The Alien in the Room

25:11All of the wire strengths you got from all the examples for wire one get averaged down to one value. All the wire strengths that you got for wire two get averaged down to one value. And if you've done this right, you can then send in any of the drawings it's seen before or new drawings it's never seen. Circles drawn by a two-year-old or a picture of an orange. And it will say, yes, there is a circle there. Holy cow. Now, that process we just went through can recognize way more sophisticated things than just a shape, like cats or dogs. And I mean, the only real difference in the model is instead of these three grids we just used, these three layers, you know, an input, a middle and an output, you just add more layers of bulbs in the middle.

26:02These multiple middle layers allow the computer to recognize progressively more complicated components of the picture. So like the first layer might just find the edges. The second might find textures. The third forms. The fourth maybe eyeballs. Because it's like because everything is made up of building blocks of the layer before it. Yes. Without, crucially, without anyone labeling any of those intermediate, like it's figuring that out itself. Exactly. And then using the same mathematical reinforcement, it can tune and tweak to get shit right. Okay, wow. Well, I need a drink after all of this to sort of let all this settle in.

26:47Great. Okay, like this, I wish my kids could learn like this. Like, the way they learn is so physical, so emotional. It matters who's saying it. It matters how they're saying it. It matters the tone. It matters all these different things. Like, this is so clean. And like crazy fast. I mean, what just took us 10, 15 minutes to explain, that all happens in seconds. So it can learn the circle thing at basically lightning speed. But like a circle, recognizing a circle is one thing. And like now we're talking like actually making, like making a, you know, a sonnet as if Shakespeare wrote it. That seems like a very wide gulf.

27:30it seems like there's still a lot of place to go for sure and our little alien is going to have to evolve here yeah but in in terms of its its architecture of how it does this it's basically the exact same huh the the only real difference is we're shifting its its focus from recognizing to a slightly different skill and we're going to get to that uh you want to predict what i'm going to say next right after a quick break exactly right after a quick break

28:28latif simon radio lab so you you asked this question to me before the break Like, how did this thing evolve from being able to recognize shapes to generate stuff? Yeah. And I posed that very question to Grant Sanderson. Okay. Yeah. Okay. So I would say there's many different ideas at play here. Who, again, YouTuber has thought a hell of a lot about this stuff. And he says the important next step is to realize that, yes, you could think of what we did with those circles as having the machine recognize them. Or you could say we were asking the machine to predict the answer we wanted. Like with the circle example, there's two things that it could predict.

29:11Circle or not. Okay. So it's not anything meaningfully different. It's just like, let's just call everything a prediction. Right. But it becomes important when we're talking about generative stuff. Okay. Like in the case of language. Predict what word comes next. So to explain, going all the way back to the 80s, IBM began playing around with these chatbots that you could type to and it would respond. Hello there. How are you today? And the way it would do what it was doing was it would take every word that you typed in as your question, turn those words into numbers. We're not going to go into how because that would take an hour in and of itself.

29:52but turn those words into numbers, send it through this multi-layered set of bulbs. But in this case, those bulbs, those layers it's passing through, they haven't been trained to categorize a sentence. Like, we don't want it to say that was a question. Instead, it has been trained to spit out the word that is most likely to come next, to predict the most likely next word. Just one word, just one. It's not even a word. Also, there's a nuance here between the notion of words and tokens, but excessive nuance. Yeah, but it's like, what is it even basing? Like, how is it predicting that? With a circle, you know it's a circle.

30:33We know the right answer. We're giving it the right answer. It's calculating back to that right answer. Right. But like, in a sentence that could go any million number of ways, how can it ever have a right answer to train back to? Well, so what IBM was doing was giving it a bunch of texts, books, transcripts, conversations, feeding that into this machine. And so then the right answer was the most likely word to follow the preceding word of words. So it's like, it's just like, here's a giant stack of human talking. And in this giant stack, what's the most likely thing that would have been said next in this exact scenario?

31:17Exactly. That's right. And just one brief aside, because it's sort of fun. I think I have this right, that a word is a big long list of like 13 ,000 numbers. What? A computer has to turn a word. one word just like a one word into 13 000 numbers yeah and so like in the way that a pixel value uh in the circle example was like basically a zero or a one it's like every word is this list of 13 000 numbers it's so weird that it like that that's that's the simpler version for it I know. Let me turn it into this, like, phone book of numbers. Which is, again, like, which points to how these things are so not us.

32:05Yeah, they're really not us. Not at all. Wow. But they're using us, though, right? Like, it's our talk that's getting turned into numbers. And it literally does it one word at a time. So after it's written the first word of its response, it just does the whole process over again. It takes all the words in your question plus the first word it predicted, sends all that through the network again. And then it just predicts the next word after that. Sends that through those bulbs again. And then the next word after that. Does the whole thing again. And plays the same game over and over and over. And one of the words in its vocabulary is the, like, end conversation token.

32:42So it has some notion of when to stop. But the act of stopping is itself just one more prediction. It's one more probability in that big list of things that should happen next. And as I said, this is how they were doing it all the way back in the 80s. And I mean, if you interacted with a chatbot, even in like the 20 teens, this is the way they were doing it as well. Really? Do you have any recollection of when you first came in contact with one? Oh, God. I feel like it must have been one of those like customer service bots on a website kind of thing. And I'm sure not just because it's a customer service experience, but because it was an early chatbot experience.

33:19It wasn't very good. No, no, no, terrible. No, terrible. And a big part of why they were bad was... They had difficulty dealing with longer stretches of text. This is Stephen Levy. Editor at large at Wired. He's been covering this stuff for... Yeah, I mean... A long time. I published a book in 1992 called Artificial Life. I was two years old, by the way. Thanks for that. Sorry. Yeah, yeah, thanks. And he says, because it predicted words one at a time and one after the other, The longer the question or the longer the answer, the more likely it was to miss or lose the larger meaning. And so eventually predict a word that just doesn't make sense or is out of place.

34:02Exactly. And so just to give one very concrete example to illustrate it, like the sentence, what sound does my dog make when I slam the door? It's like. So I can see why that would be confusing. Right. Like you have to somehow know that in that sentence, dog is really the operative term here. Right. The important noun, it's not I or door. Right. Right. Right. And so in 2017. This guy, you know, Oscar Reap, who worked at Google. Set out to solve this dog door problem. He thought that the thing should be able to figure out, oh, this is the most important part of the sentence. This is what I should pay attention to.

34:44And now the question becomes like, how the heck does one go about doing that? And what they figured out was the problem here is we're giving it one word at a time and we're having it predict one word at a time. And what we need to be able to do instead is have it somehow process the sentence as a whole so that, you know, something at the end of the sentence can sort of feed back on the weight or meaning it gives to something at the beginning of the sentence. and one way that you can just imagine it doing this is that instead of just making a prediction and giving an answer you need to take in all the information make a prediction but then just like set that aside because you're going to take in all that information again and then we're going to send it through again and again and again each time focusing on a different word in the sentence generating a different possible prediction before landing on some final prediction which god willing would be bark it's like the computer simultaneously lives in the multiverse of that sentence where where each word in that sentence is the most important yeah and like i i've i've looked at this stuff for months and i still don't totally understand exactly how a machine does this but well i mean something like that and also you know um you can say no you can tell me well i mean I mean, in the raw sense, yeah, that's the idea.

36:12Like the complexity here, you can see it's going through the roof here, like where you're like, oh God, this is so much more computing you need to do. Totally. And this was a big barrier for a long time. I mean, that's why these chatbots were almost as bad in the early 2000s as they were in the 1980s. And this is where we get to the next step in the evolution of our little alien friend here, which, as many evolutionary leaps are, was mostly a hardware upgrade. I mean, if you have been following the news about AI at all, you've probably heard this term GPU. GPUs, components that go into data centers.

36:49Or the company. Computer chip maker, NVIDIA. NVIDIA. The most valuable company in history. That makes these things. Its story, of course, wrapped up in the frenzy around the future of artificial intelligence. These things and this company have been at the center of the conflict between China and the U.S. when it comes to export controls. The idea here is for the U.S. to kind of limit the ability for China to catch up when it comes to AI. And interestingly, what these GPUs, these graphical processing units, were originally designed for was computer games. Video games, things like that. And what they're really good at is just doing a bunch of different math problems all at once.

37:27Exactly. It's just all about multiplying and adding numbers as fast as you can. There's some other things, but by and large, just do those two things and we're off to the races. In doing these math problems all at once, which is called parallel processing, that's exactly what these learning machines needed to do some version of that super complicated multiverse prediction thing we discussed. Sure, sure, sure. And so with these GPUs and this new parallelized architecture that Google named a transformer, all of a sudden they could get a machine to parse those longer sentences and give at least reasonable answers to more complicated questions.

38:04All right. But what really sent these AI chatbots into the stratosphere was a kind of knock-on effect of this parallel processing. Because when you can process everything at the same time in parallel, you can actually train on a lot more material in the same amount of time. And so eventually they just gave it basically the entire internet, almost everything we humans have ever said on the internet, as its training material, and started sending that through this network of light bulbs and wires that was just unimaginably big. Like, to get a sense. In our smaller example with the circle. There's something like a thousand and some odd parameters.

38:42Right. A thousand or so of those wires. GPT-3, which was kind of dumb by today's standards, but it came out, had 175 billion parameters. 175 billion things that could be tweaked. Yeah, and many of the ones that we have now, they're trillions of parameters. And as they fed basically all the things we humans have ever said on the internet into this thing, throwing way more training examples and way more compute than anyone would reasonably think to do, slowly they started to notice That with a sufficiently large amount of data on a sufficiently large model run with sufficiently many cycles of training, these new computers do seemingly intelligent things.

39:28Now, a lot of what I just described was written up in a paper called Attention is All You Need. And these findings are really what unlocked these large language models like ChatGPT. And that's all it was really intended for. But... There was a passage in there saying, we think this can work for images and video. And indeed, that turns out to work.

39:59That same basic model of massive parallel processing with tons of input. That could predict the next part of an image or sound. The moment civilization was transformed. And that moment, that realization, is really what triggered the explosive proliferation of artificial intelligences, different kinds, practically different species of AIs that we are living amongst today. New artificial intelligence systems. Sheens that can teach themselves superhuman skills. Chat GPT-3. GPT-4. New thing, Apple intelligence. An app called Lenza. Bard. Bard. It's called Mid Journey. Text to video art generated by...

40:44It's crazy. Look at this. So I don't know what AI it is they're using. Yes, it feels like an episode of Black Mirror.

40:53So it's like all of these different apps doing all of these different things in all these different mediums. They're taking in a huge amount of examples and then they're using fancy math to basically predict the next word, the next pixel, the next note. And from that, it's like generating this whole huge diversity of new stuff. Yeah, basically. And I mean, it's also just as we described doing something that I don't totally understand that that's more holistic than just looking at the thing that happens next. But it is drawing on the examples it's been given to decide what should happen next, which suddenly sounds not so simple.

41:43It does send you into a spiral because it's like, is what I do any different from that just spewing out, you know, some iteration of everything else I've seen before this? Yeah, but first of all, you're not pulling from the whole internet, right? Like, you have to depend on just the limited things you've experienced or can even maybe remember. That's fair. And your, like, math is also just way sloppier. It's not as accurate. Yeah. And to that point, and maybe we shouldn't even go here, but there's this one other thing that you can control in these models, which is called the temperature, which is like this final knob you get to tweak on the thing.

42:23And so if you have, I think it's if you have the temperature all the way down, it will give you the most likely thing to come next. If you turn the temperature up a little bit, though, it then is going to pick like the second or third most likely thing to come next. No, so you can control like how precise you want the math. Like you can say I want it a little stanky. Yeah, like there's a little bit of randomness in it then that it's then acting upon in what it does next. So maybe you just want the temperature turned up on like every third word. So that there's this almost spontaneous feeling, serendipitous creation, active creation that comes out of this rigid math.

43:02Like it's like something startlingly creative might just be a less right answer. A less right answer. Wow. Yep. And just by doing that, it's going to keep doing stuff that we are going to get increasingly uncomfortable with. Yeah. Like right now there is an AI generated song on the Billboard country charts. Really? I didn't hear about that. But like if that's the case, I see no way that eventually a fully AI generated film won't hit the box office like that. That's just going to happen. But when it happens, it will be only because of all of this math. To me, I think the thing that makes me, it makes me realize is when you see under the hood, what you see is less like something spooky and ethereal.

43:59Yeah. Like there are times when it gets spooky, when like, like there'll be a time, like I'll be listening to like an AI generator podcast and then one of the hosts breathes. And I'm like, wait, that's so weird. Like it doesn't even need oxygen. Why is it breathing? And now it's like, oh, because you know that like that's just the next statistical thing that would come in that sentence is a breath. That to me, that to me is like it's much less eerie because you can see where I got it from. Right. But. Well, OK, I do have one bit more for you because I don't know, I I still found myself wondering how it will feel as these things get better and better.

44:40And in particular, what it'll feel like in the moments we sit across from it and it is better than us at something we have spent our lives working on. That it is better than us at something we truly love. Yeah, many, many people. Also, all my friends tell me like, wow, you are the first professional goal player. Be famous because you lost the game. No. So, yeah, it's me. And so I got in touch with this guy. Fan Hui. I'm a professional Go player. Three-time European champion. So, real quick, Go. It is an ancient Chinese game, considered probably the most complicated board game in the world, to teach a computer to play, because of just how open-ended it is.

45:29All you really need to know is you are trying to control as much of the board as possible. You go back and forth with your opponent, placing one stone at a time, and you control portions of the board or territory by either like cordoning off sections of it or encircling your opponent's stones. It's a very simple idea, but it's difficult. Because with such simple rules, there are just this crazy number of ways the game can play out. In fact, folks like to say that there are more possible ways for a Go game to Go than there are atoms in the known universe. Yes. Anyhow, back to Fon. I remember I discovered a Go age like six in my school in Xi 'an.

46:17And I feel something, oh, this game I can play. And I progressed very quick. One year after I learned Go, for my school, I'm number one. Three years after that, I'm in the best team in the province. And not long after that, I stopped my school. I only learned Go game. I mean, for years. Every day, only thing you do is just to play Go game. 12 hours. 12 hours of playing. Yeah, it's no joke. Around age 15, he went pro. And somewhere along the way, he says, he noticed this almost magical quality of the game. Go for me, it's like a mirror. A mirror? Mirror, yeah. Because when you play, you can see your mind on the board.

47:06He says all the choices you make, whether you're aggressive in attack or are patient and waiting, you know, in a sense, how you think stares back up at you. It's like print. Mind print. And your opponent's mind, he says, it's printed there too. So I play with someone. I don't know him. I never talk with him. I play one game. I know him. This is magical. But this mirror of his, well, it was about to get shattered. 2015, that means the hazardous. Researcher at Google sent me an email like, we have some very exciting Go project. Can you go to our office, visit? We will show you our project. I tell you, yes, okay, why not?

47:50And what they showed him was this thing called AlphaGo. It was a computer that had learned how to play the game. and they asked him, like, will you play against this? So I tell him, okay, we can play together because I will win. It's just a program. That's a program. What can you do? You can win with me? Never. It's like 0 % chance to win this. 0%. And why were you so confident? Because I know the best program this moment, I can give six stone handicap. Handicap. Handicap. Handicap game. Got it, got it, got it, got it. So how you can possibly make the technique, make the future different, just months, it's impossible.

48:33And so, a month later, in this windowless office room, Fawn faced off with this computer and its human stone-placing helper in a best-of-five game match. That first game, all the game, I feel good. I think I will win. But, end of game. With just a few stones left to play. Oh, stupid. I make some mistake. And I lost my first game. But, you know, he's thinking, I was sort of arrogant going into this. I was overly confident. So next game, I will be careful. I will play more seriously. I will win the game. So the next day, next game, he sits down at the board, starts carefully placing his stones. And it's looking good.

49:21on the board, but inside his head... I feel something really difficult. Very difficult. Very difficult. Because... I like to fight, but Alpha don't fight with me. And if I want to take something, Alpha gave me very easily. Looking down at the board, he was not able to see his opponent's mind in the way he always had. No. There was no bravery. There was no subterfuge that he could sense. I see Africa won't do this. Africa won't do that. But why he won't do this? You cannot find it. You can't. And so he didn't know how to respond to it. His mind started to race. Good move, bad move. Good move, what mean?

50:12Bad move, what mean? Good move, what think my teacher? Good move, what think my student? Everybody, all my friends. And he realized that with all these emotional pushes and pulls that eventually... I will make mistake. But AlphaGo, no. Never. When you think about this, the confidence is crushed. It's crushed. All crushed. And I lost again, very, very badly. And I lost again for third, fourth, and the last one. Yeah, damn.

51:25But, you know, this experiment is really good for me. This is the moment I really see myself. Really? You think AlphaGo taught you to be more... Myself, yeah. I think AlphaGo teaches me about that. And why? Because I see myself. So it's like AlphaGo teaches me that our life, we will always lust. Lust, lust, lust. Sorry, it's real life. It's our life. I think this is human. This is important for us. I think what he saw in that game as he was losing was kind of what you were saying about seeing under the hood making AI less spooky. Like he could see it wasn't magic. It was math with no mistakes.

52:16Right. And when he saw himself, you know, like not being the perfect go player in any given moment or in every given moment, like that's what makes him a person. A person who could love something but still lose at it. Maybe feel bad about that and then use that feeling to figure out what to do next. Today, I'm teaching the go in China with a student. Why are you teaching Go? The computer will always win. Yes, yes. But be careful. Because I think all you experiment to learn is still useful. So don't worry. It will be coming. You can do nothing. Accept it and just learn. Yeah, I get that. Before we wrap this thing up, I wanted to put all of this in front of someone.

53:13and not an AI person, but somebody with a really wide scope on technology and history. And so I went to this guy. Tom Mullaney, professor of modern Chinese history at Stanford University. I worked with him years back on a story about typing in Chinese and he's just one of the most thoughtful and informed people I know. That means a lot to me. So how would you respond? Well, I mean, everyday life is at its core a study of this awful, amazing, horrifying, never-ending surprise of what it means to be born and live and die as a human. And even if, at the end of the day, an AI is orders of magnitude smarter, AI, just by definition, cannot suffer and rejoice and live and die in quite the same way that humans can, in the same way that we cannot live and die and suffer and comprehend and feel the way an octopus can.

54:23The only thing an AGI will be able to do is contemplate, my goodness, what does it mean to be an AI? And so I am not worried at all about what AI means with regard to meaning, human identity, what it means to be human, or any of that. Well, that was very beautiful. And while I love that, I'm still like, but this is going to mess everything up so badly. I don't know if this... Oh, no, I agree. Okay, go. No, it's... I mean, this is going to get weird down to the fabric. But fast forward this, you know, 20, 30 years, if we're still around at a sort of climate change level, when another future human is sitting in this fabric altered world, it will still be a group of humans rejoicing, suffering.

55:24Like it will still be that condition. and so it's kind of a it's kind of a liberate for me it's like a little bit of a liberatory time it's a great maybe we'd get to free up a little bit more space to get back to work thinking about how to be human because we have not we have not even come close to solving that issue Thank you.

56:28Thank you.

57:16Special thanks to Stephanie Yin and the New York Institute of Go for teaching us the game. To Mark, Daria, and Levon. to Barbara Svenich. And of course, thank you to Grant Sanderson for his unending patience explaining the math of neural nets to us. Grant is kind of like your favorite math nerd's favorite math nerd. His YouTube channel is 3Blue1Brown. Check it out. This story was reported and produced by Simon Adler with original scoring and sound design by Simon Adler. Which brings me to the last unsavory thing I have to say, which is goodbye to Simon Adler who happens to be one of our best reporter producers here at the show and also a friend.

58:03He's going off to, among other things, pursue his music career and this was his last episode on Staff With Us. Chances are if you list out your favorite episodes from the last 11 years at the show, more than a couple will be his. Could be some of the tech stories he did. He did stories about drones in Ukraine, about content moderation on Facebook. Could be some of the international stories he did. He reported about the hunt for an endangered rhino in Namibia. He did a story about a species of raccoon in the Caribbean islands of Guadalupe. He did a lot of stories about democracy as well. Covered a town, Seneca, Nebraska, that voted itself out of existence.

58:44He did a story back in 2017 about a New York City council race where the campaign manager was a little-known guy named Zoran Mamdani. Besides being a killer reporter, not to mention composer and interviewer, Simon has also spent so many hours coaching an entire generation and staffers and interns. He's so generous with his expertise and his time. Really someone who makes everyone around him better. Anyway, we have been so lucky to have him as part of our nerdy band for 11 years. Check out his band, Windstar Enterprises, on Instagram. That's Simon and another fellow former Radiolabber, Alex Overington.

59:30We already miss you, Simon. And good luck out there.

59:44Oh, you want me to say this? Oh, that's fun. Hi, I'm Cordelia, and I'm from New York City. And here are the staff credits. Radiolab is hosted by Lulu Miller and Latif Nasser. Soren Wheeler is our executive editor. Sarah Sandback is our executive director. Our managing editor is Pat Walters. Dylan Keefe is our director of sound design. Our staff includes Simon Adler, Jeremy Bloom, W. Harry Fortuna, David Gable, Maria Paz Gutierrez, Sindhu Nyanassam Bandhan, Matt Kielty, Mona Madgavkar, Annie McEwan, Alex Neeson, Sarah Kari, Anissa Vitsa, Arianne Wack, Molly Webster, and Jessica Young. With help from Rebecca Rand, our fact checkers are Diane Kelly, Emily Krieger, Anna Pujol-Mazzini, and Natalie Middleton.

1:00:40Leadership support for Radiolab science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation.