back to indexMichael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Lex Fridman Podcast #74
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The following is a conversation with Michael I. Jordan, a professor at Berkeley and one
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of the most influential people in the history of machine learning, statistics, and artificial
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He has been cited over 170,000 times and he has mentored many of the world class researchers
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defining the field of AI today, including Andrew Ng, Zubin Garamani, Ben Taskar, and
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All this, to me, is as impressive as the over 32,000 points in the six NBA championships
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of the Michael J. Jordan of basketball fame.
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There's a nonzero probability that I talked to the other Michael Jordan given my connection
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to and love of the Chicago Bulls of the 90s, but if I had to pick one, I'm going with
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the Michael Jordan of statistics and computer science, or as Yann LeCun calls him, the Miles
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Davis of machine learning.
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In his blog post titled Artificial Intelligence, the Revolution Hasn't Happened Yet, Michael
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argues for broadening the scope of the artificial intelligence field.
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In many ways, the underlying spirit of this podcast is the same, to see artificial intelligence
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as a deeply human endeavor, to not only engineer algorithms and robots, but to understand and
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empower human beings at all levels of abstraction, from the individual to our civilization as
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This is the Artificial Intelligence Podcast.
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If you enjoy it, subscribe and YouTube, give it five stars at Apple Podcast, support it
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on Patreon, or simply connect with me on Twitter at Lex Friedman spelled F R I D M A N.
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As usual, I'll do one or two minutes of ads now and never any ads in the middle that
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I hope that works for you and doesn't hurt the listening experience.
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organizations that is helping to advance robotics and STEM education for young people around
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And now, here's my conversation with Michael I. Jordan.
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Given that you're one of the greats in the field of AI, machine learning, computer science,
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and so on, you're trivially called the Michael Jordan of machine learning, although as you
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know, you were born first, so technically MJ is the Michael I. Jordan of basketball.
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But anyway, my favorite is Yann LeCun calling you the Miles Davis of machine learning because
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as he says, you reinvent yourself periodically and sometimes leave fans scratching their
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heads after you change direction.
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So can you put at first your historian hat on and give a history of computer science
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and AI as you saw it, as you experienced it, including the four generations of AI successes
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that I've seen you talk about?
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Yeah, first of all, I much prefer Yann's metaphor.
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Miles Davis was a real explorer in jazz and he had a coherent story.
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So I think I have one, but it's not just the one you lived, it's the one you think about
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What the historian does is they look back and they revisit.
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I think what's happening right now is not AI, that was an intellectual aspiration that's
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still alive today as an aspiration.
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But I think this is akin to the development of chemical engineering from chemistry or
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electrical engineering from electromagnetism.
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So if you go back to the 30s or 40s, there wasn't yet chemical engineering.
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There was chemistry, there was fluid flow, there was mechanics and so on.
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But people pretty clearly viewed interesting goals to try to build factories that make
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chemicals products and do it viably, safely, make good ones, do it at scale.
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So people started to try to do that, of course, and some factories worked, some didn't, some
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were not viable, some exploded, but in parallel, developed a whole field called chemical engineering.
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Electrical engineering is a field, it's no bones about it, it has theoretical aspects
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to it, it has practical aspects.
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It's not just engineering, quote unquote, it's the real thing, real concepts are needed.
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Same thing with electrical engineering.
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There was Maxwell's equations, which in some sense were everything you know about electromagnetism,
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but you needed to figure out how to build circuits, how to build modules, how to put
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them together, how to bring electricity from one point to another safely and so on and
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So a whole field that developed called electrical engineering.
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I think that's what's happening right now, is that we have a proto field, which is statistics,
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more of the theoretical side of it, algorithmic side of computer science, that was enough
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to start to build things, but what things?
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Systems that bring value to human beings and use human data and mix in human decisions.
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The engineering side of that is all ad hoc.
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That's what's emerging.
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In fact, if you wanna call machine learning a field, I think that's what it is, that it's
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a proto form of engineering based on statistical and computational ideas of previous generations.
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But do you think there's something deeper about AI in his dreams and aspirations as
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compared to chemical engineering and electrical engineering?
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Well the dreams and aspirations maybe, but those are 500 years from now.
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I think that that's like the Greeks sitting there and saying, it would be neat to get
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to the moon someday.
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I think we have no clue how the brain does computation.
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We're just a clueless.
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We're even worse than the Greeks on most anything interesting scientifically of our era.
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Can you linger on that just for a moment because you stand not completely unique, but a little
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bit unique in the clarity of that.
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Can you elaborate your intuition of why we're, like where we stand in our understanding of
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And a lot of people say, you know, scientists say we're not very far in understanding human
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brain, but you're like, you're saying we're in the dark here.
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Well, I know I'm not unique.
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I don't even think in the clarity, but if you talk to real neuroscientists that really
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study real synapses or real neurons, they agree, they agree.
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It's a hundreds of year task and they're building it up slowly and surely.
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What the signal is there is not clear.
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We think we have all of our metaphors.
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We think it's electrical, maybe it's chemical, it's a whole soup, it's ions and proteins
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And that's even around like a single synapse.
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If you look at a electron micrograph of a single synapse, it's a city of its own.
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And that's one little thing on a dendritic tree, which is extremely complicated electrochemical
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And it's doing these spikes and voltages are flying around and then proteins are taking
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that and taking it down into the DNA and who knows what.
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So it is the problem of the next few centuries.
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But we have our metaphors about it.
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Is it an economic device?
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Is it like the immune system or is it like a layered set of, you know, arithmetic computations?
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We have all these metaphors and they're fun.
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But that's not real science per se.
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There is neuroscience.
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That's not neuroscience.
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That's like the Greek speculating about how to get to the moon, fun, right?
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And I think that I like to say this fairly strongly because I think a lot of young people
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think we're on the verge because a lot of people who don't talk about it clearly let
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it be understood that, yes, we kind of, this is a brain inspired, we're kind of close,
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you know, breakthroughs are on the horizon.
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And that's scrupulous people sometimes who need money for their labs.
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That's what I'm saying, scrupulous, but people will oversell, I need money for my lab, I'm
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studying computational neuroscience, I'm going to oversell it.
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And so there's been too much of that.
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So I'll step into the gray area between metaphor and engineering with, I'm not sure if you're
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familiar with brain computer interfaces.
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So a company like Elon Musk has Neuralink that's working on putting electrodes into
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the brain and trying to be able to read, both read and send electrical signals.
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Just as you said, even the basic mechanism of communication in the brain is not something
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But do you hope without understanding the fundamental principles of how the brain works,
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we'll be able to do something interesting at that gray area of metaphor?
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So I hope in the sense, like anybody else hopes for some interesting things to happen
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from research, I would expect more something like Alzheimer's will get figured out from
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modern neuroscience.
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There's a lot of human suffering based on brain disease and we throw things like lithium
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at the brain, it kind of works, no one has a clue why.
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That's not quite true, but mostly we don't know.
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And that's even just about the biochemistry of the brain and how it leads to mood swings
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How thought emerges from that, we were really, really completely dim.
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So that you might want to hook up electrodes and try to do some signal processing on that
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and try to find patterns, fine, by all means, go for it.
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It's just not scientific at this point.
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So it's like kind of sitting in a satellite and watching the emissions from a city and
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trying to infer things about the microeconomy, even though you don't have microeconomic concepts.
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It's really that kind of thing.
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And so yes, can you find some signals that do something interesting or useful?
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Can you control a cursor or mouse with your brain?
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Yeah, absolutely, and then I can imagine business models based on that and even medical applications
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But from there to understanding algorithms that allow us to really tie in deeply from
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the brain to computer, I just, no, I don't agree with Elon Musk.
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I don't think that's even, that's not for our generations, not even for the century.
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So just in hopes of getting you to dream, you've mentioned Kolmogorov and Turing might
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pop up, do you think that there might be breakthroughs that will get you to sit back in five, 10
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years and say, wow?
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Oh, I'm sure there will be, but I don't think that there'll be demos that impress me.
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I don't think that having a computer call a restaurant and pretend to be a human is
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And people, you know, some people present it as such.
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It's imitating human intelligence.
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It's even putting coughs in the thing to make a bit of a PR stunt.
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And so fine that the world runs on those things too.
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And I don't want to diminish all the hard work and engineering that goes behind things
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like that and the ultimate value to the human race.
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But that's not scientific understanding.
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And I know the people that work on these things, they are after scientific understanding.
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In the meantime, they've got to kind of, you know, the trains got to run and they got mouths
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to feed and they got things to do and there's nothing wrong with all that.
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I would call that though, just engineering.
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And I want to distinguish that between an engineering field, like electrical engineering
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and chemical engineering that originally emerged, that had real principles and you really know
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what you're doing and you had a little scientific understanding, maybe not even complete.
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So it became more predictable and it really gave value to human life because it was understood.
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And so we don't want to muddle too much these waters of, you know, what we're able to do
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versus what we really can't do in a way that's going to impress the next.
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So I don't need to be wowed, but I think that someone comes along in 20 years, a younger
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person who's absorbed all the technology and for them to be wowed, I think they have to
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be more deeply impressed.
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A young Kolmogorov would not be wowed by some of the stunts that you see right now coming
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from the big companies.
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The demos, but do you think the breakthroughs from Kolmogorov would be, and give this question
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a chance, do you think there'll be in the scientific fundamental principles arena or
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do you think it's possible to have fundamental breakthroughs in engineering?
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Meaning, you know, I would say some of the things that Elon Musk is working with SpaceX
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and then others sort of trying to revolutionize the fundamentals of engineering, of manufacturing,
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of saying, here's a problem we know how to do a demo of and actually taking it to scale.
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So there's going to be all kinds of breakthroughs.
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I just don't like that terminology.
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I'm a scientist and I work on things day in and day out and things move along and eventually
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you say, wow, something happened, but I don't like that language very much.
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Also I don't like to prize theoretical breakthroughs over practical ones.
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I tend to be more of a theoretician and I think there's lots to do in that arena right
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And so I wouldn't point to the Kolmogorovs, I might point to the Edisons of the era and
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maybe Musk is a bit more like that.
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But you know, Musk, God bless him, also will say things about AI that he knows very little
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about and he leads people astray when he talks about things he doesn't know anything about.
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Trying to program a computer to understand natural language, to be involved in a dialogue
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we're having right now, that ain't going to happen in our lifetime.
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You could fake it, you can mimic, sort of take old sentences that humans use and retread
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them, but the deep understanding of language, no, it's not going to happen.
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And so from that, I hope you can perceive that the deeper, yet deeper kind of aspects
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and intelligence are not going to happen.
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Now will there be breakthroughs?
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No, I think that Google was a breakthrough, I think Amazon is a breakthrough, you know,
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I think Uber is a breakthrough, you know, that bring value to human beings at scale
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in new, brand new ways based on data flows and so on.
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A lot of these things are slightly broken because there's not kind of an engineering
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field that takes economic value in context of data and, you know, planetary scale and
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worries about all the externalities, the privacy, you know, we don't have that field so we don't
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think these things through very well.
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I see that as emerging and that will be, you know, looking back from 100 years, that will
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be a constituted breakthrough in this era, just like electrical engineering was a breakthrough
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in the early part of the last century and chemical engineering was a breakthrough.
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So the scale, the markets that you talk about and we'll get to will be seen as sort of breakthrough
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and we're in the very early days of really doing interesting stuff there and we'll get
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to that, but just taking a quick step back, can you give, kind of throw off the historian
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I mean, you briefly said that the history of AI kind of mimics the history of chemical
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engineering, but...
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I keep saying machine learning.
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You keep wanting to say AI, just to let you know, I don't, you know, I resist that.
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I don't think this is about AI really was John McCarthy as almost a philosopher saying,
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wouldn't it be cool if we could put thought in a computer?
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If we could mimic the human capability to think or put intelligence in, in some sense
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That's an interesting philosophical question and he wanted to make it more than philosophy.
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He wanted to actually write down a logical formula and algorithms that would do that.
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And that is a perfectly valid, reasonable thing to do.
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That's not what's happening in this era.
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So the reason I keep saying AI actually, and I'd love to hear what you think about it.
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Machine learning has a very particular set of methods and tools.
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Maybe your version of it is that mine doesn't, it's very, very open.
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It does optimization, it does sampling, it does...
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So systems that learn is what machine learning is.
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Systems that learn and make decisions.
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And make decisions.
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So it's not just pattern recognition and, you know, finding patterns, it's all about
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making decisions in real worlds and having close feedback loops.
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So something like symbolic AI, expert systems, reasoning systems, knowledge based representation,
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all of those kinds of things, search, does that neighbor fit into what you think of as
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So I don't even like the word machine learning, I think that what the field you're talking
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about is all about making large collections of decisions under uncertainty by large collections
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And there are principles for that, at that scale.
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You don't have to say the principles are for a single entity that's making decisions, single
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agent or single human.
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It really immediately goes to the network of decisions.
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Is a good word for that or no?
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No, there's no good words for any of this.
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That's kind of part of the problem.
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So we can continue the conversation to use AI for all that.
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I just want to kind of raise the flag here that this is not about, we don't know what
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intelligence is and real intelligence.
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We don't know much about abstraction and reasoning at the level of humans.
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We don't have a clue.
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We're not trying to build that because we don't have a clue.
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Eventually it may emerge.
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They'll make, I don't know if there'll be breakthroughs, but eventually we'll start
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to get glimmers of that.
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It's not what's happening right now.
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We're taking data.
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We're trying to make good decisions based on that.
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We're trying to scale.
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We're trying to economically viably, we're trying to build markets.
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We're trying to keep value at that scale and aspects of this will look intelligent.
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Computers were so dumb before, they will seem more intelligent.
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We will use that buzzword of intelligence so we can use it in that sense.
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So machine learning, you can scope it narrowly as just learning from data and pattern recognition.
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But when I talk about these topics, maybe data science is another word you could throw
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in the mix, it really is important that the decisions are as part of it.
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It's consequential decisions in the real world.
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Am I going to have a medical operation?
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Am I going to drive down the street?
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Things where there's scarcity, things that impact other human beings or other environments
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How do I do that based on data?
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How do I do that adaptively?
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How do I use computers to help those kinds of things go forward?
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Whatever you want to call that.
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So let's call it AI.
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Let's agree to call it AI, but let's not say that the goal of that is intelligence.
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The goal of that is really good working systems at planetary scale that we've never seen before.
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So reclaim the word AI from the Dartmouth conference from many decades ago of the dream
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I don't want to reclaim it.
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I want a new word.
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I think it was a bad choice.
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I mean, if you read one of my little things, the history was basically that McCarthy needed
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a new name because cybernetics already existed and he didn't like, no one really liked Norbert
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Norbert Wiener was kind of an island to himself and he felt that he had encompassed all this
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and in some sense he did.
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You look at the language of cybernetics, it was everything we're talking about.
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It was control theory and signal processing and some notions of intelligence and closed
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feedback loops and data.
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It's just not a word that lived on partly because of the maybe the personalities.
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But McCarthy needed a new word to say, I'm different from you.
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I'm not part of your show.
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Invented this word and again, thinking forward about the movies that would be made about
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it, it was a great choice.
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But thinking forward about creating a sober academic and real world discipline, it was
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a terrible choice because it led to promises that are not true that we understand.
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We understand artificial perhaps, but we don't understand intelligence.
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It's a small tangent because you're one of the great personalities of machine learning,
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whatever the heck you call the field.
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Do you think science progresses by personalities or by the fundamental principles and theories
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and research that's outside of personalities?
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And I wouldn't say there should be one kind of personality.
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I have mine and I have my preferences and I have a kind of network around me that feeds
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me and some of them agree with me and some of them disagree, but all kinds of personalities
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Right now, I think the personality that it's a little too exuberant, a little bit too ready
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to promise the moon is a little bit too much in ascendance.
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And I do think that there's some good to that.
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It certainly attracts lots of young people to our field, but a lot of those people come
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in with strong misconceptions and they have to then unlearn those and then find something
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And so I think there's just got to be some multiple voices and I wasn't hearing enough
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of the more sober voice.
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So as a continuation of a fun tangent and speaking of vibrant personalities, what would
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you say is the most interesting disagreement you have with Jan Lacune?
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So Jan's an old friend and I just say that I don't think we disagree about very much
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He and I both kind of have a let's build it kind of mentality and does it work kind of
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mentality and kind of concrete.
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We both speak French and we speak French more together and we have a lot in common.
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And so if one wanted to highlight a disagreement, it's not really a fundamental one.
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I think it's just kind of what we're emphasizing.
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Jan has emphasized pattern recognition and has emphasized prediction.
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And it's interesting to try to take that as far as you can.
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If you could do perfect prediction, what would that give you kind of as a thought experiment?
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And I think that's way too limited.
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We cannot do perfect prediction.
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We will never have the data sets that allow me to figure out what you're about ready to
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do, what question you're going to ask next.
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I will never know such things.
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Moreover, most of us find ourselves during the day in all kinds of situations we had
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no anticipation of that are kind of very, very novel in various ways.
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And in that moment, we want to think through what we want.
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And also there's going to be market forces acting on us.
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I'd like to go down that street, but now it's full because there's a crane in the street.
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I got to think about that.
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I got to think about what I might really want here.
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And I got to sort of think about how much it costs me to do this action versus this
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I got to think about the risks involved.
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A lot of our current pattern recognition and prediction systems don't do any risk evaluations.
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They have no error bars, right?
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I got to think about other people's decisions around me.
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I got to think about a collection of my decisions, even just thinking about like a medical treatment,
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you know, I'm not going to take a, the prediction of a neural net about my health, about something
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I'm not about ready to have a heart attack because some number is over 0.7.
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Even if you had all the data in the world that ever been collected about heart attacks
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better than any doctor ever had, I'm not going to trust the output of that neural net to
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predict my heart attack.
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I'm going to want to ask what if questions around that.
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I'm going to want to look at some us or other possible data I didn't have, causal things.
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I'm going to want to have a dialogue with a doctor about things we didn't think about
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when he gathered the data.
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You know, I could go on and on.
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I hope you can see.
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And I don't, I think that if you say predictions, everything that, that, that you're missing
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all of this stuff.
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And so prediction plus decision making is everything, but both of them are equally important.
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And so the field has emphasized prediction, Jan rightly so has seen how powerful that
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But at the cost of people not being aware that decision making is where the rubber really
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hits the road, where human lives are at stake, where risks are being taken, where you got
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to gather more data.
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You got to think about the error bars.
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You got to think about the consequences of your decisions on others.
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You got to think about the economy around your decisions, blah, blah, blah, blah.
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I'm not the only one working on those, but we're a smaller tribe.
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And right now we're not the one that people talk about the most.
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But you know, if you go out in the real world and industry, you know, at Amazon, I'd say
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half the people there are working on decision making and the other half are doing, you know,
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the pattern recognition.
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And the words of pattern recognition and prediction, I think the distinction there, not to linger
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on words, but the distinction there is more a constrained sort of in the lab data set
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versus decision making is talking about consequential decisions in the real world, under the messiness
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and the uncertainty of the real world.
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And just the whole of it, the whole mess of it that actually touches human beings and
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And the forces, that's the distinction.
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It helps add those, that perspective, that broader perspective.
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On the other hand, if you're a real prediction person, of course, you want it to be in the
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You want to predict real world events.
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I'm just saying that's not possible with just data sets.
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That it has to be in the context of, you know, strategic things that someone's doing, data
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they might gather, things they could have gathered, the reasoning process around data.
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It's not just taking data and making predictions based on the data.
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So one of the things that you're working on, I'm sure there's others working on it, but
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I don't hear often it talked about, especially in the clarity that you talk about it, and
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I think it's both the most exciting and the most concerning area of AI in terms of decision
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So you've talked about AI systems that help make decisions that scale in a distributed
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way, millions, billions decisions, sort of markets of decisions.
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Can you, as a starting point, sort of give an example of a system that you think about
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when you're thinking about these kinds of systems?
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Yeah, so first of all, you're absolutely getting into some territory, which I will be beyond
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And there are lots of things that are going to be very not obvious to think about.
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Just like, again, I like to think about history a little bit, but think about put yourself
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back in the sixties.
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There was kind of a banking system that wasn't computerized really.
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There was database theory emerging and database people had to think about how do I actually
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not just move data around, but actual money and have it be, you know, valid and have transactions
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that ATMs happen that are actually, you know, all valid and so on and so forth.
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So that's the kind of issues you get into when you start to get serious about sorts
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of things like this.
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I like to think about as kind of almost a thought experiment to help me think something
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simpler, which is the music market.
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And because there is, to first order, there is no music market in the world right now
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and in our country, for sure.
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There are something called things called record companies and they make money and they prop
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up a few really good musicians and make them superstars and they all make huge amounts
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But there's a long tail of huge numbers of people that make lots and lots of really good
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music that is actually listened to by more people than the famous people.
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They are not in a market.
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They cannot have a career.
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They do not make money.
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The creators, the creators, the creators, the so called influencers or whatever that
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diminishes who they are.
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So there are people who make extremely good music, especially in the hip hop or Latin
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They do it on their laptop.
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That's what they do on the weekend and they have another job during the week and they
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put it up on SoundCloud or other sites.
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Eventually it gets streamed.
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It now gets turned into bits.
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It's not economically valuable.
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The information is lost.
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It gets put up there.
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You walk around in a big city, you see people with headphones, especially young kids listening
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to music all the time.
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If you look at the data, very little of the music they are listening to is the famous
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people's music and none of it's old music.
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It's all the latest stuff.
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But the people who made that latest stuff are like some 16 year old somewhere who will
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never make a career out of this, who will never make money.
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Of course there will be a few counter examples.
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The record companies incentivize to pick out a few and highlight them.
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Long story short, there's a missing market there.
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There is not a consumer producer relationship at the level of the actual creative acts.
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The pipelines and Spotify's of the world that take this stuff and stream it along, they
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make money off of subscriptions or advertising and those things.
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They're making the money.
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And then they will offer bits and pieces of it to a few people again to highlight that
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they simulate a market.
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Anyway, a real market would be if you're a creator of music that you actually are somebody
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who's good enough that people want to listen to you, you should have the data available
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There should be a dashboard showing a map of the United States.
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So in last week, here's all the places your songs were listened to.
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It should be transparent, vetable, so that if someone down in Providence sees that you're
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being listened to 10,000 times in Providence, that they know that's real data.
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You know it's real data.
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They will have you come give a show down there.
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They will broadcast to the people who've been listening to you that you're coming.
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If you do this right, you could go down there and make $20,000.
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You do that three times a year, you start to have a career.
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So in this sense, AI creates jobs.
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It's not about taking away human jobs.
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It's creating new jobs because it creates a new market.
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Once you've created a market, you've now connected up producers and consumers.
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The person who's making the music can say to someone who comes to their shows a lot,
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hey, I'll play at your daughter's wedding for $10,000.
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They'll say 9,000.
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Then again, you can now get an income up to $100,000.
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You're not going to be a millionaire.
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And now even think about really the value of music is in these personal connections,
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even so much so that a young kid wants to wear a tshirt with their favorite musician's
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So if they listen to the music on the internet, the internet should be able to provide them
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with a button that they push and the merchandise arrives the next day.
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And now why should we do that?
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Well, because the kid who bought the shirt will be happy, but more the person who made
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the music will get the money.
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There's no advertising needed.
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So you can create markets between producers and consumers, take 5% cut.
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Your company will be perfectly sound.
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It'll go forward into the future and it will create new markets and that raises human happiness.
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Now this seems like, well, this is easy, just create this dashboard, kind of create some
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connections and all that.
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But if you think about Uber or whatever, you think about the challenges in the real world
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of doing things like this, and there are actually new principles going to be needed.
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You're trying to create a new kind of two way market at a different scale that's ever
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There's going to be unwanted aspects of the market.
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There'll be bad people.
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There'll be the data will get used in the wrong ways, it'll fail in some ways, it won't
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You have to think that through.
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Just like anyone who ran a big auction or ran a big matching service in economics will
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think these things through.
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And so that maybe doesn't get at all the huge issues that can arise when you start to create
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markets, but it starts to, at least for me, solidify my thoughts and allow me to move
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forward in my own thinking.
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So I talked to the head of research at Spotify actually, and I think their longterm goal,
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they've said, is to have at least one million creators make a comfortable living putting
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So I think you articulate a really nice vision of the world and the digital and the cyberspace
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What do you think companies like Spotify or YouTube or Netflix can do to create such markets?
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Is it an AI problem?
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Is it an interface problem for interface design?
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Is it some other kind of, is it an economics problem?
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Who should they hire to solve these problems?
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Well, part of it's not just top down.
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So the Silicon Valley has this attitude that they know how to do it.
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They will create the system just like Google did with the search box that will be so good
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that they'll just, everyone will adopt that.
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It's everything you said, but really I think missing that kind of culture.
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So it's literally that 16 year old who's able to create the songs.
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You don't create that as a Silicon Valley entity.
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You don't hire them per se.
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You have to create an ecosystem in which they are wanted and that they belong.
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And so you have to have some cultural credibility to do things like this.
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Netflix, to their credit, wanted some of that credibility and they created shows, content.
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They call it content.
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It's such a terrible word, but it's culture.
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And so with movies, you can kind of go give a large sum of money to somebody graduating
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from the USC film school.
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It's a whole thing of its own, but it's kind of like rich white people's thing to do.
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And American culture has not been so much about rich white people.
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It's been about all the immigrants, all the Africans who came and brought that culture
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and those rhythms to this world and created this whole new thing.
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And so companies can't artificially create that.
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They can't just say, hey, we're here.
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We're going to buy it up.
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You've got a partner.
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And so anyway, not to denigrate, these companies are all trying and they should, and I'm sure
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they're asking these questions and some of them are even making an effort.
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But it is partly a respect the culture as a technology person.
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You've got to blend your technology with cultural meaning.
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How much of a role do you think the algorithm, so machine learning has in connecting the
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consumer to the creator, sort of the recommender system aspect of this?
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It's a great question.
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I think pretty high.
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There's no magic in the algorithms, but a good recommender system is way better than
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a bad recommender system.
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And recommender systems is a billion dollar industry back even 10, 20 years ago.
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And it continues to be extremely important going forward.
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What's your favorite recommender system, just so we can put something, well, just historically
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I was one of the, when I first went to Amazon, I first didn't like Amazon because they put
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the book people out of business or the library, the local booksellers went out of business.
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I've come to accept that there probably are more books being sold now and poor people
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reading them than ever before.
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And then local book stores are coming back.
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So that's how economics sometimes work.
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You go up and you go down.
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But anyway, when I finally started going there and I bought a few books, I was really pleased
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to see another few books being recommended to me that I never would have thought of.
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And I bought a bunch of them.
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So they obviously had a good business model.
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But I learned things and I still to this day kind of browse using that service.
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And I think lots of people get a lot, that is a good aspect of a recommendation system.
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I'm learning from my peers in an indirect way.
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And their algorithms are not meant to have them impose what we learn.
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It really is trying to find out what's in the data.
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It doesn't work so well for other kinds of entities, but that's just the complexity of
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Like shirts, I'm not going to get recommendations on shirts, but that's interesting.
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If you try to recommend restaurants, it's hard.
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It's hard to do it at scale.
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But a blend of recommendation systems with other economic ideas, matchings and so on
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is really, really still very open research wise.
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And there's new companies that are going to emerge that do that well.
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What do you think is going to the messy, difficult land of say politics and things like that,
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that YouTube and Twitter have to deal with in terms of recommendation systems?
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Being able to suggest, I think Facebook just launched Facebook news.
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So recommend the kind of news that are most likely for you to be interesting.
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Do you think this is AI solvable, again, whatever term we want to use, do you think it's a solvable
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problem for machines or is it a deeply human problem that's unsolvable?
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So I don't even think about it at that level.
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I think that what's broken with some of these companies, it's all monetization by advertising.
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They're not, at least Facebook, I want to critique them, but they didn't really try
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to connect a producer and a consumer in an economic way, right?
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No one wants to pay for anything.
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And so they all, you know, starting with Google and Facebook, they went back to the playbook
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of, you know, the television companies back in the day.
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No one wanted to pay for this signal.
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They will pay for the TV box, but not for the signal, at least back in the day.
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And so advertising kind of filled that gap and advertising was new and interesting and
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it somehow didn't take over our lives quite, right?
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Fast forward, Google provides a service that people don't want to pay for.
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And so somewhat surprisingly in the nineties, they made, they ended up making huge amounts
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so they cornered the advertising market.
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It didn't seem like that was going to happen, at least to me.
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These little things on the right hand side of the screen just did not seem all that economically
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interesting, but that companies had maybe no other choice.
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The TV market was going away and billboards and so on.
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So they've, they got it.
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And I think that sadly that Google just has, it was doing so well with that at making such
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They didn't think much more about how, wait a minute, is there a producer consumer relationship
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to be set up here?
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Not just between us and the advertisers market to be created.
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Is there an actual market between the producer consumer?
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They're the producers, the person who created that video clip, the person that made that
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website, the person who could make more such things, the person who could adjust it as
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a function of demand, the person on the other side who's asking for different kinds of things,
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So you see glimmers of that now there's influencers and there's kind of a little glimmering of
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a market, but it should have been done 20 years ago.
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It should have been thought about.
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It should have been created in parallel with the advertising ecosystem.
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And then Facebook inherited that.
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And I think they also didn't think very much about that.
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So fast forward and now they are making huge amounts of money off of advertising.
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And the news thing and all these clicks is just feeding the advertising.
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It's all connected up to the advertiser.
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So you want more people to click on certain things because that money flows to you, Facebook.
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You're very much incentivized to do that.
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And when you start to find it's breaking, people are telling you, well, we're getting
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into some troubles.
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You try to adjust it with your smart AI algorithms, right?
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And figure out what are bad clicks.
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So maybe it shouldn't be click through rate, it should be something else.
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I find that pretty much hopeless.
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It does get into all the complexity of human life and you can try to fix it.
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You should, but you could also fix the whole business model.
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And the business model is that really, what are, are there some human producers and consumers
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Is there some economic value to be liberated by connecting them directly?
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Is it such that it's so valuable that people will be able to pay for it?
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And micro payments, like small payments.
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Micro, but even have to be micro.
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So I like the example, suppose I'm going, next week I'm going to India.
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Never been to India before.
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I have a couple of days in Mumbai, I have no idea what to do there.
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And I could go on the web right now and search.
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It's going to be kind of hopeless.
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I'm not going to find, you know, I have lots of advertisers in my face.
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What I really want to do is broadcast to the world that I am going to Mumbai and have someone
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on the other side of a market look at me and, and there's a recommendation system there.
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So I'm not looking at all possible people coming to Mumbai.
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They're looking at the people who are relevant to them.
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So someone in my age group, someone who kind of knows me in some level, I give up a little
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privacy by that, but I'm happy because what I'm going to get back is this person can make
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a little video for me, or they're going to write a little two page paper on here's the
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cool things that you want to do and move by this week, especially, right?
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I'm going to look at that.
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I'm not going to pay a micro payment.
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I'm going to pay, you know, a hundred dollars or whatever for that.
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It's like journalism.
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Um, and as an honest subscription, it's that I'm going to pay that person in that moment.
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Company's going to take 5% of that.
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And that person has now got it.
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It's a gig economy, if you will, but you know, done for it, you know, thinking about a little
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bit behind YouTube, there was actually people who could make more of those things.
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If they were connected to a market, they would make more of those things independently.
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You don't have to tell them what to do.
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You don't have to incentivize them any other way.
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Um, and so, yeah, these companies, I don't think have thought long and hard about that.
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So I do distinguish on Facebook on the one side, who just not thought about these things
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I think, uh, thinking that AI will fix everything, uh, and Amazon thinks about them all the time
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because they were already out in the real world.
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They were delivering packages, people's doors.
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They were, they were worried about a market.
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They were worried about sellers and, you know, they worry and some things they do are great.
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Some things maybe not so great, but you know, they're in that business model.
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And then I'd say Google sort of hovers somewhere in between.
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I don't, I don't think for a long, long time they got it.
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I think they probably see that YouTube is more pregnant with possibility than, than,
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than they might've thought and that they're probably heading that direction.
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Um, but uh, you know, Silicon Valley has been dominated by the Google Facebook kind of mentality
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and the subscription and advertising and that is, that's the core problem, right?
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The fake news actually rides on top of that because it means that you're monetizing with
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clip through rate and that is the core problem.
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You got to remove that.
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So advertisement, if we're going to linger on that, I mean, that's an interesting thesis.
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I don't know if everyone really deeply thinks about that.
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The thought is the advertising model is the only thing we have, the only thing we'll ever
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We have to fix, we have to build algorithms that despite that business model, you know,
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find the better angels of our nature and do good by society and by the individual.
link |
But you think we can slowly, you think, first of all, there's a difference between should
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So you're saying we should slowly move away from the advertising model and have a direct
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connection between the consumer and the creator.
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The question I also have is, can we, because the advertising model is so successful now
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in terms of just making a huge amount of money and therefore being able to build a big company
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that provides, has really smart people working that create a good service.
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Do you think it's possible?
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And just to clarify, you think we should move away?
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Well, I think we should.
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But we is the, you know, me.
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Well, the companies, I mean, so first of all, full disclosure, I'm doing a day a week at
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Amazon because I kind of want to learn more about how they do things.
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So, you know, I'm not speaking for Amazon in any way, but, you know, I did go there
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because I actually believe they get a little bit of this or trying to create these markets.
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And they don't really use, advertising is not a crucial part of it.
link |
Well, that's a good question.
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So it has become not crucial, but it's become more and more present if you go to Amazon
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And, you know, without revealing too many deep secrets about Amazon, I can tell you
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that, you know, a lot of people in the company question this and there's a huge questioning
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You do not want a world where there's zero advertising.
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That actually is a bad world.
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So here's a way to think about it.
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You're a company that like Amazon is trying to bring products to customers, right?
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And the customer, at any given moment, you want to buy a vacuum cleaner, say, you want
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to know what's available for me.
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And, you know, it's not going to be that obvious.
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You have to do a little bit of work at it.
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The recommendation system will sort of help, right?
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But now suppose this other person over here has just made the world, you know, they spent
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a huge amount of energy.
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They had a great idea.
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They made a great vacuum cleaner.
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They know they really did it.
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It's an MIT, you know, whiz kid that made a great new vacuum cleaner, right?
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It's not going to be in the recommendation system.
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No one will know about it.
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The algorithms will not find it and AI will not fix that.
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How do you allow that vacuum cleaner to start to get in front of people, be sold well advertising.
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And here, what advertising is, it's a signal that you're, you believe in your product enough
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that you're willing to pay some real money for it.
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And to me as a consumer, I look at that signal.
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I say, well, first of all, I know these are not just cheap little ads cause we have now
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I know that, you know, these are super cheap, you know, pennies.
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If I see an ad where it's actually, I know the company is only doing a few of these and
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they're making, you know, real money is kind of flowing and I see an ad, I may pay more
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And I actually might want that because I see, Hey, that guy spent money on his vacuum cleaner.
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Maybe there's something good there.
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So I will look at it.
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And so that's part of the overall information flow in a good market.
link |
So advertising has a role, but the problem is of course that that signal is now completely
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gone because it just, you know, dominant by these tiny little things that add up to big
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money for the company, you know?
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So I think it will just, I think it will change because the societies just don't, you know,
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stick with things that annoy a lot of people and advertising currently annoys people more
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than it provides information.
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And I think that a Google probably is smart enough to figure out that this is a dead,
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this is a bad model, even though it's a hard, huge amount of money and they'll have to figure
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out how to pull it away from it slowly.
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And I'm sure the CEO there will figure it out, but they need to do it.
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And they needed it to, so if you reduce advertising, not to zero, but you reduce it at the same
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time you bring up producer, consumer, actual real value being delivered.
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So real money is being paid and they take a 5% cut that 5% could start to get big enough
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to cancel out the lost revenue from the kind of the poor kind of advertising.
link |
And I think that a good company will do that, will realize that.
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And Facebook, you know, again, God bless them.
link |
They bring, you know, grandmothers, they bring children's pictures into grandmothers lives.
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But they need to think of a new business model and that's the core problem there.
link |
Until they start to connect producer consumer, I think they will just continue to make money
link |
and then buy the next social network company and then buy the next one and the innovation
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level will not be high and the health issues will not go away.
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So I apologize that we kind of returned to words, I don't think the exact terms matter,
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but in sort of defense of advertisement, don't you think the kind of direct connection between
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consumer and creator producer is what advertisement strives to do, right?
link |
So that is best advertisement is literally now Facebook is listening to our conversation
link |
and heard that you're going to India and will be able to actually start automatically for
link |
you making these connections and start giving this offer.
link |
So like, I apologize if it's just a matter of terms, but just to draw a distinction,
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is it possible to make advertisements just better and better and better algorithmically
link |
to where it actually becomes a connection, almost a direct connection?
link |
That's a good question.
link |
So let's component on that.
link |
First of all, what we just talked about, I was defending advertising.
link |
So I was defending it as a way to get signals into a market that don't come any other way,
link |
especially algorithmically.
link |
It's a sign that someone spent money on it, it's a sign they think it's valuable.
link |
And if I think that if other things, someone else thinks it's valuable, and if I trust
link |
other people, I might be willing to listen.
link |
I don't trust that Facebook though, who's an intermediary between this.
link |
I don't think they care about me.
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I don't think they do.
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And I find it creepy that they know I'm going to India next week because of our conversation.
link |
Why do you think that is?
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So what, could you just put your PR hat on?
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Why do you think you find Facebook creepy and not trust them as do majority of the population?
link |
So they're out of the Silicon Valley companies, I saw like not approval rate, but there's
link |
ranking of how much people trust companies and Facebook is in the gutter.
link |
In the gutter, including people inside of Facebook.
link |
So what do you attribute that to?
link |
Come on, you don't find it creepy that right now we're talking that I might walk out on
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the street right now that some unknown person who I don't know kind of comes up to me and
link |
says, I hear you're going to India.
link |
I mean, that's not even Facebook.
link |
That's just, I want transparency in human society.
link |
I want to have, if you know something about me, there's actually some reason you know
link |
something about me.
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That's something that if I look at it later and audit it kind of, I approve.
link |
You know something about me because you care in some way.
link |
There's a caring relationship even, or an economic one or something.
link |
Not just that you're someone who could exploit it in ways I don't know about or care about
link |
or I'm troubled by or whatever.
link |
We're in a world right now where that happens way too much and that Facebook knows things
link |
about a lot of people and could exploit it and does exploit it at times.
link |
I think most people do find that creepy.
link |
It's not for them.
link |
It's not that Facebook is not doing it because they care about them in a real sense.
link |
And they shouldn't.
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They should not be a big brother caring about us.
link |
That is not the role of a company like that.
link |
Wait, not the big brother part, but the caring, the trusting.
link |
I mean, don't those companies, just to link on it because a lot of companies have a lot
link |
of information about us.
link |
I would argue that there's companies like Microsoft that has more information about
link |
us than Facebook does and yet we trust Microsoft more.
link |
Well, Microsoft is pivoting.
link |
Microsoft, you know, under Satya Nadella has decided this is really important.
link |
We don't want to do creepy things.
link |
Really want people to trust us to actually only use information in ways that they really
link |
would approve of, that we don't decide, right?
link |
And I'm just kind of adding that the health of a market is that when I connect to someone
link |
who produces a consumer, it's not just a random producer or consumer, it's people who see
link |
They don't like each other, but they sense that if they transact, some happiness will
link |
go up on both sides.
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If a company helps me to do that in moments that I choose of my choosing, then fine.
link |
So, and also think about the difference between, you know, browsing versus buying, right?
link |
There are moments in my life I just want to buy, you know, a gadget or something.
link |
I need something for that moment.
link |
I need some ammonia for my house or something because I got a problem with a spill.
link |
I want to just go in.
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I don't want to be advertised at that moment.
link |
I don't want to be led down various, you know, that's annoying.
link |
I want to just go and have it be extremely easy to do what I want.
link |
Other moments I might say, no, it's like today I'm going to the shopping mall.
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I want to walk around and see things and see people and be exposed to stuff.
link |
So I want control over that though.
link |
I don't want the company's algorithms to decide for me, right?
link |
I think that's the thing.
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There's a total loss of control if Facebook thinks they should take the control from us
link |
of deciding when we want to have certain kinds of information, when we don't, what information
link |
that is, how much it relates to what they know about us that we didn't really want them
link |
I don't want them to be helping me in that way.
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I don't want them to be helping them by they decide they have control over what I want
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Facebook, by the way, I have this optimistic thing where I think Facebook has the kind
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of personal information about us that could create a beautiful thing.
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So I'm really optimistic of what Facebook could do.
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It's not what it's doing, but what it could do.
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So I don't see that.
link |
I think that optimism is misplaced because there's not a bit, you have to have a business
link |
model behind these things.
link |
Create a beautiful thing is really, let's be, let's be clear.
link |
It's about something that people would value.
link |
And I don't think they have that business model and I don't think they will suddenly
link |
discover it by what, you know, a long hot shower.
link |
I disagree in terms of, you can discover a lot of amazing things in a shower.
link |
So I didn't say that.
link |
I said, they won't come, they won't do it, but in the shower, I think a lot of other
link |
people will discover it.
link |
I think that this guy, so I should also, full disclosure, there's a company called United
link |
Masters, which I'm on their board and they've created this music market and I have a hundred
link |
thousand artists now signed on and they've done things like gone to the NBA and the NBA,
link |
the music you find behind NBA clips right now is their music, right?
link |
That's a company that had the right business model in mind from the get go, right?
link |
And from day one, there was value brought to, so here you have a kid who made some songs
link |
who suddenly their songs are on the NBA website, right?
link |
That's real economic value to people.
link |
And so, you know, so you and I differ on the optimism of being able to sort of change the
link |
direction of the Titanic, right?
link |
So I, yeah, I'm older than you, so I've seen some Titanic's crash, got it.
link |
But and just to elaborate, cause I totally agree with you and I just want to know how
link |
difficult you think this problem is of, so for example, I want to read some news and
link |
I would, there's a lot of times in the day where something makes me either smile or think
link |
in a way where I like consciously think this really gave me value.
link |
Like I sometimes listen to the daily podcasts in the New York times, way better than the
link |
New York times themselves, by the way, for people listening.
link |
That's like real journalism is happening for some reason in the podcast space.
link |
It doesn't make sense to me, but often I listen to it 20 minutes and I would be willing to
link |
pay for that, like $5, $10 for that experience.
link |
And how difficult, that's kind of what you're getting at is that little transaction.
link |
How difficult is it to create a frictionless system like Uber has, for example, for other
link |
What's your intuition there?
link |
So I, first of all, I pay little bits of money to, you know, to send, there's something
link |
called courts that does financial things.
link |
I like medium as a site, I don't pay there, but I would.
link |
You had a great post on medium.
link |
I would have loved to pay you a dollar and not others.
link |
I wouldn't have wanted it per se because there should be also sites where that's not actually
link |
The goal is to actually have a broadcast channel that I monetize in some other way if I chose
link |
I mean, I could now people know about it.
link |
I could, I'm not doing it, but that's fine with me.
link |
Also the musicians who are making all this music, I don't think the right model is that
link |
you pay a little subscription fee to them, right?
link |
Because people can copy the bits too easily and it's just not that somewhere the value
link |
The value is that a connection was made between real human beings, then you can follow up
link |
And create yet more value.
link |
So no, I think there's a lot of open questions here, hot open questions, but also, yeah,
link |
I do want good recommendation systems that recommend cool stuff to me.
link |
But it's pretty hard, right?
link |
I don't like them to recommend stuff just based on my browsing history.
link |
I don't like the based on stuff they know about me, quote unquote.
link |
What's unknown about me is the most interesting.
link |
So this is the, this is the really interesting question.
link |
We may disagree, maybe not.
link |
I think that I love recommender systems and I want to give them everything about me in
link |
a way that I trust.
link |
But you, but you don't, because, so for example, this morning I clicked on a, you know, I was
link |
pretty sleepy this morning.
link |
I clicked on a story about the queen of England.
link |
I do not give a damn about the queen of England.
link |
But it was clickbait.
link |
It kind of looked funny and I had to say, what the heck are they talking about?
link |
I don't want to have my life, you know, heading that direction.
link |
Now that's in my browsing history.
link |
The system in any reasonable system will think that I care about the queen of England.
link |
That's browsing history.
link |
But, but you're saying all the trace, all the digital exhaust or whatever, that's been
link |
kind of the models.
link |
If you collect all this stuff, you're going to figure all of us out.
link |
Well, if you're trying to figure out like kind of one person like Trump or something,
link |
maybe you could figure him out.
link |
But if you're trying to figure out, you know, 500 million people, you know, no way, no way.
link |
I think we are, humans are just amazingly rich and complicated.
link |
Every one of us has our little quirks, every one of us has our little things that could
link |
intrigue us that we don't even know it will intrigue us.
link |
And there's no sign of it in our past, but by God, there it comes and you know, you fall
link |
And I don't want a company trying to figure that out for me and anticipate that I want
link |
them to provide a forum, a market, a place that I kind of go and by hook or by crook,
link |
this happens, you know, I I'm walking down the street and I hear some Chilean music being
link |
played and I never knew I liked Chilean music, but wow.
link |
So there is that side and I want them to provide a limited, but you know, interesting place
link |
And so don't try to use your AI to kind of, you know, figure me out and then put me in
link |
a world where you figured me out, you know, no, create huge spaces for human beings where
link |
our creativity and our style will be enriched and come forward and it'll be a lot of more
link |
I won't have people randomly, anonymously putting comments up and I'll special based
link |
on stuff they know about me, facts that, you know, we are so broken right now.
link |
If you're, you know, especially if you're a celebrity, but you know, it's about anybody
link |
that anonymous people are hurting lots and lots of people right now.
link |
That's part of this thing that Silicon Valley is thinking that, you know, just collect all
link |
this information and use it in a great way.
link |
So no, I'm not, I'm not a pessimist, I'm very much an optimist by nature, but I think that's
link |
just been the wrong path for the whole technology to take.
link |
Be more limited, create, let humans rise up.
link |
Don't try to replace them.
link |
That's the AI mantra.
link |
Don't try to anticipate them.
link |
Don't try to predict them because you're, you're, you're not going to, you're not going
link |
to be able to do those things.
link |
You're going to make things worse.
link |
So right now, just give this a chance.
link |
Right now, the recommender systems are the creepy people in the shadow watching your
link |
So they're looking at traces of you.
link |
They're not directly interacting with you, sort of the, your close friends and family,
link |
the way they know you is by having conversation, by actually having interactions back and forth.
link |
Do you think there's a place for recommender systems sort of to step, cause you, you just
link |
emphasize the value of human to human connection, but yeah, just give it a chance, AI human
link |
Is there a role for an AI system to have conversations with you in terms of, to try to figure out
link |
what kind of music you like, not by just watching what you listening to, but actually having
link |
a conversation, natural language or otherwise.
link |
Yeah, no, I'm, I'm, so I'm not against it.
link |
I just wanted to push back against the, maybe you're saying you have options for Facebook.
link |
So there I think it's misplaced, but, but I think that distributing, yeah, no, so good
link |
That's a hard spot to be in.
link |
Human interaction, like on our daily, the context around me in my own home is something
link |
that I don't want some big company to know about at all, but I would be more than happy
link |
to have technology help me with it.
link |
Which kind of technology?
link |
Well, you know, just, Alexa, Amazon, well, a good, Alexa's done right.
link |
And I think Alexa is a research platform right now more than anything else.
link |
But Alexa done right, you know, could do things like I, I leave the water running in my garden
link |
and I say, Hey, Alexa, the water's running in my garden.
link |
And even have Alexa figure out that that means when my wife comes home, that she should be
link |
That's a little bit of a reasoning.
link |
I would call that AI and by any kind of stretch, it's a little bit of reasoning and it actually
link |
kind of would make my life a little easier and better.
link |
And you know, I don't, I wouldn't call this a wow moment, but I kind of think that overall
link |
rises human happiness up to have that kind of thing.
link |
But not when you're lonely, Alexa, knowing loneliness.
link |
No, no, I don't want Alexa to be, feel intrusive.
link |
And I don't want just the designer of the system to kind of work all this out.
link |
I really want to have a lot of control and I want transparency and control.
link |
And if a company can stand up and give me that in the context of new technology, I think
link |
First of all, be way more successful than our current generation.
link |
And like I said, I was mentioning Microsoft, I really think they're, they're pivoting to
link |
kind of be the trusted old uncle, but you know, I think that they get that this is a
link |
way to go, that if you let people find technology, empowers them to have more control and have
link |
and have control, not just over privacy, but over this rich set of interactions, that that
link |
people are going to like that a lot more.
link |
And that's, that's the right business model going forward.
link |
What does control over privacy look like?
link |
Do you think you should be able to just view all the data that?
link |
No, it's much more than that.
link |
I mean, first of all, it should be an individual decision.
link |
Some people don't want privacy.
link |
They want their whole life out there.
link |
Other people's want it.
link |
Privacy is not a zero one.
link |
It's not a legal thing.
link |
It's not just about which data is available, which is not.
link |
I like to recall to people that, you know, a couple hundred years ago, everyone, there
link |
was not really big cities, everyone lived in on the countryside and villages and villages.
link |
Everybody knew everything about you.
link |
Very, you didn't have any privacy.
link |
Are we better off now?
link |
Well, you know, arguably no, because what did you get for that loss of certain kinds
link |
Well, people help each other if they, because they know everything about you.
link |
They know something's bad's happening, they will help you with that.
link |
And now you live in a big city, no one knows about that.
link |
So it kind of depends the answer.
link |
I want certain people who I trust and there should be relationships.
link |
I should kind of manage all those, but who knows what about me?
link |
I should have some agency there.
link |
It shouldn't, I shouldn't be a drift in a sea of technology where I have no agency.
link |
I don't want to go reading things and checking boxes.
link |
So I don't know how to do that.
link |
And I'm not a privacy researcher per se.
link |
I just, I recognize the vast complexity of this.
link |
It's not just technology.
link |
It's not just legal scholars meeting technologists.
link |
There's gotta be kind of a whole layers around it.
link |
And so I, when I alluded to this emerging engineering field, this is a big part of it.
link |
When electrical engineering came, I'm not one around at the time, but you just didn't
link |
plug electricity into walls and all kinds of work.
link |
You don't have to have like underwriters laboratory that reassured you that that plug's not going
link |
to burn up your house and that that machine will do this and that and everything.
link |
There'll be whole people who can install things.
link |
There'll be people who can watch the installers.
link |
There'll be a whole layers, you know, an onion of these kinds of things.
link |
And for things as deep and interesting as privacy, which is as least as interesting
link |
as electricity, that's going to take decades to kind of work out, but it's going to require
link |
a lot of new structures that we don't have right now.
link |
So it's kind of hard to talk about it.
link |
And you're saying there's a lot of money to be made if you get it right.
link |
So something you should look at.
link |
A lot of money to be made in all these things that provide human services and people recognize
link |
them as useful parts of their lives.
link |
So yeah, the dialogue sometimes goes from the exuberant technologists to the no technology
link |
And that's, you know, in our public discourse, you know, and as far as you see too much of
link |
this kind of thing and the sober discussions in the middle, which are the challenge he
link |
wants to have or where we need to be having our conversations.
link |
And you know, there's just not actually, there's not many forum fora for those.
link |
You know, there's, that's, that's kind of what I would look for.
link |
Maybe I could go and I could read a comment section of something and it would actually
link |
be this kind of dialogue going back and forth.
link |
You don't see much of this, right?
link |
Which is why actually there's a resurgence of podcasts out of all, because people are
link |
really hungry for conversation, but there's technology is not helping much.
link |
So comment sections of anything, including YouTube is not hurting and not helping.
link |
And you think technically speaking, it's possible to help.
link |
I don't know the answers, but it's a, it's a, it's a less anonymity, a little more locality,
link |
you know, worlds that you kind of enter in and you trust the people there in those worlds
link |
so that when you start having a discussion, you know, not only is that people are not
link |
going to hurt you, but it's not going to be a total waste of your time because there's
link |
a lot of wasting of time that, you know, a lot of us, I pulled out of Facebook early
link |
on cause it was clearly going to waste a lot of my time even though there was some value.
link |
And so, yeah, worlds that are somehow you enter in and you know what you're getting
link |
and it's kind of appeals to you and you might, new things might happen, but you kind of have
link |
some, some trust in that world.
link |
And there's some deep, interesting, complex psychological aspects around anonymity, how
link |
that changes human behavior that's quite dark.
link |
I think a lot of us are, especially those of us who really loved the advent of technology.
link |
I love social networks when they came out.
link |
I was just, I didn't see any negatives there at all.
link |
But then I started seeing comment sections.
link |
I think it was maybe, you know, with the CNN or something.
link |
And I started to go, wow, this, this darkness I just did not know about and, and our technology
link |
is now amplifying it.
link |
So sorry for the big philosophical question, but on that topic, do you think human beings,
link |
cause you've also, out of all things, had a foot in psychology too, the, do you think
link |
human beings are fundamentally good?
link |
Like all of us have good intent that could be mind or is it depending on context and
link |
environment, everybody could be evil.
link |
So my answer is fundamentally good.
link |
But fundamentally limited.
link |
All of us have very, you know, blinkers on.
link |
We don't see the other person's pain that easily.
link |
We don't see the other person's point of view that easily.
link |
We're very much in our own head, in our own world.
link |
And on my good days, I think the technology could open us up to, you know, more perspectives
link |
and more less blinkered and more understanding, you know, a lot of wars in human history happened
link |
because of just ignorance.
link |
They didn't, they, they thought the other person was doing this while their person wasn't
link |
And we have a huge amounts of that.
link |
But in my lifetime, I've not seen technology really help in that way yet.
link |
And I do, I do, I do believe in that, but you know, no, I think fundamentally humans
link |
The people suffer, people have grievances because you have grudges and those things
link |
cause them to do things they probably wouldn't want.
link |
They regret it often.
link |
So no, I, I think it's a, you know, part of the progress of technology is to indeed allow
link |
it to be a little easier to be the real good person you actually are.
link |
Well, but do you think individual human life or society could be modeled as an optimization
link |
Not the way I think typically, I mean, that's, you're talking about one of the most complex
link |
phenomenon in the whole, you know, in all of which the individual human life or society
link |
I mean, individual human life is amazingly complex.
link |
And so you know, optimization is kind of just one branch of mathematics that talks about
link |
certain kinds of things.
link |
And it just feels way too limited for the complexity of such things.
link |
What properties of optimization problems do you think, so do you think most interesting
link |
problems that could be solved through optimization, what kind of properties does that surface
link |
have non convexity, convexity, linearity, all those kinds of things, saddle points?
link |
Well, so optimization is just one piece of mathematics.
link |
You know, there's like, you just, even in our era, we're aware that say sampling is
link |
coming up, examples of something coming up with a distribution.
link |
What's optimization?
link |
Well, they, you can, if you're a kind of a certain kind of mathematician, you can try
link |
to blend them and make them seem to be sort of the same thing.
link |
But optimization is roughly speaking, trying to find a point that, a single point that
link |
is the optimum of a criterion function of some kind.
link |
And sampling is trying to, from that same surface, treat that as a distribution or density
link |
and find points that have high density.
link |
So I want the entire distribution in a sampling paradigm and I want the, you know, the single
link |
point, that's the best point in the optimization paradigm.
link |
Now if you were optimizing in the space of probability measures, the output of that could
link |
be a whole probability distribution.
link |
So you can start to make these things the same.
link |
But in mathematics, if you go too high up that kind of abstraction hierarchy, you start
link |
to lose the, you know, the ability to do the interesting theorems.
link |
So you kind of don't try that.
link |
You don't try to overly over abstract.
link |
So as a small tangent, what kind of worldview do you find more appealing?
link |
One that is deterministic or stochastic?
link |
Well, that's easy.
link |
I mean, I'm a statistician.
link |
You know, the world is highly stochastic.
link |
I don't know what's going to happen in the next five minutes, right?
link |
Because what you're going to ask, what we're going to do, what I'll say.
link |
Due to the uncertainty.
link |
Massive uncertainty.
link |
You know, massive uncertainty.
link |
And so the best I can do is have come rough sense or probability distribution on things
link |
and somehow use that in my reasoning about what to do now.
link |
So how does the distributed at scale when you have multi agent systems look like?
link |
So optimization can optimize sort of, it makes a lot more sense, sort of at least from my
link |
from robotics perspective, for a single robot, for a single agent, trying to optimize some
link |
objective function.
link |
When you start to enter the real world, this game theoretic concept starts popping up.
link |
That's how do you see optimization in this?
link |
Because you've talked about markets in a scale.
link |
What does that look like?
link |
Do you see it as optimization?
link |
Do you see it as sampling?
link |
Do you see like, how should you mark?
link |
These all blend together.
link |
And a system designer thinking about how to build an incentivized system will have a blend
link |
of all these things.
link |
So, you know, a particle in a potential well is optimizing a functional called a Lagrangian,
link |
The particle doesn't know that.
link |
There's no algorithm running that does that.
link |
And so it's a description mathematically of something that helps us understand as analysts
link |
what's happening, right?
link |
And so the same thing will happen when we talk about, you know, mixtures of humans and
link |
computers and markets and so on and so forth, there'll be certain principles that allow
link |
us to understand what's happening, whether or not the actual algorithms are being used
link |
by any sense is not clear.
link |
Now at some point, I may have set up a multi agent or market kind of system.
link |
And I'm now thinking about an individual agent in that system.
link |
And they're asked to do some task and they're incentivized in some way, they get certain
link |
signals and they have some utility.
link |
What they will do at that point is they just won't know the answer, they may have to optimize
link |
to find an answer.
link |
Okay, so an artist could be embedded inside of an overall market.
link |
You know, and game theory is very, very broad.
link |
It is often studied very narrowly for certain kinds of problems.
link |
But it's roughly speaking, this is just the, I don't know what you're going to do.
link |
So I kind of anticipate that a little bit, and you anticipate what I'm anticipating.
link |
And we kind of go back and forth in our own minds.
link |
We run kind of thought experiments.
link |
You've talked about this interesting point in terms of game theory, you know, most optimization
link |
problems really hate saddle points, maybe you can describe what saddle points are.
link |
But I've heard you kind of mentioned that there's a there's a branch of optimization
link |
that you could try to explicitly look for saddle points as a good thing.
link |
Oh, not optimization.
link |
That's just game theory that that so there's all kinds of different equilibria in game
link |
And some of them are highly explanatory behavior.
link |
They're not attempting to be algorithmic.
link |
They're just trying to say, if you happen to be at this equilibrium, you would see certain
link |
And we see that in real life.
link |
That's what an economist wants to do, especially behavioral economists in continuous differential
link |
game theory, you're in continuous spaces, a some of the simplest equilibria are saddle
link |
points and Nash equilibrium as a saddle point.
link |
It's a special kind of saddle point.
link |
So classically, in game theory, you were trying to find Nash equilibria and an algorithmic
link |
game theory, you're trying to find algorithms that would find them.
link |
And so you're trying to find saddle points.
link |
I mean, so that's literally what you're trying to do.
link |
But you know, any economist knows that Nash equilibria have their limitations.
link |
They are definitely not that explanatory in many situations.
link |
They're not what you really want.
link |
There's other kind of equilibria.
link |
And there's names associated with these because they came from history with certain people
link |
working on them, but there will be new ones emerging.
link |
So you know, one example is a Stackelberg equilibrium.
link |
So you know, Nash, you and I are both playing this game against each other or for each other,
link |
maybe it's cooperative, and we're both going to think it through and then we're going to
link |
decide and we're going to do our thing simultaneously.
link |
You know, in a Stackelberg, no, I'm going to be the first mover.
link |
I'm going to make a move.
link |
You're going to look at my move and then you're going to make yours.
link |
Now since I know you're going to look at my move, I anticipate what you're going to do.
link |
And so I don't do something stupid, but then I know that you are also anticipating me.
link |
So we're kind of going back and forth on why, but there is then a first mover thing.
link |
And so those are different equilibria, right?
link |
And so just mathematically, yeah, these things have certain topologies and certain shapes
link |
that are like, what's it, algorithmically or dynamically, how do you move towards them?
link |
How do you move away from things?
link |
You know, so some of these questions have answers, they've been studied, others do not.
link |
And especially if it becomes stochastic, especially if there's large numbers of decentralized
link |
things, there's just, you know, young people get in this field who kind of think it's all
link |
done because we have, you know, TensorFlow.
link |
Well, no, these are all open problems and they're really important and interesting.
link |
And it's about strategic settings.
link |
How do I collect data?
link |
Suppose I don't know what you're going to do because I don't know you very well, right?
link |
Well, I got to collect data about you.
link |
So maybe I want to push you into a part of the space where I don't know much about you
link |
so I can get data.
link |
Cause, and then later I'll realize that you'll never, you'll never go there because of the
link |
way the game is set up.
link |
You know, that's part of the overall, you know, data analysis context is that.
link |
Even the game of poker is fascinating space, whenever there's any uncertainty, a lack of
link |
information, it's a super exciting space.
link |
Just to linger on optimization for a second.
link |
So when we look at deep learning, it's essentially minimization of a complicated loss function.
link |
So is there something insightful or hopeful that you see in the kinds of function surface
link |
that loss functions, the deep learning and in the real world is trying to optimize over?
link |
Is there something interesting as it's just the usual kind of problems of optimization?
link |
I think from an optimization point of view, that surface, first of all, it's pretty smooth.
link |
And secondly, if there's over, if it's over parameterized, there's kind of lots of paths
link |
down to reasonable Optima.
link |
And so kind of the getting downhill to the, to an optimum is viewed as not as hard as
link |
you might've expected in high dimensions.
link |
The fact that some Optima tend to be really good ones and others not so good.
link |
And you tend to, it's not, sometimes you find the good ones is sort of still needs explanation.
link |
But, but the particular surface is coming from the particular generation of neural nets.
link |
I kind of suspect those will, those will change in 10 years.
link |
It will not be exactly those surfaces.
link |
There'll be some others that are an optimization theory will help contribute to why other surfaces
link |
or why other algorithms.
link |
Years of arithmetic operations with a little bit of nonlinearity, that's not, that didn't
link |
come from neuroscience per se.
link |
I mean, maybe in the minds of some of the people working on it, they were thinking about
link |
brains, but they were arithmetic circuits in all kinds of fields, computer science control
link |
And that layers of these could transform things in certain ways.
link |
And that if it's smooth, maybe you could find parameter values is a sort of big discovery
link |
that it's working, it's able to work at this scale.
link |
But I don't think that we're stuck with that and we're, we're certainly not stuck with
link |
that cause we're understanding the brain.
link |
So in terms of on the algorithm side sort of gradient descent, do you think we're stuck
link |
with gradient descent as a variance of it?
link |
What variance do you find interesting or do you think there'll be something else invented
link |
that is able to walk all over these optimization spaces in more interesting ways?
link |
So there's a co design of the surface and the, or the architecture and the algorithm.
link |
So if you just ask if we stay with the kind of architectures that we have now and not
link |
just neural nets, but you know, phase retrieval architectures or matrix completion architectures
link |
You know, I think we've kind of come to a place where yeah, a stochastic gradient algorithms
link |
are dominant and there are versions that are a little better than others.
link |
They have more guarantees, they're more robust and so on.
link |
And there's ongoing research to kind of figure out which is the best arm for which situation.
link |
But I think that that'll start to co evolve, that that'll put pressure on the actual architecture.
link |
And so we shouldn't do it in this particular way, we should do it in a different way because
link |
this other algorithm is now available if you do it in a different way.
link |
So that I can't really anticipate that co evolution process, but you know, gradients
link |
are amazing mathematical objects.
link |
They have a lot of people who start to study them more deeply mathematically are kind of
link |
shocked about what they are and what they can do.
link |
Think about it this way, suppose that I tell you if you move along the x axis, you go uphill
link |
in some objective by three units, whereas if you move along the y axis, you go uphill
link |
by seven units, right?
link |
Now I'm going to only allow you to move a certain unit distance, right?
link |
What are you going to do?
link |
Well, most people will say that I'm going to go along the y axis, I'm getting the biggest
link |
bang for my buck, you know, and my buck is only one unit, so I'm going to put all of
link |
it in the y axis, right?
link |
And why should I even take any of my strength, my step size and put any of it in the x axis
link |
because I'm getting less bang for my buck.
link |
That seems like a completely clear argument and it's wrong because the gradient direction
link |
is not to go along the y axis, it's to take a little bit of the x axis.
link |
And to understand that, you have to know some math and so even a trivial so called operator
link |
like gradient is not trivial and so, you know, exploiting its properties is still very important.
link |
Now we know that just pervading descent has got all kinds of problems, it gets stuck in
link |
many ways and it had never, you know, good dimension dependence and so on.
link |
So my own line of work recently has been about what kinds of stochasticity, how can we get
link |
dimension dependence, how can we do the theory of that and we've come up pretty favorable
link |
results with certain kinds of stochasticity.
link |
We have sufficient conditions generally.
link |
We know if you do this, we will give you a good guarantee.
link |
We don't have necessary conditions that it must be done a certain way in general.
link |
So stochasticity, how much randomness to inject into the walking along the gradient?
link |
And what kind of randomness?
link |
Why is randomness good in this process?
link |
Why is stochasticity good?
link |
Yeah, so I can give you simple answers but in some sense again, it's kind of amazing.
link |
Stochasticity just, you know, particular features of a surface that could have hurt you if you
link |
were doing one thing deterministically won't hurt you because by chance, there's very little
link |
chance that you would get hurt.
link |
So here stochasticity, it just kind of saves you from some of the particular features of
link |
In fact, if you think about surfaces that are discontinuous in our first derivative,
link |
like an absolute value function, you will go down and hit that point where there's nondifferentiability.
link |
And if you're running a deterministic algorithm at that point, you can really do something
link |
Whereas stochasticity just means it's pretty unlikely that's going to happen, that you're
link |
going to hit that point.
link |
So it's again, nontrivial to analyze but especially in higher dimensions, also stochasticity,
link |
our intuition isn't very good about it but it has properties that kind of are very appealing
link |
in high dimensions for a lot of large number of reasons.
link |
So it's all part of the mathematics to kind of, that's what's fun to work in the field
link |
is that you get to try to understand this mathematics.
link |
But long story short, you know, partly empirically, it was discovered stochastic gradient is very
link |
effective and theory kind of followed, I'd say, that but I don't see that we're getting
link |
clearly out of that.
link |
What's the most beautiful, mysterious, a profound idea to you in optimization?
link |
I don't know the most.
link |
But let me just say that Nesterov's work on Nesterov acceleration to me is pretty surprising
link |
Can you elaborate?
link |
Well Nesterov acceleration is just that, suppose that we are going to use gradients
link |
to move around in a space.
link |
For the reasons I've alluded to, they're nice directions to move.
link |
And suppose that I tell you that you're only allowed to use gradients, you're not going
link |
to be allowed to use this local person that can only sense kind of the change in the surface.
link |
But I'm going to give you kind of a computer that's able to store all your previous gradients.
link |
And so you start to learn some something about the surface.
link |
And I'm going to restrict you to maybe move in the direction of like a linear span of
link |
all the gradients.
link |
So you can't kind of just move in some arbitrary direction, right?
link |
So now we have a well defined mathematical complexity model.
link |
There's certain classes of algorithms that can do that and others that can't.
link |
And we can ask for certain kinds of surfaces, how fast can you get down to the optimum?
link |
So there's answers to these.
link |
So for a smooth convex function, there's an answer, which is one over the number of steps
link |
You will be within a ball of that size after k steps.
link |
Gradient descent in particular has a slower rate, it's one over k.
link |
So you could ask, is gradient descent actually, even though we know it's a good algorithm,
link |
is it the best algorithm?
link |
And the answer is no.
link |
Well, not clear yet, because one over k squared is a lower bound.
link |
That's probably the best you can do.
link |
Gradient is one over k, but is there something better?
link |
And so I think as a surprise to most, Nesterov discovered a new algorithm that has got two
link |
It's two gradients and puts those together in a certain kind of obscure way.
link |
And the thing doesn't even move downhill all the time.
link |
It sometimes goes back uphill.
link |
And if you're a physicist, that kind of makes some sense.
link |
You're building up some momentum and that is kind of the right intuition, but that intuition
link |
is not enough to understand kind of how to do it and why it works.
link |
It achieves one over k squared and it has a mathematical structure and it's still kind
link |
of to this day, a lot of us are writing papers and trying to explore that and understand
link |
So there are lots of cool ideas and optimization, but just kind of using gradients, I think
link |
is number one that goes back, you know, 150 years.
link |
And then Nesterov, I think has made a major contribution with this idea.
link |
So like you said, gradients themselves are in some sense, mysterious.
link |
They're not as trivial as...
link |
Coordinate descent is more of a trivial one.
link |
You just pick one of the coordinates.
link |
That's how we think.
link |
That's how our human mind thinks.
link |
That's how our human minds think.
link |
And gradients are not that easy for our human mind to grapple with.
link |
An absurd question, but what is statistics?
link |
So here it's a little bit, it's somewhere between math and science and technology.
link |
It's somewhere in that convex hole.
link |
So it's a set of principles that allow you to make inferences that have got some reason
link |
to be believed and also principles that allow you to make decisions where you can have some
link |
reason to believe you're not going to make errors.
link |
So all of that requires some assumptions about what do you mean by an error?
link |
What do you mean by the probabilities?
link |
But after you start making some of those assumptions, you're led to conclusions that, yes, I can
link |
guarantee that if you do this in this way, your probability of making an error will be
link |
Your probability of continuing to not make errors over time will be small.
link |
And the probability that you found something that's real will be small, will be high.
link |
So decision making is a big part of that.
link |
Decision making is a big part.
link |
So statistics, short history was that, it goes back as a formal discipline, 250 years
link |
It was called inverse probability because around that era, probability was developed
link |
sort of especially to explain gambling situations.
link |
Of course, interesting.
link |
So you would say, well, given the state of nature is this, there's a certain roulette
link |
board that has a certain mechanism and what kind of outcomes do I expect to see?
link |
And especially if I do things long amounts of time, what outcomes will I see?
link |
And the physicists started to pay attention to this.
link |
And then people said, well, let's turn the problem around.
link |
What if I saw certain outcomes, could I infer what the underlying mechanism was?
link |
That's an inverse problem.
link |
And in fact, for quite a while, statistics was called inverse probability.
link |
That was the name of the field.
link |
And I believe that it was Laplace who was working in Napoleon's government who needed
link |
to do a census of France, learn about the people there.
link |
So he went and gathered data and he analyzed that data to determine policy and said, well,
link |
let's call this field that does this kind of thing statistics because the word state
link |
In French, that's etat, but it's the study of data for the state.
link |
So anyway, that caught on and it's been called statistics ever since.
link |
But by the time it got formalized, it was sort of in the 30s.
link |
And around that time, there was game theory and decision theory developed nearby.
link |
People in that era didn't think of themselves as either computer science or statistics or
link |
They were all the above.
link |
And so Von Neumann is developing game theory, but also thinking of that as decision theory.
link |
Wald is an econometrician developing decision theory and then turning that into statistics.
link |
And so it's all about, here's not just data and you analyze it, here's a loss function.
link |
Here's what you care about.
link |
Here's the question you're trying to ask.
link |
Here is a probability model and here's the risk you will face if you make certain decisions.
link |
And to this day, in most advanced statistical curricula, you teach decision theory as the
link |
starting point and then it branches out into the two branches of Bayesian and frequentist.
link |
But that's all about decisions.
link |
In statistics, what is the most beautiful, mysterious, maybe surprising idea that you've
link |
Yeah, good question.
link |
I mean, there's a bunch of surprising ones.
link |
There's something that's way too technical for this thing, but something called James
link |
Stein estimation, which is kind of surprising and really takes time to wrap your head around.
link |
Can you try to maybe...
link |
I think I don't want to even want to try.
link |
Let me just say a colleague at Steven Stigler at University of Chicago wrote a really beautiful
link |
paper on James Stein estimation, which helps to...
link |
It's views a paradox.
link |
It kind of defeats the mind's attempts to understand it, but you can and Steve has a
link |
nice perspective on that.
link |
So one of the troubles with statistics is that it's like in physics that are in quantum
link |
physics, you have multiple interpretations.
link |
There's a wave and particle duality in physics and you get used to that over time, but it
link |
still kind of haunts you that you don't really quite understand the relationship.
link |
The electron's a wave and electron's a particle.
link |
Well the same thing happens here.
link |
There's Bayesian ways of thinking and frequentist, and they are different.
link |
They sometimes become sort of the same in practice, but they are physically different.
link |
And then in some practice, they are not the same at all.
link |
They give you rather different answers.
link |
And so it is very much like wave and particle duality, and that is something that you have
link |
to kind of get used to in the field.
link |
Can you define Bayesian and frequentist?
link |
Yeah in decision theory you can make, I have a video that people could see.
link |
It's called are you a Bayesian or a frequentist and kind of help try to make it really clear.
link |
It comes from decision theory.
link |
So you know, decision theory, you're talking about loss functions, which are a function
link |
of data X and parameter theta.
link |
They're a function of two arguments.
link |
Neither one of those arguments is known.
link |
You don't know the data a priori.
link |
It's random and the parameters unknown.
link |
So you have a function of two things you don't know, and you're trying to say, I want that
link |
function to be small.
link |
I want small loss, right?
link |
Well what are you going to do?
link |
So you sort of say, well, I'm going to average over these quantities or maximize over them
link |
or something so that, you know, I turn that uncertainty into something certain.
link |
So you could look at the first argument and average over it, or you could look at the
link |
second argument and average over it.
link |
That's Bayesian and frequentist.
link |
So the frequentist says, I'm going to look at the X, the data, and I'm going to take
link |
that as random and I'm going to average over the distribution.
link |
So I take the expectation loss under X. Theta is held fixed, right?
link |
That's called the risk.
link |
And so it's looking at other, all the data sets you could get, right?
link |
And say, how well will a certain procedure do under all those data sets?
link |
That's called a frequentist guarantee, right?
link |
So I think it is very appropriate when like you're building a piece of software and you're
link |
shipping it out there and people are using it on all kinds of data sets.
link |
You want to have a stamp, a guarantee on it that as people run it on many, many data sets
link |
that you never even thought about that 95% of the time it will do the right thing.
link |
Perfectly reasonable.
link |
The Bayesian perspective says, well, no, I'm going to look at the other argument of the
link |
loss function, the theta part, okay?
link |
That's unknown and I'm uncertain about it.
link |
So I could have my own personal probability for what it is, you know, how many tall people
link |
are there out there?
link |
I'm trying to infer the average height of the population while I have an idea roughly
link |
what the height is.
link |
So I'm going to average over the theta.
link |
So now that loss function as only now, again, one argument's gone, now it's a function of
link |
X and that's what a Bayesian does is they say, well, let's just focus on the particular
link |
X we got, the data set we got, we condition on that.
link |
Conditional on the X, I say something about my loss.
link |
That's a Bayesian approach to things.
link |
And the Bayesian will argue that it's not relevant to look at all the other data sets
link |
you could have gotten and average over them, the frequentist approach.
link |
It's really only the data sets you got, right?
link |
And I do agree with that, especially in situations where you're working with a scientist, you
link |
can learn a lot about the domain and you're really only focused on certain kinds of data
link |
and you gathered your data and you make inferences.
link |
I don't agree with it though, that, you know, in the sense that there are needs for frequentist
link |
guarantees, you're writing software, people are using it out there, you want to say something.
link |
So these two things have to got to fight each other a little bit, but they have to blend.
link |
So long story short, there's a set of ideas that are right in the middle that are called
link |
And empirical Bayes sort of starts with the Bayesian framework.
link |
It's kind of arguably philosophically more, you know, reasonable and kosher.
link |
Write down a bunch of the math that kind of flows from that, and then realize there's
link |
a bunch of things you don't know because it's the real world and you don't know everything.
link |
So you're uncertain about certain quantities.
link |
At that point, ask, is there a reasonable way to plug in an estimate for those things?
link |
And in some cases, there's quite a reasonable thing to do, to plug in, there's a natural
link |
thing you can observe in the world that you can plug in and then do a little bit more
link |
mathematics and assure yourself it's really good.
link |
So based on math or based on human expertise, what's, what, what are good?
link |
Oh, they're both going in.
link |
The Bayesian framework allows you to put a lot of human expertise in, but the math kind
link |
of guides you along that path and then kind of reassures you the end, you could put that
link |
stamp of approval under certain assumptions, this thing will work.
link |
So you asked the question, what's my favorite, you know, or what's the most surprising, nice
link |
So one that is more accessible is something called false discovery rate, which is, you
link |
know, you're making not just one hypothesis test or making one decision, you're making
link |
a whole bag of them.
link |
And in that bag of decisions, you look at the ones where you made a discovery, you announced
link |
that something interesting had happened.
link |
That's going to be some subset of your big bag.
link |
In the ones you made a discovery, which subset of those are bad?
link |
Or false, false discoveries.
link |
You'd like the fraction of your false discoveries among your discoveries to be small.
link |
That's a different criterion than accuracy or precision or recall or sensitivity and
link |
It's a different quantity.
link |
Those latter ones are almost all of them have more of a frequentist flavor.
link |
They say, given the truth is that the null hypothesis is true.
link |
Here's what accuracy I would get, or given that the alternative is true, here's what
link |
So it's kind of going forward from the state of nature to the data.
link |
The Bayesian goes the other direction from the data back to the state of nature.
link |
And that's actually what false discovery rate is.
link |
It says, given you made a discovery, okay, that's conditioned on your data.
link |
What's the probability of the hypothesis?
link |
It's going the other direction.
link |
And so the classical frequency look at that, well, I can't know that there's some priors
link |
And the empirical Bayesian goes ahead and plows forward and starts writing down these formulas
link |
and realizes at some point, some of those things can actually be estimated in a reasonable
link |
And so it's kind of, it's a beautiful set of ideas.
link |
So I, this kind of line of argument has come out.
link |
It's not certainly mine, but it sort of came out from Robbins around 1960.
link |
Brad Efron has written beautifully about this in various papers and books.
link |
And the FDR is, you know, Benjamin in Israel, John Story did this Bayesian interpretation
link |
And he used to absorb these things over the years and find it a very healthy way to think
link |
Let me ask you about intelligence to jump slightly back out into philosophy, perhaps.
link |
You said that maybe you can elaborate, but you said that defining just even the question
link |
of what is intelligence is a very difficult question.
link |
Is it a useful question?
link |
Do you think we'll one day understand the fundamentals of human intelligence and what
link |
it means, you know, have good benchmarks for general intelligence that we put before our
link |
So I don't work on these topics so much that you're really asking the question for a psychologist
link |
And I studied some, but I don't consider myself at least an expert at this point.
link |
You know, a psychologist aims to understand human intelligence, right?
link |
And I think many psychologists I know are fairly humble about this.
link |
They might try to understand how a baby understands, you know, whether something's a solid or liquid
link |
or whether something's hidden or not.
link |
And maybe how a child starts to learn the meaning of certain words, what's a verb, what's
link |
a noun and also, you know, slowly but surely trying to figure out things.
link |
But humans ability to take a really complicated environment, reason about it, abstract about
link |
it, find the right abstractions, communicate about it, interact and so on is just, you
link |
know, really staggeringly rich and complicated.
link |
And so, you know, I think in all humility, we don't think we're kind of aiming for that
link |
in the near future.
link |
A certain psychologist doing experiments with babies in the lab or with people talking has
link |
a much more limited aspiration.
link |
And you know, Kahneman and Tversky would look at our reasoning patterns and they're not
link |
deeply understanding all the how we do our reasoning, but they're sort of saying, hey,
link |
here's some oddities about the reasoning and some things you should think about it.
link |
But also, as I emphasize in some things I've been writing about, you know, AI, the revolution
link |
hasn't happened yet.
link |
I've been emphasizing that, you know, if you step back and look at intelligent systems
link |
of any kind and whatever you mean by intelligence, it's not just the humans or the animals or,
link |
you know, the plants or whatever, you know, so a market that brings goods into a city,
link |
you know, food to restaurants or something every day is a system.
link |
It's a decentralized set of decisions.
link |
Looking at it from far enough away, it's just like a collection of neurons.
link |
Every neuron is making its own little decisions, presumably in some way.
link |
And if you step back enough, every little part of an economic system is making all of
link |
And just like with the brain, who knows what an individual neuron does and what the overall
link |
But something happens at some aggregate level, same thing with the economy.
link |
People eat in a city and it's robust.
link |
It works at all scales, small villages to big cities.
link |
It's been working for thousands of years.
link |
It works rain or shine, so it's adaptive.
link |
So all the kind of, you know, those are adjectives one tends to apply to intelligent systems.
link |
Robust, adaptive, you know, you don't need to keep adjusting it, self healing, whatever.
link |
You know, intelligences are never perfect and markets are not perfect.
link |
But I do not believe in this era that you cannot, that you can say, well, our computers
link |
are, our humans are smart, but you know, no markets are not, more markets are.
link |
So they are intelligent.
link |
Now we humans didn't evolve to be markets.
link |
We've been participating in them, right?
link |
But we are not ourselves a market per se.
link |
The neurons could be viewed as the market.
link |
There's economic, you know, neuroscience kind of perspective.
link |
That's interesting to pursue all that.
link |
The point though is, is that if you were to study humans and really be the world's best
link |
psychologist studied for thousands of years and come up with the theory of human intelligence,
link |
you might have never discovered principles of markets, you know, supply demand curves
link |
and you know, matching and auctions and all that.
link |
Those are real principles and they lead to a form of intelligence that's not maybe human
link |
It's arguably another kind of intelligence.
link |
There probably are third kinds of intelligence or fourth that none of us are really thinking
link |
too much about right now.
link |
So if you really, and then all of those are relevant to computer systems in the future.
link |
Certainly the market one is relevant right now.
link |
Whereas the understanding of human intelligence is not so clear that it's relevant right now.
link |
So if you want general intelligence, whatever one means by that, or, you know, understanding
link |
intelligence in a deep sense and all that, it is definitely has to be not just human
link |
It's gotta be this broader thing.
link |
And that's not a mystery.
link |
Markets are intelligent.
link |
So, you know, it's definitely not just a philosophical stance to say we've got to move beyond intelligence.
link |
That sounds ridiculous.
link |
And in that blog post, you define different kinds of like intelligent infrastructure,
link |
AI, which I really like is some of the concepts you've just been describing.
link |
Do you see ourselves, if we see earth, human civilization as a single organism, do you
link |
think the intelligence of that organism, when you think from the perspective of markets
link |
and intelligence infrastructure is increasing, is it increasing linearly?
link |
Is it increasing exponentially?
link |
What do you think the future of that intelligence?
link |
Yeah, I don't know.
link |
I don't tend to think, I don't tend to answer questions like that because you know, that's
link |
I'm hoping to catch you off guard.
link |
Well again, because you said it's so far in the future, it's fun to ask and you'll probably,
link |
you know, like you said, predicting the future is really nearly impossible.
link |
But say as an axiom, one day we create a human level, a superhuman level intelligent, not
link |
the scale of markets, but the scale of an individual.
link |
What do you think it is, what do you think it would take to do that?
link |
Or maybe to ask another question is how would that system be different than the biological
link |
human beings that we see around us today?
link |
Is it possible to say anything interesting to that question or is it just a stupid question?
link |
It's not a stupid question, but it's science fiction.
link |
And so I'm totally happy to read science fiction and think about it from time in my own life.
link |
I loved, there was this like brain in a vat kind of, you know, little thing that people
link |
were talking about when I was a student, I remember, you know, imagine that, you know,
link |
between your brain and your body, there's a, you know, there's a bunch of wires, right?
link |
And suppose that every one of them was replaced with a literal wire.
link |
And then suppose that wire was turned in actually a little wireless, you know, there's a receiver
link |
So the brain has got all the senders and receiver, you know, on all of its exiting, you know,
link |
axons and all the dendrites down to the body have replaced with senders and receivers.
link |
Now you could move the body off somewhere and put the brain in a vat, right?
link |
And then you could do things like start killing off those senders and receivers one by one.
link |
And after you've killed off all of them, where is that person?
link |
You know, they thought they were out in the body walking around the world and they moved
link |
So those are science fiction things.
link |
Those are fun to think about.
link |
It's just intriguing about where is, what is thought, where is it and all that.
link |
And I think every 18 year old should take philosophy classes and think about these things.
link |
And I think that everyone should think about what could happen in society that's kind of
link |
But I really don't think that's the right thing for most of us that are my age group
link |
to be doing and thinking about.
link |
I really think that we have so many more present, you know, first challenges and dangers and
link |
real things to build and all that such that, you know, spending too much time on science
link |
fiction, at least in public for like this, I think is not what we should be doing.
link |
Maybe over beers in private.
link |
Well, I'm not going to broadcast where I have beers because this is going to go on Facebook
link |
and I don't want a lot of people showing up there.
link |
But yeah, I'll, I love Facebook, Twitter, Amazon, YouTube.
link |
I have I'm optimistic and hopeful, but maybe, maybe I don't have grounds for such optimism
link |
But let me ask, you've mentored some of the brightest sort of some of the seminal figures
link |
Can you give advice to people who are undergraduates today?
link |
What does it take to take, you know, advice on their journey if they're interested in
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machine learning and in the ideas of markets from economics and psychology and all the
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kinds of things that you've exploring?
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What steps should they take on that journey?
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Well, yeah, first of all, the door is open and second, it's a journey.
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I like your language there.
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It is not that you're so brilliant and you have great, brilliant ideas and therefore
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that's just, you know, that's how you have success or that's how you enter into the field.
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It's that you apprentice yourself, you spend a lot of time, you work on hard things, you
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try and pull back and you be as broad as you can, you talk to lots of people.
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And it's like entering in any kind of a creative community.
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There's years that are needed and human connections are critical to it.
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So, you know, I think about, you know, being a musician or being an artist or something,
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you don't just, you know, immediately from day one, you know, you're a genius and therefore
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No, you, you know, practice really, really hard on basics and you be humble about where
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you are and then, and you realize you'll never be an expert on everything.
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So you kind of pick and there's a lot of randomness and a lot of kind of luck, but luck just kind
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of picks out which branch of the tree you go down, but you'll go down some branch.
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So yeah, it's a community.
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So the graduate school is, I still think is one of the wonderful phenomena that we have
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in our, in our world.
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It's very much about apprenticeship with an advisor.
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It's very much about a group of people you belong to.
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It's a four or five year process.
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So it's plenty of time to start from kind of nothing to come up to something, you know,
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more, more expertise, and then to start to have your own creativity start to flower,
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even surprising your own self.
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And it's a very cooperative endeavor.
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I think a lot of people think of science as highly competitive and I think in some other
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fields it might be more so.
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Here it's way more cooperative than you might imagine.
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And people are always teaching each other something and people are always more than
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happy to be clear that, so I feel I'm an expert on certain kinds of things, but I'm very much
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not expert on lots of other things and a lot of them are relevant and a lot of them are,
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I should know, but should in some society, you know, you don't.
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So I'm always willing to reveal my ignorance to people around me so they can teach me things.
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And I think a lot of us feel that way about our field.
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So it's very cooperative.
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I might add it's also very international because it's so cooperative.
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We see no barriers.
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And so that the nationalism that you see, especially in the current era and everything
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is just at odds with the way that most of us think about what we're doing here, where
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this is a human endeavor and we cooperate and are very much trying to do it together
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for the, you know, the benefit of everybody.
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So last question, where and how and why did you learn French and which language is more
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beautiful English or French?
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So first of all, I think Italian is actually more beautiful than French and English.
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And I also speak that.
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So I'm married to an Italian and I have kids and we speak Italian.
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Anyway, all kidding aside, every language allows you to express things a bit differently.
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And it is one of the great fun things to do in life is to explore those things.
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So in fact, when I kids or teens or college students ask me what they study, I say, well,
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do what your heart, where your heart is, certainly do a lot of math.
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Math is good for everybody, but do some poetry and do some history and do some language too.
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You know, throughout your life, you'll want to be a thinking person.
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You'll want to have done that.
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For me, French I learned when I was, I'd say a late teen, I was living in the middle of
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the country in Kansas and not much was going on in Kansas with all due respect to Kansas.
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And so my parents happened to have some French books on the shelf and just in my boredom,
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I pulled them down and I found this is fun.
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And I kind of learned the language by reading.
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And when I first heard it spoken, I had no idea what was being spoken, but I realized
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I had somehow knew it from some previous life and so I made the connection.
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But then I traveled and just I love to go beyond my own barriers and my own comfort
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And I found myself on trains in France next to say older people who had lived a whole
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life of their own.
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And the ability to communicate with them was special and the ability to also see myself
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in other people's shoes and have empathy and kind of work on that language as part of that.
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So after that kind of experience and also embedding myself in French culture, which
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is quite amazing, languages are rich, not just because there's something inherently
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beautiful about it, but it's all the creativity that went into it.
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So I learned a lot of songs, read poems, read books.
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And then I was here actually at MIT where we're doing the podcast today and a young
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professor not yet married and not having a lot of friends in the area.
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So I just didn't have, I was kind of a bored person.
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I said, I heard a lot of Italians around.
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There's happened to be a lot of Italians at MIT, an Italian professor for some reason.
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And so I was kind of vaguely understanding what they were talking about.
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I said, well, I should learn this language too.
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And then later met my spouse and Italian became a part of my life.
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But I go to China a lot these days.
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I go to Asia, I go to Europe and every time I go, I kind of am amazed by the richness
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of human experience and the people don't have any idea if you haven't traveled, kind of
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how amazingly rich and I love the diversity.
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It's not just a buzzword to me.
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It really means something.
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I love to embed myself with other people's experiences.
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And so yeah, learning language is a big part of that.
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I think I've said in some interview at some point that if I had millions of dollars and
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infinite time or whatever, what would you really work on if you really wanted to do
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And for me, that is natural language and really done right.
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Deep understanding of language.
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That's to me, an amazingly interesting scientific challenge.
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One we're very far away on.
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One we're very far away, but good natural language.
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People are kind of really invested then.
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I think a lot of them see that's where the core of AI is that if you understand that
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you really help human communication, you understand something about the human mind, the semantics
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that come out of the human mind and I agree, I think that will be such a long time.
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So I didn't do that in my career just cause I kind of, I was behind in the early days.
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I didn't kind of know enough of that stuff.
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I was at MIT, I didn't learn much language and it was too late at some point to kind
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of spend a whole career doing that, but I admire that field and so in my little way
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by learning language, you know, kind of that part of my brain has been trained up.
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You truly are the Miles Davis of machine learning.
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I don't think there's a better place than it.
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Mike it was a huge honor talking to you today.
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It's been my pleasure.
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Thanks for listening to this conversation with Michael I. Jordan and thank you to our
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presenting sponsor, Cash App.
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Download it, use code LEXPodcast, you'll get $10 and $10 will go to FIRST, an organization
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that inspires and educates young minds to become science and technology innovators of
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If you enjoy this podcast, subscribe on YouTube, give it five stars on Apple Podcast, support
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on Patreon, or simply connect with me on Twitter at Lex Friedman.
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And now let me leave you with some words of wisdom from Michael I. Jordan from his blog
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post titled Artificial Intelligence, the revolution hasn't happened yet, calling for broadening
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the scope of the AI field.
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We should embrace the fact that what we are witnessing is the creation of a new branch
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The term engineering is often invoked in a narrow sense in academia and beyond with overtones
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of cold, effectless machinery and negative connotations of loss of control by humans.
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But an engineering discipline can be what we want it to be.
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In the current era, we have a real opportunity to conceive of something historically new,
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a human centric engineering discipline.
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I will resist giving this emerging discipline a name, but if the acronym AI continues to
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be used, let's be aware of the very real limitations of this placeholder.
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Let's broaden our scope, tone down the hype, and recognize the serious challenges ahead.
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Thank you for listening and hope to see you next time.