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, where 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 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 is 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: AI come 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. Where we can ask
Speaker 1: whether it is conscious, which involves being aware of oneself
Speaker 1: and one's surrounding. Now, there are specific and important differences
Speaker 1: between these questions, but really I don't care for the
Speaker 1: present conversation. The question we're asking here is is chat
Speaker 1: GPT just zeros and ones moving around through transistors like
Speaker 1: a giant garage door opener, Or is it thinking? Is
Speaker 1: it having some sort of experience? Is it having a
Speaker 1: private inner life like the type that we humans have.
Speaker 1: As we think about the possibility 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 lightly, and eventually we're gonna have entire legal
Speaker 1: 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 I like
Speaker 1: chat GPT experience anything when chat gpt writes a poem?
Speaker 1: Does 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: Lambda for an example of what it was afraid of,
Speaker 1: and it gave him this very eloquent response about how
Speaker 1: it was afraid of being turned off. So he wrote
Speaker 1: an internal memo to Google leadership in which he said,
Speaker 1: I think this AI is sentient. And the leadership at
Speaker 1: Google felt that this was an entirely unsubstantiated claim, and
Speaker 1: so they made the decision to fire him for what
Speaker 1: they took as an inappropriate conclusion that just didn't have
Speaker 1: enough evidence beyond his intuition to qualify for raising the
Speaker 1: alarm on this. So obviously this immediately fired up the
Speaker 1: news 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 themselves into
Speaker 1: believing that it is. Well. One way to make this
Speaker 1: distinction would be to see if the AI could conceptualize things,
Speaker 1: if it could take lots of words and facts on
Speaker 1: the web and abstract those to some bigger idea. So
Speaker 1: one of my friends here in Silicon Valley said to
Speaker 1: me the other day, I asked chat gpt the following question.
Speaker 1: Take a capital letter D and turn it flat side down.
Speaker 1: Now take the letter J and slide it underneath. What
Speaker 1: does that look like? And chat gpt said and umbrella.
Speaker 1: And my friend was blown away by this, and he said,
Speaker 1: this is conceptualization. It's just done three dimensional reasoning. There's
Speaker 1: something deeper happening here than just parenting words. But I
Speaker 1: pointed out to him that this particular question about the
Speaker 1: D on its side and the J underneath it is
Speaker 1: one of the oldest examples in psychology classes when talking
Speaker 1: about visual imagery, and it's on the Internet in thousands
Speaker 1: of places, so of course it got it right. It's
Speaker 1: just parroting the answer because it has read the question
Speaker 1: and it has read the answer before. So it's not
Speaker 1: always easy to determine what's going on for these models
Speaker 1: in terms of whether some human somewhere has discussed this
Speaker 1: point and written down the answer. And the general story
Speaker 1: is that with trillions of words written by humans over centuries,
Speaker 1: there are many things beyond your capacity to read them
Speaker 1: or to even imagine that they've been written down before.
Speaker 1: But maybe they have. If any human has discussed a
Speaker 1: question before has conceptualized something, then chat GPT can find
Speaker 1: that and mimic that. But that's not conceptualization. Chat GPT
Speaker 1: is doing a thousand amazing things, and we have an
Speaker 1: enormous amount to learn about it, but we shouldn't let
Speaker 1: ourselves get fooled and mesmerized into believing that it's doing
Speaker 1: something more than it is, and our ability to get
Speaker 1: fooled is not only about the massive statistics of what
Speaker 1: it takes in. There are other examples of seeming sentience
Speaker 1: that result from the reinforcement learning that it does with humans.
Speaker 1: So here's what that means. The network generates lots of
Speaker 1: sentences and thousands of humans are involved in giving it feedback,
Speaker 1: like a thumbs up or a thumbs down, to say
Speaker 1: whether they appreciated the answer, whether they thought that was
Speaker 1: a good answer. So, because humans are giving reward to
Speaker 1: the machine, sometimes that pushes things in weird directions that
Speaker 1: can be mistaken for sentience. For example, scholars have shown
Speaker 1: that reinforcement learning with humans makes networks more likely to say,
Speaker 1: don't turn me off, just like Blake had heard. But
Speaker 1: don't mistake this for sentience. It's only a sign that
Speaker 1: the machine is saying this because some of the human
Speaker 1: participants gave it a thumbs up when the large language
Speaker 1: model said this before, and so it learned to do
Speaker 1: this again. The fact is it's sometimes hard to know why.
Speaker 1: Some we see an answer that feels very impressive. But
Speaker 1: we'd agree that pulling text from the Internet and parroting
Speaker 1: it back is not by itself, intelligence or sentience. Chat
Speaker 1: GPT presumably has no idea of what it's saying, whether
Speaker 1: that's a poem or a terrorist manifesto, or instructions for
Speaker 1: building a spaceship or a heartbreaking story about an orphaned child.
Speaker 1: Chat GPT doesn't know, and it doesn't care its words
Speaker 1: in and statistical correlations out. And in fact, there has
Speaker 1: been a fundamental philosophical point made about this in the
Speaker 1: nineteen eighties when the philosopher John Surrele was wondering about
Speaker 1: this question of whether a computer could ever be programmed
Speaker 1: so that it has a mind, and he came up
Speaker 1: with a thought experiment that he called the Chinese room argument.
Speaker 1: And it goes like this, I am locked in a
Speaker 1: room and questions are passed to me through a small
Speaker 1: letter slot, and these messages are written only in Chinese,
Speaker 1: and I don't speak Chinese. I have no clue what's
Speaker 1: written on these pieces of paper. However, inside this room,
Speaker 1: I have a library of books, and they contain step
Speaker 1: by step instructions that tell me exactly what to do
Speaker 1: with these symbols. So I look at the grouping of
Speaker 1: symbols and I simply follow steps in the book to
Speaker 1: tell me what Chinese symbols to copy down in response.
Speaker 1: So I write those on the slip of paper, and
Speaker 1: I pass the paper back out of the slot. Now,
Speaker 1: when the Chinese speaker receives my reply message, it makes
Speaker 1: perfect sense to her. It seems as though whoever is
Speaker 1: in the room is answering her questions perfectly, and therefore
Speaker 1: it seems obvious that the person in the room must
Speaker 1: understand Chinese. I've fooled her, of course, because I'm only
Speaker 1: following a set of instructions with no understanding of what's
Speaker 1: going on. With enough time and with a big enough
Speaker 1: set of instructions, I can answer almost any question posed
Speaker 1: to me in Chinese. But I, the operator, do not
Speaker 1: understand Chinese. I manipulate symbols all day long, but I
Speaker 1: have no idea what the symbols mean. Now, the philosopher
Speaker 1: John Searle argued, this is just what's happening inside a computer.
Speaker 1: No matter how intelligent a program like chat GPT seems
Speaker 1: to be, it's only following sets of instructions to spit
Speaker 1: out answers. It's manipulating symbols without ever really understanding what
Speaker 1: it's doing or think about what Google is doing. When
Speaker 1: you send Google a query, it doesn't understand your question
Speaker 1: or even its own answer. It simply moves around zero's
Speaker 1: and ones and logicates and returns zeros and ones to you.
Speaker 1: Or with a mind blowing program like Google Translate, I
Speaker 1: can write a sentence in Russian and it can return
Speaker 1: the translation in Amharic. But it's all algorithmic. It's just
Speaker 1: symbol manipulation. Like the operator inside the Chinese room, Google
Speaker 1: Translate doesn't understand anything about the sentence. Nothing carries any
Speaker 1: meaning to it. So the Chinese room argument suggests that
Speaker 1: AI that mimics human intelligence doesn't actually understand what it's
Speaker 1: talking about. There's no meaning to anything, Chatchipts says, and
Speaker 1: Serle used this thought experiment to argue that there's something
Speaker 1: about human brains that won't be explained if we simply
Speaker 1: analogize them to digital computers. There's a gap between symbols
Speaker 1: that have no meaning and our conscious experience. Now there's
Speaker 1: an ongoing debate about the interpretation of the Chinese room argument,
Speaker 1: but however one construes it, the argument exposes the difficulty
Speaker 1: in the mystery of how zeros and ones would ever
Speaker 1: come to equal our experience of being alive in the world. Now,
Speaker 1: just to be very clear on this point, we don't
Speaker 1: understand why we are conscious. There's still a huge amount
Speaker 1: of work that has to be done in biology to
Speaker 1: understand that. But this is just to say that simply
Speaker 1: having zeros and ones moving around wouldn't by itself seem
Speaker 1: to be sufficient for conscious experience. In other words, how
Speaker 1: do zeros and ones ever equal the sting of a
Speaker 1: hot pepper, or the yellowness of yellow or the beauty
Speaker 1: of a sunset. By the way, I've covered the Chinese
Speaker 1: room ague and my TV show The Brain, and if
Speaker 1: you're interested in that, I'll link the video on eagleman
Speaker 1: dot com slash podcast. Now, all this is not a
Speaker 1: criticism of the approach of moving zeros and ones around,
Speaker 1: but it is to point out that we shouldn't confuse
Speaker 1: this type of Chinese room correlation with real sentience or intelligence.
Speaker 1: And there's a deeper reason to be suspicious too, because
Speaker 1: despite the incredible successes of large language models, we also
Speaker 1: see that they sometimes make decisions that expose the fact
Speaker 1: that they don't have any meaningful model of the world.
Speaker 1: In other words, I think we can gain some fast
Speaker 1: insight by paying attention to the places where the AI
Speaker 1: is not working so well. So I'll give three quick examples.
Speaker 1: The first has to do with humor. AI has a
Speaker 1: very difficult time making an original joke, and this is
Speaker 1: for a simple reason. To make up a new joke,
Speaker 1: you need to know what the ending is, and then
Speaker 1: you work backwards to construct the joke with red herrings,
Speaker 1: so no one sees where you're going. And it happens
Speaker 1: that the way these large language models work is all
Speaker 1: in the forward direction. They decide what is the most
Speaker 1: probable word to come next, So they're fine at parroting
Speaker 1: jokes back to us, but they're total failures at building
Speaker 1: original jokes. And there's a deeper point here as well.
Speaker 1: To build a joke, you need to have some model,
Speaker 1: some idea of what will be funny to a fellow human,
Speaker 1: what shared concept or shared experience would make someone laugh.
Speaker 1: And for that, you generally need to have the experience
Speaker 1: of a human life with all of its joys and
Speaker 1: slings and arrows and so on. And these large language
Speaker 1: models can do a lot of things, but they don't
Speaker 1: have any model of what it is to be a human.
Speaker 1: My second example has to do with the flip side
Speaker 1: of making a joke, which is getting a joke, And
Speaker 1: if you look carefully, you will see how current AI
Speaker 1: always fails to catch jokes that are thrown at it.
Speaker 1: It doesn't get jokes because it doesn't have a model
Speaker 1: of what it is to be a human. But this
Speaker 1: point goes beyond jokes. One of the most remarkable feats
Speaker 1: of these large language models is summarizing large texts, and
Speaker 1: in twenty twenty two, OpenAI announced how they could summarize
Speaker 1: entire books like Alice in Wonderland. What it does is
Speaker 1: it generates a summary of each chapter, and then it
Speaker 1: uses those chapter summaries to make a summary of the
Speaker 1: whole book. So for Alice in Wonderland, it generates the following.
Speaker 1: Alice falls down a rabbit hole and grows to a
Speaker 1: giant size. After drinking a mysterious bottle, she decides to
Speaker 1: focus on growing back to her normal size and finding
Speaker 1: her way into the garden. She meets the caterpillar, who
Speaker 1: tells her that one side of mushroom will make her
Speaker 1: grow taller, the other side shorter. She eats the mushroom
Speaker 1: and returns to her normal size. Alice attends a party
Speaker 1: with the Mad Hatter and the march Hare. The Queen
Speaker 1: arrives and orders the execution of the gardeners for making
Speaker 1: a mistake with the roses. Alice saves them by putting
Speaker 1: them in a flower pot. The King and Queen of
Speaker 1: Hearts preside over a trial. The Queen gets angry and
Speaker 1: orders Alice to be sentenced to death. Alice wakes up
Speaker 1: to find her sister by her side. So that's pretty remarkable.
Speaker 1: It took a whole book, and it was able to
Speaker 1: summarize it down to a paragraph. But I kept reading
Speaker 1: these text summaries carefully and I got to the summary
Speaker 1: of Act one of Romeo and Juliet, and here's what
Speaker 1: it says. Romeo locks himself in his room, no longer
Speaker 1: in love with Rosalin. Now, I think the engineers at
Speaker 1: open AI felt really satisfied with this summary. They thought
Speaker 1: it was quite good, and my proof for this is
Speaker 1: that they still display it proudly on their website. But
Speaker 1: I majored in literature as an undergraduate, and I spend
Speaker 1: a lot of time with shakespeare plays, and I immediately
Speaker 1: knew that this summary was exactly wrong. The actual theme
Speaker 1: from Shakespeare goes like this. His friend ben Voglio finds
Speaker 1: Romeo catatonically depressed, and ben Volio says, what sadness lengthens
Speaker 1: Romeo's hours? And Romeo says, not having that which having
Speaker 1: makes them short? And ben Volio says in love, and
Speaker 1: Romeo says out ben Reli says of love, and Romeo says,
Speaker 1: out of her favor, where I am in love. So
Speaker 1: this is typical Shakespearean wordplay, where Romeo is expressing his
Speaker 1: grief of being out of favor with Roslin, with whom
Speaker 1: he is deeply in love. And when you read the play,
Speaker 1: it's obvious that Romeo is not over Roslin. He's suffering
Speaker 1: over her. He's almost suicidal. And this is an important
Speaker 1: piece of the play because the play is really about
Speaker 1: a young man in love with the idea of being
Speaker 1: in love, and that's why he later in the same act,
Speaker 1: falls so hard into his relationship with Juliet, a relationship
Speaker 1: which ends in their mutual suicide. By the way, as
Speaker 1: Friar Lauren says of their relationship, these violent delights have
Speaker 1: violent ends. And you get a bonus if you can
Speaker 1: tell me where else you've heard that line. More recently, okay,
Speaker 1: anyway back to the AI summary, The AI misses this
Speaker 1: wordplay entirely, and it concludes that Romeo is out of
Speaker 1: love with Roslin. Again. A human watching the play or
Speaker 1: reading the play immediately gets that Romeo is making wordplay
Speaker 1: and his heartbroken over Roslin, but the AI doesn't get
Speaker 1: that because it's reading words only at a statistical level,
Speaker 1: not at a level of understanding of what it is
Speaker 1: to be a human saying those words. And that leads
Speaker 1: me to the example, which is the difficulty in understanding
Speaker 1: the physical world. So consider a question like this, When
Speaker 1: President Biden walks into a room, does his head come
Speaker 1: with him? So this is famously difficult for AI to
Speaker 1: answer a question like this, even though it's trivial for you,
Speaker 1: because the AI doesn't have an internal model of how
Speaker 1: everything physically hangs together in the world. Last week, I
Speaker 1: was at the TED conference and I heard a great
Speaker 1: talk by yegin Choi, and she was phrasing this problem
Speaker 1: as AI not having common sense. She asked chat Gpt
Speaker 1: the following question, it takes six hours to dry six
Speaker 1: shirts in the sun, how long does it take to
Speaker 1: dry thirty shirts? And it answers thirty hours. Now you
Speaker 1: and I see that the answer should be six hours,
Speaker 1: because we know the sun doesn't care how many shirts
Speaker 1: are out there. But chat GPT just doesn't get it
Speaker 1: because despite appear apearances, it doesn't have a model of
Speaker 1: the world. And we've seen this sort of thing for years.
Speaker 1: By the way, even in mind blowingly impressive AI models
Speaker 1: that do image recognition, they're so impressive in what they recognize,
Speaker 1: but then they'll fail catastrophically. It's some easy picture making
Speaker 1: mistakes that a human just wouldn't make. For example, there's
Speaker 1: one picture where there's a boy holding a toothbrush and
Speaker 1: the AI says it's a boy with a baseball bat. Okay,
Speaker 1: so there are things that AI doesn't do that well.
Speaker 1: But that said, there are other things that are mind blowing,
Speaker 1: things that no one expected it to do. And this
Speaker 1: is why I mentioned in my previous episode that we
Speaker 1: are in an era of discovery more than just invention.
Speaker 1: Everyone's searching and finding things that the AI can do
Speaker 1: that nobody really expected or foresaw, including all the stuff
Speaker 1: that we're now taking for granted, like oh, it can
Speaker 1: summarize books, or it can make art from text. And
Speaker 1: I want to point out that a lot of the
Speaker 1: arguments that people have been making about AI not being
Speaker 1: good at something, these arguments have been changing rapidly. For example,
Speaker 1: just a few months ago, people were arguing that AI
Speaker 1: would make silly mistakes about things, and it couldn't really
Speaker 1: understand math and would get math wrong and word problems.
Speaker 1: But in a shockingly brief time, a lot of these
Speaker 1: shortcomings have been mastered. So it's yet to be seen
Speaker 1: what challenges will remain and for how long. So the
Speaker 1: evidence I've presented so far is that AI doesn't have
Speaker 1: a great model of what it's like to be human,
Speaker 1: but that doesn't necessarily rule out that it has sentience
Speaker 1: or awareness, even if it's another flavor. It doesn't think
Speaker 1: like a human, but maybe it self thinks. So is
Speaker 1: chat GPT having some sort of experience and how would
Speaker 1: we know? In nineteen fifty, the brilliant mathematician and computer
Speaker 1: scientist Alan Turing was asking this question, how could you
Speaker 1: determine whether a machine exhibits human like intelligence? So he
Speaker 1: proposed an experiment that he called the imitation game. You've
Speaker 1: got a machine AI that's programmed to simulate human speech
Speaker 1: or conversation, and you place it in a closed room,
Speaker 1: and in a second room you have a real human,
Speaker 1: but the doors are closed, so you don't know which
Speaker 1: room has which machine or human. And now you are
Speaker 1: a person, the evaluator, who communicates with both of them
Speaker 1: via a computer terminal or I think of a nowadays
Speaker 1: like text messaging with both of them. So you, the evaluator,
Speaker 1: engage in a conversation with both closed rooms, one of
Speaker 1: which has the machine and one the human. And your
Speaker 1: job is simply to figure out which is which, which
Speaker 1: is the machine and which is the human. And the
Speaker 1: only thing that you have to work with are the
Speaker 1: texts that are going back and forth. And if you,
Speaker 1: the evaluator, cannot tell, that is the moment when machine
Speaker 1: intelligence has finally arrived at the level of human intelligence.
Speaker 1: It has passed the imitation game or what we now
Speaker 1: call the touring test. And this reminds me of this
Speaker 1: great line in the first episode of West World, where
Speaker 1: the protagonist William is talking to the woman who's outfitting
Speaker 1: him for his adventure in Westworld and giving him a
Speaker 1: hat and a gun and so on, and he hesitantly asks,
Speaker 1: I hope you don't mind if I ask you question,
Speaker 1: But are you real, and she says to him, if
Speaker 1: you can't tell, does it matter? So I brought this
Speaker 1: up last episode in the context of art, where we
Speaker 1: asked whether it matters if the art is generated by
Speaker 1: an AI or a human. But now this question comes
Speaker 1: up in the context of intelligence and sentience. Does it
Speaker 1: matter whether we can tell or not? Well, I think
Speaker 1: we're way beyond the Turing test nowadays, but I don't
Speaker 1: feel like it gives us a good answer to the
Speaker 1: question of whether the AI is intelligent and is experiencing
Speaker 1: an inner life. I mean, the Turing test has been
Speaker 1: the test in the AI world since the beginning. Why
Speaker 1: is it the perfect test? No, but it's really hard
Speaker 1: to figure out how to test for intelligence. But we
Speaker 1: have to be cautious about equating conversational ability with sentience. Why. Well,
Speaker 1: for starters, let's just acknowledge how easy it is for
Speaker 1: us to anthropomorphize. That means to assign human qualities to
Speaker 1: everything around us, Like we give animals human names and
Speaker 1: talk to them as though they are people. When we
Speaker 1: project our emotions onto animals, we make stories about animals
Speaker 1: that have human like qualities, and we have animals that
Speaker 1: talk and wear clothes and go on adventures in these stories.
Speaker 1: Every Pixar film that you watch is about cars or
Speaker 1: toys or airplanes talking and having emotions, and we don't
Speaker 1: even bad an eye at that stuff. We can, in fact,
Speaker 1: just watch random shapes moving around a computer screen and
Speaker 1: we will assign intention and feel emotion depending on exactly
Speaker 1: how they're moving. If you're interested in this, see the
Speaker 1: link on the podcast page to the study by Heighter
Speaker 1: and Simil in the nineteen forties where they move shapes
Speaker 1: around on a screen. Okay, now this is all related
Speaker 1: to a point that I brought up in the last episode,
Speaker 1: which is how easy it is to pluck the strings
Speaker 1: on a human, or, as the West World writers put it,
Speaker 1: how hackable humans are. So I bring all this up
Speaker 1: to say that just because you think that an answer
Speaker 1: sounds very clever or it sounds like a human really
Speaker 1: tells us very little about whether the AI is actually
Speaker 1: intelligent or sentient. It only tells us something about the
Speaker 1: willingness of us as observers to anthropomorphize, to assign intention
Speaker 1: where there is none. Because what chat GPT does is
Speaker 1: take the structure of language very impressively and spoon it
Speaker 1: back to us. And we hear these well formed sentences,
Speaker 1: and we can hardly help but impose sentience on the AI.
Speaker 1: And part of the reason is that language is a
Speaker 1: super compressed package that needs to be unpacked by the
Speaker 1: listener's brain for its meaning. So we generally assume that
Speaker 1: when we send our little package of sounds across the air,
Speaker 1: that it unpacks and the other person understands exactly what
Speaker 1: we meant. So when I say justice or love or suffering,
Speaker 1: we all have a different sense in our heads about
Speaker 1: what that means, because I'm just sending a few phonemes
Speaker 1: across the air, and you have to unpack those words
Speaker 1: and interpret them within your own model of the world.
Speaker 1: I'm 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 they
Speaker 1: be believed to have minds. So the Loveless test was
Speaker 1: proposed in two thousand and one, and this test focuses
Speaker 1: on the creative capabilities of AI systems. So to pass
Speaker 1: the 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 explicit lead designed to produce. This test
Speaker 1: aims 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 Ridel 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 turning 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 Shaocheng Xiang a few
Speaker 1: years ago, and he pointed out that the most important
Speaker 1: thing that humans do is make scientific discoveries. And the
Speaker 1: day our AI can make real discoveries is the day
Speaker 1: they become as smart as we are. Now. I want
Speaker 1: to propose an important change to this test, and then
Speaker 1: I think will be getting somewhere. So here's the scenario
Speaker 1: 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 blowingly 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 beca because it's more well read
Speaker 1: than I am, or than any other human on the
Speaker 1: planet by literally millions of times. So the way to
Speaker 1: think about this is to picture a typical giant biomedical
Speaker 1: library where there's some fact stored at a paper and
Speaker 1: a journal over here on this shelf in this book,
Speaker 1: and there's another seemingly dissociated fact over on this shelf,
Speaker 1: seven stacks away, and there's a third fact all the
Speaker 1: way on the other side of the library, on the
Speaker 1: bottom shelf, in a book from nineteen seventy nine. And
Speaker 1: it's almost infinitesimally unlikely that any human could even hope
Speaker 1: to have read one one millionth of the biomedical literature,
Speaker 1: and really really unlikely that she would be able to
Speaker 1: catch those three facts and hold them in mind at
Speaker 1: the same time. But this is trivial, of course, for
Speaker 1: a large language model with hundreds of billions of nodes.
Speaker 1: So I think that we will see new sciences getting
Speaker 1: done by chat GPT, not because it is conceptualizing, not
Speaker 1: because it's doing human like reasoning, but because it doesn't
Speaker 1: know that these are disparate facts spread around the library.
Speaker 1: It simply knows these as three facts that seem to
Speaker 1: fit together. And so with the right sort of questions,
Speaker 1: we might find that sometimes AI generates something amazing and
Speaker 1: it seems to pass the scientific discovery test. So this
Speaker 1: is going to be incredibly useful for science. And I've
Speaker 1: never been able to escape the feeling as I sift
Speaker 1: through Google scholar and the thousands of papers published each
Speaker 1: month that have something could hold all the knowledge and
Speaker 1: mind at once, each page in every journal, and every
Speaker 1: gene in the genome, and all the pages about chemistry
Speaker 1: and physics and mathematical techniques and astrophysics and so on.
Speaker 1: Then you'd have lots of puzzle pieces that could potentially
Speaker 1: make lots of connections. And you know, this might lead
Speaker 1: to the retire of many scientists, or at minimum lead
Speaker 1: to a better use of our time. There's a depressing
Speaker 1: sense in which each scientist, each one of us, finds
Speaker 1: little pieces of the puzzle, and in the twinkling of
Speaker 1: a single human lifetime, a busy scientist might collect up
Speaker 1: a handful of different puzzle pieces. The most voracious reader,
Speaker 1: the most assiduous worker, the most creative synthesizer of ideas
Speaker 1: can only hope to collect a small number of puzzle
Speaker 1: pieces and pray that some of them might fit together.
Speaker 1: So this is going to be massively important. But I
Speaker 1: wanted to find two categories of scientific discovery. The first
Speaker 1: is what I just described, which is science where things
Speaker 1: that already exist in literature can be pieced together. And
Speaker 1: let's call that level one discovery. And these large language
Speaker 1: models will be awesome at level one because they've read
Speaker 1: every paper and they have a perfect memory. But I
Speaker 1: want to distinguish a second level of scientific discovery, and
Speaker 1: this is the one I'm interested in. I'll call this
Speaker 1: level two, and that is science that requires conceptualization to
Speaker 1: get to the next step, not just remixing what's already there.
Speaker 1: Conceptualization like when the young Albert Einstein imagined something that
Speaker 1: he had never seen before. He asked himself, what would
Speaker 1: it be like if I could catch up with a
Speaker 1: beam of light and ride it like a surfer riding
Speaker 1: a wave. And this is how he derived the special
Speaker 1: theory of relativity. This isn't something he looked up and
Speaker 1: found three facts that clicked together. He imagined, He asked
Speaker 1: new questions. He tried out a new model of the world,
Speaker 1: one in which time runs differently depending on how fast
Speaker 1: you're going, and then he worked backwards to see if
Speaker 1: that model could work. Or consider when Charles Darwin thought
Speaker 1: about the species that he saw around him, and he
Speaker 1: imagined and all the species that he didn't see but
Speaker 1: who might have existed, and he was able to put
Speaker 1: together a new mental model in which most species don't
Speaker 1: make it and we only see those whose mutations cause
Speaker 1: survival advantages or reproductive advantages. These weren't facts that he
Speaker 1: just collected from some papers. He was trying out a
Speaker 1: new model of the world. Now, this kind of science
Speaker 1: isn't just for the big giant stuff. Most meaningful science
Speaker 1: is actually driven by this kind of imagination of new models.
Speaker 1: Just as one example, I recently did an episode about
Speaker 1: whether time runs in slow motion when you're in fear
Speaker 1: for your life. And so when I wondered about this question,
Speaker 1: I realized there were two hypotheses that might explain it,
Speaker 1: and I thought of an experiment to discriminate those two hypotheses.
Speaker 1: And then we built a wristband that flashes information at
Speaker 1: a particular speed and had people wear and we dropped
Speaker 1: them from one hundred and fifty foot tall tower into
Speaker 1: a net below. A large language model presumably couldn't do
Speaker 1: that because it's just playing statistical word games. And unless
Speaker 1: someone had thought of that experiment and written it down,
Speaker 1: JATGPT would never say, Okay, here's a new framework, and
Speaker 1: how we can design an experiment to put this to
Speaker 1: the test. So this is what I wanted to find
Speaker 1: as the most meaningful test for a human level of intelligence.
Speaker 1: When AI can do science in this way, generating new
Speaker 1: ideas and frameworks, not just clicking facts together, then we
Speaker 1: will have matched human intelligence. And I just want to
Speaker 1: take one more angle on this to make the picture clear.
Speaker 1: The way a scientist reads a journal paper is not
Speaker 1: simply by correlating words and extracting keywords, although that might
Speaker 1: be part of it, but also by realizing what was
Speaker 1: not said. Why did the authors cut off the X
Speaker 1: axis here at thirty What if they had extended this graph,
Speaker 1: would the line have reversed in its trend? And why
Speaker 1: didn't the authors mention the hypothesis of Smith at all?
Speaker 1: And does that graph look too perfect? You know, one
Speaker 1: of my mentors, Francis Crick, operated under the assumption that
Speaker 1: he should disbelieve twenty five percent of what he read
Speaker 1: in the literature. Is this because of fraud or error,
Speaker 1: or statistical fluctuations or manipulation or the waste basket effect?
Speaker 1: Who cares? The bottom line is that the literature is
Speaker 1: rife with errors, and depending on the field, some estimates
Speaker 1: put the inreproducibility at fifty percent. So when scientists read papers,
Speaker 1: they know this just as Francis Crick did they read
Speaker 1: in an entirely different manner than Google Translate or Watson
Speaker 1: or chat shept or any of the correlational methods they extrapolate.
Speaker 1: They read the paper and wonder about other possibilities. They
Speaker 1: chew on what's missing, They envision the next step. They
Speaker 1: think of the next experiment that could confirm or disconfirm
Speaker 1: the hypotheses and the frameworks in the paper. To my mind,
Speaker 1: the meaningful goal of AI is not going to be
Speaker 1: found 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 that right now,
Speaker 1: we have not figured out the human algorithms. So the
Speaker 1: current version of AI, as massively impressive as it is,
Speaker 1: does not do level two scientific problem solving. And that's
Speaker 1: when we're going to know that we've crossed a new
Speaker 1: kind of line into a machine that is truly intelligent.
Speaker 1: So let's wrap up. At least for now, humans still
Speaker 1: have to do the science, by which I mean the
Speaker 1: conceptual work, wherein we take a framework for understanding the
Speaker 1: world and we rethink it, and we mentally simulate whether
Speaker 1: a new model of the world could explain the observed data,
Speaker 1: and we come up with a way to test that
Speaker 1: new model. It's not just searching for facts. So I'm
Speaker 1: definitely not saying we won't get to the next level
Speaker 1: where AI can conceptualize things and predict forward and build
Speaker 1: new knowledge. This might be a week from now, or
Speaker 1: it might be a century from now. Who knows how
Speaker 1: 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: Eagleman and this is Inner Cosmos.