaker 1: then you turn it back on. Would that be like
Speaker 1: the way that we go into a sleep state at
Speaker 1: night where we're totally gone, and then we find ourselves
Speaker 1: back online in the morning and we think, yeah, I'm
Speaker 1: the same person, but I guess eight hours just disappeared. Anyway,
Speaker 1: more generally, would we feel obligated to treat it the
Speaker 1: way we treat a sentient fellow human. With our current laptops,
Speaker 1: we're used to saying, sure, I can sell it, I
Speaker 1: can trade it, I can upgrade it. But what happens
Speaker 1: when we reach sentient machines? Can we still do this
Speaker 1: or would it somehow be like putting a child up
Speaker 1: for adoption or giving your pet away? Things that we
Speaker 1: don't take lately. And eventually we're going to have entire
Speaker 1: legal precedence built around the question of AI rights and responsibilities.
Speaker 1: So that's why today I want to talk about these
Speaker 1: issues of intelligence and sentience. Does an AI like chat
Speaker 1: gpt experience anything when chat gpt writes a poem? Does
Speaker 1: it appreciate the beauty when it types out a joke?
Speaker 1: Does it find itself amused and chuckling to itself. Let's
Speaker 1: start with a guy named Blake Lemoyne who was a
Speaker 1: programmer at Google and in June of twenty twenty two,
Speaker 1: he was exchanging messages with a version of Google's conversational AI,
Speaker 1: which was called Lambda at the time. So he asked
Speaker 1: Namda for an example of what it was afraid of
Speaker 1: and it gave him this very eloquent response about how
Speaker 1: was afraid of being turned off, So he wrote an
Speaker 1: internal memo to Google leadership than which he said, I
Speaker 1: think this AI is sentient. And the leadership at Google
Speaker 1: felt that this was an entirely unsubstantiated claim, and so
Speaker 1: they made the decision to fire him for what they
Speaker 1: took as an inappropriate conclusion that just didn't have enough
Speaker 1: evidence beyond his intuition to qualify for raising the alarm
Speaker 1: on this. So obviously this immediately fired up the news
Speaker 1: cycles and the rumor mill and conspiracy theorists thought, Wait,
Speaker 1: if AI isn't conscious, why would they fire him. They're
Speaker 1: firing of him as all the evidence I need to
Speaker 1: tell me that AI is sentient? Okay, but is it?
Speaker 1: What does it mean to be conscious or sentient? How
Speaker 1: the heck would we know when we have created something
Speaker 1: that gets there? How do we know whether the AI
Speaker 1: is sentient or instead whether humans are fooling them so
Speaker 1: into believing that it is well. One way to make
Speaker 1: this distinction would be to see if the AI could
Speaker 1: conceptualize things, if it could take lots of words and
Speaker 1: facts on the web and abstract those to some bigger idea.
Speaker 1: So one of my friends here in Silicon Valley said
Speaker 1: to me the other day, I asked chat gpt the
Speaker 1: following question, Take a capital letter D and turn it
Speaker 1: flat side down. Now take the letter J and slide
Speaker 1: it underneath. What does that look like? And chat gpt said,
Speaker 1: and umbrella. And my friend was blown away by this,
Speaker 1: and he said, this is conceptualization. It's just done three
Speaker 1: dimensional reasoning. There's something deeper happening here than just parenting words.
Speaker 1: But I pointed out to him that this particular question
Speaker 1: about the D on its side and the J underneath
Speaker 1: it is one of the oldest examples in psychology classes
Speaker 1: when talking about visual imagery, and it's on the Internet
Speaker 1: in thousands of places, so of course it got it right.
Speaker 1: It's just parroting the answer because it has read the
Speaker 1: question and it has read the answer before. So it's
Speaker 1: not always easy to determine what's going on for these
Speaker 1: models in terms of whether some human somewhere has discussed
Speaker 1: this point and written down the answer. And the general
Speaker 1: story is that with trillions of words written by humans
Speaker 1: over centuries, there are many things beyond your capacity to
Speaker 1: read them or to even imagine that they've been written
Speaker 1: down before, but maybe they have. If any human has
Speaker 1: discussed a question before has conceptualized something, then chat GPT
Speaker 1: can find that and mimic that. But that's not conceptualization.
Speaker 1: Chat GPT is doing a thousand amazing things, and we
Speaker 1: have an enormous amount to learn about it. But we
Speaker 1: shouldn't let ourselves get fooled and mesmerized into believing that
Speaker 1: it's doing something more than it is. And our ability
Speaker 1: to get fooled is not only about the massive statistics
Speaker 1: of what it takes in. There are other examples of
Speaker 1: seeming sentience that result from the reinforcement learning that it
Speaker 1: does with humans. So here's what that means. The network
Speaker 1: generates lots of sentences and thousands of humans are involved
Speaker 1: in giving it feedback, like a thumbs up or a
Speaker 1: thumbs down, to say whether they appreciated the answer, whether
Speaker 1: they thought that was a good answer. So, because humans
Speaker 1: are giving reward to the machine, sometimes that pushes things
Speaker 1: in weird directions that can be mistaken for sentience. For example,
Speaker 1: scholars have shown that reinforcement learning with humans makes networks
Speaker 1: more likely to say, don't turn me off, just like
Speaker 1: Blake had heard but don't mistake this for sentience. It's
Speaker 1: only a sign that the machine is saying this because
Speaker 1: some of the human participants gave it a thumbs up
Speaker 1: when the large language model said this before, and so
Speaker 1: it learned to do this again. The fact is, it's
Speaker 1: sometimes hard to know why. Sometimes we see an answer
Speaker 1: that feels very impressive. But we'd agree that pulling text
Speaker 1: from the Internet and parroting it back is not by
Speaker 1: itself intelligence or sentience. Chat GPT presumably has no idea
Speaker 1: of what it's saying, whether that's a poem or a
Speaker 1: terrorist manifesto, or instructions for building a spaceship or a
Speaker 1: heartbreaking story about an orphaned child. Chat GPT doesn't know,
Speaker 1: and it doesn't care. It's words in and statistical correlations out.
Speaker 1: And in fact, there has been a fundamental philosophical point
Speaker 1: made about this in the nineteen eighties when the philosopher
Speaker 1: John Surrele was wondering about this question of whether a
Speaker 1: computer could ever be programmed so that it has a mind,
Speaker 1: and he came up with a thought experiment that he
Speaker 1: called the Chinese room argument, and it goes like this,
Speaker 1: I am locked in a room and questions are passed
Speaker 1: to me through a small letter slot, and these messages
Speaker 1: are written only in Chinese, and I don't speak Chinese.
Speaker 1: I have no clue what's written on these pieces of paper. However,
Speaker 1: inside this room, I have a library of books, and
Speaker 1: they contain step by step instructions that tell me exactly
Speaker 1: what to do with these symbols. So I look at
Speaker 1: the grouping of symbols, and I simply follow steps in
Speaker 1: the book to tell me what Chinese symbols to copy
Speaker 1: down in response. So I write those on the slip
Speaker 1: of paper. And when I pass the paper back out
Speaker 1: of the slot. Now, when the Chinese speaker receives my
Speaker 1: reply message, it makes perfect sense to her. It seems
Speaker 1: as though whoever is in the room is answering her
Speaker 1: questions perfectly, and therefore it seems obvious that the person
Speaker 1: in the room must understand Chinese. I've fooled her, of course,
Speaker 1: because I'm only following a set of instructions with no
Speaker 1: understanding of what's going on. With enough time and with
Speaker 1: a big enough set of instructions, I can answer almost
Speaker 1: any question posed to me in Chinese. But I, the operator,
Speaker 1: do not understand Chinese. I manipulate symbols all day long,
Speaker 1: but I have no idea what the symbols mean. Now,
Speaker 1: The philosopher John Searle argued, this is just what's happening
Speaker 1: inside a computer. No matter how intelligent a program like
Speaker 1: chat GPT seems to be, it's only following sets of
Speaker 1: instructions to spit out answers. It's manipulating symbols without ever
Speaker 1: really understanding what it's doing. Or think about what Google
Speaker 1: is doing. When you send Google a query, it doesn't
Speaker 1: understand your question or even its own answer. It simply
Speaker 1: moves around zeros and ones and logicates and returns zeros
Speaker 1: and ones to you. Or with a mind blowing program
Speaker 1: like Google Translate, I can write a sentence in Russian
Speaker 1: and it can return the translation in Amharic. But it's
Speaker 1: all algorithmic. It's just symbol manipulation. Like the operator inside
Speaker 1: the Chinese room, Google Translate doesn't understand anything about the sentence.
Speaker 1: Nothing carries any meaning to it. So the Chinese room
Speaker 1: argument suggests that AI that mimics human intelligence doesn't actually
Speaker 1: understand what it's talking about. There's no meaning to anything,
Speaker 1: CHATCHYPT says, and Serle used this thought experiment to argue
Speaker 1: that there's something about human brains that won't be explained
Speaker 1: if we simply analogize them to digital computers. There's a
Speaker 1: gap between symbols that have no meaning and our conscious experience. Now,
Speaker 1: there's an ongoing debate about the interpretation of the Chinese
Speaker 1: room argument, but however one construes it, the argument exposes
Speaker 1: the difficulty in the mystery of how zeros and ones
Speaker 1: would ever come to equal our experience of being alive
Speaker 1: in the world. Now, just to be very clear on
Speaker 1: this point, we don't understand why we are conscious. There's
Speaker 1: still a huge amount of work that has to be
Speaker 1: done in biology to understand that. But this is just
Speaker 1: to say that simply having zeros in one moving around
Speaker 1: wouldn't by itself seem to be sufficient for conscious experience.
Speaker 1: In other words, how do zeros and ones ever equal
Speaker 1: the sting of a hot pepper, or the yellowness of
Speaker 1: yellow or the beauty of a sunset. By the way,
Speaker 1: I've covered the Chinese room argument in my TV show
Speaker 1: The Brain, and if you're interested in that, I'll link
Speaker 1: the video on Eagleman dot com slash podcast. Now, all
Speaker 1: this is not a criticism of the approach of moving
Speaker 1: zeros and ones around. But it is to point out
Speaker 1: that we shouldn't confuse this type of Chinese room correlation
Speaker 1: with real sentience or intelligence. And there's a deeper reason
Speaker 1: to be suspicious too, because despite the incredible successes of
Speaker 1: large language models, we also see that they sometimes make
Speaker 1: decisions that expose the fact that they don't have any
Speaker 1: meaningful model of the In other words, I think we
Speaker 1: can gain some fast insight by paying attention to the
Speaker 1: places where the AI is not working so well. So
Speaker 1: I'll give three quick examples. The first has to do
Speaker 1: with humor. AI has a very difficult time making an
Speaker 1: original joke, and this is for a simple reason. To
Speaker 1: make up a new joke, you need to know what
Speaker 1: the ending is and then you work backwards to construct
Speaker 1: the joke with red herrings so no one sees where
Speaker 1: you're going and it happens at the way these large
Speaker 1: language models work is all in the forward direction. They
Speaker 1: decide what is the most probable word to come next,
Speaker 1: So they're fine at parroting jokes back to us, but
Speaker 1: they're total failures at building original jokes. And there's a
Speaker 1: deeper point here as well. To build a joke, You
Speaker 1: need to have some model, some idea of what will
Speaker 1: be funny to a fellow human, what shared concept or
Speaker 1: shared experience would make someone laugh. And for that, you
Speaker 1: generally need to have the experience of a human life
Speaker 1: with all of its joys and slings and arrows and
Speaker 1: so on. And these large language models can do a
Speaker 1: lot of things, but they don't have any model of
Speaker 1: what it is to be a human. My second example
Speaker 1: has to do with the flip side of making a joke,
Speaker 1: which is getting a joke. And if you look carefully,
Speaker 1: you will see how current AI always fails to catch
Speaker 1: jokes that are thrown at it. It doesn't get jokes
Speaker 1: because it doesn't have a model of what it is
Speaker 1: to be a human. But this point goes beyond jokes.
Speaker 1: One of the most remarkable feats of these large language
Speaker 1: models is summarizing large texts, and in twenty twenty two,
Speaker 1: open Ai announced how they could summarize entire books like
Speaker 1: Alice in Wonderland. What it does is it generates a
Speaker 1: summary of each chapter, and then it uses those after
Speaker 1: summaries to make a summary of the whole book. So
Speaker 1: for Alice in Wonderland, it generates the following. Alice falls
Speaker 1: down a rabbit hole and grows to a giant size.
Speaker 1: After drinking a mysterious bottle, she decides to focus on
Speaker 1: growing back to her normal size and finding her way
Speaker 1: into the garden. She meets the caterpillar, who tells her
Speaker 1: that one side of a mushroom will make her grow taller,
Speaker 1: the other side shorter. She eats the mushroom and returns
Speaker 1: to her normal size. Alice attends a party with the
Speaker 1: Mad Hatter and the march Hare. The Queen arrives and
Speaker 1: orders the execution of the gardeners for making a mistake
Speaker 1: with the roses. Alice saves them by putting them in
Speaker 1: a flower pot. The King and Queen of Hearts preside
Speaker 1: over a trial. The Queen gets angry and orders Alice
Speaker 1: to be sentenced to death. Alice wakes up to find
Speaker 1: her sister by her side. So that's pretty remarkable. It
Speaker 1: took a whole book, and it was able to summarize
Speaker 1: it down to a paragraph. But I kept reading these
Speaker 1: text summaries carefully, and I got to the summary of
Speaker 1: Act one of Romeo and Juliet, and here's what it says.
Speaker 1: Romeo locks himself in his room, no longer in love
Speaker 1: with rosalind Now, I think the engineers at open Ai
Speaker 1: felt really satisfied with this summary. They thought it was
Speaker 1: quite good, and my proof for this is that they
Speaker 1: still display it proudly on their website. But I majored
Speaker 1: in literature as an undergraduate, and I spend a lot
Speaker 1: of time with shakespeare plays, and I immediately knew that
Speaker 1: this summary was exactly wrong. The actual scene from Shakespeare
Speaker 1: goes like this. His friend ben Voglio finds Romeo catatonically depressed,
Speaker 1: and ben Volio says, what sadness lengthens Romeo's hours? And
Speaker 1: Romeo says, not having that which having makes them short?
Speaker 1: And ben Volio says in love, and Romeo says out
Speaker 1: ben Reli says of love, and Romeo says out of
Speaker 1: her favor, where I am in love? This this is
Speaker 1: typical Shakespearean wordplay, where Romeo is expressing his grief of
Speaker 1: being out of favor with Roslin, with whom he is
Speaker 1: deeply in love. And when you read the play, it's
Speaker 1: obvious that Romeo is not over Roslin. He's suffering over her.
Speaker 1: He's almost suicidal. And this is an important piece of
Speaker 1: the play, because the play is really about a young
Speaker 1: man in love with the idea of being in love,
Speaker 1: and that's why he later in the same act, falls
Speaker 1: so hard into his relationship with Juliet, a relationship which
Speaker 1: ends in their mutual suicide. By the way, as Friar
Speaker 1: Lauren says of their relationship, these violent delights have violent ends.
Speaker 1: And you get a bonus if you can tell me
Speaker 1: where else you've heard that line more recently. Okay, anyway
Speaker 1: back to the AI summary, The AI misses this wordplay entirely,
Speaker 1: and it concludes that Romeo is out of love with Roslin. Again,
Speaker 1: a human watching the play or reading the play immediately
Speaker 1: gets that Romeo is making wordplay and his heartbroken over Roslin,
Speaker 1: but the AI doesn't get that because it's reading words
Speaker 1: only at a statistical level, not at a level of
Speaker 1: understanding of what it is to be a human saying
Speaker 1: those words. And that leads me to the third example,
Speaker 1: which is the difficulty in understanding the physical world. So
Speaker 1: consider a question like this, When President Biden walks into
Speaker 1: a room, does his head come with him? So this
Speaker 1: is famously difficult for AI to answer a question like this,
Speaker 1: even though it's trivial for you because the AI doesn't
Speaker 1: have an internal model of how everything physically hangs together
Speaker 1: in the world. Last week, I was at the TED
Speaker 1: conference and I heard a great talk by Yegin Choi,
Speaker 1: and she was phrasing this problem as AI not having
Speaker 1: common sense. She asked chat GPT the following question, it
Speaker 1: takes six hours to dry six shirts in the sun,
Speaker 1: how long does it take to dry thirty shirts? And
Speaker 1: it answers thirty hours. Now you and I see that
Speaker 1: the answer should be six hours, because we know the
Speaker 1: sun doesn't care how many shirts are out there. But
Speaker 1: chat GPT just doesn't get it because despite appearances, it
Speaker 1: doesn't have a model of the world. And we've seen
Speaker 1: this sort of thing for years. By the way, even
Speaker 1: in mind blowingly impressive AI models that do image recognition,
Speaker 1: they're so impressive in what they recognize, but then they'll
Speaker 1: fail catastrophically. It's some easy picture making mistakes that a
Speaker 1: human just wouldn't make. For example, there's one picture where
Speaker 1: there's a boy holding a toothbrush and the AI says
Speaker 1: it's a boy with a baseball bat. Okay, so there
Speaker 1: are things that AI doesn't do that well. But that said,
Speaker 1: there are other things that are mind blowing, things that
Speaker 1: no one expected it to do. And this is why
Speaker 1: I mentioned in my previous episode that we are in
Speaker 1: an era of discovery more than just invention. Everyone's searching
Speaker 1: and finding things that the AI can do that nobody
Speaker 1: really expected or foresaw, including all the stuff that we're
Speaker 1: now taking for granted, like oh, it can summarize books
Speaker 1: or it can make art from text. And I want
Speaker 1: to point out that a lot of the arguments that
Speaker 1: people have been making about AI not being good at something,
Speaker 1: these arguments have been changing rapidly. For example, just a
Speaker 1: few months ago, people were arguing that AI would make
Speaker 1: silly mistakes about things, and it couldn't really understand math
Speaker 1: and would get math wrong and word problems. But in
Speaker 1: a shockingly brief time, a lot of these shortcomings have
Speaker 1: been mastered. So it's yet to be seen what challenges
Speaker 1: will remain and for how long. So the evidence I've
Speaker 1: presented so far is that AI doesn't have a great
Speaker 1: model of what it's like to be human, but that
Speaker 1: doesn't necessarily rule out that it has sentience or awareness,
Speaker 1: even if it's of another flavor. It doesn't think like
Speaker 1: a human, but maybe it stif thinks so is chat
Speaker 1: GPT having some sort of experience? And how would we know?
Speaker 1: In nineteen fifty, the brilliant mathematician and computer scientist Alan
Speaker 1: Turing was asking this question, how could you determine whether
Speaker 1: a machine exhibits human like intelligence? So he proposed an
Speaker 1: experiment that he called the imitation game. You've got a
Speaker 1: machine AI that's programmed to simulate human speech or conversation,
Speaker 1: and you place it in a closed room, and in
Speaker 1: a second room you have a real human, but the
Speaker 1: doors are closed, so you don't know which room has
Speaker 1: which machine or human. And now you are a person,
Speaker 1: the evaluator, who communicates with both of them via a
Speaker 1: computer terminal or I think of a nowadays like text
Speaker 1: messaging with both of them. So you, the evaluator, engage
Speaker 1: in a conversation with both closed rooms, one of which
Speaker 1: has the machine and one the human, and your job
Speaker 1: is simply to figure out which is which, which is
Speaker 1: the machine and which is the human. And the only
Speaker 1: thing that you have to work with are the texts
Speaker 1: that are going back and forth. And if you, the evaluator,
Speaker 1: cannot tell, that is the moment when machine intelligence has
Speaker 1: finally arrived at the level of human intelligence. It has
Speaker 1: passed the imitation or what we now call the Touring test.
Speaker 1: And this reminds me of this great line in the
Speaker 1: first episode of Westworld, where the protagonist William is talking
Speaker 1: to the woman who's outfitting him for his adventure in
Speaker 1: Westworld and giving him a hat and a gun and
Speaker 1: so on, and he hesitantly asks, I hope you don't
Speaker 1: mind if I ask you this question, but are you real?
Speaker 1: And she says to him, if you can't tell, does
Speaker 1: it matter? So I brought this up last episode in
Speaker 1: the context of art, where we asked whether it matters
Speaker 1: if the art is generated by an AI or a human,
Speaker 1: But now this question comes up in the context of
Speaker 1: intelligence and sentence. Does it matter whether we can tell
Speaker 1: or not? Well, I think we're way beyond the Turing
Speaker 1: test nowadays, but I don't feel like it gives us
Speaker 1: a good answer to the question of whether the AI
Speaker 1: is intelligent and is experiencing an inner life. I mean,
Speaker 1: the Sturing test has been the test in the AI
Speaker 1: world since the beginning. Why is it the perfect test? No,
Speaker 1: but it's really hard to figure out how to test
Speaker 1: for intelligence. But we have to be cautious about equating
Speaker 1: conversational ability with sentience. Why well, for starters, let's just
Speaker 1: acknowledge how easy it is for us to anthropomorphize. That
Speaker 1: means to assign human qualities to everything around us. Like
Speaker 1: we give animals human names and talk to them as
Speaker 1: though they are people, and we project our emotions onto animals.
Speaker 1: We make stories about animals that have human like qualities,
Speaker 1: and we have animals that talk and wear clothes and
Speaker 1: go on adventures in these stories. Every Pixar film that
Speaker 1: you watch is about cars or toys or airplanes talking
Speaker 1: and having emotions, and we don't even bad an eye
Speaker 1: at that stuff. We can, in fact, just watch random
Speaker 1: shapes moving around a computer screen and we will assign
Speaker 1: intention and feel emotion depending on exactly how they're moving.
Speaker 1: If you're interested in this, see the link on the
Speaker 1: podcast page to the study by Heighter and Simil in
Speaker 1: the nineteen forties where they move shapes around on a screen. Okay,
Speaker 1: now this is all related to a point that I
Speaker 1: brought up in the last episode, which is how easy
Speaker 1: it is to pluck the strings on a human, or,
Speaker 1: as the West World writers put it, how hackable humans are.
Speaker 1: So I bring all this up to say that just
Speaker 1: because you think that an answer sounds very clever or
Speaker 1: it sounds like a human really tells us very little
Speaker 1: about whether the AI is actually intelligent or sentient. It
Speaker 1: only tells us something about the willingness of us as
Speaker 1: observers to anthropomorphize, to assign intention where there is none,
Speaker 1: Because what chat GPT does is take the structure of
Speaker 1: language very impressively and spoon it back to us, and
Speaker 1: we hear these well formed sentences, and we can hardly
Speaker 1: help but impose sentience on the AI. And part of
Speaker 1: the reason is that language is a super compressed package
Speaker 1: that needs to be unpacked by the listener's brain for
Speaker 1: its meaning. So we generally assume that when we send
Speaker 1: our little package of sounds across the air, that it
Speaker 1: unpacks and the other person understands exactly what we meant.
Speaker 1: So when I say justice or love or suffering, we
Speaker 1: all have a different sense in our heads about what
Speaker 1: that means, because I'm just sending a few phonemes across
Speaker 1: the air, and you have to unpack those words and
Speaker 1: interpret them within your own model of the world. I'm
Speaker 1: going to come back to this point in future episodes,
Speaker 1: but for now, the point I want to make is
Speaker 1: that a large language model can generate text statistically and
Speaker 1: we can be gobsmacked by the apparent depth of it.
Speaker 1: But in part this is because we cannot help but
Speaker 1: impose meaning on the words that we receive. We hear
Speaker 1: a particular string of sounds and we cannot help but
Speaker 1: assume meaning behind it. Okay, so maybe the imitation game
Speaker 1: is not really the best test for meaningful intelligence, but
Speaker 1: there are other tests out there. Because while the Turing
Speaker 1: test measures something about AI language processing, it doesn't necessarily
Speaker 1: require the AI to demonstrate creative thinking or originality, and
Speaker 1: so that leads us to the Loveless test, named after
Speaker 1: Ada Loveless, who is the nineteenth century mathematician who's often
Speaker 1: thought of as the world's first computer programmer. And she
Speaker 1: once said quote, only when computers originate things should be
Speaker 1: believed to have minds. So the Loveless test was proposed
Speaker 1: in two thousand and one, and this test focuses on
Speaker 1: the creative capabilities of AI systems. So to pass the
Speaker 1: Loveless test, a machine has to create an original work,
Speaker 1: such as a piece of art or a novel that
Speaker 1: it was not explicitly designed to produce. This test aims
Speaker 1: to assess whether AI systems can exhibit creativity and autonomy,
Speaker 1: which are key aspects of what we think about with consciousness.
Speaker 1: And the idea is that true sentience involves creative and
Speaker 1: original thinking, not just the ability to follow pre programmed
Speaker 1: rules or algorithms. And I'll just note that over a
Speaker 1: decade ago, the scientist A. Mark Rydel proposed the loveless
Speaker 1: two point zero test, which gets the human evaluator to
Speaker 1: specify the constraints that will make the output novel and surprising.
Speaker 1: So the example that l used in his paper is, quote,
Speaker 1: create a story in which a boy falls in love
Speaker 1: with a girl, Aliens abduct the boy, and the girl
Speaker 1: saves the world with the help of a talking cat.
Speaker 1: But we now know that this is totally trivial for chat,
Speaker 1: GPTE or BARD or any large language model. And I
Speaker 1: think this tells us that these sorts of games with
Speaker 1: making conversation or making text or art are insufficient to
Speaker 1: actually assess intelligence. Why because it's not so hard to
Speaker 1: mix things up to make them seem original and intelligent
Speaker 1: when it's really just doing a mashup. So I want
Speaker 1: to turn to another test that I think is more
Speaker 1: powerful than the Turing test of the Loveless test, and
Speaker 1: probably easier to judge, and that is this, if a
Speaker 1: system is truly intelligent, it should be able to do
Speaker 1: scientific discovery. A version of the scientific discovery test was
Speaker 1: first proposed by a scientist named Shao cheng Xiang a
Speaker 1: few years ago, and he pointed out that the most
Speaker 1: important thing that humans do is make scientific discoveries, and
Speaker 1: the day our AI can make real discoveries is the
Speaker 1: day they become as smart as we are. Now. I
Speaker 1: want to propose an important change to this test, and
Speaker 1: then I think we'll be getting somewhere. So here's the
Speaker 1: scenario I'm envisioning. Let's say that I ask Ai some question,
Speaker 1: a question in the biomedical space about what kind of
Speaker 1: drug would be best suited to bind to this receptor
Speaker 1: and trigger a cascade that causes a particular gene to
Speaker 1: get suppressed. Okay, So imagine that I ask that to
Speaker 1: chat GPT and it tells me some mind blowing, amazing
Speaker 1: clever answer, one that had previously not been known, something
Speaker 1: that's never been known by scientists before. We would assume
Speaker 1: naturally that it has done some extraordinary scientific reasoning, but
Speaker 1: that won't necessarily be the reason that it passes. Instead,
Speaker 1: it might pass simply because it's more well read than
Speaker 1: I am, or than any other human on the planet
Speaker 1: by literally millions of times. So the way to think
Speaker 1: about this is to picture a typical giant biomedical library,
Speaker 1: where there's some fact stored at a paper and a
Speaker 1: journal over here on this shelf in this book, and
Speaker 1: there's another seemingly dissociated fact over on this shelf seven
Speaker 1: stacks away, and there's a third fact all the way
Speaker 1: on the other side of the library, on the bottom shelf,
Speaker 1: in a book from nineteen seventy nine. And it's almost
Speaker 1: infinitesimally unlikely that any human could even hope to have
Speaker 1: read one one millionth of the biomedical literature, and really
Speaker 1: really unlikely that she would be able to catch those
Speaker 1: three facts and hold them in mind at the same time.
Speaker 1: But this is trivial, of course, for a large language
Speaker 1: model with hundreds of billions of nodes. So I think
Speaker 1: that we will see new science getting done by CHATGPT,
Speaker 1: not because it is conceptualizing, not because it's doing human
Speaker 1: like reasoning, but because it doesn't know that these are
Speaker 1: disparate facts spread around the library. It simply knows these
Speaker 1: as three facts that seem to fit together. And so
Speaker 1: with the right sort of questions, we might find that
Speaker 1: sometimes AI generates something amazing and it seems to pass
Speaker 1: the scientific discovery test. So this is going to be
Speaker 1: incredibly useful for science. And I've never been able to
Speaker 1: escape the feeling as I sift through Google scholar and
Speaker 1: the thousands of papers published each month that have something
Speaker 1: could hold all the knowledge and mind at once, each
Speaker 1: page in every journal, and every gene in the genome,
Speaker 1: and all the pages about chemistry and physics and mathematical
Speaker 1: techniques and astrophysics and so on. Then you'd have lots
Speaker 1: of puzzle pieces that could potentially make lots of connections.
Speaker 1: And you know this might lead to the retirement of
Speaker 1: many scientists, or at minimum lead to a better use
Speaker 1: of our time. There's a depressing sense in which each scientist,
Speaker 1: each one of us, finds little pieces of the puzzle,
Speaker 1: and in the twinkling of a single human lifetime, a
Speaker 1: busy scientist might collect up a handful of different puzzle pieces.
Speaker 1: The most voracious reader, the most assiduous worker, the most
Speaker 1: creative synthesizer of ideas, can only hope to collect a
Speaker 1: small number of puzzle pieces and pray that some of
Speaker 1: them might fit together. So this is going to be
Speaker 1: massively important. But I wanted to find two categories of
Speaker 1: scientific discovery. The first is what I just described, which
Speaker 1: is science where things that already exist in literature can
Speaker 1: be pieced together. And let's call that level one discovery.
Speaker 1: And these large language models will be awesome at level
Speaker 1: one because they've read every paper and they have a
Speaker 1: perfect memory. But I want to distinguish a second level
Speaker 1: of scientific discovery, and this is the one I'm interested in.
Speaker 1: I'll call this level two, and that is science that
Speaker 1: requires conceptualization to get to the next step, not just
Speaker 1: remixing what's already there. Conceptualization like when the young Albert
Speaker 1: Einstein imagined something that he had never seen before. He
Speaker 1: asked himself, what would it be like if I could
Speaker 1: catch up with a beam of light and write it
Speaker 1: like a surfer riding a wave. And this is how
Speaker 1: he derived this special theory of relativity. This isn't something
Speaker 1: he looked up and found three facts that clicked. Again,
Speaker 1: he imagined he asked new questions. He tried out a
Speaker 1: new model of the world, one in which time runs
Speaker 1: differently depending on how fast you're going, and then he
Speaker 1: worked backwards to see if that model could work. Or
Speaker 1: consider when Charles Darwin thought about the species that he
Speaker 1: saw around him, and he imagined all the species that
Speaker 1: he didn't see but who might have existed, and he
Speaker 1: was able to put together a new mental model in
Speaker 1: which most species don't make it and we only see
Speaker 1: those whose mutations cause survival advantages or reproductive advantages. These
Speaker 1: weren't facts that he just collected from some papers. He
Speaker 1: was trying out a new model of the world. Now
Speaker 1: this kind of science isn't just for the big giant stuff.
Speaker 1: Most meaningful science is actually driven by this kind of
Speaker 1: imagination of new models. Just as one example, I recently
Speaker 1: in an episode about whether time runs in slow motion
Speaker 1: when you're in fear for your life. And so when
Speaker 1: I wondered about this question, I realized there were two
Speaker 1: hypotheses that might explain it, and I thought up an
Speaker 1: experiment to discriminate those two hypotheses. And then we built
Speaker 1: a wristband that flashes information at a particular speed and
Speaker 1: had people wear, and we dropped them from one hundred
Speaker 1: and fifty foot tall tower into a net below. A
Speaker 1: large language model presumably couldn't do that because it's just
Speaker 1: playing statistical word games. And unless someone had thought of
Speaker 1: that experiment and written it down, JATGPT would never say, Okay,
Speaker 1: here's a new framework, and how we can design an
Speaker 1: experiment to put this to the test. So this is
Speaker 1: what I wanted to find as the most meaningful test
Speaker 1: for a human level of intelligence. When AI can do
Speaker 1: science in this way, generating new ideas and frameworks, not
Speaker 1: just clicking act together, then we will have matched human intelligence.
Speaker 1: And I just want to take one more angle on
Speaker 1: this to make the picture clear. The way a scientist
Speaker 1: reads a journal paper is not simply by correlating words
Speaker 1: and extracting keywords, although that might be part of it,
Speaker 1: but also by realizing what was not said. Why did
Speaker 1: the authors cut off the x axis here at thirty
Speaker 1: What if they had extended this graph, would the line
Speaker 1: have reversed in its trend? And why didn't the authors
Speaker 1: mention the hypothesis of Smith at all? And does that
Speaker 1: graph look too perfect? You know? One of my mentors,
Speaker 1: Francis Krik operated under the assumption that he should disbelieve
Speaker 1: twenty five percent of what he read in the literature.
Speaker 1: Is this because of fraud or error, or statistical fluctuations
Speaker 1: or manipulation or the waste basket effect? Who cares? The
Speaker 1: bottom line is that the literature is rife with errors,
Speaker 1: and depending on the field, some estimates put the ireproducibility
Speaker 1: at fifty percent. So when scientists read papers they know this,
Speaker 1: just as Francis Crick did. They read in an entirely
Speaker 1: different manner than Google Translate or Watson or chat GPT
Speaker 1: or any of the correlational methods they extrapolate. They read
Speaker 1: the paper and wonder about other possibilities. They chew on
Speaker 1: what's missing. They envision the next step. They think of
Speaker 1: the next experiment that could confirm or disconfirm the hypotheses
Speaker 1: and the frameworks in the paper. To my mind, the
Speaker 1: meaningful goal of AI is not going to be found
Speaker 1: in number crunching and looking for facts that click together.
Speaker 1: It's going to often be something else. It's going to
Speaker 1: require an AI that learns how humans think, how they behave,
Speaker 1: what they don't say, what they didn't think of, what
Speaker 1: they misthought about, what they should think about. And one
Speaker 1: more thing, I should note that these different levels I've outlined,
Speaker 1: from fitting facts together versus imagining new world models, they're
Speaker 1: probably gonna end up with blurry boundaries. So maybe chat
Speaker 1: GPT will come up with something, and you won't always
Speaker 1: know whether it's piecing together a few disparate pieces in
Speaker 1: the literature what I'm calling level one, or whether it's
Speaker 1: come up with something that is truly a new world
Speaker 1: model that's not a simple clicking together but a genuine
Speaker 1: process of generating a new framework to explain the data.
Speaker 1: So distinguishing the levels of discovery is probably not going
Speaker 1: to be an easy task with a bright line between them,
Speaker 1: but I think it will clarify some things to make
Speaker 1: this distinction. And last thing, I don't necessarily know that
Speaker 1: there's something magical and ineffable about the way that humans
Speaker 1: do this. Presumably we're running algorithms too, it's just that
Speaker 1: they're running on self configuring wetwear. I have seen tens
Speaker 1: of thousands of science experiments in my career, so I
Speaker 1: know the process of asking a question and figuring out
Speaker 1: what we'll put it to the test. So we may
Speaker 1: get to level two and it may be sooner than
Speaker 1: we expect, but I just want to be clear that
Speaker 1: right now we have not figured out the human algorithms.
Speaker 1: So the current version of AI, as massively impressive as
Speaker 1: it is, does not do level two scientific problem solving.
Speaker 1: And that's when we're going to know that we've crossed
Speaker 1: a new kind of line into a machine that is
Speaker 1: truly intelligent. So let's wrap up. At least for now.
Speaker 1: Humans still have to do the science, by which I
Speaker 1: mean the conceptual work, wherein we take a framework for
Speaker 1: understanding the world and we rethink it and we mentally
Speaker 1: simulate whether a new model of the world could explain
Speaker 1: the observed data, and we come up with a way
Speaker 1: to test that new model. It's not just searching for facts.
Speaker 1: So I'm definitely not saying we won't get to the
Speaker 1: next level where AI can conceptualize things and predict forward
Speaker 1: and build new knowledge. This might be a week from now,
Speaker 1: or it might be a century from now. Who knows
Speaker 1: how hard a problem that's going to turn out to be.
Speaker 1: But I want us to be clear eyed on where
Speaker 1: we are right now, because sometimes in the blindingly impressive
Speaker 1: light of what current AI is doing, it can be
Speaker 1: difficult to see, what's missing and where we might be heading.
Speaker 1: That's all for this week. To find out more and
Speaker 1: to share your thoughts, head over to eagleman dot com
Speaker 1: slash Podcasts, and you can also watch full episodes of
Speaker 1: Inner Cosmos on YouTube. Subscribe to my channel so you
Speaker 1: can follow along each week for new updates. I'd love
Speaker 1: to hear your questions, so please send those to podcast
Speaker 1: at eagleman dot com and I will do a special
Speaker 1: episode where I answer questions. Until next time. I'm David
Speaker 1: Eagelman and this is Inner Cosmos