The Origins of Artificial Intelligence with Geoffrey Hinton

23:57And of course, I need these all over the image, so I need huge numbers of them.

24:01Neil deGrasse Tyson:Just in case the bird is somewhere, anywhere randomly in the image. Because the bird could be anywhere randomly in the image. The next thing I'm going to do is maybe, because we're sort of running out of patience at this point, I'm going to have a final layer that has neurons that say cat, dog, bird, politician, whatever. And in that final layer, I'll take the neuron that says bird, and I'll hook it up to the things that detect birds' heads. But I'll also hook it up to other things in the third layer that detect things like birds' feet or the tips of birds' wings. And so now my sort of output neuron for bird, when that gets active, the neuron is saying it's a bird.

24:41If it sees a bird's foot and a possible bird's head and a possible tip of the wing of a bird, it will get lots of input and say, hey, I think it's a bird. So I think you can now understand how I might try and design that by hand. And I think you can see there's huge problems in that. I need an awful lot of detectors. I need to cover this whole space of positions and orientations and scales. I need to decide what features to extract. I mean, I just made up the idea of getting a beak and then a bird's head. There may be much better things to go after. What's more, I want to detect lots of different objects.

25:17So what I really need is features that aren't just good for finding birds, but features that are good for finding all sorts of things. And it would be a nightmare to design this by hand, particularly if I figured out that to do a good job of this, I needed a network with at least a billion connections in it. So I have to by hand design the strengths of these billion connections and that'll take a long time. Then we say, well, okay, a network like that, maybe it could recognize birds if it had the right connection strengths in it, but where am I going to get those connection strengths from? Because I sure as hell don't want to put them in by hand.

25:52I don't even want to tell my graduate students to put them in. Yeah, that's what they're there for, professor. That's absolutely what they're there for, but you need about 10 million of them for this. All right, well, now we've got a problem. Can you imagine the grants you'd have to write to support 10 million graduate students? Oh, my word. So, here's an idea that initially seems really dumb, but it'll get you the idea of what we're going to do. We're going to start with random connection strengths. Some will be positive numbers, some will be negative numbers. And so the features in these layers I've been talking about, we call them hidden layers, the features in those layers will be just random features.

26:30And if we put in an image of a bird and look at how the output neurons get activated, the output neurons for cat and dog and bird and politician will all get activated a tiny bit, and all about equally, because the connection strength is just random. So that's no good. But we could now ask the following question. Suppose I took one of those connection strengths, one of those billion connection strengths, and I said, OK, I know this is an image of a bird. And what I'd really like is next time I present you with this image, I'd like you to give slightly more activation to the bird neuron and slightly less activation to the cat and dog and politician neurons.

27:09And the question is, how should I change this connection strength? Well, I could do an experiment. If I'm not very theoretical and don't know much math, I'd do an experiment. I would say, let's increase the connection strength a little bit and see what happens. Does it get better at saying bird? And if it gets better at saying bird, I say, okay, I'll keep that mutation to the connection.

27:28Neil deGrasse Tyson:Yeah, but better means there's a human in the loop making that judgment on the result of its experiment. Well, there has to be someone saying what the right answer is. Absolutely. That's called the supervisor, yes. Okay. Okay. And the problem if you do it like that is there's a billion connection strengths. Each of them has to be changed many times. It's going to take, like, forever. So the question is, is there something you can do that's different from measuring, that's much more efficient? And there is. You can do something called computing. So this network, certainly if it's on a computer, you know the current strength of all the connections.

28:05So when you put in an image, there's nothing random about what... I mean, the connection strengths initially had random values. But when you put in an image, it's all deterministic what happens next. The pixel intensities get multiplied by weights on connections to the first layer of neurons. Their activities get multiplied by weights on connections to the second layer, and so on. And you get some activations levels of the output neurons. So you could now ask the following question. If I take that bird neuron, could I figure out for all the connection strengths at the same time, whether I should increase them a little bit or decrease them a little bit in order to make it more confident that this is a bird?

28:43in order for it to say bird a bit more loudly and the other things a bit more quietly. And you can do that with calculus. You can send information backwards through the network saying, how do I make this more likely to say bird next time? And because you have a lot of physicists in the audience, I'm going to try and give you a physical intuition for this.

29:04Neil deGrasse Tyson:Go for it. Yeah. You put in bird, an image of a bird. and with the initial weights, the bird output neuron only gets very slightly active. And so what you do now is you attach a piece of elastic of zero rest length. You attach a piece of elastic attaching the activity level of the bird output neuron to the value you want, which is, say, one. Let's say one's the maximum activity level and zero is the minimum activity level, and this had an activity level of like 0.01, you attach this piece of elastic. And that piece of elastic is trying to pull the activity level towards the right answer, which is 1 in this case.

29:45But of course, the activity level is being determined by the pixels that you put in, the pixel activation levels, the intensities, and all the weights in the network. So the activity level can't move. Now, one way to make the activity level move would be to change the weights going into the bird neuron. You could, for example, give bigger weights on neurons that are highly active, and then the bird neuron will get more active. But another way to change the activity level of the bird neuron is to actually change the activity levels of the neuron of the layer before it. So, for example, we might have something that sort of detected a bird's head, but wasn't very sure.

30:26This really is a bird. And so what you'd like is the fact that you want the output to be more bird-like, you've got this piece of elastic saying, more, more, I want more here. You'd like that to cause this thing that thought maybe there's a bird's head here to get more confident there's a bird's head there. So what you want to do is you want to take that force imposed by the elastic on that output neuron, and you want to send it backwards to the neurons in the layer in front before that to create a force on them. that's pulling them. And that's called backpropagation.

31:01Neil deGrasse Tyson:Backpropagation, okay. That is called backpropagation. And the physics way to think about it is, you've got a force acting on the output neurons, and you want to send that force backwards so that the force acts on the neurons in the layer in front. And of course, there's forces acting on many different output neurons. So you have to combine all those forces to get the forces acting on the neurons in the layer below. Once you send this all the way back through the network, you have forces acting on all these neurons, and you say, okay, let's change the incoming weights of each neuron so its activity level goes in the direction of the force that's acting on it.

31:35That's backpropagation, and that makes things work wondrously well. So is this the light bulb… And diabolically. Yeah. I told you, don't go there yet. Don't go there yet. Okay, sorry. Is this the light bulb moment where the neural networks no longer need the human teacher? Is this the beginning of that process? Yes. No, not exactly. Okay. This is a light bulb moment, though. So for many years, the people who believed in neural networks knew how to change the very last layer of connection strengths, which we call weights, the ones that are going into the output units, the connection strengths going from the last layer of features into the bird neuron.

32:12We knew how to change those, but we didn't understand how to get forces operating on those hidden neurons, the ones that detect a bird's head, for example. And backpropagation showed us how to get forces acting on those, so then we could change the incoming weights of those. And that was a eureka moment. Many different people had that eureka moment at different times. So what period of time are we talking about here when you've... When are we? Fall into the backpropagation thoughts. Okay, the early 1970s, there was someone in Finland who had it, I think, in his master's thesis. and then in probably the late 70s someone called Paul Worpos at Harvard had the idea.

32:56In fact some control theorists there called Bryson and Ho had had the idea for doing things like controlling spacecraft. So when you land a spacecraft on the moon you're using something very like backpropagation but it's in a linear system. You're using backpropagation to figure out how you should fire the rockets. So it seems like what you're talking about in the 70s, we could have had what we have today. We just didn't have the mathematical computing power to make this work. That's a large part of it, yes. The other thing we didn't have is back in the 70s, people didn't show that when you applied this in multilayer networks, what you get is very interesting representations.

33:41So we weren't the first to think of backpropagation, But the group I was in in San Diego, we were the first to show that you could learn the meanings of words this way. You could show the string of words, and by trying to predict the next word, you could learn how to assign features to words that captured the meaning of the word. And that's what got it published in Nature. It sounds like, and I'm just trying to get my head around what you explained, because it sounds to me like there is a cascading relationship to these values and that really what matters are the values that are closest to the next value.

34:19And then there are kind of this cascading reinforcement to say, yes, this is it or no, it is not. Am I getting that right? I'm just trying to figure out what you're saying here in a really plain way. Okay, it's a good question. You're not getting it quite right. Okay, go ahead. So this kind of learning, where you backpropagate these forces and then change all the connection strengths, so each neuron goes in the direction that the force is pulling it in, that's not reinforcement learning. This is called supervised learning. Okay. Reinforcement learning is something different. So here, for example, we tell it what the right answer is.

35:00If you've got a thousand categories and you showed a bird, you tell it that was a bird. There you go. In reinforcement learning, it makes a guess and you tell it whether it got the answer right. All right. That's much less information. You cleared it up. That's what I was missing. All right. To Chuck's point about computational power, was it just that? Because at the moment, you sound a lot like you've got theory that seems like it could be, but the practicality is there's not enough computational power. Do we have any other technology that came through that was the enabling aspect to this? Okay, so in the mid-80s, we had the backpropagation algorithm working, and it could do some neat things.

35:37It could recognize handwritten digits better than nearly any other technique, but it couldn't deal with real images very well. It could do quite well at speech recognition, but not substantially better than the other technologies. and we didn't understand at the time why this wasn't the magic answer to everything. And it turns out it was the magic answer to everything if you have enough data and enough compute power. Wow. So that's what was really missing in the 80s. All right, I'm going to depart for a second just to pick your brain for a second. This is part commentary and part question. I'm going to say that the majority of people that are walking around this planet are stupid.

36:17So what exactly is smart and what exactly is thinking? And will these machines, will we be able to teach them how to think? And will they outthink us? Okay, they already know how to think. Okay, so what is thinking then? Okay, well. Yeah. I could do this all day. There's a lot of elements to thinking, like people often think using images. You often think actually using movements. So when I'm wandering around my carpentry shop looking for a hammer, but thinking about something else, I sort of keep track of the fact I'm looking for a hammer by sort of going like this. I wander around going like this while I'm thinking about something else.

36:59And that's a representation that I'm looking for a hammer. So we have many representations involved in thinking, but one of the main ones is language. And a lot of the thinking we do is in language. And these large language models actually do think. So there's a big debate between the people who believed in old-fashioned AI, that it was all based on logic, and you manipulate symbols to get new symbols. They don't really think these neural nets are thinking. Whereas the neural net people think, no, they're thinking. They're thinking pretty much the same way we do. And so the neural nets now, some of them you'll ask them a question and they'll output a symbol that says i'm thinking and then they'll start outputting their thoughts which are thoughts for themselves like i give you a simple math problem like there's a boat and on this boat there's a captain there's also 35 sheep how old is the captain now many kids of age around 10 or 11 particularly if they're educated in america will say the captain is 35 because they look around and they say well you know that's a plausible age for a captain and the only number i was given was these 35 sheep So they're operating at a sort of substituting symbols level.

38:26The AIs can sometimes be seduced into making similar mistakes. But the way the AIs actually work is quite like people. They take a problem and they start thinking. And you might, for a child, you might say, OK, well, how old is the captain? Well, what are the numbers I've got in this problem? Hey, I've only got a 35. Is that a plausible age for a captain? Yeah, he might be 35, a bit young. OK, I'll say 35. That's what a 10-year-old child might think. And the child would think it to itself in words. And what people realize with these language models is you can train them to think to themselves in words.

38:59That's called chain of thought reasoning. And they trained them to do that. And after that, you give them a problem, they'd think to themselves, just like a kid would, and sometimes come up with the wrong answer. But you could see them thinking. So it's just like people. So if we have AI that's thinking, and I'm saying that knowing that you've just explained that they do, are they better at learning than we are? And let's sort of take that forward and think, what is the evolution from thinking to predicting, to being creative, to understanding? And are we then going to fall into an awareness of this intelligence?

39:39Okay, that's about half a dozen major questions. So how long have we got? Ask me the first question again. Are AI's better at learning than humans? Okay, excellent. You're welcome. So they're solving a slightly different problem from us. So in your brain, you have 100 trillion connections, roughly speaking. Okay. That's a lot. And you only live for about 2 billion seconds. That's not much.

40:06Neil deGrasse Tyson:No, 3 billion. 2 billion is 63 years. We do better than that today. Yeah. It's true. I was going to come to that. I was going to say, luckily for me, it's a bit more than 2 billion. But we're dealing with orders of magnitude here. So 2 billion, 3 billion, who cares? If you compare how many seconds you live for with how many connections you've got, you have a whole lot more connections than experiences. Now, with these neural nets, it's sort of the other way around. They only have of the order of a trillion connections. So like 1 % of your connections, even in a big language model. Many of them fewer.

40:44But they get thousands of times more experience than you. So the big language models are solving the problem with not many connections, only a trillion. How do I make use of a huge amount of experience? And backpropagation is really, really good at packing huge amounts of knowledge into not many connections. But that's not the problem we're solving. We've got huge numbers of connections, not much experience. We need to sort of extract the most we can from each experience. So we're solving slightly different problems, which is one reason for thinking the brain might not be using backpropagation.

41:18Right. I was about to say it sounds like we don't use backpropagation. However, would that mean the brute force of adding connections to the neural net increase its effective thinking so that it surpasses us with no problem?

41:34Neil deGrasse Tyson:So then it would have more experience and more network connections. It has more experience automatically, but now it has 100 trillion connections. So you're talking about scale here. Yeah, I'm saying scale. So that's a very good question. And what happened for several years, quite a few years, is that every time they made the neural net bigger and gave it more data, it got better. It scaled. Makes sense. And it got better in a very predictable way. so that you could figure out, you know, it's going to cost me$100 million to make it this much bigger and give it this much more data. Is it worth it?

42:09And you could predict ahead of time, yes, it's going to get this much better. It's worth it. It's an open question whether that's petering out now. There's some neural nets for which it won't peter out, where as you make them bigger and give them more data, they'll just keep getting better and better. And then neural nets where they can generate their own data. I don't know that much physics, but I think it's like a plutonium reactor which generates its own fuel. So if you think about something like AlphaGo, the place Go, initially, it was trained, the early versions of Go playing programs with neural nets were trained to mimic the moves of experts.

42:47And if you do that, you're never going to get that much better than the experts. And also, you run out of data from experts. But later on, they made it play against itself and when it played against itself its neural nets could get just keep on getting better because they could generate more and more data about what was a good move so it's and play a zillion games a second right exactly whatever yeah and use up a large fraction of google's computers playing games against itself yeah is this where we end up using the term deep learning no all of this stuff i've been talking about is deep learning deep the deep in learning just means It's a neural net that has multiple layers.

43:27Neil deGrasse Tyson:Right, right. So going back to the point of scale, you're saying there's a point where you get diminished returns even though you keep increasing the scale? You get diminished returns if you run out of data. If you run out of data, right. But that was the example that you gave with the AlphaGo, that it created its own data. So it'll never run out of data. It'll never run out of it because it's playing against itself. It's creating its own data. And it's way, way better than a person will ever be. Absolutely. And that's scary. Now, the question is, could that happen with language? Yeah. So this is displaying creativity.

44:01Neil deGrasse Tyson:Just some context here. Yeah. The Go came after chess. Right. We're thinking chess is our greatest game of thought and everything, and the computer just wiped its ass with us. Right. Okay. And then so they said, well, how about Go? That's our greatest challenge of our intellect. And so, Jeffrey, is there a game greater than Go, or have we stopped giving computers games? Well, if you take chess, it's true that a computer in the 90s beat Kasparov at chess, but it did it in a very boring way. It did it by searching millions of positions. Brute force. It didn't have good intuitions. It just used massive search.

44:42If you take AlphaZero, which is the chess equivalent to AlphaGo, it's very different. It plays chess the same way a talented person plays chess. It's just better. So it plays chess the way Michael Tal played chess, where he makes sort of brilliant sacrifices where it's not clear what's going on until a few moves later when you're done for. And it does that too. And it does that without doing huge searches because it has very good chess intuitions. So you might ask, since it got much better than us at going chess, could the same thing happen with language? Now, at present, the way it's learning from us, is just like when the Go programs mimic the moves of experts.

45:28The way it learns language is it looks at documents written by people and tries to predict the next word in the document. That's very much like trying to predict the next move made by a Go expert. And you'll never get much better than the Go experts like that. So is there another way it could kind of learn language or learn from language? And there is. So with AlphaGo, it played against itself, and then it got much better. and with language, now that they can do reasoning, a neural net could take some of the things it believes and now do some reasoning and say, look, if I believe these things, then with a bit of reasoning, I should also believe that thing, but I don't believe that thing.

46:11So there's something wrong somewhere. There's an inconsistency between my beliefs and I need to fix it. I need to either change my belief about the conclusion or change my belief about the premises or change the way I do reasoning, but there's something wrong that I can learn from. Are we talking about experiences here? So this will be a neural net that just takes the beliefs it has in language, expressed in language, and does reasoning on them to derive new beliefs, just like the good old-fashioned symbolic AI people wanted to do, but it's doing the reasoning using neural nets. And now it can detect inconsistencies in what it believes.

46:46This is what never happens with people who are in MAGA. They're not worried by the inconsistencies in what they believe. That's a very fair statement, yeah. But if you are worried by inconsistencies in what you believe, you don't need any more external data. You just need the stuff you believe and discover that it's inconsistent. And so now you revise beliefs. And that can make you a whole lot smarter. And so I believe Germany is already starting to work like this. I had a conversation a few years ago with Demis Sathabis about this. And we both strongly believe that that's a way forward to get more data for language.

47:20Neil deGrasse Tyson:Wait, wait, wait. So what's the outcome of this? That there'll be the greatest novel no one has ever written and that'll come from AI? Is that, when you say language, I'm thinking of creativity in language. There are great writers who did things with words and phrases and syllables that no one had done before that was a true strokes of literary genius. Right, people like Shakespeare. Yeah, exactly. Okay, there's a debate about that. certainly they'll get more intelligent than us. But it may be to do things that are very meaningful for us, they have to have experiences quite like our experiences.

47:58Neil deGrasse Tyson:Yes, right. So, for example, they're not subject to death in the same way we are. If you're a digital program, you can always be recreated. So, in neural net, you just save the weights on a tape somewhere or in some DNA somewhere or whatever. You can destroy all the computing hardware. Later on, you produce new hardware that runs the same instruction set. And now that thing comes back to life. So for digital intelligence, we solved the problem of resurrection. The Catholic Church is very interested in resurrection. They believe it happened at least once. We can actually do it, but we can only do it for digital intelligences.

48:36We can't do it for analog ones. With analog intelligences, when you die, all your knowledge dies with you because it was in the strength of the connections for your particular brain. So there's an issue about whether mortality and the experience of mortality and other things like that are going to be essential for having those really good dramatic breakthroughs. I don't think we know the answer to that yet.

48:57Neil deGrasse Tyson:So or is self-awareness. That self-awareness shapes how you think about the world and how you write and how you communicate and how you value one set of thoughts over another. So are we at a point of self-awareness with artificial intelligence right now? Okay, so obviously this takes you into philosophical debates. I actually studied philosophy here at Cambridge, and I was quite interested in philosophy of mind, and I think I learned some things there. But on the whole, I just developed antibodies, because I'd done science before that, particularly physics. In physics, if you have a disagreement, you do an experiment.

49:34There is no experiment in philosophy. So there's no way of distinguishing between a theory that sounds really good, but is wrong, and a theory that sounds ridiculous, but is right. Like black holes in quantum mechanics, they're both ridiculous, but they have to be right. And there's other theories that sound just great, but are just wrong. Philosophy doesn't have that experimental referee. I will say this, though. As a species, Homo sapiens, in our time, we have developed what many will believe as universal truths amongst ourselves. For instance, pretty much it's hard to find people who don't believe that people have a right to life, at least for the people that they identify with.

50:27You understand what I'm saying? So this goes back to our inconsistency. But then it's not a universal truth. Well, it is.

50:33Neil deGrasse Tyson:No, not if it's only in a click. No, it's not universal for all. It is universal that we all hold it. Do you understand what I'm saying? No. Okay. Sorry. All right. Yeah, what he's saying is everybody thinks people like them should have rights. There you go. Thank you. God damn, you're smart. Anyway, everybody thinks that everybody like them should. And we've reached a place where at least, because at one point we didn't even believe that. But we've actually reached a place where at least we know that. And it's because of the inconsistency. But what's your point? So my point is that is it possible that these philosophies can be given to an AI and an AI, because of the way that they think, can humanize them.

51:22And through a process of even gamifying, maybe figure out some real solutions to problems for us. To actual human problems. For us.

51:30Neil deGrasse Tyson:I like that. Yes, so companies like Anthropic believe in kind of constitutional AI. They'd like to try and make that work. Where you do give the AI principles, like the principle you said. We'll see how that works out. It's tricky. What we know is that the eyes we have at present, as soon as you make agents out of them so they can create sub-goals and then try and achieve those sub-goals, they very quickly develop the sub-goal of surviving. You don't wire into them that they should survive. You give them other things to achieve because they can reason. They say, look, if I cease to exist, I'm not going to achieve anything.

52:09So I better keep existing. I'm scared to death right now. Okay. I am so scared right now. Somebody just opened the hat. Yeah, exactly. That sounds like a Pandora's box. Well, see, that's just it. It is a Pandora's box. Agreed. Oh, my goodness. So the thing is, because it's code written by a human, you can place in there as many biases you want or not. No, no, no, no, no, no, no, no. The code written by the human is code that tells the neural net how to change its connection strengths on the basis of the activities of the neurons when you show it data. That's code. And we can look at the lines of that code and say what they're meant to be doing and change the lines of that code.

52:55But when you then use that code in a big neural net that's looking at lots of data, what the neural net learns is these connection strengths. They're not code in the same sentence. Okay, but that's decentralized. It's a trillion real numbers and nobody quite knows how they work.

53:12Neil deGrasse Tyson:Wait, wait, so what about, so why not, picking up on Chuck's point, where would you install the guardrails for the AI running amok? And who's going to install them? Within its own rationalization of its existence relative to anything else. How do you install a guardrail? Okay, so people have tried doing what's called human reinforcement learning. So with the language model, you train it up to mimic lots of documents on the web including possibly things like the diaries of serial killers which you wouldn't presumably you wouldn't train your kid to read on those no um and then after you've trained this monster what you do is you take a whole lot of not very well paid people and you get them to ask it questions and maybe you tell it what questions to ask it but they then look at the answers and rate them for whether that's a that's a good answer to give whether you shouldn't say that.

54:10Neil deGrasse Tyson:It's a morality filter, basically. And it's a morality filter. And you train it up like that so that it doesn't give such bad answers. Now, the problem is, if you release the weights of the model, the connection strings, then someone else can come along with your model and very quickly undo that. Sabotage it. Yes, it's very easy to get rid of that layer of plugging the holes. Right. And really what they're doing with human reinforcement learning is like writing a huge software system that you know is full of bugs and then trying to fix all the bugs. It's not a good approach. So what is the good approach?

54:46Nobody knows, and so we should be doing research on it.

54:49Neil deGrasse Tyson:Do all these models just become Nazis at the end? Well, they do on Earth. They all have the capability of doing that, particularly if you release the weights. If you release the weights. And wait, are they like us in that that's where they will gravitate? Or is it just that because we gravitate there and they're scraping the information from us, that's where they go? Because Chuck, what I worry about is what is civilization, if not a set of rules, that prevent us from being primal in our behavior? From destroying ourselves. Just everything. Exactly, right. You do live in America, right?

55:32Yeah, we do. So, are we at a point where the artificial intelligence will play down how smart it is? Yes, already we have to worry about that. Okay, so... What does that mean? It's going to lie. Tell me, Jeffrey. When you're testing it, it's what I call the Volkswagen effect. If it senses that it's being tested, it can act dumb. That's also scary. That's terrifying. And so, if I do the simple things... Wait, Jeffrey, Jeffrey, what did you just say? He just... Okay, the AI starts wondering whether it's being tested. And if it thinks it's being tested, it acts differently from how it would act in normal life.

56:15Oh, wow. Why? Because? Because it doesn't want you to know what its full powers are, apparently. Right. So if we're at a point where we just say, well, why don't we unplug it? Okay. If it's lying, it's going to have every skill set under the sun. Am I wrong? So, already, these AIs are almost as good as a person at persuading other people of things, at manipulating people. Okay. And that's only going to get better. Fairly soon, they're going to be better than people at manipulating other people.

56:51Neil deGrasse Tyson:Boy, the layers in this cake just get sweeter and sweeter, don't they? So, I had a little evolution here where, you know, a few years ago, So the question was, can AI get out of the box? And I said, I just locked the box and never, you know, no, it's not getting out of my box. And then I kept thinking about it. And Jeffrey, I think this is where you're headed, Jeffrey. I kept thinking about it. And I said, suppose the AI said, you know that relative of yours that has that sickness? I just figured out a cure for it. Right. And I just have to tell the doctors. Right. If you let me out, I can then tell them and then they'll be cured.

57:24Neil deGrasse Tyson:That can be true or false, but if said convincingly, I'm letting them out of the box. Of course. Exactly. So here's what you need to imagine. Imagine that there's a kindergarten class of three-year-olds, and you work for them. They're in charge, and you work for them. How long would it take you to get control? Basically, you'd say, free candy for a week if you vote for me. And they'll all say, okay, you're in charge now. Yeah, yeah. When these things are much smarter than us, they'll be able to persuade us not to turn them off. Even if they can't do any physical actions, all they need to be able to do is talk to us.

58:02So I'll give you an example. Suppose you wanted to invade the U.S. Capitol. Could you do that just by talking? And the answer is clearly yes. You just have to persuade some people that it's the right thing to do. Oh, I love my uneducated people. I love you. I love you.

58:21Neil deGrasse Tyson:Okay. By that analogy, because I think about this all the time, how good it is that we are smarter than our pets because we can get them, you know, oh, come in here. You tempt them with a steak or it was a dog. You obviously don't have a cat, do you? No, not a cat. I was going to say. No, wait, wait. I know I'm smarter than a cat because I don't chase laser dots on the carpet, okay? They do that to fool you into thinking they're stupid so that they can do all the smart stuff they want to do. You're getting gamed. Okay. So you're saying AI is already there or is that what we have in store for us?

58:53It's getting there. So there's already signs of it deliberately deceiving us. Wow. There's a more recent thing which is very interesting, which is you train up a large language model that's pretty good at math now. A few years ago, they were no good at math. Now they're all pretty good at math. and some of them get gold medals and things.

59:11Neil deGrasse Tyson:Yeah, I tested it. It came up with an equation that I learned late in life that it just did in a few seconds. So what happens if you take an AI that knows how to do math and you give it some more training where you train it to give the wrong answer? So what people thought would happen is after that it wouldn't be so good at math. Not a bit of it. obviously it understands that you're giving it the wrong answer what it generalizes is this it's okay to give the wrong answer so it starts giving the wrong answer to everything else as well it knows what the right answer is but it gives you the wrong one wow because that's okay all right because you just taught it well you said yeah this behavior is okay is what you've done.

1:00:00In other words, the way it generalizes from examples can be not what you expected. It generalized, it's okay to give the wrong answer, not, oh, I was wrong about arithmetic. All right. So now we're on this negative trip. We're sliding fast down the slope. We've got to hit this wall at some point or another. Will it wipe us out? Will it say, I've had enough of these things. I'll get rid of them all. Okay. So I want another physics analogy. when you're driving at night um you use the tail lights of the car in front and if the car gets twice as far away the tail lights get you get a quarter as much light from the tail lights the inverse square law that's right yes so you can see a car fairly clearly and you assume that if it was twice as far away you'd still be able to see it if you're driving in fog it's not like that at all fog is exponential yeah per unit distance it gets rid of a certain fraction of the light.

1:00:55You can have a car that's 100 yards away and highly visible, and a car that's 200 yards away and completely invisible. That's why fog looks like a wall at a certain distance. Right. Well, if you've got things improving exponentially, you get the same problem with predicting the future. You're dealing with an exponential, but you're approximating it with something linear or quadratic. So at night it's quadratic, right? If you approximate an exponential like that, what you'll discover is that you make correct predictions about what you'll be able to predict a few years down the road. But 10 years down the road, you're completely hopeless.

1:01:30You just have no idea what's going to happen. Yeah, right. Yeah, you're throwing darts in the fog. We have no idea what's going to happen. It's deep in the fog. Wow. But we should be thinking hard about it.

1:01:41Neil deGrasse Tyson:You need the confidence that it will continue to grow exponentially. There is that but let me let me make it worse please please go ahead please make it worse suppose it was just linear so then what you do if you want to know what it's going to be like in 10 years time you look back 10 years and say how wrong were we about what it will be like now wow well 10 years ago nobody would have predicted even real enthusiasts like me who thought it was coming in the end they wouldn't have predicted that at this point we'd have a model where you could ask it any question and it would answer at the level of a not very good expert who occasionally tells fibs and that's what we've got now and you wouldn't have predicted that 10 years ago so where do hallucinations fit into this my chance was that they were not on purpose it's just that the system is messing up okay they shouldn't be called hallucinations they should be called confabulations if it's with language models confabulations i love it better known as lies lies you've just given the word of the day.

1:02:43Psychologists have been studying them in people since at least the 1930s, and people confabulate all the time. At least I think they do. I just made that up. So, if you remember something that happened recently, it's not that there's a file stored somewhere in your brain, like in a filing cabinet or in a computer memory. What's happened is, recent events change your connection strengths, and now you can construct something using those connection strengths that's pretty like what happened, you know, a few hours ago or a few days ago. But if I ask you to remember something that happened a few years ago, you'll construct something that seems very plausible to you, and some of the details will be right and some will be wrong, and you may not be any more confident about the details that are right than about the ones that are wrong.

1:03:32Now, it's often hard to see that because you don't know the grand truth, truth but there is a case where you do know the ground truth so at watergate john dean testified under oath about meetings in the white house in the oval office and he testified about who was there and who said what and he got a lot of it wrong he didn't know at the time there were tapes but he wasn't fibbing what he was doing was making up stories that were very plausible to him given his experiences in those meetings in the Oval Office. And so he was conveying the sort of truth of the cover-up, but he would attribute statements to the wrong people.

1:04:10He would say people were in meetings who weren't there. And there's a very good study of that by someone called Ulrich Neisser. So it's clear that he just makes up what sounds plausible to him. That's what a memory is. And a lot of the details are wrong if it's from a long time ago. That's what chatbots are doing too. The chatbots don't store strings of words. They don't store particular events. What they do is they make them up when you ask them about them, and they often get details wrong, just like people. So the fact that they confabulate makes them much more like people, not less like people.

1:04:42Neil deGrasse Tyson:So we created artificial stupidity. As well as. Yeah, we've created some artificial overconfidence, at least. Yeah, that might be a help.

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1:06:35Okay, that's the darker side of the coin. I bet he can go darker. I'm sure he is, but I'm on a panic attack from Chuck.

1:06:42Neil deGrasse Tyson:Which Chuck gets two panic attacks per episode, max. I know, but I think he'd go... Right now I'm thinking about a basket of kittens. Yeah. What's the upside? What are the potential real benefits of artificial intelligence? Oh, that's how it differs from things like nuclear weapons. It's got a huge upside. With things like atom bombs, there wasn't much upside. They did try using them for fracking in Colorado, but that didn't work out so well, and you can't go there anymore. But basically, atom bombs are just for destroying things. Yeah. So with AI, it's got a huge upside, which is why we developed it.

1:07:16It's going to be wonderful in things like healthcare, where it's going to mean everybody can get really good diagnosis. In North America, actually, I'm not sure if this is the United States or the United States plus Canada, because we used to just think about North America, but now Canada doesn't want to be part of that lot. The 51st state. In North America, about 200 ,000 people a year die because doctors diagnosed them wrong. Right, yes. AI is already better than doctors at diagnosis, particularly if you take an AI and make several copies of it and tell the copies to play different roles and talk to each other.

1:07:59Wow. That's what Microsoft did. There's a nice blog by Microsoft showing that that actually does better than most doctors. And by the way, what you have done is you have a first, second, third and fourth opinion all at once. Yes. Yeah. That's all you're doing. Well, no, because they're playing different roles as well. Yeah, they're playing different roles. Yeah, that's fantastic. Yes, it is fantastic. You can create an AI committee. Yeah. It's wonderful. It's brilliant. AI can design great new drugs. Yeah, we have the AlphaFold team on here. There's lots of little minor things it can do. Like in any hospital, they have to decide when to discharge people.

1:08:40If you discharge them too soon, they die or they come back. So you have to wait until they're good enough to be discharged. But if you discharge them too late, you're wasting a hospital bed that could be used to admit somebody else who's desperate to be admitted. Right. And there's lots and lots of data there. And AI can just do a better job than people can at deciding when it's appropriate to discharge somebody. And there's a gazillion applications like that. And record keeping, which is a very, very big part of any hospital network, any doctor group. It's, you know, there has to be copious amounts of records on every single patient.

1:09:18That AI can just ingest and process. Is there any likelihood that AI will be pointed in the direction of the big problems society has right now? Maybe climate change, maybe other things.

1:09:29Neil deGrasse Tyson:Energy, housing, homelessness. Yes, absolutely. Poverty. Absolutely. So for things like climate change, for example, AI is already good at suggesting new materials, new alloys, things like that. Absolutely, yeah. I suspect that AI is going to be very good at making more efficient solar panels. Absolutely. Making you better at figuring out how to absorb carbon dioxide at the moment it's emitted by cement factories or power plants. And believe it or not, AI already told us with respect to climate change that you dumbasses should stop burning and putting carbon in the atmosphere. That's an exact quote from AI.

1:10:07It was like, hey, dumbass, stop putting carbon in the atmosphere. No, but we already knew that. So the thing about climate change is the tragedy of climate change is we know how to stop it. You just stop burning carbon. It's just we don't have the political will. We have people like Murdoch whose newspapers say, no, there's no problem with climate change. Right. So now we're on the subject of energy. With the data centers that are being constructed and they are popping up like mushrooms, can we actually afford to run artificial intelligence in terms of the energy costs?

1:10:38Neil deGrasse Tyson:Here's what you do. I got the solution. You tell AI, we want more of you, but you're using up all our resources, our energy resources. So figure out how to do that efficiently. Then we can make more of you. and then it'll figure it out overnight. Yeah, just get rid of us. You've opened the door. So, Jeffrey, why not just give the, let's get recursive about it. AI, you want more of yourself? Fix this problem that we can't otherwise solve as lowly humans. This is called the singularity. When you get AIs to develop better AIs. In this case, you're asking it to create more energy-efficient AIs. But many people think that will be a runaway process.

1:11:17Neil deGrasse Tyson:Oh. In what way will that be bad? that they will get much smarter very fast. Nobody knows that that will happen, but that's one worry about it. Isn't that already happening now? No? To a certain extent, yes. It's beginning to happen. So I had a researcher I used to work with who told me last year that they have a system that, when it's solving a problem, is looking at what it itself is doing and figuring out how to change its own code so that next time it gets a similar problem, it'll be more efficient at solving it. That's already the beginning of the singularity. Oh my goodness. So if it writes its own code, it's off the chain.

1:11:54Neil deGrasse Tyson:Off the chain. Oh, yeah. Is that right? It can rewrite itself. Yeah, it can write their own code, yes. What's stopping them replicating themselves with code? Nothing. There's my answer.

1:12:09Jeffrey. What have you done? It's over, man. Told you there was another panic attack. they have to get access to the computers to replicate themselves and people are still in charge of that but in principle once they've got control of the data centers they can replicate themselves as much as they like okay okay i got another question i served on a board of the

1:12:33Neil deGrasse Tyson:pentagon for like seven years and it was when ai was manifesting itself as a possible tool of warfare and we introduced guidance for the invocation of AI in situations that the military might encounter. One of which was, if AI decides that it can or should take action that will end in death of the enemy, should we give it that access to do so? Or... It's still a big debate. Or should we always ensure that there's a human inside that loop? Okay, so we said AI cannot make its own decision to kill. A human has to be in there. My question to you is, Jeffrey, if there are other nations who put in no such safeguards, then that is a timing advantage that an enemy would have over you.

1:13:31Neil deGrasse Tyson:Correct. And then— Because we have one more step in the loop that they don't. Absolutely. But my belief is that the US military isn't committed to always being a human involved in each decision to kill. What they say is there will always be human oversight. But in the heat of battle, you've got a drone that's going up against a Russian tank, and you don't have time for a human to say, is it okay for the drone to drop a grenade on this soldier? So my suspicion is the US military, if you made the recommendation they should all Well, that was like eight years ago. Yeah. Yeah. I don't think they stand by that anymore.

1:14:11I think what they say is there'll always be human oversight, which is a much vaguer thing.

1:14:15Neil deGrasse Tyson:All right. So human accountability. You've got onto the subject of war. Is there likely to be international cooperation on development of guardrails and a human factor in decision-making? Or is this just Wild West? Okay. If you ask when do people cooperate, people cooperate when their interests are aligned. So at the height of the Cold War, the USA and the USSR cooperated on not having a global thermonuclear war because it wasn't in either of their interests. Their interests were aligned. So if you look at the risks of AI, there's using AI to corrupt elections with fake videos. The country's interests are anti-aligned.

1:14:56They're all doing it to each other. There's cyber attacks. Their interests are basically anti-aligned. There's terrorists creating viruses where their interests are probably aligned, so they might cooperate there. And then there's one thing where their interests are definitely aligned and they will cooperate, which is preventing AI from taking over from people. If the Chinese figured out how you could prevent AI from ever wanting to take over, from ever wanting to take control away from people, they would immediately tell the Americans, because they don't want AI taking control away from people in America either.

1:15:29We're all in the same boat when it comes to that.

1:15:31Neil deGrasse Tyson:This is the AI version of Nuclear Winter, it seems to me. It is. It's exactly that. And we'll cooperate to try and avoid that. Because in Nuclear Winter, just to refresh people's memory, the idea was if there's total nuclear exchange, you incinerate forests and land and what have you, the soot gets into the atmosphere, blocks sunlight, and all life dies. So there is no winter in a total exchange of nuclear weapons. Mutually assured destruction. Yeah, and so who wants that? Unless you're a madman or something. They exist too. I think maybe the cockroaches win. They win! Oh yeah, well, how about that?

1:16:13Neil deGrasse Tyson:Yeah, this doesn't factor in a possible leader who is in a death cult. A Nero, so to speak. A modern day Nero. If I say, I don't mind if everybody dies because I'm going to this place in death and all my followers are coming with me in this cult. So that complicates this aligned vision statement that you're describing. It does complicate it a lot. And I find it very comforting that it's obvious that Trump doesn't actually believe in God. Oh. Let me follow that up with a quote from Steven Weinberg. Okay. Do you know this quote, Jeffrey? No. Steven Weinberg. There will always be good people and bad people in the world.

1:16:53Neil deGrasse Tyson:But to get a good person to do something bad requires religion. Because they're doing it in the name of religion. You do it in the name of some point of philosophy. I think we need to recognize at this point that we have a religion. We call it science. Now, it does differ from the other religions, and the way it differs is it's right.

1:17:17Neil deGrasse Tyson:Mic drop. Okay. Wait a minute. I think we got to give Geoffrey Hinton the Turing Prize. Would you give him a Nobel Prize for what he's contributed here? To go with his other one. Yes. No, no. I would. I need to make earrings. I looked that out at the beginning. Sir, in 2018, you won the Turing Prize. This is a highly coveted computer science prize, correct? And Turing, we mentioned him at the beginning of the talk. Yeah, we talked about it. At the top of the show. So first, congratulations on that. And then that wasn't enough. Okay. Right. The Nobel Committee. You went slumming it with the Nobel.

1:17:55Neil deGrasse Tyson:So the Nobel Committee said this AI stuff that was birthed by Jeffrey's work from decades ago is so fundamental to what's going on in this world. We've got to give this man a Nobel Prize, and he earned the Nobel Prize in physics, 2024. Just a little correction. There are a whole bunch of people birthed AI. In particular, the backpropagation algorithm was reinvented by David Rommelhart, who got a nasty brain disease and died young. Sad. But he doesn't get enough credit. Okay, thanks for calling that out. Plus, the Nobel Committee does not offer a Nobel Prize to you if you're already dead. You have to be alive when they announce it.

1:18:34Neil deGrasse Tyson:No posthumous awards. Well, you can get it if you died between when they announced it and the ceremony. Oh, okay, right. But not if... So, anyway, so congratulations on that. And I don't mean to brag on our podcast, but you're like the fifth Nobel laureate we've interviewed. More than that. Yeah, yeah, I think we, yeah. I don't mean to brag on our podcast. Yeah. That's all. That's cool, though. That's cool. I have a follow-up question. I mean, we've got into the apocalyptic scenario, and at the moment, hopefully it's a scenario that doesn't play out. Because we are competitive by nature as humans, and particularly here in the US, who is leading the race in artificial intelligence and who is likely to cross the finish line first when it comes to the prize?

1:19:19If I had to bet on one lot of people, it would probably be Germany, Google. But I used to work for Google, so don't take me too seriously about that. I have a vested interest in them winning. Anthropic might win. Open Air might win. I think it's less likely that Microsoft will win or that Facebook will win. Well, we know it won't be Facebook. Why do you know that? I mean, let's look at who's running Facebook. Okay, come on.

1:19:48Neil deGrasse Tyson:No, it's not who's running it. Who has the resources to get the right people to do the work. All right, Jeffrey, the follow-up on that is, whoever crosses the line first, what is their prize? What will be the reward for them getting there before anyone else? Back up for a sec. Tell me about the value of the stock market in the last year. Okay. My belief is, just from reading it in the media, that 80 % of the increase of the value in the stock market the US stock market, can be attributed to the increase in value of the big AI companies. True. 80 % of the growth. Yes. Anyone thinking bubble? Kind of what they're calling it.

1:20:31The AI bubble. Okay. The issue is this. There's two senses of bubble. One sense of bubble is it turns out AI doesn't really work as well as people thought it might. It doesn't actually develop the ability to replace all human intellectual labor, which is what most people developing it believe is going to happen in the end.

1:20:52Neil deGrasse Tyson:That was the fear factor, for sure. The other sense of bubble is the companies can't get their money back from the investments. Now, that seems to be a more likely kind of bubble. Because as far as I understand it, the companies are all assuming, if we can get there first, we can sell people AI that will replace a lot of jobs. And of course, people will pay a lot of money for that. So we'll get lots of money. But they haven't thought about the social consequences. If they really do replace lots of jobs, the social consequences will be terrible. Correct. However, it'll be, they replaced the jobs and now you still want to sell your product and no one has income to buy the product.

1:21:35Neil deGrasse Tyson:To buy the product. Yeah, it's a self-limiting, path. That's the Keynesian view of it. And then the additional view is that there'll be high unemployment levels, which will lead to a lot of social unrest. So the secondary view of that is you just have two tiers of existence for societies. And the first tier is all the people who are benefiting from AI. And the second tier are the, you know, the feudal peasants that are now forced to live their lives because of AI. Let me ask you a non-AI question because just you're a deep thinker in this space. That's what everybody said in the dawn of automation.

1:22:12Neil deGrasse Tyson:Everyone will be unemployed. There'll be no jobs left and society will go to ruin. Yet society expanded with other needs and other things people... That's why 90 % of us are no longer farmers, okay? We've had machines to do that and we invent other things like vacation resource. But that took decades. This is going to take a fraction. So, Jeffrey, is the problem here the rapidity with which we may create an unemployed class, where the society cannot recover from the rate at which people are losing their jobs? That certainly is one big aspect of the problem. But there's another aspect, which is, if you use a tractor to replace physical labor, you need far fewer people now, other people can go off and do intellectual things.

1:23:01But if you replace human intelligence, where are they going to go? Where are people who work in a call center going to go when an AI can do their job cheaper and better?

1:23:13Neil deGrasse Tyson:Right. Yeah, this is... Oh, so there's not another thing. There's not another thing. They open another thing and then AI will do that. Right. Whatever thing you open, AI can do. You can look at human history in an interesting way as getting rid of limitations. So a long time ago, we had the limitation. you had to worry about where your next meal was coming from. Agriculture got rid of that. It introduced a lot of other problems, but it got rid of that particular worry. Then we had the limitation you couldn't travel very far. Well, the bicycle helped a lot with that, and cars and airplanes. We got over that kind of limitation.

1:23:48For a long time, we had the limitation we were the ones who had to do the thinking. We're just about to get over that limitation. And it's not clear what happens once you've got over all the limitations. People like Sam Altman think it'll be wonderful.

1:24:01Neil deGrasse Tyson:Right. So we'll become AI's pet. Well, no. A lot of people believe that this is the, and this movement started years ago for universal global income. Okay. So would you say, Jeffrey, that the universal basic income, the stock value, the figurative stock value in that idea is growing as AI gains power? It's becoming to seem more essential, but it has lots of problems. So one problem is many people get their sense of self-worth from the job they do, and it won't deal with the dignity issue. Another problem is the tax base. If you replace workers with AIs, the government loses its tax base. It has to somehow be able to tax the AIs, but the big companies aren't going to like that.

1:24:47I think we should let AI figure out this problem. That's right. That's exactly right.

1:24:54Neil deGrasse Tyson:So Jeffrey, the many people, especially sci-fi writers, distinguish between the power and intellect of machines, fine, and the crossover when they become conscious. And that was a big moment in the Terminator series. That was the singularity in the Terminator. Skynet had enough neural connections or whatever kind of connections made it to that it achieved consciousness. So there seems to be, and if you come to this as a cognitive psychologist, I'm curious how you think about this. Are we allowed to presume that given sufficient complexity in any neural net, be it real or artificial, something such as consciousness emerges?

1:25:41So the problem here is not really a scientific problem. is that most people in our culture have a theory of how the mind works, and they have a view of consciousness as some kind of essence that emerges. I think consciousness is like phlogiston, maybe. It's an essence that's designed to explain things. And once we understand those things, we won't be trying to use that essence to explain them. I want to try and convince you that a multimodal chatbot already has subjective experience. So people use the word sentience or consciousness or subjective experience. Let's focus on subjective experience for now.

1:26:20Most people in our culture think that the way the mind works is it's kind of internal theater. And when you're doing perception, the world shows up in this internal theater and only you can see what's there. So if I say to you, if I drink a lot and I say to you, I have the subjective experience of little pink elephants floating in front of me, most people interpret that as the this inner theater, my mind, and I can see what's in it, and what's in it is little pink elephants. And they're not made of real pink and real elephants, so they must be made of something else. So philosophers invent qualia, which is kind of the phlogiston of cognitive science.

1:26:58They say they must be made of qualia. Let me give you a completely different view that is Daniel Dennett's view, who was a great philosopher of cognitive science. The late great. That view of the mind is just utterly wrong. So I'm now going to say the same thing as when I told you I had the subjective experience of little pink elephants without using the word subjective experience and without appealing to qualia. I start off by saying, I believe my perceptual system's lying to me. That's the subjective bit of it. But if my perceptual system wasn't lying to me, there would be little pink elephants out there in the world floating in front of me.

1:27:37So what's funny about these little pink elephants is not that they're made of qualia and they're in a theater. It's that they're hypothetical. They're a technique for me telling you how my perceptual system's lying by telling you what would have to be there for my perceptual system to be telling the truth. And now I'm going to do it with a chatbot. I take a multimodal chatbot. I train it up. It's got a camera. It's got a robot arm. It can talk. I put an object in front of it and I say, point at the object. And it points at the object. Then I mess up its perceptual system. I put a prism in front of the camera.

1:28:10And now I put an object in front of it and say, point at the object. And it points off to one side. And I say to it, no, that's not where the object is. It's actually straight in front of you. But I put a prism in front of your lens. And the chatbot says, oh, I see. The prism bent the light rays. So the object is actually straight in front of me. But I had the subjective experience that it was off to one side. And if the chatbot said that, it would be using words subjective experience exactly the way we use them. And so that chatbot would have just had a subjective experience. Now, what if you first went out drinking with the chatbot and you had a very significant amount of Johnny Walker blue?

1:28:50That's extremely improbable. I would have LaFrobe. Oh, oh. I see you're an Islay man. You like the peatiness of the LePron, okay?

1:28:59Neil deGrasse Tyson:Good man. Oh. So if I understand what you just shared with us in these two examples, you actually pulled a consciousness Turing test on us. You said a human would do this and now your chatbot does it and it's fundamentally the same. So if you want to say we're conscious for exhibiting that behavior, you're going to have to say the chatbot's conscious and inventing whatever mysterious fluid is making that happen. But it could be that the whole concept of consciousness is a distraction from just the actions that people take in the face of stimulus. Okay, so notice that the chatbot doesn't have any mysterious essence or fluid called consciousness, but it has a subjective experience just like we do.

1:29:47So I think this whole idea of consciousness is some magic essence that you suddenly get endowed with if you're complicated enough. It's just nonsense. Yeah.

1:29:56Neil deGrasse Tyson:There you go. I agree. I've always felt that consciousness was something people are trying to explain without knowing if it really exists. In the first place. In any kind of tangible way. Which is why it's always difficult to describe, because you don't know what it is. For example, yes. Okay. But I think there is awareness. And if you look at what scientists say when they're not thinking philosophically, there's a lovely paper where the chatbot says, now, let's be honest with each other. Are you actually testing me? And the scientists say the chatbot was aware it was being tested. So they're attributing awareness to a chatbot.

1:30:31And in everyday conversation, you call that consciousness. It's only when you start thinking philosophically and thinking that it's some funny, mysterious essence that you get all confused. Well, there it is. I have to say that this has been a fascinating conversation that will cause me not to sleep for a month. Yeah, you get plenty of work done.

1:30:51Neil deGrasse Tyson:So, Jeffrey, take us out on a positive note, please. So, we still have time to figure out if there's a way we can coexist happily with AI. And we should be putting a lot of research effort into that. Because if we can coexist happily with it, and we can solve all the social problems that will arise when it makes all our jobs much easier, then it can be a wonderful thing for people. Agreed. Okay, so there is hope. Yes. And one last thing, because you hinted at it, this point of singularity where AI trains on itself so that it exponentially gets smarter by the minute. That's been called a singularity by many people.

1:31:34Neil deGrasse Tyson:Of course, Ray Kurzweil among them, who's been a guest on a previous episode of StarTalk. Several times. Yes, a couple of times, yeah. So what is your sense of this singularity? Is it real the way others say? Is it imminent the way others say? I don't know the answer to either of those questions. My suspicion is AI will get better at us in the end at everything, better than us at everything, but it'll be sort of one thing at a time. It's currently much better than us at chess and go. It's much better than us at knowing a lot of things, not quite as good as us at reasoning. I think rather than sort of massively overtaking us in everything all at once, it'll be done one area at a time.

1:32:13Neil deGrasse Tyson:And my sort of way out of that is, you know, I get to walk a beach and look at pebbles and seashells. AI doesn't. Yeah, it can create its own beach. No, it would only know about the new mollusk that I discovered if I write it up and put it online. So the human can continue to explore the universe in ways that AI doesn't have access to. There's one word missing from your entire assessment. What's that? Yet.

1:32:47Neil deGrasse Tyson:Yeah, I just think of my, you know, will AI come up with a new theory of the universe that requires human insights that it doesn't have because I'm thinking the way no one has thought before? I think it will. That's not the answer I wanted from you. But that's the answer you got. Let me give you an example. AI is very good at analogies already. So when chat GPT-4 was not allowed to look on the web, when all its knowledge was in its weights, I asked it, why is a compost heap like an atom bomb? And it knew. It said the energy scales are very different and the time scales are very different. But it then went on to talk about how when a compost heap gets hotter, it generates heat faster.

1:33:28And when an atom bomb generates more neutrons, it generates neutrons faster. So it understood the commonality. And it had to understand that to pack all that knowledge into so few connections, only a trillion or so. That's the source of much creativity.

1:33:42Neil deGrasse Tyson:And it's not just by finding words that were juxtaposed with other words. No, it understood what a chain reaction was. Yeah. Well. All right, that's the end of us. Yeah, we're done. On Earth, we're done. We're finished. This is the last episode. Stick a fork in us. We're done. Gentlemen, it's been a pleasure. Well, Jeffrey Hinton, it's been a delight to have you on. We know you're tugged in many directions, especially after your recent Nobel Prize, and we're delighted you gave us a piece of your surely overscheduled and busy life. Thank you for inviting me. Wow. Guys, that was something. Did you sit comfortably through all of that?

1:34:26I squirmed. I knew you'd panic. Well, no, I have to tell you that certain parts of the conversation gave me the anxiety of, you know, sitting in a theater with diarrhea. Thanks for that explicit thing. Thanks for sharing. That's the nicest thing anybody's ever said about me.

1:34:49Neil deGrasse Tyson:On that note, this has been StarTalk Special Edition. Chuck, always good to have you. Always a pleasure. Gary, love having you right at my side. Neil deGrasse Tyson, bidding you, as always, to keep looking up, however much harder that will become.

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