Michael Littman: Reinforcement Learning and the Future of AI | Lex Fridman Podcast #144
Michael Littman, a Brown computer scientist, discusses reinforcement learning's evolution from early neural nets through AlphaGo, and argues that near-term superintelligence risks are overblown. He's skeptical of Bostrom and Musk's warnings, pointing instead to social media as an existing example of algorithmic influence on society.
Chapters
The following is a conversation with Michael Littman, a computer science professor at Brown University doing research on and teaching machine learning, reinforcement learning, and artificial intelligence. He enjoys being silly and light hearted in conversation, so this was definitely a fun one. Quick mention of his sponsor followed by some thoughts related to the episode. Thank you to SimpliSafe, a home security company I use to monitor and protect my apartment, ExpressVPN, the VPN I've used for many years to protect my privacy on the Internet, masterclass online courses that I enjoy from some of the most amazing humans in history and betterhelp online therapy with a licensed professional. Please check out these sponsors in the description to get a discount and to support this podcast. As a side note, let me say that I may experiment with doing some solo episodes in the coming month or two. The three ideas I have floating in my head currently is to use 1 a particular moment in history, 2 a particular movie or 3 a book to drive a conversation about a set of related concepts. For example, I could use 2001 A Space Odyssey or Ex Machina to to talk about AGI for 1, 2, 3 hours. Or I could do an episode on the yes, rise and fall of Hitler and Stalin, each in a separate episode using relevant books and historical moments for reference. I find the format of a solo episode very uncomfortable and challenging, but that just tells me that it's something I definitely need to do and learn from the experience. Of course, I hope you come along for the ride. Also, since we have all this momentum built up on announcements, I'm giving a few lectures on machine learning at MIT this January. In general, if you have ideas for the episodes, for the lectures, or for just short videos on YouTube, let me know in the comments that I still definitely read, despite my better judgment and the wise sage device of the great Joe Rogan. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcast, follow on Spotify, support on Patreon, or connect with me on Twitter lexfriedman. And now here's my conversation with Michael Littman. I saw a video of you talking to Charles Isbell about Westworld, the TV series you guys were doing. Kind of thing where you're watching new things together. But let's rewind back. Is there a sci fi movie or book or shows that you that was profound, that had an impact on you philosophically or just like specifically something you enjoyed nerding out about?
Yeah, interesting. I think a lot of us have been inspired by robots in movies. The one that I really like is there's a movie called Robot and Frank, which I think is really interesting because it's very near term a future where robots are being deployed as helpers in people's homes. And it was. It was. And we don't know how to make robots like that at this point, but it seemed very plausible, it seemed very realistic or imaginable. And I thought that was really cool because they did. They're awkward, they do funny things that raise some interesting issues. But it seemed like something that would ultimately be helpful and good if we could do it. Right.
Yeah. He was an older, cranky gentleman, right?
He was an older, cranky jewel thief. Yeah.
It's kind of funny little thing, which is, you know, he's a jewel thief. And so he pulls the robot into his life, which is like. Which is something you can imagine taking a home robotics thing and pulling into whatever quirky thing that's involved in your.
Meaningful to you. Exactly. So, yeah. And I think. I think from that perspective, I mean, not all of us are jewel thieves. And so when we bring our robots into ourselves.
Yeah.
It explains a lot about this apartment, actually. But no, the idea that people should have the ability to, you know, make this technology their own, that it becomes part of their lives. And I think that's. It's hard for us as technologists to make that kind of technology. It's easier to mold people into what we need them to be. And just that opposite vision, I think, is really inspiring.
And then there's a anthropomorphization where we project certain things on them, because I think the robot was kind of dumb. But I have a bunch of roombas I play with. And you immediately project stuff onto them. Much greater level of intelligence. We'll probably do that with each other too. Much. Much great. Much greater degree of compassion.
That's right. One of the things we're learning from AI is where we are smart and where we are not smart.
Yeah. You also enjoy, as people can see, and I enjoyed myself watching you sing and even dance a little bit. A little bit. A little bit. A little bit of dancing, a little bit of dancing. That's not quite my thing as a method of education or just in life in general. So easy question. What's the definitive, objectively speaking, top three songs of all time? Maybe something that. To walk that back a little bit. Maybe something that others might be surprised by. Three songs that you kind of enjoy.
That is a great question that I cannot answer. But instead, let me tell you a story.
Pick a question you do want to answer.
That's right. Watching the presidential debates and vice presidential debates and turns out, yeah, it's really. You can just answer any question you want. It's a related question. Yeah. Well said. I really like pop music. I've enjoyed pop music ever since I was very young. So 60s music, 70s music, 80s music. This is all awesome. And then I had kids and I think I stopped listening to music. And I was starting to realize that my musical taste had sort of frozen out. And so I decided in 2011, I think, to start listening to the top 10 Billboard songs each week. So I'd be on the treadmill and I would listen to that week's top 10 songs so I could find out what was popular now. And what I discovered is that I have no musical taste whatsoever. I like what I'm familiar with. And so the first time I'd hear a song is the first week that it was on the charts, I'd be like, ugh. And then the second week I was into it a little bit and the third week I was loving it. And by the fourth week it was like just part of me. And so I'm afraid that I can't tell you the most, my favorite song of all time because it's whatever I heard most recently.
Yeah, that's interesting. People have told me that there's an art to listening to music as well. And you can start to. If you listen to a song just carefully, like explicitly, just force yourself to really listen. You start to. I did this when I was part of jazz band and fusion band in college. Is the. There's they. You. You start to hear the layers of the instruments. You start to hear the individual instruments. And you start to. You can listen to classical music or to orchestra this way. You can listen to jazz this way. I mean, it's. It's funny to imagine you now to walking that forward to listening to pop hits now as like a scholar listening to like Cardi B or something like that. Or Justin Timberlake. Is he. No, not Timberlake. Bieber. I get my.
They've both been in the top 10 since I've been listening.
They're still up there. Oh my God, I'm so cool.
If you haven't heard Justin Timberlake's top 10 in the last few years, there was one song that he did where the music video was set at essentially Neurips.
Oh, wow. Oh, the one with the robotics. Yeah, yeah, yeah, yeah, yeah, yeah, yeah, yeah.
He's like at an academic conference and he's doing it.
He was presenting.
It was Sort of a cross between the Apple like Steve Jobs kind of talk and neurips. So it's always fun when AI shows up in pop culture.
I wonder if he consulted somebody for that. That's really interesting. So maybe on that topic, I've seen your celebrity multiple dimensions, but one of them is you've done cameos in different places. I've seen you in a TurboTax commercial as I guess the brilliant Einstein character. And the point is that TurboTax doesn't need somebody like you. It doesn't need a brilliant person.
Very few things need someone like me. But yes, they were specifically emphasizing the idea that you don't need to be a computer expert to be able to use their software.
How did you end up in that world?
I think it's an interesting story. So I was teaching my class. It was an intro computer science class for non concentrators, non majors. And sometimes when people would visit campus, they would check in to say, hey, we want to see what a class is like. Can we sit on your class? So a person came to my class who was the daughter of the brother of the husband of the best friend of my wife. Anyway, basically a family friend came to campus to check out Brown and asked to come to my class and came with her dad. Her dad is who I've known from various kinds of family events and so forth. But he also does advertising and he said that he was recruiting scientists for this ad, this TurboTax set of ads. And he said, we wrote the ad with the idea that we get the most brilliant researchers, but they all said no. So can you help us find B level scientists? And I'm like, sure, that's, that's who I hang out with, so that should be fine. So I put together a list and I did what some people called a Dick Cheney. So I included myself on the list of possible candidates, you know, with a little blurb about each one and why I thought that would make sense for them to do it. And they reached out to a handful of them, but then they ultimately they YouTube stalked me a little bit and they thought, oh, I think he could do this. And they said, okay, we're going to offer you the commercial. I'm like, what? So it was, it was such an interesting experience because it's. They have another world. The people who do nationwide kind of ad campaigns and television shows and movies and so forth. It's quite a remarkable system that they have going because it's like a set. Yeah. So I went to. It was just somebody's house that they rented in New Jersey. But in the commercial, it's just me and this other woman. In reality, there were 50 people in that room and another, I don't know, half a dozen, kind of spread out around the house in various ways. There were people whose job it was to control the sun. They were in the backyard on ladders, putting filters up to try to make sure that the sun didn't glare off the window in a way that would wreck the shot. So there was like, six people out there doing that. There was three people out there giving snacks. The craft table. There was another three people giving healthy snacks because that was a separate craft table. There was one person whose job it was to keep me from getting lost. And I think the reason for all this is because so many people are in one place at one time. They have to be time efficient. They have to get it done. The morning they were going to do my commercial. In the afternoon, they were going to do a commercial of a mathematics professor from Princeton. They had to get it done. No wasted time or energy. And so there's just a fleet of people all working as an organism. And it was fascinating. I was just, the whole time just looking around like, this is so neat. Like, one person whose job it was to take the camera off of the cameraman and so that someone else whose job it was to remove the film canister because every couple's takes, they had to replace the film because, you know, film gets used up. It was just. I don't know. I was geeking out the whole time. It was so fun.
How many takes did it take? It looked the opposite. Like, there was more than two people there. It was very relaxed.
Right? Right. Yeah. I mean, the person who I was in the scene with is a professional. She's a, you know, she's an actor. Improv comedian, New York City. And when I got there, they had given me a script as such as it was. And then I got there and they said, we're gonna do this as improv. I'm like, I don't know how to improv. Like, this is not. I don't know what this. I don't know what you're telling me to do here. Don't worry, she knows. Okay, we'll see how this goes.
I guess I got pulled into the story because, like, where the heck did you come from? I guess in the scene, like, how did you show up in this random person's house? I don't know.
Yeah, Well, I mean, the reality of it is I stood outside in the blazing sun. There was someone whose job it was to keep an umbrella over me because I started to schwitz. I started to sweat. And so I would wreck the shot because my face was all shiny with sweat. So there was one person who would dab me off, had an umbrella. But yeah, like, the reality of it, like, why is this strange stalkery person hanging around outside somebody's house?
We're not sure when you have to look in. We'll have to wait for the book. But are you. So you make. You make, like you said, YouTube. You make videos yourself. You make awesome parody sort of parody songs that kind of focus in on particular aspects of computer science. How much those seem really natural. How much production value goes into that. Do you also have a team of 50 people videos?
Almost all the videos except for the ones that people would have actually seen are just me. I write the lyrics, I sing the song. I generally find a. Like a backing track online because, unlike you can't really play an instrument. And then I do. In some cases, I'll do visuals using just like PowerPoint, lots and lots of PowerPoint to make it sort of like an animation. The most produced one is the one that people might have seen, which is the overfitting video that I did with Charles Isbel, and that was produced by the Georgia Tech and Udacity people because we were doing a class together. It was kind of. I usually do parody songs kind of to cap off a class at the end of a class.
So that one you're wearing. So it was just a thriller.
Yeah.
You're wearing the Michael Jackson. The red leather jacket. The interesting thing with podcasting that you're also into is that I really enjoy is that there is not a team of people. It's kind of more. Because, you know, the. The. There's something that happens when there's more people involved than just one person. That just the way you start acting, I don't know, there's a censorship you're not given especially for, like, slow thinkers like me. You're not. And I think most of us are. If we're trying to actually think we're a little bit slow and careful, it kind of large teams get in the way of that. And I don't know what to do with it. Like, that's the. To me, like, if, you know, it's very popular to criticize, quote, unquote, mainstream media, I. But there is legitimacy to criticizing them the same. I love listening to npr, for example. But every. It's clear that there's a Team behind it. There's a commerce. There's constant commercial breaks. There's this kind of like, rush of like. Okay, I have to interrupt you now because we have to go to commercial. Just this whole. It creates. It destroys the possibility of nuanced conversation. Yeah, exactly. Evian, which Charles Isbell, who I talked to yesterday, told me that Evian is naive backwards, which the fact that his mind thinks this way is just. Is quite brilliant. Anyway, there's a freedom to this podcast.
He's Dr. Awkward, which, by the way, is a palindrome. That's a palindrome that I happen to know from other parts of my life. And I just threw it out. Well, you know, use it against Charles. Dr. Awkward.
So what was the most challenging parody song to make? Was it the Thriller one?
Mm, no. That was really fun. I wrote the lyrics really quickly and then I gave it over to the production team. They recruited an acapella group to sing. That went. It went really smoothly. It's great having a team because then you can just focus on the part that you really love, which in my case is writing the lyrics. For me, the most challenging one, not challenging in a bad way, but challenging in a really fun way, was I did. One of the parody songs I did is about the halting problem in computer science. The fact that you can't create a program that can tell for any other arbitrary program whether it actually going to get stuck in an infinite loop or whether it's going to eventually stop. And so I did it to an 80s song because that's. I hadn't started my new thing of learning current songs, and it was Billy Joel's the Piano Man.
Nice.
Which is a great song.
Great song.
Yeah, yeah.
And sing me a song. You're the piano man. Yeah, it's a great song.
So the lyrics are great because first of all, it rhymes. Not all songs rhyme. I did. I've done Rolling Stone songs which turn out to have no rhyme scheme whatsoever. They're just sort of yelling and having a good time, which makes it not fun from a parody perspective because, like, you can say anything, but the lines rhymed. And there was a lot of internal rhymes as well. And so figuring out how to sing with internal rhymes, a proof of the halting problem, was really challenging, and I really enjoyed that process.
What about last question on this topic? What about the dancing and the Thriller video? How many takes? That take.
So I wasn't planning to dance. They had me in the studio and they gave me the jacket, and it's like well, you can't. If you have the jacket and the glove, there's not much you can do. So I think I just danced around, and then they said, why don't you dance a little bit? There was a scene with me and Charles dancing together. They did not use it in the video, but we recorded it.
I remembered.
Yeah, yeah. No, it was pretty funny. And Charles, who has this beautiful, wonderful voice, doesn't really sing. He's not really a singer. And so that was why I designed the song with him doing a spoken section and me doing the singing.
It's very like, Barry White.
Yeah. Smooth baritone. Yeah.
Yeah.
It's great.
It was awesome. So one of the other things Charles said is that, you know, everyone knows you as, like, a super nice guy, super passionate about teaching and so on. What he said. Don't know if it's true that despite the fact that you're.
You are cold blood, like, okay, I will admit this. Finally, for the first time, that was.
That was me, the Johnny Cash song, the Mano, just to watch him die, that you actually do have some strong opinions on some topics. So if this, in fact, is true, what strong opinions would you say you have? Is there ideas you think maybe in artificial intelligence, machine learning, maybe in life that you believe is true, that others might. You know, some number of people might disagree with you on?
So I try very hard to see things from multiple perspectives. There's this great Calvin and Harbs, Calvin and Hobbes cartoon. Where do you know? Okay, so Calvin's dad is always kind of a bit of a foil. And he talked Calvin into. Calvin had done something wrong. The dad talks him into, like, seeing it from another perspective. And Calvin, like, this breaks Calvin because he's like, oh. Oh, my gosh. Now I can see the opposite sides of things. And so it becomes like a cubist cartoon where there is no front and back. Everything's just exposed, and it really freaks him out. And finally he settles back down. He's like, oh, good. No, I can make that go away. But, like, I'm. That I live in that world where I'm trying to see everything from every perspective all the time. So there are some things that I've formed opinions about that would be harder, I think, to disavow me of. One is the superintelligence argument, and the existential threat of AI is one where I feel pretty confident in my feeling about that one. Like, I'm willing to hear other arguments, but, like, I am not particularly moved by the idea that if we're not careful, we will accidentally create a superintelligence that will destroy human life.
Let's talk about that. Let's get you in trouble and record you on video. It's like Bill Gates, I think he said, like some quote about the Internet, that that's just going to be a small thing. It's not going to really go anywhere. And I think Steve Ballmer said, I don't know why I'm sticking on Microsoft. That's something that, like, smartphones are useless. There's no reason why Microsoft should get into smartphones. That kind of. So let's get. Let's talk about AGI as AGI is destroying the world. We'll look back at this video and see. No, I think it's really interesting to actually talk about because nobody really knows the future. So you have to use your best intuition. It's very difficult to predict it. But you have spoken about AGI and the existential risks around it and sort of based on your intuition that we're quite far away from that being a serious concern relative to the other concerns we have. Can you maybe unpack that a little bit?
Yeah, sure, sure, sure. So, as I understand it, the. For example, I read Bostrom's book and a bunch of other reading material about this sort of general way of thinking about the world, and I think the story goes something like this, that we will at some point create computers that are smart enough that they can help design the next version of themselves, which itself will be smarter than the previous version of themselves and eventually bootstrapped up to being smarter than us, at which point we are essentially at the mercy of this sort of more powerful intellect, which, in principle, we don't have any control over what its goals are. And so if its goals are at all out of sync with our goals, like, for example, the continued existence of humanity, we won't be able to stop it. It'll be way more powerful than us and we will be toast. So there's some, I don't know, very smart people who have signed on to that story. And it's a compelling story. Now I can really get myself in trouble. I once wrote an op ed about this, specifically responding to some quotes from Elon Musk, who has been on this.
Very podcast more than once, and AI summoning the demon.
But then he came to Providence, Rhode island, which is where I live, and. And said to the governors of all the states, you know, you're worried about entirely the wrong thing. You need to be worried about AI. You need to be very, very worried about AI. So. And journalists kind of reacted to that and they wanted to get people's, people's take. And I was like, okay. My belief is that one of the things that makes Elon Musk so successful and so remarkable as an individual is that he believes in the power of ideas. He, he believes that you can have, you can, if, you know, if you have a really good idea for getting into space, you can get into space. If you have a really good idea for a company or for how to change the way that people drive, you just have to do it and it can happen. It's really natural to apply that same idea to AI. You see these systems that are doing some pretty remarkable computational tricks demonstrations, and then to take that idea and just push it all the way to the limit and think, okay, where does this go? Where is this going to take us next? And if you're a deep believer in the power of ideas, then it's really natural to believe that those ideas could be taken to the extreme and kill us. So I think his strength is also his undoing, because that doesn't mean it's true, it doesn't mean that that has to happen, but it's natural for him to think that.
So another way to phrase the way he thinks and I, I find it very difficult to argue with that line of thinking. So Sam Harris is another person from neuroscience perspective that thinks like that is saying, well, is there something fundamental in the physics of the universe that prevents this from eventually happening? And this Nick Bostrom thinks in the same way. That kind of zooming out. Yeah. Okay. We humans now are existing in this like time scale of minutes and days. And so our intuition is in this time scale of minutes, hours and days. But if you look at the span of human history, is there any reason we, you can't see this in, in a hundred years? And like, is there, is there something fundamental about the laws of physics that prevent this? And if it doesn't, then it eventually will happen or will we will destroy ourselves in some other way. And it's very difficult, I find, to actually argue against that.
Yeah, me too.
And not sound like, not sound like you're just like rolling your eyes, like I have, like science fiction.
We don't have to think about it.
But even, even worse than that, which is like, I don't have kids, but like, I gotta pick up my kids now like this. Okay, I see.
There's more pressing short term.
Yeah, there's more pressing short term things that like, stop it with this existential crisis. We have much shorter things like now, especially this year, there's Covid. So any kind of discussion like that is there's pressing things today. And then. So the Sam Harris argument. Well, any day the exponential singularity can occur. It's very difficult to argue against. I mean, I don't know, but part.
Of his story is also he's not going to put a date on it. It could be in a thousand years, it could be in 100 years, it could be in two years. It's just that as long as we keep making this kind of progress, it ultimately has to become a concern. I kind of am on board with that. But the piece that I feel like is missing from that way of extrapolating from the moment that we're in is that I believe that in the process of actually developing technology that can really get around in the world and really process and do things in the world in a sophisticated way, we're going to learn a lot about what that means, which that we don't know now because we don't know how to do this right now. If you believe that you can just turn on a deep learning network and eventually give it enough compute and it'll eventually get there, well, sure, that seems really scary because we won't be in the loop at all. We won't be helping to design or target these kinds of systems. But I don't see that feels like it is against the laws of physics because these systems need help. They need to surpass the difficulty, the wall of complexity that happens in arranging something in the form that will happen in. Yeah, I believe in evolution. I believe that there's an argument. Right. So there's another argument, just to look at it from a different perspective, that people say, well, I don't believe in evolution. How could evolution. It's sort of like a random set of parts assemble themselves into a 747, and that could just never happen. Yeah. So it's like, okay, that's maybe hard to argue against, but clearly 747s do get assembled. They get assembled by us, basically. The idea being that there's a process by which we will get to the point of making technology that has that kind of awareness. And in that process, we're going to learn a lot about that process and we'll have more ability to control it or to shape it, or to build it in our own image. It's not something that is going to spring into existence like that 747, and we're just going to have to contend with it. Completely unprepared.
Now, it's very possible that in the context of the long arc of human history, it will in fact spring into existence. But that springing might take like, if you look at nuclear weapons, like even 20 years is a springing in the context of human history. And it's very possible, just like with nuclear weapons, that we could have. I don't know what percentage you want to put at it, but the possibility of.
Could have knocked ourselves out.
Yeah. The possibility of humanity, beings destroying themselves in the 20th century with nuclear weapons. I don't know. You can, if you really think through it, you could really put it close to like, I don't know, 30, 40% given like the certain moments of crisis that happen. So like, I think one like fear in the shadows that's not being acknowledged is. It's not so much the AI will run away is, is that as it's running away, we won't have enough time to think through how to stop it.
Right. Fast takeoff or foom.
Yeah. I mean my much bigger concern, I wonder what you think about it, which is we won't know it's happening. So I kind of that argument think that there is an AGI situation already happening with social media. That our minds, our collective intelligence of human civilization is already being controlled by an algorithm and we're already super like the level of collective intelligence thanks to Wikipedia. People should donate to Wikipedia to feed the AGI.
Man. If we had a superintelligence that was in line with Wikipedia's values, it's a lot better than a lot of other things I can imagine. I trust Wikipedia more than I trust Facebook or YouTube. As far as trying to do the right thing from a rational perspective.
Yeah.
Now that's not where you were going. I understand that. But it does strike me that there's sort of smarter and less smart ways of exposing ourselves to each other on the Internet.
Yeah. The interesting thing is that Wikipedia and social media, very different forces. You're right. I mean Wikipedia, if AGI was Wikipedia, it'd be just like this cranky, overly competent editor of articles. There's something to that. But the social media media aspect is not. So the vision of AGI is as a separate system that's super intelligent. That's super intelligent. That's one key little thing. I mean, there's the paperclip argument that's super dumb, but super powerful systems. But with social media you have a relatively. Algorithms we may talk about today, very simple algorithms that when something Charles talks a lot about, which is interactive AI, when they start having at scale like tiny little interactions with human beings, they can start controlling these human beings. So a single algorithm can control the minds of human beings slowly to where we might not realize. It can start wars, it can start, it can change the way we think about things. It feels like in the long arc of history, if I were to sort of zoom out from all the outrage and all the tension on social media, the that it's progressing us towards better and better things. It feels like chaos and toxic and all that kind of stuff.
But it's chaos and toxic.
Yeah, but it feels like actually the chaos and toxic is similar to the kind of debates we had from the founding of this country. You know, there was a civil war that happened over that, over that period. And ultimately it was all about this tension of like something doesn't feel right about our implementation of the core values we hold as human beings and they're constantly struggling with this. And that results in people calling each other like just, just being shitty to each other on Twitter. But I, ultimately the algorithm is managing all that and it feels like there's a possible future in which that algorithm controls us to. Into the direction of self destruction and whatever that looks like.
Yeah, so. So, all right. I do believe in the power of social media to screw us up royally. I do believe in the power of social media to benefit us too. I do think that we're in a. Yeah, it's sort of almost got dropped on top of us and now we're trying to, as a culture figure out how to cope with it. There's a sense in which, I don't know, there's some arguments that say that, for example, I guess college age students now, late college age students now, people who were in middle school when social media started to really take off, maybe, maybe really damaged like this may have really hurt their development in a way that we don't have all the implications of quite yet. That's the generation who if, and I hate to make it somebody else's responsibility, but like they're the ones who can fix it. They're the ones who can figure out how do we keep the good of this kind of technology without letting it eat us alive. And if they're successful, we move on to the next phase, the next level of the game. If they're not successful, then yeah, then we're going to wreck each other. We're going to destroy society.
So you're going to, in your old age, sit on a porch and watch the world burn because of the TikTok generation. That I believe.
Well, so this is my kid's age, right, and certainly my daughter's age. And she's very tapped in to social stuff, but she's also. She's trying to find that balance, right, of participating in it and then getting the positives of it, but without letting it eat her alive. And I think sometimes she ventures. I hope she doesn't watch this. Sometimes I think she ventures a little too far and is consumed by it. And other times she gets a little distance. And if there's enough people like her out there, they're going to navigate this choppy waters.
That's. That's an interesting skill, actually to develop. I talked to my dad about it. You know, I've now somehow this podcast in particular, but other reasons, has received a little bit of attention. And with that, apparently in this world, even though I don't shut up about love and I'm just all about kindness, I. I have now a little mini army of trolls. Oh, it's kind of hilarious, actually. But it also doesn't feel good. But it's a skill to learn to not look at that, like, to moderate, actually, how much you look at that. The discussion I had with my dad, it's similar to. It doesn't have to be about trolls. It could be about checking email, which is like, if you're anticipating, you know, there's. My dad runs a large institute at Drexel University. And there could be stressful, like, emails. You're waiting. Like, there's drama of some kinds. And so, like, there's a temptation to check the email. If you send an email, you got it and that pulls you in. Into it doesn't feel good. And it's a skill that he actually complains that he hasn't learned. I mean, he grew up without it, so he hasn't learned the skill of how to shut off the Internet and walk away. And I think young people, while they're also being, quote, unquote, damaged by, like, you know, being bullied online, all those stories which are very, like, horrific. You basically can't escape your bullies these days when you're growing up, but at the same time, they're also learning that skill of how to be able to shut off the, like, disconnect with it, be able to laugh at it, not take it too seriously. It's fascinating. Like, we're all trying to figure this out. Just like you said, it's been dropped on us and we're trying to figure it out.
Yeah, I think that's really interesting. And I guess I've become a believer in the human design, which I feel like I don't completely understand. Like, how do you make something as robust as us? Like, we're so flawed in so many ways and yet, and yet, you know, we dominate the planet and we do seem to manage to get ourselves out of scrapes eventually. Not necessarily the most elegant possible way, but somehow we get to the next step. And I don't know how I'd make a machine do that. Generally speaking, if I train one of my reinforcement learning agents to play a video game and it works really hard on that first stage over and over and over again, and it makes it through, it succeeds on that first level, and then the new level comes and it's just like, okay, I'm back to the drawing board. And somehow humanity, we keep leveling up and then somehow managing to put together the skills necessary to achieve success, some semblance of success in that next level too. And, you know, I hope we can keep doing that.
You mentioned reinforcement learning. So you've have a couple years in the field? No, quite, you know, quite a few, Quite a long career in artificial intelligence broadly. But reinforcement learning specifically, can you maybe give a hint about your sense of the history of the field in some ways has changed with the advent of deep learning, but has long roots. Like how is it weaved in and out of your own life? How have you seen the community change or maybe the ideas that it's playing with change?
I've had the privilege, the pleasure of being, of having almost a front row seat to a lot of this stuff. And it's been really, really fun and interesting. So when I was in College in the 80s, early 80s, the neural net thing was starting to happen and I was taking a lot of psychology classes and a lot of computer science classes as a college student. And I thought something that can play tic tac TOE and just learn to get better at it, that ought to be a really easy thing. So I spent almost all of my, what would have been vacations during college, hacking on my home computer, trying to teach it how to play tic tac toe.
Programming language basic.
Oh, yeah, I was, I. That's my first language. That's my native language.
Is that when you first fell in love with computer science? Just like programming BASIC on that. What was the computer? Do you remember?
I had, I had a TRS 80 Model 1 before they were called model ones because there was nothing else. I got my computer in 1979 instead. So I was, I would have been bar mitzvahed. But instead of having a big party that my parents threw on my behalf, they just got me a computer because that's what I really, really, really wanted. I saw them in the mall in Radio Shack and I thought, what? How are they doing that? I would try to stump them. I would give them math problems like one plus and then in parentheses, two plus one. And I would always get it right. I'm like, how do you know so much? Like, I've had to go to algebra class for the last few years to learn this stuff. And you just seem to know. So I was, I was, I was smitten and got a computer. And I think ages 13 to 15. I have no memory of those years. I think I just was in my room with the computer listening to Billy Joel, communing, possibly listening to the radio, listening to Billy Joel. That was the one album I had on vinyl at that time. And. And then I got it on cassette tape. And that was really helpful because then I could play it. I didn't have to go down to my parents. Wi Fi or hi Fi.
Sorry.
And at age 15, I remember kind of walking out and like, okay, I'm ready to talk to people again. Like, I've learned what I need to learn here. And so, yeah, so. So that was, that was my home computer. And so I went to college and I was like, oh, I'm totally going to study computer science. And I opted. The college I chose specifically had a computer science major. The one that I really wanted. The college I really wanted to go to didn't. So bye bye to them.
Which college did you go to?
So I went to Yale. Princeton would have been way more convenient. And it was just beautiful campus and it was close enough to home. And I was really excited about Princeton and I visited. I said, so computer science major. Like, well, we have computer engineering. I'm like, oh, I don't like that word engineering. I like computer science. I really, I want to do like you're saying hardware and software. They're like, yeah. I'm like, I just want to do software. I couldn't care less about hardware.
And you grew up in Philadelphia?
I grew up outside, yeah. Yeah.
Okay.
So the local schools were like Penn and Drexel and Temple. Everyone in my family went to temple, at least at one point in their lives, except for me. So, yeah, Philly family.
Yale had a computer science department. And that's when you. It's kind of interesting. You said 80s and neural networks. That's when neural networks was A hot new thing or a hot thing, period. So what is that in college when you first learned about neural networks? When she learned, like.
And it was in a psychology class, not in a CS class. Yeah.
Was it psychology or cognitive science or like, do you remember, like, what context it was?
Yeah, yeah, yeah. So. So I was a. I've always been a bit of a cognitive psychology groupie. So, like, I study computer science, but I like, I like to hang around where the cognitive scientists are because I don't know. Brains, man, they're like, they're wacky cool.
And they have a bigger picture view of things. They're a little less engineer y. I would say they're more, they're more interested in the nature of cognition and intelligence and perception and how the vision system works. They're asking always bigger questions. Now with the deep learning community, I think more. There's a lot of intersections, but I do find that the neuroscience folks actually, and cognitive psychology, cognitive science folks are starting to learn how to program how to use neuron artificial neural networks, and they are actually approaching problems in like, totally new, interesting ways. It's fun to watch that grad students from those departments, like approach to problem of Machine learning.
Right. They come in with a different perspective.
Yeah. They don't care about like your ImageNet data set or whatever. They, they want, like to understand the, the, the, like the basic mechanisms at the, at the neuronal level, at the functional level of intelligence. So it's kind of, it's kind of cool to see them work, but.
Yeah.
Okay. So you were always a groupie of cognitive psychology.
Yeah, yeah. And so it was in a class by Richard Gehrig. He was kind of my favorite psych professor in college, and I took like three different classes with him. And yeah, so they were talking specifically. The class, I think was kind of a. There was a big paper that was written by Steven Pinker and Prince. I'm blanking on Prince's first name, but Prince and Pinker and Prince, they wrote kind of a. They were at that time kind of like. I'm blanking on the names of the current people, the cognitive scientists who are complaining a lot about deep networks.
Oh, Gary.
Gary Marcus.
Gary Marcus, Yeah. And who else? I mean, there's a few. But Gary. Gary's the most feisty.
Sure, Gary's very feisty. And with this, with his co author, they, you know, they're kind of doing these kind of takedowns where they say, okay, well, yeah, it does all these amazing things, but here's a Shortcoming. Here's a shortcoming. Here's a shortcoming. And so the Pinker Prince paper is kind of like that generation's version of Marcus and Davis, right, where they're trained as cognitive scientists, but they're looking skeptically at the results in the artificial intelligence neural net kind of world and saying, yeah, it can do this and this and this, but it can't do that, and it can't do that, and it can't do that. But maybe in principle or maybe just in practice at this point, but the fact of the matter is, you've narrowed your focus too far to be impressed. You're impressed with the things within that circle, but you need to broaden that circle a little bit. You need to look at a wider set of problems. So I was in this seminar in college that was basically a close reading of the Pinker Prince paper, which was really thick. There was a lot going on in there.
And.
It talked about the reinforcement learning idea a little bit. I'm like, oh, that sounds really cool. Because behavior is what is really interesting to me about psychology anyway, so making programs that. I mean, programs are things that behave. People are things that behave. I want to make learning that learns to behave.
In which way was reinforcement learning presented? Is this talking about human and animal behavior, or are we talking about actual mathematical constructs?
Ah, right. So that's a good question. Right. So this is. I think it wasn't actually talked about as behavior in the paper that I was reading. I think that it just talked about learning. And to me, learning is about learning to behave. But really, neural nets at that point were about learning, like supervised learning, so learning to produce outputs from inputs. So I kind of tried to invent reinforcement learning. When I graduated, I joined a research group at Bellcore, which had spun out of Bell Labs recently at that time because of the divestiture of the. Of long distance and local phone service in the 1980s, 1984. And I was in a group with Dave Ackley, who was the first author of the Boltzmann machine paper. So the very first neural net paper that could handle Xor.
Right.
So XOR sort of killed neural nets. The very first.
The 0 with the first winter.
Yeah, the Perceptrons paper. And Hinton, along with his student Dave Ackley, and I think there was other authors as well, showed that, no, no, no. With Boltz machines, we can actually learn nonlinear concepts. And so everything's back on the table again. And that kind of started that second wave of neural networks. So Dave Ackley was. He became my mentor at Bellcore, and we talked a lot about learning and life and computation and how all these things fit together. Now Dave and I have a podcast together, so. So I get to kind of enjoy that sort of his perspective once again, even all these years later. And so I said I was really interested in learning, but in the concept of behavior. And he's like, oh, well, that's reinforcement learning here. And he gave me Rich Sutton's 1984 TD paper. So I read that paper. I honestly didn't get all of it, but I got the idea. I got that they were using. That he was using ideas that I was familiar with in the context of neural nets and sort of backprop, but with this idea of making predictions over time. I'm like, this is so interesting, but I don't really get all the details. I said to Dave, and Dave said, oh, well, why don't we have him come and give a talk? And I was like, wait, what? You can do that? These are real people. I thought they were just words. I thought it was just ideas that somehow magically seeped into paper. He's like, no, I know Rich. We'll just have him come down and he'll give a talk. And so I was, you know, my mind was blown. And so Rich came and he gave a talk at Bellcore and he talked about what? He was super excited, which was they had just figured out at the time, Q learning. So Watkins had visited the Rich Sutton's lab at UMass or Andy Bartow's lab that Rich was a part of, and he was really excited about this because it resolved a whole bunch of problems that he didn't know how to resolve in the earlier paper.
And so for people who don't know TD temporal difference, these are all just algorithms for reinforcement learning.
Right. And TD temporal difference in particular is about making predictions over time. And you can try to use it for making decisions. Right. Because if you can predict how good a future action. An action outcomes will be in the future, you can choose one that has better. But the theory didn't really support changing your behavior. Like, the predictions had to be of a consistent process if you really wanted it to work. And one of the things that was really cool about Q learning, another algorithm for reinforcement learning, is it was off policy, which meant that you could actually be learning about the environment and what the value of different actions would be while actually figuring out how to behave optimally.
Yeah.
So that was a revelation.
Yeah. And the proof of that is kind of interesting. I mean, that's really surprising to me when I first read that and then in Richard Rich Sutton's book on the matter, it's, it's kind of beautiful that a single equation can capture one equation.
One line of code and like you can learn anything.
Yeah, like enough time. So equation and code. You're right. Like you can. The code that you can arguably, at least if you like squint your eyes can say, this is all of intelligence. That you can implement that in a single. I think I started with Lisp, which is shout out to Lisp. Like a single line of code, key piece of code, maybe a couple that you could do that. It's kind of magical. It feels too good to be true.
Well, and it sort of is, yeah, kind of. It seems to require an awful lot of extra stuff supporting it. But, but nonetheless the idea is the, the idea is really good and as far as we know it is. It is a very reasonable way of trying to create adaptive behavior, behavior that gets better at something over time.
Did you find the idea of optimal at all compelling, that you could prove that it's optimal? So like one part of computer science, that it makes people feel warm and fuzzy inside, is when you can prove something like that a sorting algorithm, worst case runs in n log n. And it makes everybody feel so good, even though in reality it doesn't really matter what the worst case is. What matters is like, does this thing actually work in practice on this particular actual set of data that I, that I enjoy? Did you.
So here's that, here's a place where I have maybe a strong opinion, which is like, you're right, of course, but no, no. So what makes worst case so great? Right. If you have a worst case analysis so great is that you get modularity. You can take that thing and plug it into another thing and still have some understanding of what's going to happen when you click them together. If it just works well in practice, in other words, with respect to some distribution that you care about, when you go plug it into another thing, that distribution can shift, it can change, and your thing may not work well anymore and you want it to, and you wish it does, and you hope that it will, but it might not.
And then, ah, so you're so, so you're saying you don't like machine learning.
But we have some positive theoretical results for these things. You know, you can come back at me with, yeah, but they're really weak and yeah, they're really weak. And you can even say that, you know, sorting algorithms, like if you do the optimal sorting algorithm. It's not really the one that you want. And that might be true as well, but.
But it is. The modularity is a really powerful statement. I really like an engineer. You can then assemble different things you can count on be. I mean it's interesting. It's, it's a balance like with everything else in life. You don't want to get too obsessed. I mean this is what computer scientists do, which they tend to like get obsessed and they over optimize things or they start by optimizing and they over optimize. So it's, it's easy to like get really granular about this thing. But like the step from an N squared to an n log n sorting algorithm is a big leap for most real world systems. No matter what the actual behavior of the system is. That's a big leap. And the same can probably be said for other kind of first leaps that you would take on a particular problem. It's picking the low hanging fruit or whatever the equivalent of doing not the dumbest thing, but the next to the dumbest thing.
Picking the most delicious reachable fruit.
Fruit. Yeah. Most delicious reachable fruit.
I don't know why that's not a saying.
And yeah. Okay, so, so you then this is the 80s and this kind of idea starts to percolate of.
Yeah, at that point I got to meet, I got to meet Rich Sutton. So everything was sort of downhill from there. I mean that was, that was really the pinnacle of everything. But then I, you know, then I felt like I was kind of on the inside. So then as interesting results were happening, I could like check in with, with Rich or with Jerry Tesaro who had a huge impact on kind of early thinking in temporal difference learning and reinforcement learning and showed that you could solve problems that we didn't know how to solve any other way. And so that was really cool. So good things were happening. I would hear about it from either the people who were doing it or the people who were talking to the people who were doing it. And so I was able to track things pretty well through the 90s.
So what wasn't most of the excitement on reinforcement learning in the 90s era with what is it? TD Gamma. Like what's the role of these kind of little like fun game playing things and breakthroughs about exciting the community was that like what were your. Because you've also built a crossword or part of building a crossword puzzle solver program.
Yeah.
Solving program called Proverb. So. So you were interested in this as A problem like in forming, in using games to understand how to build intelligent systems. So, like, what did you think about TD Gamma? Like, what did you think about that whole thing in the 90s?
Yeah, I mean, I found the TD Gammon result really just remarkable. So I had known about some of Jerry's stuff before he did TD Gammon. He did a system just more vanilla. Well, not entirely vanilla, but a more classical back propy kind of network for playing backgammon where he was training it on expert moves. So it was kind of supervised, but the way that it worked was not to mimic the actions, but to learn internally an evaluation function. So to learn well, if the expert chose this over this, that must mean that the expert values this more than this. And so let me adjust my weights to make it so that the network evaluates this as being better than this. So it could learn from human preferences, it could learn its own preferences. And then when he took the step from that to actually doing it as a full on reinforcement learning problem where you didn't need a trainer, you could just let it play, that was remarkable. And so I think, as humans often do, as we've done in the recent past as well, people extrapolate. It's like, oh, well, if you can do that, which is obviously very hard, then obviously you could do all these other problems that we want to solve that we know are also really hard. And it turned out very few of them ended up being practical. Partly because I think neural nets certainly at the time were struggling to be consistent and reliable. And so training them in a reinforcement learning setting was a bit of a mess. I had, I don't know, generation after generation of like master students who wanted to do value function approximation, basically reinforcement learning with neural nets. And over and over and over again we were failing. We couldn't get the good results that Jerry Tesaro got. I now believe that Jerry is a neural net whisperer. He has a particular ability to get neural networks to do things that other people would find impossible. And it's not the technology, it's the technology and Jerry together, which I think.
Speaks to the role of the human expert in the process of machine learning.
Right. It's so easy. We're so drawn to the idea that it's the technology that is where the power is coming from that I think we lose sight of the fact that sometimes you need a really good. Just like, I mean, no one would think, hey, here's this great piece of software, here's like, I don't know, GNU Emacs or whatever. And doesn't that prove that computers are super powerful and basically going to take over the world? It's like, no. Stallman is a hell of a hacker. Right. Who have just a particular set of.
Skills on that topic. By the way, as a small side note, I tweeted emacs is greater than Vim yesterday and deleted. Deleted the tweet 10 minutes later when I realized you were on fire. Started a war.
Yeah.
I was like, oh, I was just kidding. I was just being provocative. Walk back. And so people still feel passionately about that particular piece of.
Yeah, I don't get that. Because Emacs is clearly so much better. I don't understand. But you know, why do I say that? Because I spent a block of time in the 80s making my fingers know the Emacs keys and now, like, that's part of the thought process for me. Like, I need to express. And if you take that, if you take my Emacs key bindings away, I become. I can't express myself.
I'm the same way with the. I don't know if you know what it is, but it's a Kinesis keyboard, which is this butt shaped keyboard.
Yes, I've seen them.
Yeah, they're very.
I don't know, sexy, elegant. They're just beautiful.
Yeah, they're. They're gorgeous. Way too expensive. But the, the problem with them, similar with Emacs, is when you, once you learn to use it, it's harder to use other things. It's hard to use other things. There's this absurd thing where I have like small, elegant, lightweight, beautiful little laptops and I'm sitting there in a coffee shop with a giant Kinesis keyboard and, and a sexy little laptop. It's absurd, but I used to feel bad about it. But at the same time, you just kind of have to. Sometimes it's back to the Billy Joel thing. You just have to throw that Billy Joel record and throw Taylor Swift and Justin Bieber to the wind. See?
But I like them now because again, I have no musical taste now that I've heard Justin Bieber enough. I really like his songs. And Taylor Swift, not only do I like her songs, but my daughter's convinced that she's a genius. And so now I basically have. I'm signed on to that. So.
So, yeah, that, that speaks to the back to the robustness of the human brain. That speaks to the neuroplasticity that you can just. You could just like a mouse, teach yourself to. Or Pavlov's dog teach yourself to enjoy Taylor Swift. I'll try it out. I don't know. I try. You know what it has to do with just, like, acclimation, Right? Just like you said, a couple weeks.
Yeah.
That's an interesting experiment. I'll actually try that. Like, I'll listen.
That wasn't the intent of the experiment. Just like social media. It wasn't intended as an experiment to see what we can take as a. But it turned out that way.
I don't think I'll be the same person on the other side of the week listening to Taylor Swift, but let's try.
It's more compartmental. Don't be so worried. I get that you can be worried, but don't be so worried because we compartmentalize really well and so it won't bleed into other parts of your life. You won't start, I don't know, wearing red lipstick or whatever. It's fine.
You change fashion and everything.
It's fine. But you know what the thing you have to watch out for is? You'll walk into a coffee shop once.
We can do that again and recognize.
The song, and you'll be. No, you won't know that you're singing along until everybody in the coffee shop is looking at you. And then you're like, that wasn't me.
Yeah, that's the. You know, people are afraid of AGI. I'm afraid of the.
Taylor Swift takeover.
Yeah. And I mean, people should know that TD Gammon was, I guess, would you call it. Do you like the terminology of self play, by any chance? So, like, systems that learn by playing themselves just. I don't know if it's the best word, but.
So what's. What's the problem with that term? Okay.
Silly. It's like the big Bang. Like, it's. It's like talking to serious physicists. Do you like the term big Bang? And when. When it was early. I feel like it's the early days of self play. I don't know, maybe it was used previously, but I think it's been used by only a small group of people. And so, like, I think we're still deciding, is this ridiculously silly name a good name for the potentially one of the most important concepts in artificial intelligence?
Okay. It depends how broadly you apply the term. So I used the term in my 1996 PhD dissertation.
Wow. The actual terms of.
Yeah, because Tesoro's paper was something like training up an expert backgammon player through self play. So I think it was in the title of his paper. If not in the title, it was definitely a term that he used to. There's another term that we got from that work is rollout. So I don't know if you ever hear the term rollout. That's a backgammon term that has now applied generally in computers, well, at least in AI because of TDGammon.
That's fascinating.
So how is self play being used now and why is it. Does it feel like a more general, powerful concept? Sort of the idea of well, the machine's just going to teach itself to be smart.
Yeah. So that's where maybe you can correct me, but that's where the continuation of the spirit and actually literally the Exact algorithms of TD Gammon are applied by DeepMind and OpenAI to learn games that are a little bit more complex that when I was learning artificial intelligence Go was presented to me with artificial intelligence, the modern approach. I don't know if they explicitly pointed to Go in those books as like unsolvable kind of thing. Like implying that these approaches hit their limit in this with these particular kind of games. So something, I don't remember if the book said it or not, but something in my head or if it was the professors instilled in me the idea like this is the limits of artificial intelligence of the field. Like it instilled in me the idea that if we can create a system that can solve the game of God, we've achieved AGI. That was kind of. I didn't explicitly like say this, but that was the feeling. And so from I was one of the people that it seemed magical when a learning system was able to beat a human world champion at the game of Go. And even more so from that, that was AlphaGo even more so with AlphaGo Zero then kind of renamed and advanced into AlphaZero. Beating a world champion or world class player without any supervised learning on expert games, doing only through by playing itself. So that is. I don't know what to make of it. I think it would be interesting to hear what your opinions are on just how exciting, surprising, profound, interesting or boring. The breakthrough performance of AlphaZero was okay.
So AlphaGo knocked my socks off. That was so remarkable which aspect of.
It.
That they got it to work that they actually were able to leverage a whole bunch of different ideas, integrate them into one giant system. Just the software engineering aspect of it is mind blowing. I I've never been a part of a program as complicated as the program that they built for that. And just the, you know, like Jerry Tesaro is a neural net Whisperer. Like, you know, David Silver is a kind of neural net whisperer too. He was able to coax these networks and these new way out there architectures to do these, you know, solve these problems that as you said, you know, when we were learning from AI, no one had an idea how to make it work. It was remarkable that these techniques that were so good at playing chess and that could beat the world champion in chess couldn't beat your typical GO playing teenager in go. So the fact that in a very short number of years we kind of ramped up to trouncing people in GO just blew me away.
So you're kind of focusing on the engineering aspect, which is also very surprising. I mean, there's something different about large, well funded companies. I mean, there's a compute aspect to it too.
Sure.
Like that, of course. I mean, that's similar to Deep Blue. Right. With, with IBM. Like there's something important to be learned and remembered about a large company taking the ideas that are already out there and investing a few million dollars into it or, or more. And so you're kind of saying the engineering is kind of fascinating both on the Wadafago is probably just gathering all the data. Right. Of the expert games, like organizing everything, actually doing distributed supervised learning. And to me, see, the engineering I kind of took for granted to me philosophically being able to persist in the, in the face of like long odds because it feels like for me, I'll be one of the skeptical people in the room thinking that you can learn your way to, to beat go. Like it sounded like, especially with David Silver, it sounded like David was not confident at all. It's like, it was like not. It's funny how confidence works.
Yeah.
It's like you're not like cocky about it, like, but.
Right. Because if you're cocky about it, you kind of stop and stall and don't get anywhere. Yeah.
But there's like a hope that's unbreakable. Maybe that's better than confidence. It's a kind of wishful hope and a little dream and you almost don't want to do anything else. You kind of keep doing it. That's. That seems to be the story and.
But with enough skepticism that you're looking for where the problems are and fighting through them.
Yeah.
Because you know, there's got to be a way out of this thing. Yeah.
And for him it was probably. There's, there's a bunch of little factors that come into play. It's funny how these stories just all come together like Everything he did in his life came into play, which is like a love for video games and also a connection to. So that the 90s had to happen with TD Gammon and so on.
Yeah.
In some ways it's surprising. Maybe you can provide some intuition to it that not much more than TD Gammon was done for quite a long time on the reinforcement learning front. Is that weird to you?
I mean, like I said, the students who I worked with, we tried to get basically apply that architecture to other problems, and we consistently failed. There were a couple really nice demonstrations that ended up being in the literature. There was a paper about controlling elevators where it's like, okay, can we modify the heuristic that elevators use for deciding, like a bank of elevators for deciding which floors we should be stopping on to maximize throughput, essentially. And you can set that up as a reinforcement learning problem. And you can have a neural net represent the value function so that it's taking. Where are all the elevators? Where the button pushes this high dimensional. Well, at the time, high dimensional, input a couple dozen dimensions and turn that into a prediction as to, oh, is it going to be better if I stop at this floor or not? And ultimately it appeared as though for the standard simulation distribution for people trying to leave the building, at the end of the day, that the neural net learned a better strategy than the standard one that's implemented in elevator controllers. So that was nice. There was some work that Satinder, Singh et al did on handoffs with cell phones, deciding when should you handoff from this cell tower to this cell tower communication networks. Yeah, yeah. And so a couple things seemed like they were really promising. None of them made it into production that I'm aware of. And neural nets as a whole started to kind of implode around then. And so there just wasn't a lot of air in the room for people to try to figure out, okay, how do we get this to work in the RL setting?
And then they found their way back in 10 plus years. So you said AlphaGo was impressive. Like it's a big spectacle. Is there?
Right. So then AlphaZero. So I think I may have a slightly different opinion on this than some people. So I talked to Satinder Singh in particular about this. So Satinder was like Rich Sutton, a student of Auntie Bartow. So they came out of the same lab, very influential machine learning, reinforcement learning researcher now at DeepMind, as is Rich, though different sites, the two of them.
He's in Alberta, Rich is in Alberta.
And Sitinder would be in England. But I think he's in England from Michigan at the moment. But the, but he was, yes, he was much more impressed with AlphaGo Zero, which didn't get a kind of a bootstrap in the beginning with human trained games just was purely self play. Though the first one, Alpha Go was also a tremendous amount of self play. Right. They started off, they kick started the action network that was making decisions, but then they trained it for a really long time using more traditional temporal difference methods. So as a result, it didn't seem that different to me. It seems like, yeah, why wouldn't that work? Once it works, it works. But he found that removal of that extra information to be breathtaking. That's a game changer. To me, the first thing was more of a game changer.
But the open question, I mean, I guess that's the assumption is the expert games might contain within them a humongous amount of information, but we know that.
It went beyond that, right? We know that it somehow got away from that information because it was learning strategies. I don't think it's, I don't think AlphaGo is just better at implementing human strategies. I think it actually developed its own strategies that were more effective. And so from that perspective. Okay, well, so it made at least one quantum leap in terms of strategic knowledge. Okay, so now maybe it makes three. Like, okay, but that first one is the doozy, right? Getting it to work reliably and for the networks to hold on to the value. Well, enough like that was a big step.
Well, isn't maybe you could speak to this on the reinforcement learning front. So the starting from scratch and learning to do something like the first like, like random behavior, to like crappy behavior, to like somewhat okay behavior. It's not obvious to me that that's not like impossible to take those steps. Like if you just think about the intuition, like how the heck does random behavior become somewhat basic? Intelligent behavior? Not, not human level, not superhuman level, but just basic. But you're saying to you kind of the intuition is like if, if you can go from human to superhuman level intelligence on the, on this particular task of game playing, then so you're good at taking leaps so you can take many of them.
That the system, I believe that the system can take that kind of leap. Yeah. And also I think that beginner knowledge in Go, like you can start to get a feel really quickly for the idea that certain parts of the being in certain parts of the board seems to be more associated with winning. Right. Because it's not stumbling upon the concept of winning. It's told that it wins or that it loses. Well, it's self play. So it both wins and loses. It's told which side won and the information is kind of there to start percolating around to make a difference as to well, these things have a better chance of helping you win and these things have a worse chance of helping you win. And so it can get to basic play, I think pretty quickly. Then once it has basic play, well now it's kind of forced to do some search to actually experiment with. Okay, well what gets me that next increment of, of improvement?
How far do you think, okay, this is where you kind of bring up the, the Elon Musk and the Sam Harris is right. How far is your intuition about these kinds of self play mechanisms being able to take us? Because it feels One of the ominous but stated calmly things that when I talked to David Silver he said is that they have not yet discovered a ceiling for Alphazero, for example, on the game of Go or chess, it keeps, no matter how much the compute they throw at it, it keeps improving. So it's possible, it's very possible that if you throw some like 10x compute that it will improve by 5x or something like that. And when stated calmly it's so like oh yeah, I guess so. But then you think.
