Michael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Lex Fridman Podcast #74

the following is a conversation with michael i jordan a professor at berkeley and one of the most influential people in the history of machine learning statistics and artificial intelligence he has been cited over 170 thousand times and has mentored many of the world-class researchers defining the field of ai today including andrew eng zubin garamani bentascar and yoshio banjo all this to me is as impressive as the over 32 000 points and the six nba championships of the michael j jordan of basketball fame there's a non-zero probability that i talked to the other michael jordan given my connection to and love the chicago bulls of the 90s but if i had to pick one i'm going with the michael jordan of statistics and computer science or as john le calls him the miles davis of machine learning in his blog post titled artificial intelligence the revolution hasn't happened yet michael argues for broadening the scope or the artificial intelligence field in many ways the underlying spirit of this podcast is the same to see artificial intelligence as a deeply human endeavor to not only engineer algorithms and robots but to understand and empower human beings at all levels of abstractions from the individual to our civilization as a whole this is the artificial intelligence podcast if you enjoy it subscribe on youtube give it five stars at apple podcast support it on patreon or simply connect with me on twitter at lex friedman spelled friday as usual i'll do one or two minutes of ads now and never any ads in the middle that can break the flow of the conversation i hope that works for you and doesn't hurt the listening experience this show is presented by cash app the number one finance app in the app store when you get it use code lex podcast cash app unless you send money to friends buy bitcoin and invest in the stock market with as little as one dollar since cash app does fractional share trading let me mention that the order execution algorithm that works behind the 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calling you the miles davis of machine learning because as he says you reinvent yourself periodically and sometimes leave fans scratching their heads after you change direction so can you put at first your historian hat on and give a history of computer science and ai as you saw it as you experienced it including the four generations of ai successes that i've seen you talk about sure yeah first of all i much prefer yon's metaphor um miles davis is uh was a real explorer in jazz and um he had a coherent story so i think i have one and but it's not just the one you live it's the one you think about later what a good historian does is they look back and they revisit um i think what happening right now is not ai that was an intellectual aspiration um that's still alive today is an aspiration but i think this is akin to the development of chemical engineering from chemistry or electrical engineering from from electromagnetism so if you go back to the 30s or 40s there wasn't yet chemical engineering there was chemistry there was fluid flow there was mechanics and so on but people pretty clearly viewed interesting goals try to build factories that you make chemicals products and do it viably safely make good ones do it at scale so people started to try to do that of course and some factories worked some didn't you know some were not viable some exploded but in parallel developed a whole field called chemical engineering right and chemical engineering is a field it's it's no no bones about it it has theoretical aspects to it it has practical aspects it's not just engineering quote unquote it's the real thing real concepts are needed same thing with electrical engineering you know there was maxwell's equations which in some sense were everything you know about electromagnetism but you needed to figure out how to build circuits how to build modules how to put them together how to bring electricity from one point to another safely and so on so forth so whole field is developed called electrical engineering all right i think that's what's happening right now is that we have we have a proto field which is statistics compute more the theoretical side of the algorithmic side of computer science that was enough to start to build things but what things systems that bring value to human beings and use human data and mix in human decisions the engineering side of that is all ad hoc that's what's emerging in fact if you want to call machine learning a field i think that's what it is that's a proto form of engineering based on statistical and computational ideas of previous generations but do you think there's something deeper about ai in his dreams and aspirations as compared to chemical engineering and electrical engineering well the dreams and aspirations maybe but those are from those are 500 years from now i think that that's like the greek sitting there and saying it would be neat to get to the moon someday right um i hate we have no clue how the brain does computation uh we're just a clueless we're like we're even worse than the greeks almost anything interesting uh scientifically of our era can you linger on that just for a moment because you stand not completely unique but a little bit unique in that in the clarity of that can you can you elaborate your intuition of why we like where we stand in our understanding of the human brain and a lot of people say you know scientists say we're not very far in understanding human brain but you're like you're saying we're in the dark here well i know i'm not unique i don't even think in the clarity but if you talk to real neuroscientists that really study real synapses or real neurons they agree they agree it's a hundred year hundreds of year tasks and they're building it up slowly surely what the signal is there is not clear we think we have all of our metaphors we think it's electrical maybe it's chemical it's a whole soup it's ions and proteins and it's a cell and that's even around like a single synapse if you look at a electromicrograph of a single synapse it's a it's a city of its own and that's one little thing on a dendritic tree which is extremely complicated you know electrochemical thing and it's doing these spikes and voltages have been flying around and then proteins are taking that and taking it down into the dna and who knows what so it is the problem of the next few centuries it is fantastic but we have our metaphors about it is it an economic device is it like the immune system or is it like a layered you know set of copy you know arithmetic computations what we have all these metaphors and they're fun but that's not real science per se there is neuroscience that's not neuroscience all right that that's that's like the greek speculating about how to get to the moon fun right and i think that i like to say this fairly strongly because i think a lot of young people think we're on the verge because a lot of people who don't talk about it clearly let it be understood that yes we kind of this is brain inspired we're kind of close you know breakthroughs are on the horizon and unscrupulous people sometimes who need money for their labs um as i'm saying scrupulous but people will oversell um i need money from a lab i'm gonna i'm studying here you know computational neuroscience um i'm gonna oversell it and so there's been too much of that so i'll step into the slight the gray area between metaphor and engineering with uh i'm not sure if you're familiar with brain computer interfaces so a company like elon musk has neural link that's working on putting electrodes into the brain and trying to be able to read both read and send electrical signals just as you said even the basic mechanism of communication in the brain is not something we understand but do you hope without understanding the fundamental principles of how the brain works we'll be able to do something interesting at that gray area of metaphor it's not my area so i i hope in the sense like anybody else hopes for some interesting things to happen from research i would expect more something like alzheimer's will get figured out from modern neuroscience that you know a lot of there's a lot of human suffering based on brain disease and we throw things like lithium at the brain it kind of works no one has a clue why that's not quite true but you know mostly we don't know and that's even just about the biochemistry of the brain and how it leads to mood swings and so on how thought emerges from that we just we were really really completely dim so that you might want to hook up electrodes and try to do some signal processing on that and try to find patterns fine you know by all means go for it it's just not scientific at this point it's just it's so it's like kind of sitting in a satellite and watching the emissions from a city and trying to affirm things about the micro economy even though you don't have microeconomic concepts i mean it's really that kind of thing and so yes can you find some signals that do something interesting or useful can you control a cursor or mouse with your brain yeah absolutely you know and then i can imagine business models based on that and even you know medical applications of that but from there to understanding algorithms that allow us to really tie in deeply to from the brain to computer you know i just no i don't agree with elon musk i don't think that's even that's not for our generation it's not even for the century so just uh in hopes of getting you to dream uh you've mentioned kolmogorov and touring might pop up do you think that there might be breakthroughs they'll get you to sit back in five ten years and say wow oh i'm sure there will be but i don't think that there'll be demos that impress me i don't think that having a computer call a restaurant and pretend to be a human is a breakthrough and people you know some people present it as such it's imitating human intelligence it's even putting coughs in the thing to make a bit of a pr stunt and so fine the world runs on those things too and i don't want to diminish all the hard work and engineering that goes behind things like that and and the ultimate value to the human race but that's not scientific understanding and and i know the people who work on these things they are after a scientific understanding you know in the meantime they've got to kind of you know the trains got to run and they got miles to feed and they got things to do and there's nothing wrong with all that i would call that though just engineering and i want to distinguish that between an engineering field like electoral internet chemical injury that originally that originally emerged that had real principles and you really knew what you're doing and you had a little scientific understanding maybe not even complete so it became more predictable and it was really gave value to human life because it was understood and and so we have to we don't want to muddle too much these waters of you know what we're able to do versus what we really can do in a way that's going to impress the next so i don't i don't need to be wowed but i i think that someone comes along in 20 years a younger person who's absorbed all the uh the technology and for them to be wowed i think they have to be more deeply impressed a young kulmogorov would not be wowed by some of the stunts that you see right now coming from the big companies the demos but do you think the breakthroughs from kolmogorov would be and give this question a chance do you think they'll be in the scientific fundamental principles arena or do you think it's possible to have fundamental breakthroughs in engineering meaning you know i would say some of the things that elon musk is working with spacex and then others sort of trying to revolutionize the fundamentals of engineering of manufacturing of of saying here's a problem we know how to do a demo of and actually taking it to scale yeah so so there's going to be all kinds of breakthroughs i just don't like that terminology i'm a scientist and i work on things day in and day out and things move along and eventually say wow something happened but it's i don't like that language very much also i don't like to prize theoretical breakthroughs over practical ones um i tend to be more of a theoretician and i think there's lots to do in that arena right now um and so i wouldn't point to the komo gurus i might point to the edisons of the era and maybe musk is a bit more like that but um you know musk god bless him also we'll say things about ai that he knows very little about and and he doesn't know what he's he he is you know leads people astray when he talks about things he doesn't know anything about trying to program a computer to understand natural language to be involved in a dialogue we're having right now that can happen in our lifetime you could fake it you can mimic sort of take old sentences that humans use and retread them with the deep understanding of language now it's not going to happen and so from that you know i hope you can perceive that the deeper yet deeper kind of aspects and intelligence are not going to happen now will there be breakthroughs you know i think that google was a breakthrough i think amazon's a breakthrough you know i think uber is a breakthrough you know that bring value to human beings at scale in new brand new ways based on data flows and and so on a lot of these things are slightly broken because there's not a kind of a engineering field that takes economic value in context of data and and at you know planetary scale and and worries about all the externalities the privacy you know we don't have that field so we don't think these things through very well but i see that is emerging and that will be cons that will you know looking back from 100 years that will be constituted a breakthrough in this era just like electrical engineering was a breakthrough in the early part of the last century and chemical injury was a breakthrough so the scale the markets that you talk about and we'll get to will be seen as sort of breakthrough and we're in the very early days of really doing interesting stuff there and we'll get to that but it's just taking a quick step back can you give uh we kind of threw off the historian hat i mean you briefly said that uh in the history of ai kind of mimics the history of chemical engineering but i keep saying machine learning you keep want to say ai just to let you know i don't you know i i'd resist that i don't think this is about ai really was john mccarthy as almost a philosopher saying wouldn't it be cool if we could put thought in a computer if we could mimic the human capability to think or put intelligence in in some sense into a computer that's an interesting philosophical question and he wanted to make it more than philosophy he wanted to actually write down logical formula and algorithms that would do that and that is a perfectly valid reasonable thing to do that's not what's happening in this era right so so the reason i keep saying ai actually and i'd love to hear what you think about it machine learning has uh has a very particular set of methods and tools maybe your version of it is that mine doesn't no it does it's very very open it does optimization it does sampling it does so systems that learn is what machine learning is systems that learn and make decisions and make decisions so what is pattern recognition and from you know finding patterns it's all about making decisions in real worlds and having close feedback loops so something like symbolic ai expert systems reading systems knowledge based representation all of those kinds of things search does that neighbor fit into what you think of as machine learning so i don't even like the word but you know i think that with the field you're talking about is all about making large collections of decisions under uncertainty by large collections of entities yes right and there are principles for that at that scale you don't have to say the principles are for a single entity that's making decisions a single agent or a single human it really immediately goes to the network of decisions is a good award for that or no no there's no good words for any of this that's kind of part of the problem um so we can continue the conversation use ai for all that i just want to kind of raise our flag here that this is not about we don't know what intelligence is and real intelligence we don't know much about abstraction and reasoning at the level of humans we don't have a clue we're not trying to build that because we don't have a clue eventually it may emerge they'll make i don't know if there'll be breakthroughs but eventually we'll start to get glimmers of that it's not what's happening right now though okay we're taking data we're trying to make good decisions based on that we're trying to scale we're trying to do it economically viably we're trying to build markets we're trying to keep value at that scale and aspects of this will look intelligent it will look computers were so dumb before they will seem more intelligent we will use that buzz word of intelligence so we can use it in that sense but you know so machine learning you can scope it narrowly is just learning from data and pattern recognition but whatever i when i talk about these topics i maybe data science is another word you could throw in the mix it really is important that the decisions are as part of it it's consequential decisions in the real world are i have a medical operation am i going to drive down the street you know things that where their scarcity things that impact other human beings or other you know the environment and so on how do i do that based on data how do i do that adaptively how i use computers to help those kind of things go forward whatever you want to call that so let's call it ai let's agree to call it ai but it's um let's let's not say that what the goal of that is is intelligence the goal of that is really good working systems at planetary scale we've never seen before so reclaiming the word ai from the dartmouth conference from many decades ago of the dream of humanity i don't want to reclaim it i want a new word i think it was a bad choice i mean i i you know i if you read one of my little things um the history was basically that uh mccarthy needed a new name because cybernetics already existed and he didn't like you know no one really liked norbert wiener you know ravina was kind of an island to himself and he felt that he had encompassed all this and in some sense he did you look at the language of cybernetics it was everything we're talking about it was control theory and single processing and some notions of intelligence and close feedback loops and data it was all there it's just not a word that lived on partly because of maybe the personalities but mccarthy needed a new word to say i'm different from you i'm not part of your your show i got my own invented this word um and again as a kind of a thinking forward about the movies that would be made about it uh it was a great choice but thinking forward about creating a sober academic and real world discipline it was a terrible choice because it led to promises that are not true that we understand we understand artificial perhaps but we don't understand intelligence it's a small tangent because you're one of the great personalities of machine learning whatever the heck you call the field the do you think science progresses by personalities or by the fundamental principles and theories and research that's outside of personality both and i wouldn't say there should be one kind of personality i have mine and i have my preferences and i have a kind of network around me that feeds me and and some of them agree with me and some disagree but all kinds of personalities are needed um right now i think the personality that's a little too exuberant a little bit too ready to promise the moon is a little bit too much in ascendance um and i do i do think that that's there's some good to that it certainly attracts lots of young people to our field but a lot of those people come in with strong misconceptions and they have to then unlearn those and then find something you know to do um and so i think there's just got to be some multiple voices and there's i didn't i wasn't hearing enough of the more sober voice so uh as a continuation of a fun tangent and speaking of vibrant personalities what would you say is the most interesting disagreement you have with yon lacoon so john's an old friend and i just say that i i don't think we disagree about very much really he and i both kind of have a let's build that kind of mentality and does it work kind of mentality and uh kind of concrete um we both speak french and we speak french more together and we have we have a lot a lot in common um and so you know if one wanted to highlight a a disagreement it's not really a fundamental one i think it's just kind of where we're emphasizing um jan has uh emphasized pattern recognition and uh has emphasized prediction all right so you know um and it's interesting to try to take that as far as you can if you could do perfect prediction what would that give you kind of as a thought experiment um and um i think that's way too limited um we cannot do perfect prediction we will never have the data sets allow me to figure out what you're about ready to do what question you're going to ask next i have no clue i will never know such things moreover most of us find ourselves during the day in all kinds of situations we had no anticipation of that are kind of very very novel in various ways and in that moment we want to think through what we want and also there's going to be market forces acting on us i'd like to go down that street but now it's full because there's a crane in the street i gotta i gotta think about that i gotta think about what i might really want here and i gotta sort of think about how much it cost me to do this action versus this action i got to think about the risks involved you know a lot of our current pattern recognition and prediction systems don't do any risk evaluations they have no error bars right i got to think about other people's decisions around me i got to think about a collection of my decisions even just thinking about like a medical treatment you know i'm not going to take the prediction of a neural net about my health about something consequential am i about to have a heart attack because some number is over 0.7 even if you had all the data in the world never been collected about heart attacks better than any doctor ever had i'm not going to trust the output of that neural net to predict my heart attack i'm going to want to ask what if questions around that i'm going to want to look at some us or other possible data i didn't have causal things i'm going to have a dialogue with a doctor about things we didn't think about we gathered the data you know it i could go on and on i hope you can see and i don't i think that if you say predictions everything that that you're missing all of this stuff and so prediction plus decision making is everything but both of them are equally important and so the field has emphasized prediction yan rightly so has seen how powerful that is but at the cost of people not being aware that decision making is where the rubber really hits the road where human lives are at stake where risks are being taken where you got to gather more data you got to think about the arab bars you got to think about the consequences of your decisions on others you about the economy around your decisions blah blah blah i'm not the only one working on those but we're a smaller tribe and right now we're not the the one that people talk about the most um but you know if you go out in the real world in industry um you know at amazon i'd say half the people there are working on decision making and the other half are doing you know the pattern recognition it's important and the words of pattern recognition and prediction i think the distinction there not to linger on words but the distinction there is more a constrained sort of in the lab data set versus decision making is talking about consequential decisions in the real world under the messiness and the uncertainty of the real world and just the whole of it the whole mess of it that actually touches human beings and scale like you said market forces that's the that's the distinction yeah it helps add those that perspective that broader perspective you're right i totally agree uh on the other hand if you're a real prediction person of course you want it to be in the real world you want to predict real world events i'm just saying that's not possible with just data sets uh that it has to be in the context of you know uh strategic things that someone's doing data they might gather things they could have gathered the reasoning process around data it's not just taking data and making predictions based on the data so one of the the things that you're working on i'm sure there's others working on it but i don't hear often it talked about especially in the clarity that you talk about it and i think it's both the most exciting and the most concerning area of ai in terms of decision making so you've talked about ai systems that help make decisions that scale in a distributed way millions billions decisions it's sort of markets of decisions can you as a starting point sort of give an example of a system that you think about when you're thinking about these kinds of systems uh yeah so first of all you're absolutely getting into some territory which i will be beyond my expertise and and there are lots of things that are going to be very non-obvious to think about just like just again i like to think about history a little bit but think about put yourself back in the 60s there was kind of a banking system that wasn't computerized really there was there was database theory emerging and database people had to think about how do i actually not just move data around but actual money and have it be you know valid and have transactions that atms happen that are actually you know all valid and so on so forth so that's the kind of issues you get into when you start to get serious about sort of things like this i like to think about as kind of almost a thought experiment to help me think uh something simpler which is a music market and uh because there is the first door there is no music market in the world right now or in the con in our country for sure uh there are uh something called things called record companies and they make money uh and they prop up a few um really good musicians and make them superstars and they all make huge amounts of money but there's a long tale of huge numbers of people that make lots and lots of really good music that is actually listened to by more people than the famous people um they are not in a market they cannot have a career they do not make money the creators the creators the creators the so-called influencers or whatever that diminishes who they are right so there are people who make extremely good music especially in the hip-hop or latin world these days uh they do it on their laptop that's what they do on the weekend and they have another job during the week and they put it up on soundcloud or other sites eventually it gets streamed it down gets turned into bits it's not economically valuable the information is lost it gets put up there people stream it you walk around in a big city you see people with headphones all you know especially young kids listen to music all the time if you look at the data none of them very little the music they listen to is the famous people's music and none of it's old music it's all the latest stuff but the people who made that latest stuff are like some 16 year old somewhere who will never make a career out of this who will never make money of course there will be a few counter examples the record companies incentivize to pick out a few and highlight them long story short there's a missing market there there is not a consumer producer relationship at the level of the actual creative acts um the pipelines and spotifys of the world that take this stuff and stream it along they make money off of subscriptions or advertising and those things they're making the money all right and then they will offer bits and pieces of it to a few people again to highlight that you know they're they simulate a market anyway a real market would be if you're a creator of music that you actually are somebody who's good enough that people want to listen to you you should have the data available to you there should be a dashboard showing a map of the united states so in last week here's all the places your songs were listened to it should be transparent um vettable so that if someone in down in providence sees that you're being listened to ten thousand times in providence that they know that's real data you know it's real data they will have you come give a show down there they will broadcast to the people who've been listening to you that you're coming if you do this right you could you could you know go down there make twenty thousand dollars you do that three times a year you start to have a career so in this sense ai creates jobs it's not about taking away human jobs it's creating new jobs because it creates a new market once you've created a market you've now connected up producers and consumers you know the new person who's making the music can say to someone who comes to their shows a lot hey i'll play your daughter's wedding for ten thousand dollars you'll say eight thousand they'll say nine thousand um then you again you you can now get an income up to a hundred thousand dollars you're not going to be a millionaire all right and and now even think about really the value of music is in these personal connections even so much so that um a young kid wants to wear a t-shirt with their favorite musician's signature on it right so if they listen to the music on the internet the internet should be able to provide them with a button as they push and the merchandise arrives the next day we can do that right and now why should we do that well because the kid who bought the shirt will be happy but more the person who made the music will get the money there's no advertising needed right so you could create markets between personal consumers take five percent cut your company will be perfectly uh sound it'll go forward into the future and it will create new markets and that raises human happiness um now this seems like it was easy just create this dashboard kind of create some connections and all that but you know if you think about uber or whatever you think about the challenges in the real world of doing things like this and there are actually new principles going to be needed you're trying to create a new kind of two-way market at a different scale that's ever been done before there's going to be you know unwanted aspects of the market there'll be bad people they'll be you know the data will get used in the wrong ways you know it'll fail in some ways it won't deliver value you have to think that through just like anyone who like ran a big auction or you know ran a big matching service in economics will think these things through and so that maybe doesn't get at all the huge issues that can arise when you start to create markets but it starts for at least for me solidify my thoughts and allow me to move forward in my own thinking yeah so i talked to how to research at spotify actually i think their long-term goal they've said is to uh have at least 1 million creators make a make a comfortable living putting on spotify so in and i think you articulate a really nice vision of uh the world and the digital and the cyber space of markets what what do you think companies like spotify or youtube or netflix can do to create such markets is it an ai problem is it an interface problem so interface design is it uh some other kind of it was an economics problem who should they hire to solve these problems well part of it's not just top down so the silicon valley has its attitude that they know how to do it they will create the system just like google did with the search box that will be so good that they'll just everyone will adopt that right um it's not it's it's everything you said but really i think missing the kind of culture all right so it's literally that 16 year old who's able to create the songs you don't create that as a silicon valley entity you don't hire them per se right you have to create an ecosystem in which they are wanted and that they belong right so you have to have some cultural credibility to do things like this you know netflix to their credit wanted some of that sort of credibility they created shows you know content they call it content it's such a terrible word but it's called it's culture right and so with movies you can kind of go give a large sum of money to somebody graduate from the usc film school it's a whole thing of its own but it's kind of like rich white people's thing to do you know and you know american culture has not been so much about rich white people it's been about all the immigrants all the africans who came and brought that culture and those those rhythms and and that that to to this world and created this whole new thing you know american culture and and so companies can't artificially create that they can't just say hey we're here we're going to buy it up you got a partner right and um so but anyway you know not to integrate these companies are all trying and they should and they they are i'm sure they're asking these questions and some of them are even making an effort but it is it is partly a respect the culture as you were as a technology person you got to blend your technology with cultural with cultural uh you know meaning how much of a role do you think the algorithm machine learning has in connecting the consumer to the creator sort of uh the recommender system aspect of this yeah it's a great question i think pretty high recommend you know um there's no magic in the algorithms but a good recommender system is way better than a bad recommender system and uh recommender systems was a billion dollar industry back even you know 10 20 years ago um and it continues to be extremely important going forward what's your favorite recommender system just so we can put something well just historically i was one of the you know when i first went to amazon and you know i first didn't like amazon because they put the book people out of business or the library you know the local book sellers went out of business um i've come to accept that they're you know there probably are more books being selled now and more people reading them than ever before and then local books stores are coming back so you know that's how economics sometimes work you go up and you go down but anyway when i finally started going there and i bought a few books i was really pleased to see another few books being recommended to me that i never would have thought of and i bought a bunch of them so they obviously had a good business model but i learned things and i still to this day kind of browse using that service um and i think lots of people get a lot you know they're that that is a good aspect of a recommendation system i'm learning from my peers in a in an indirect way and their algorithms are not meant to have them impose what we what we learn it really is trying to find out what's in the data uh it doesn't work so well for other kind of entities but that's just the complexity of human life like shirts you know i'm not gonna get recommendations on shirts and uh but that's that's that's interesting uh if you try to recommend um uh restaurants it's it's it's it's it's hard it's hard to do it at scale and and um but uh a blend of recommendation systems with other economic ideas matchings and so on is really really still very open research-wise and there's new companies that could emerge that do that well what what do you think is going to the messy difficult land of say politics and things like that that youtube and twitter have to deal with in terms of recommendation systems being able to suggest i think facebook just launched facebook news so they're having recommend the kind of news that are most likely for you to be interesting you think this is this ai solvable again whatever term want to use do you think it's a solvable problem for machines or is it a deeply human problem that's unsolvable uh so i don't even think about it that level i think that what's broken with some of these companies it's all monetization by advertising they're not at least facebook let's i want to critique them they didn't really try to connect a producer and a consumer in an economic way right no one wants to pay for anything and so they all you know starting with google and facebook they went back to the playbook of you know the the television companies back in the day no one wanted to pay for this signal they will pay for the tv box but not for the signal at least back in the day and so advertising kind of filled that gap but advertising was new and interesting and it somehow didn't take over our lives quite right fast forward google provides a service that people don't want to pay for um and so somewhat surprising in the 90s they made end up making huge amounts they cornered the advertising market it didn't seem like that was going to happen at least to me um these little things on the right hand side of the screen just did not seem all that economically interesting but that companies had maybe no other choice the tv market was going away and billboards and so on um so they've they got it and i think that sadly that uh google just has me it was doing so well with that and making such right they didn't think much more about how wait a minute is there a producer consumer relationship to be set up here not just uh between us and the advertisers market to be created is there an actual market between the producer and consumer they're the producers the person who created that video clip the person that made that website the person who could make more such things the person who could adjust it and as a function of demand the person on the other side who's asking for different kinds of things you know so you see glimmers of that now there's influencers and there's kind of a little glimmering of a market but it should have been done 20 years ago it should have been thought about it should have been created in parallel with the advertising ecosystem and then facebook inherited that and i think they also didn't think very much about that so fast forward and now they are making huge amounts of money off of advertising and the news thing and all these clicks is just is feeding the advertising it's all connected up to the advertiser so you want more people to click on certain things because that money flows to you facebook you're very much incentivized to do that and when you start to find it's breaking people are telling you well we're getting into some troubles you try to adjust it with your smart ai algorithms right and figure out what are bad clicks though maybe shouldn't be click-through rate it should be something i find that pretty much hopeless it does get into all the complexity in life and you can try to fix it you should but you could also fix the whole business model and the business model is that really what are are there some human producers and consumers out there is there some economic value to be liberated by connecting them directly is it such that it's so valuable that uh people are willing to pay for it all right and micro payments like smart micro but even have to be micro so i i like the example suppose i'm going next week i'm going to india never been to india before right uh i have a couple days in in mumbai um i have no idea what to do there right and i could go on the web right now and search it's going to be kind of hopeless i'm not going to find you know um i'll have lots of advertisers in my face right what i really want to do is broadcast to the world that i am going to mumbai and have someone on the other side of a market look at me and and there's a recommendation system there so i'm not looking at all possible people coming to mumbai they're looking at the people who are relevant to them so someone my age group someone who kind of knows me in some level i give up a little privacy by that but i'm happy because what i'm going to get back is this person's going to make a little video for me or they're going to write a little two-page paper on here's the cool things that you want to do and move by this week especially right i'm going to look at that i'm not going to pay a micro payment i'm going to pay you know 100 or whatever for that it's real value it's like journalism um as i'm not a subscription it's that i'm gonna pay that person in that moment company's gonna take five percent of that and that person has now got it it's a gig economy if you will but you know done for in you know thinking about a little bit behind youtube there was actually people who could make more of those things if they were connected to a market they would make more of those things independently you have to tell them what to do you don't have to incentivize them any other way um and so yeah these companies i don't think have thought long long and hard about that so i do distinguish on you know facebook on the one side who just not thought about these things at all i think uh thinking that ai will fix everything uh and amazon thinks about them all the time because they were already out in the real world they were delivering packages people's doors they were they were worried about a market they were worrying about sellers and you know they worry and some things they do are great some things maybe not so great but you know they're in that business model and then i'd say google sort of hover somewhere in between i don't i don't think for a long long time they they got it i think they probably see that youtube is more pregnant with possibility than than than they might have thought and that they're probably heading that direction um but uh you know silicon valley's been dominated by the google facebook kind of mentality and the subscription and advertising and that is that's the core problem right the fake news actually rides on top of that because it means that you're monetizing with click-through rate and that is the core problem you got to remove that so advertisement if you're going to linger on that i mean that's an interesting thesis i don't know if everyone really deeply thinks about that so you're right the thought is the advertising model is the only thing we have the only thing we'll ever have so we have to fix we have to build algorithms that despite that business model you know find the better angels of our nature and do good by society and by the individual but you think we can slowly you think first of all there's a difference between should and could so you're saying we should slowly move away from the advertising model and have a direct connection between the consumer and the creator the the question i also have is can we because the advertising model is so successful now in terms of just making a huge amount of money and therefore being able to build a big company that provides has really smart people working that create a good service do you think it's possible and just to clarify you think we should move away well i think we should yeah but uh we is you know me so society yeah will the companies um i mean so first of all full disclosure i'm doing a day a week at amazon because i kind of want to learn more about how they do things so you know i'm not speaking for amazon in any way but um you know i did go there because i actually believe they get a little bit of this are trying to create these markets and they don't really use advertising is not a crucial part of it that's a good question so it has become not crucial but it's become more and more present if you go to amazon website and you know without revealing too many deep secrets about amazon i can tell you that you know a lot of people company question this and there's a huge questioning going on you do not want a world where there's zero advertising that actually is a bad world okay so here's a way to think about it you're a company that like amazon is trying to bring products to customers all right and the customer at any given you want to buy a vacuum cleaner say you want to know what's available for me and you know it's not gonna be that obvious you have to do a little bit of work at it the recommendation system will sort of help all right but now suppose this other person over here has just made the world you know they spent a huge amount of energy they had a great idea they made a great vacuum cleaner they know they they really did it they nailed it it's an mit you know whiz kid that made a great new vacuum cleaner all right it's not going to be in the recommendation system no one will know about it the algorithms will not find it and ai will not fix that okay at all right how do you allow that vacuum cleaner to start to get in front of people be sold well advertising and here what advertising is it's a signal that you're you believe in your product enough that you're willing to pay some real money for it and to me as a consumer i look at that signal i say well first of all i know these are not just cheap little ads because we have now right now i know that you know these are super cheap you know pennies uh if i see an ad where it's actually i know the company is only doing a few of these and they're making you know real money is kind of flowing and i see an ad i may pay more attention to it and i actually might want that because i see hey that guy spent money on his vacuum cleaner or maybe there's something good there so i will look at it and and so that's part of the overall information flow in a good market uh so advertising has a role um but the problem is of course that that signal is now completely gone because it just you know dominar by these tiny little things that add up to big money for the company you know so i i think it will just i think it will change because the societies just don't you know stick with things that annoy a lot of people and advertising currently annoys people more than it provides information and i think that at google probably is smart enough to figure out that this is a dead this is a bad model even though it's a hard huge amount of money and they'll have to figure out how to pull it away from it and slowly and i'm sure the ceo there will figure it out but um they need to do it and uh they need to so if you reduce advertising not to zero but you reduce it at the same time you bring up producer consumer actual real value being delivered so real money is being paid and they take a five percent cut that five percent could start to get big enough to cancel out the lost revenue from the the kind of the poor kind of advertising and i think that a good company will will do that we'll realize that um and they're com you know facebook you know again god bless them they they bring you know grandmother's uh you know uh they bring children's pictures into grandmother's lives it's fantastic um but they need to think of a new business model and and they that's that's the core problem there um until they start to connect producer consumer i think they will just just continue to make money and then buy the next social network company and then buy the next one and the innovation level will not be high and the health the health issues will not go away so i apologize that we kind of return to words i don't think the exact terms matter but in sort of defensive advertisement don't you think the kind of direct connection between consumer and creator producer is the best like the is what advertisement strives to do right so that is the best advertisement is literally now facebook is listening to our conversation and heard that you're going to india and we'll be able to actually start automatically for you making these connections and start giving this offer so like i apologize if it's just a matter of terms but just to draw a distinction is it possible to make advertisements just better and better and better algorithmically to where it actually becomes a connection almost address that's a good question so let's component all that push first of all i i what we just talked about i was defending advertising okay so i was defending it as a way to get signals into a market that don't come any other way especially algorithmically it's a sign that someone spent money on it it's a sign they think it's valuable and if i think that if other things someone else thinks it's valuable and if i trust other people i might be willing to listen i don't trust that facebook though is who's an intermediary between this i don't think they care about me okay i don't think they do and i find it creepy that they know i'm going to india next week because of our conversation why do you think that can we so what can you just put your pr hat on why do you think you find facebook uh creepy and not trust them as as do majority of the population so they're out of the silicon valley companies i saw like not approval rate but there's there's ranking of how much people trust companies and facebook is in in the gutter in the gutter including people inside of facebook so what uh what do you attribute that to because when i come on you don't find it creepy that right now we're talking i might walk out on the street right now that some unknown person who i don't know kind of comes up to me and says i hear you going to india i mean that's not even facebook that's just a if i want transparency in human society i want to have if you know something about me there's actually some reason you know something about me that's something that if i look at it later and audit it kind of i approve you know something about me because you care in some way there's a caring relationship even or an economic one or something not just that you're someone who could exploit it in ways i don't know about or care about or or i'm troubled by or or whatever and we're in a world right now where that happens way too much and that facebook knows things about a lot of people and could exploit it and does exploit it at times i think most people do find that creepy it's not for them it's not it's not that it's facebook that's not doing it because they care about them right in any real sense and they shouldn't they should not be a big brother caring about us that is not the role of a company like that why not wait not the big brother part but the sharing the trust thing i mean don't those companies just to linger because a lot of companies have a lot of information about us i would argue that there's companies like microsoft that has more information about us than facebook does and yet we trust microsoft more well microsoft is pivoting microsoft you know under satya nadella has decided this is really important we don't want to do creepy things we really want people to trust us to actually only use information in ways that they really would approve of that we don't decide right and um i'm just kind of adding that the health the health of a market is that uh when i connect to someone who produces a consumer it's not just a random producer consumer it's people who see each other they don't like each other but they sense that if they transact some happiness will go up on both sides if a company helps me to do that and moments that i choose of my choosing then fine so and also think about the difference between you know browsing versus buying right there are moments in my life i just want to buy you know a gadget or something i need something for that moment i need some ammonia for my house or something because i got a problem a spill i want to just go in i don't want to be advertised at that moment i don't want to be led down very dire you know that's annoying i want to just go and have it extremely easy to do what i want um other moments i might say no it's like today i'm going to the shopping mall i want to walk around and see things and see people and be exposed to stuff so i want control over that though i don't want the company's algorithms to decide for me right and i think that's the thing we it's a total loss of control if facebook thinks they should take the control from us of deciding when we want to have certain kinds of information when we don't what information that is how much it relates to what they know about us that we didn't really want them to know about us they're not i don't want them to be helping me in that way i don't want them to be helping them but they decide well they have control over um um what i want and when i totally agree so facebook by the way i have this optimistic thing where i think facebook has the kind of personal information about us that could create a beautiful thing so i i'm really optimistic of what facebook could do uh it's not what it's doing but what it could do so i don't see that i think that optimism is misplaced because there's not a bit you have to have a business model behind these things yes create a beautiful thing is really let's be let's be clear it's about something that people would value and and i don't think they have that business model and i don't think they they will suddenly discover it by what you know have a long hot shower i disagree i disagree in terms of uh you can discover a lot of amazing things in the shower so if i didn't say that i said they won't come they won't they won't do it but in the shower i think a lot of other people will discover it i think that this guy so i should also uh full disclosure there's a company called united masters which i'm on their board and they've created this music market yes they have a hundred thousand artists now signed on and they've done things like gone to the nba and the nba the music you find behind me the eclipse right now is their music right that's a company that had the right business model in mind from the get-go right executed on that and and from day one there was value brought to so here you have a kid who made some songs who suddenly their songs are on the nba website right that that's real economic value to people and uh so you know so you and i differ on the optimism of being able to sort of uh um change the direction of the titanic right so i yeah i'm older than you so i think titanic's crash got it but uh so and just to elaborate because i totally agree with you and i just want to know how difficult you think this problem is of so for example i um i want to read some news and i would there's a lot of times in the day where something makes me either smile or think in a way where i like consciously think this really gave me value like i sometimes listen to uh the daily podcast in the new york times way better than the new york times themselves by the way for people listening that's like real journalism is happening for some reason in the podcast space it doesn't make sense to me but often i'll listen to it 20 minutes and i i would be willing to pay for that like five dollars ten dollars for that experience absolutely and how difficult that's kind of what you're getting at is that little transaction how difficult is it to create a frictionless system like uber has for example for other things what's your intuition there uh so i first of all i pay little bits of money to you know to say there's something called courts that does financial things i like medium as a site i don't pay there but um i would you had a great post on medium i would have loved to pay you a dollar and but i wouldn't want it i wouldn't have wanted it per se because um there should be also sites where that's not actually the goal the goal is to actually have a broadcast channel that i monetize in some other way if i chose to i mean i could now people know about it i could i'm not doing it but um that's fine with me there also the musicians who are making all this music i don't think the right model is that you pay a little subscription fee to them all right because because people can copy the bits too easily and it's just not that somewhere the value is the value is that a connection was made between real human beings then you can follow up on that right and create yet more value so no i think um there's a lot of open questions here hot open questions but also yeah i do want good recommendation systems that recommend cool stuff to me and but it's pretty hard right i don't like them to recommend stuff just based on my browsing history i don't like that based on stuff they know about me quote quote what's unknown about me is the most interesting so this is the this is the really interesting question we may disagree maybe not i think that i love recommender systems and i want to give them everything about me in a way that i trust yeah but you but you don't because so for example this morning i clicked on i you know i was pretty sleepy this morning um i clicked on a story about the queen of england yes right i do not give a damn about the queen of england i really do not but it was clickbait it kind of looked funny and i had to say what the heck are they talking about i don't want to have my life you know heading that direction now that's in my browsing history the system in any reasonable system we'll think about history right but but you're saying all the trace all the digital exhaust or whatever that's been kind of the models if you collect all this stuff you're gonna figure all of us out well if you're trying to figure out like kind of one person like trump or something maybe you could figure him out but if you're trying to figure out you know 500 million people you know no way no way do you think so no i do i think so i think we are humans are just amazingly rich and complicated every one of us has our little quirks everyone else has our little things that could intrigue us that we don't even know and will intrigue us and there's no sign of it in our past but by god there it comes and you know you fall in love with it and i don't want a company trying to figure that out for me and anticipate that okay well i want them to provide a forum a market a place that i kind of go and by hook or by crook this happens you know i i'm walking down the street and i hear some chilean music being played and i never knew i like chili music but wow so there is that side and i want them to provide a limited but you know interesting place to go right and so don't try to use your ai to kind of you know figure me out and then put me in a world where you figured me out you know no create huge spaces for human beings where our creativity and our style will be enriched and come forward and it'll be a lot more transparency i won't have people randomly anonymously putting comments up and especially based on stuff they know about me facts that you know we are so broken right now if you're you know especially if you're a celebrity but you know it's about anybody that uh anonymous people are hurting lots and lots of people right now and that's part of this thing that silicon valley is thinking that you know just collect all this information and use it in a great way so no i i'm i'm not i'm not a pessimism i'm very much an optimist by nature but i think that's just been the wrong path for the whole technology to take be more limited create let humans rise up don't don't try to replace them that's the ai mantra don't try to anticipate them don't try to predict them because you're you're not good at you're not going to do those things you're going to make things worse okay so right now just give this a chance uh right now the recommender systems are the creepy people in the shadow watching your every move so they're looking at traces of you they're not directly interacting with you sort of the your close friends and family the way they know you is by having conversation by actually having interactions back and forth do you think there's a place for recommender systems sort of to step because you you just emphasize the value of human to human connection but yeah just give it a chance ai human connection is there a role for an ass system to have conversations with you in terms of to try to figure out what kind of music you like not by just watching what you're listening but actually having a conversation natural language or otherwise yeah no i'm i'm so i'm not against it i just want to push back against them maybe you're saying you have options for facebook so there i think it's misplaced but but um i think that this one pending facebook yeah now so good for you um go for it that's a hard spot to be yeah no good human interaction like on our daily the context around me in my own home is something that i don't want some big company to know about at all but i would be more than happy to have technology help me with it which kind of technology well you know just alexa amazon well a good alexa's done right i think alex is a research platform right now more than anything else but alexa done right you know could do things like i i leave the water running in my garden and i say hey alex so the waters are in my garden um and even have alexa figure out that that means when my wife comes home that she should be told about that that's a little bit of a reasoning i call that ai and by any kind of stretch it's a little bit of reasoning and it actually kind of makes my life a little easier and better and you know i don't i wouldn't call this a wow moment but i kind of think that overall rises human happiness up to have that kind of thing um but not when you're lonely alexa knowing loneliness no no there i don't want to let you that be feel intrusive and i and i don't want just the designer of the system to kind of work all this out i really want to have a lot of control and i want transparency and control and if the company can stand up and give me that in the context of new technology i think they're good first of all be way more successful than our current generation and like i said i was mentioning microsoft earlier i really think they're pivoting to kind of be the trusted old uncle but you know i think that they get that this is the way to go that if you let people find technology empowers them to have more control and have and have control not just over privacy but over this rich set of interactions um that that people gonna like that a lot more and that's that's the right business model going forward what does control over privacy look like do you think you should be able to just view all the data that no it's much more than that i mean first of all it should be an individual decision some people don't want privacy they want their whole life out there other people's want it um privacy is not a zero one it's not a legal thing it's not just about which data is available which is not um i like to recall to people that you know a couple hundred years ago everyone there was not really big cities everyone lived down the countryside and villages um and in villages everybody knew everything about you very you didn't have any privacy is that bad are we better off now well you know arguably no because what did you get for that loss of at least certain kinds of privacy um well uh people helped each other if they because they know everything about you they know something's bad's happening they will help you with that right and now you live in a big city no one knows their mouth you get no help um so uh it kind of depends the answer i want certain people who i trust and there should be relationships i should kind of manage all those but who knows what about me i should have some agency there it shouldn't i shouldn't be a drift in a sea of technology where i have no idea i don't want to go reading things and checking boxes so i don't know how to do this and i'm not a privacy researcher per se i just i recognize the vast complexity of this it's not just technology it's not just legal scholars meeting technologists there's got to be kind of whole layers around it and so i when i allude to this emerging engineering field this is a big part of it um like when electrical engineering come came i'm not wasn't around in the time but you just didn't plug electricity you know into walls and it all kind of worked you don't have to have like underwriter's laboratory that reassured you that that plug's not going to burn up your house and that that machine will do this and that and everything there'll be whole people who can install things there'll be people who can watch the installers there'll be a whole layers you know an onion of these kind of things and for things as deeply interesting as privacy which is his least essential electricity um that that's gonna take decades to kind of work out but it's gonna require a lot of new structures that we don't have right now so it's kind of hard to talk about it and you're saying there's a lot of money to be made if you get it right so absolutely a lot of money to be made and all these things that provide human services and people recognize them as useful parts of their lives uh so yeah um so yeah the dialect sometimes goes from the exuberant technologists to the no technology is good kind of and that's you know in our public discourse you know in newspapers you see too much of this kind of thing and and the sober discussions in the middle which are the challenging ones to have are where we need to be having our conversations and you know there's not actually there's not many forum forum for those um you know there's that's that's kind of what i would look for maybe i could go and i could read a comment section of something and it would actually be this kind of dialogue going back and forth you don't see much of this right which is why actually there's a resurgence of podcasts out of all because good people are really hungry for conversation but their technology is not helping much so comment sections of anything including youtube yeah is not hurting i'm not hurting yeah and you think technically speaking it's possible to help i don't know the answers but it's it's a it's a less anonymity a little more locality um you know worlds that you kind of enter in and you trust the people there in those worlds so that when you start having a discussion you know not only is that people not gonna hurt you but it's not gonna be a total waste your time because there's a lot of wasting of time that you know a lot of us i i pulled out of facebook early on because it was clearly going to waste a lot of my time even though there was some value um and so yeah worlds that are somehow you enter in you know what you're getting and it's kind of appeals to you might new things might happen but you kind of have some some trust in that world and there's some deep interesting complex psychological aspects around anonymity uh how that changes human behavior indeed quite dark and quite dark yeah i think a lot of us are especially those of us who really love the advent of technology i loved social networks when it came out i was just i didn't see any negatives there at all but then i started uh seeing comment sections i think it was maybe you know cnn or something and i started going wow this this darkness i just did not know about and um and our technology is now amplifying it so sorry for the big philosophical question but on that topic do you think human beings because you've also out of all things had a foot in psychology too the do you think human beings are fundamentally good like all of us have good intent that could be mined or is it depending on context and environment everybody could be evil thought my answer is fundamentally good um but fundamentally limited all of us have very you know blinkers on we don't see the other person's pain that easily we don't see the other person's point of view that easily we're very much in our own head in our own world and on my good days i think that technology could open us up to you know more perspectives and more less blinkered and more understanding you know a lot of wars in human history happen because of just ignorance they didn't they they thought the other person was doing this well other person wasn't doing this and we have huge amounts of that um but in my lifetime i've not seen technology really help in that way yet and i do i do i do believe in that but you know no i think fundamentally humans are good the people suffer people have grievances because you have grudges and those things cause them to do things they probably wouldn't want they regret it often um so no i i i think it's a you know part of the progress of technology is to indeed allow it to be a little easier to be the real good person you actually are well but do you think individual human life or society could be modeled as an optimization problem um not the way i think typically i mean that's your time one of the most complex phenomena in the whole you know in all ways individual human life for society as a whole both both i mean individual human life is amazingly complex and um so uh you know optimization is kind of just one branch of mathematics that talks about certain kind of things and uh it just feels way too limited for the complexity of such things what properties of optimization problems do you think so do you think most interesting problems that could be solved through optimization uh what kind of properties does that surface have non-convexity convexity linearity all those kinds of things saddle points well so optimization's just one piece of mathematics you know there's like you just even in our era we're aware that say sampling um is coming up with examples of something um coming up with a description what's sampling well you they you can if you're a kind of a certain kind of mathematician you can try to blend them and make them seem to be sort of the same thing but optimization is roughly speaking trying to uh find a point that um a single point that is the optimum of a criterion function of some kind um and sampling is trying to from that same surface treat that as a distribution or density and find prop points that have high density so um i i want the entire distribution and the sampling paradigm and i want the um you know the the single point that's the best point in the par in the sample in the uh optimization paradigm now if you were optimizing in the space of probability measures the output of that could be a whole probability distribution so you can start to make these things the same but in mathematics if you go too high up that kind of abstraction arc you start to lose the uh you know the ability to do the interesting theorems so you kind of don't try to you don't try to overly over abstract so as a small tangent what kind of world view do you find more appealing one that is deterministic or stochastic well that's easy i mean i'm a statistician you know the world is highly stochastic wait i don't know what's going to happen in the next five minutes right because you're going to ask what we're going to do massive uncertainty yeah you know massive uncertainty and so the best i can do is have come rough sense or probability distribution on things and somehow use that in my reasoning about what to do now so how does the distributed at scale when you have multi-agent systems look like so optimization can optimize sort of it makes a lot more sense sort of uh at least from from a robotics perspective for a single robot for a single agent trying to optimize some objective function when you start to enter the real world this game theoretic concept starts popping up and that how do you see optimization in this because you've talked about markets in a scale what does that look like do you see this optimization do you see it as sampling do you see like how how should you modify these all blend together um and a system designer thinking about how to build an incentivized system will have a blend of all these things so you know a particle in a potential well is optimizing a function called lagrangian right the particle doesn't know that there's no algorithm running that does that it just happens it's so it's a description mathematically of something that helps us understand as analysts what's happening right and so the same will happen when we talk about you know mixtures of humans and computers and markets and so on so forth there'll be certain principles that allow us to understand what's happening and whether or not the actual algorithms are being used by any sense it's not clear now at some point i may have set up a multi-agent or market kind of system and i'm now thinking about an individual agent in that system and they're asked to do some tasks and they're incentivized in some way they get certain signals and they they have some utility maybe what they will do at that point is they just won't know the answer they may have to optimize to find an answer okay so an autism could be embedded inside of an overall market you know and game theory is is very very broad it is often studied very narrowly for certain kinds of problems but it's roughly speaking this is just the i don't know what you're going to do so i kind of anticipate that a little bit and you anticipate what i'm anticipating and we kind of go back and forth in our own minds we run kind of thought experiments you talked about this interesting point in terms of game theory you know most optimization problems really hate saddle points maybe you can describe what saddle points are but i've heard you kind of mentioned that there's a there's a branch of optimization you could try to explicitly look for saddle points that's a good thing oh not optimization that's just game theory that that's so uh there's all kinds of different equilibria in game theory and some of them are highly explanatory behavior they're not attempting to be algorithmic they're just trying to say if you happen to be at this equilibrium you would see certain kind of behavior and we see that in real life that's what an economist wants to do especially behavioral economist um uh in in continuous uh differential game theory you're in continuous spaces a um some of the simplest equilibria are saddle points a nash equilibrium is a saddle point it's a special kind of salon point so classically in game theory you were trying to find nash equilibria and algorithmic games here you're trying to find algorithms that would find them and so you're trying to find saddle points i mean so that's literally what you're trying to do um but you know any economist knows that nash equilibria have their limitations they are definitely not that explanatory in many situations they're not what you really want um there's other kind of equilibria and there's names associated with these because they came from history with certain people working on them but there will be new ones emerging so you know one example is a stackelberg equilibrium so you know nash you and i are both playing this game against each other or for each other maybe it's cooperative and we're both going to think it through and then we're going to decide and we're going to off you know do our thing simultaneously you know in a stackelberg no i'm going to be the first mover i'm going to make a move you're going to look at my move and then you're going to make yours now since i know you're going to look at my move i anticipate what you're going to do and so i don't do something stupid but and but then i know that you were also anticipating me so we're kind of going back and so far am i but there is then a first mover thing and so there's a those are different equilibria all right and uh so just mathematically yeah these things have certain topologies certain shapes they're like southwest and algorithmically or dynamically how do you move towards them how do you move away from things um you know so some of these questions have answers they've been studied others do not and especially if it becomes stochastic especially if there's large numbers of decentralized things there's just uh you know young people getting in this field who kind of think it's all done because we have you know tensorflow well no these are all open problems and they're really important and interesting and it's about strategic settings how do i collect data suppose i don't know what you're going to do because i don't know you very well right well i got to kind of date about you so maybe i want to push you in a part of the space where i don't know much about you so i can get data because and then later i'll realize that you'll never you'll never go there because of the way the game is set up but you know that's part of the overall you know data analysis context is that yeah even the game of poker is fascinating space whenever there's any uncertainty your lack of information is it's a super exciting space yeah uh just uh lingard optimization for a second so if we look at deep learning it's essentially minimization of a complicated loss function so is there something insightful or hopeful that you see in the kinds of function surface that loss functions that deep learning in in the real world is trying to optimize over is there something interesting this is just the usual kind of problems of optimization i think from an optimization point of view that surface first of all it's pretty smooth um and secondly if there's over if it's over parameterized there's kind of lots of paths down to reasonable optima and so kind of the getting downhill to the to an optimum is viewed as not as hard as you might have expected in high dimensions the fact that some optima tend to be really good ones and others not so good and you tend to it's not sometimes you find the good ones is sort of still needs explanation yes but but the particular surface is coming from the particular generation of neural nets i kind of suspect those will this those will change in 10 years it will not be exactly those surfaces there'll be some others that are and optimization theory will help contribute to why other surfaces are why other algorithms layers of arithmetic operations with a little bit of nonlinearity that's not that didn't come from neuroscience per se i mean maybe in the minds of some of the people working on it they were thinking about brains but uh they were arithmetic circuits in all kinds of fields you know uh computer science control theory and so on and that layers of these could transform things in certain ways and that if it's smooth maybe you could uh you know find parameter values um you know it's a big is a is a sort of big discovery that it's it's working it's able to work at this scale but um um i don't think that we're stuck with that and we're certainly not stuck with that because we're understanding the brain so in terms of uh on the algorithm size of gradient descent do you think we're stuck with gradient descent this is uh variants of it what variants do you find interesting or do you think there'll be something else invented that uh is able to walk all over these optimization spaces in more interesting ways so there's a co-design of the surface and or the architecture and the algorithm so if you just ask if we stay with the kind of architectures we have now and not just neural nets but you know phase retrieval architectures or materials completion architectures and so on um you know i think we've kind of come to a place where yeah a stochastic gradient algorithms are dominant and um there are versions uh they're you know that are a little better than others they you know have more guarantees they're more robust and and so on and there's ongoing research to kind of figure out which is the best downforce situation um but i think that that'll start to co-evolve that that'll put pressure on the actual architecture and so we shouldn't do it in this particular way we should do it in a different way because this other algorithm is now available if you do it in a different way um so uh that that i can't really anticipate that co-evolution process but you know gradients are amazing uh mathematical objects um they uh have a lot of people who uh start to study them more deeply mathematically are kind of shocked about what what they are and what they can do um i mean to think about this way if uh suppose that i tell you if you move along the x-axis you get uh uh uh you know you go uphill in some objective by you know three units whereas if you move on the y-axis you go uphill by seven units right now i'm gonna only allow you to move a certain you know unit distance all right what are you gonna do well the most not people will say i'm gonna go along the y-axis i'm getting the biggest bang for my buck you know and my buck is only one unit so i'm gonna put all of it in the y-axis right and uh why should i even take any of my strength my step size and put any of it in the x-axis because i'm getting less bang for my buck that seems like a completely you know clear cl argument and it's wrong because the gradient direction is not to go along the y-axis it's to take a little bit of the x-axis uh and that to understand that you have to you have to know some math and um so even a you know trivial so so-called operator like grading is not trivial and so you know exploiting its properties is still very very important um now we know that just providing descent has got all kinds of problems it gets stuck in many ways and it hadn't have you know good dimension dependence and so on so um my own line of work recently has been about what kinds of stochasticity how can we get dimension dependence how can we do the theory of that um and we've come up pretty favorable results with certain kinds of stochasticity we have sufficient conditions generally we know if you if you do this we will give you a good guarantee we don't have necessary conditions that it must be done a certain way in general so stochasticity how much randomness to inject into the into the walking along the gradient and what kind of randomness why is randomness good in this process why is stochasticity good yeah so um i give you simple answers but in some sense again it's kind of amazing stochasticity just uh um you know particular features of a surface that could have hurt you if you were doing one thing um deterministically it won't hurt you because uh you know by chance there's very little chance that you would get hurt and um you know so here stochasticity um you know is just kind of saves you from some of the particular features of surfaces that um you know and in fact if you think about you know surfaces that are discontinuous in a first derivative like you know absolute value function um you will go down and hit that point where there's non-differentiability right and if you're running a deterministic argument at that point you can really do something bad right whereas stochasticity just means it's pretty unlikely that's going to happen you're going to you're going to hit that point so you know it's again not trivially analyzed but um especially in higher dimensions also stochasticity our intuition isn't very good about it but it has properties that kind of are very appealing in high dimensions for a lot of large number of reasons um so it's it's all part of the mathematics to kind of that's what's fun to work in the field is that you get to try to understand this mathematics and um but long story short you know partly empirically it was discovered stochastic gradient is very effective and theory kind of followed i'd say um that but i don't see that we're getting clearly out of that uh what's the most beautiful mysterious a profound idea to you in optimization i don't know the most but let me just say that uh you know nestorov's work on nest drive acceleration to me is uh pretty pretty surprising and pretty deep um can you elaborate well install acceleration is just that um i suppose that we are going to use gradients to move around into space for the reasons i've alluded to there there are nice directions to move and suppose that i tell you that you're only allowed to use gradients you're not going to be allowed to you'll see this local person it can only sense kind of a change in the surface um but i'm going to give you kind of a computer that's able to store all your previous gradients and so you start to learn some something about the the surface um and i'm going to restrict you to maybe move in the direction of like a linear span of all the gradients so you can't kind of just move in some arbitrary direction right so now we have a well-defined mathematical complexity model there's a certain classes of algorithms that can do that and others that can't and we can ask for certain kinds of surfaces how fast can you get down to the optimum so there's an answers to these so for a you know a smooth convex function there's an answer which is one over the number of steps squared you will be within a ball of that size after after k steps um gradient descent in particular has a slower rate it's one over k okay um so you could ask is gradient is said actually even though we know it's a good algorithm is it the best algorithm in the sense of the answer is no well well not clear yet because what one of our case score is a lower bound that's that's probably the best you can do what gradient is one over k but is there something better and so i think as a surprise to most though nest drove discovered a new algorithm that is got two pieces to it it uses two gradients um and uh puts those together in a certain kind of obscure way and uh the thing doesn't even move downhill all the time it sometimes goes back uphill and if you're a physicist that kind of makes some sense you're building up some momentum and that is kind of the right intuition but that that intuition is not enough to understand kind of how to do it and why it works um but it does it achieves one over k squared and uh it has a mathematical structure and it's still kind of to this day a lot of us are writing papers and trying to explore that and understand it um so there are lots of cool ideas in optimization but just kind of using gradients i think is number one that goes back you know 150 years um and then nest drive i think has made a major contribution with this idea so like you said gradients themselves are in some sense mysterious yeah they're not uh they're not as trivial as they're not as trivial coordinate descent is more of a trivial one you just pick one of the coordinates that's how we think that's our human mind that's our human minds think and gradients are not that easy for our human mind to grapple with an absurd question but uh what is statistics so the here it's a little bit it's somewhere between math and science and technology it's somewhere in that convex hole so it's a set of principles that allow you to make inferences that have got some reason to be believed and also principles allow you make decisions where you can have some reason to believe you're not going to make errors so all of that requires some assumptions about what do you mean by an error what do you mean by you know the probabilities and um but you know you start after you start making some of those assumptions you're led to uh conclusions that yes i can guarantee that you know you know if you do this in this way your probability of making error will be small your probability of continuing to not make errors over time will be small and probability you found something that's real will be small uh will be high so decision making is a big part of the big part yeah so uh the original so statistics uh you know short history was that you know it's kind of goes back as a formal discipline you know 250 years or so it was called inverse probability because around that era probability was developed sort of especially to explain gambling situations of course and um interesting so you would say well given the state of nature is this there's a certain roulette board that has a certain mechanism in it uh what kind of outcomes do i expect to see uh and um especially if i do things long long amounts of time what outcomes i see and the physicists start to pay attention to this um and then people say well given let's turn the problem around what if i saw certain outcomes could i infer what the underlying mechanism was that's an inverse problem and in fact for quite a while statistics was called inverse probability that was the name of the field and i believe that uh it was laplace uh who was working in napoleon's government who was trying to who needed to do a census of france learn about the people there so he went and gathered data and he analyzed that data to determine policy and uh said let's call this field that does this kind of thing statistics because um the the word state is in there in french that's eta but you know it's the study of data for the state so anyway that caught on and um it's been called statistics ever since but um uh but by the time it got formalized it was sort of in the 30s um and uh around that time there was game theory and decision theory developed nearby people in that era didn't think of themselves as either computer science or statistics or controlled or econ they were all they were all the above and so you know von neumann is developing game theory but also thinking of that as decision theory wall is an econometrician developing decision theory and then you know turning that into statistics and so it's all about here's a here's not just data and you analyze it here's a loss function here's what you care about here's the question you're trying to ask here is a probability model and here's the risk you will face if you make certain decisions um and to this day in most advanced statistical curricula you teach decision theory is the starting point and then it branches out into the two branches of bazin or frequentist but um that's it's all about decisions in statistics what is the most beautiful mysterious maybe surprising idea that you've come across uh yeah good question um i mean there's a bunch of surprising ones there's something that's way too technical for this thing but something called james stein estimation which is kind of surprising and really takes time to wrap your head around can you try to maybe i think i don't even want to try um let me just say a colleague at steve steven stickler at university of chicago wrote a really beautiful paper on james stein estimation which helps to its views of paradox it kind of defeats the mind's attempts to understand it but you can and steve has a nice perspective on that um there uh so one of the troubles with statistics is that it's like in physics that are in quantum physics you have multiple interpretations there's a wave and particle duality in physics and you get used to that over time but it still kind of haunts you that you don't really you know quite understand the relationship the electrons away when electrons a particle well um well the same thing happens here there's bayesian ways of thinking and frequentist and they are different they they all they sometimes become sort of the same in practice but they are physically different and then in some practice they are not the same at all they give you rather different answers um and so it is very much like wave and particle duality and that is something you have to kind of get used to in the field can you define beijing and frequencies yeah in decision theory you can make i have a like i have a video that people could see it's called are you a bayesian or a frequentist and kind of help try to to make it really clear it comes from decision theory so you know decision theory uh you're talking about loss functions which are a function of data x and parameter theta it's a function of two arguments okay neither one of those arguments is known you don't know the data a priori it's random and the parameter is unknown all right so you have this function of two things you don't know and you're trying to say i want that function to be small i want small loss right well um what are you gonna do so you sort of say well i'm gonna average over these quantities or maximize over them or something so that you know i turn that uncertainty into something certain so you could look at the first argument an average over it or you could look at the second argument average over it that's bayesian frequencies so the frequencies says i'm going to look at the x the data and i'm going to take that as random and i'm going to average over the distribution so i take the expectation loss under x theta is held fixed all right that's called the risk and so it's looking at other all the data sets you could get all right and say how well will a certain procedure do under all those data sets that's called a frequency guarantee all right so i think it is very appropriate when like you're building a piece of software and you're shipping it out there and people are using all kinds of data sets you want to have a stamp a guarantee on it that as people run it on many many data sets that you never even thought about that 95 of the time it will do the right thing um perfectly reasonable the bayesian perspective says well no i'm going to look at the other argument of the loss function the theta part okay that's unknown and i'm uncertain about it so i could have my own personal probability for what it is you know how many tall people are there out there i'm trying to infer the average height of the population well i have an idea roughly what the height is so i'm going to average over the um the theta so now that loss function has only now again one argument's gone now it's a function of x and that's what a bayesian does is they say well let's just focus on the particular x we got the data set we got we condition on that conditional on the x i say something about my loss that's a bayesian approach to things and the bayesian will argue that it's not relevant to look at all the other data sets you could have gotten and average over them the frequentest approach it's really only the data set you got all right and i do agree with that especially in situations where you're working with a scientist you can learn a lot about the domain and you really only focus on certain kinds of data and you've gathered your data and you make inferences i don't agree with it though that it you know in the sense that there are needs for frequency guarantees you're writing software people are using it out there you want to say something so these two things have to go out to fight each other a little bit but they have to blend so long story short there's a set of ideas that are right in the middle they're called empirical bays and empirical base sort of starts with the bayesian framework it's it's kind of arguably philosophically more you know reasonable and kosher write down a bunch of the math that kind of flows from that and then realize there's a bunch of things you don't know because it's the real world then you don't know everything so you're uncertain about certain quantities at that point ask is there a reasonable way to plug in an estimate for those things okay and in some cases there's quite a reasonable thing to do to plug in there's a natural thing you can observe in the world that you can plug in and then do a little bit more mathematics and assure yourself it's really good so my math are based on human expertise what's what are good they're both going in the bayesian framework allows you to put a lot of human expertise in but the math kind of guides you along that path and then kind of reassures you at the end you could put that stamp of approval under certain assumptions this thing will work so perhaps you asked question what's my favorite you know or what's the most surprising nice idea so one that is more accessible is something called false discovery rate which is um you know you're making not just one hypothesis test or making one decision you're making a whole bag of them and in that bag of decisions you look at the ones where you made a discovery you announced that something interesting it happened all right that's gonna be some subset of your big bag in the ones you made a discovery which subset of those are bad there are false false discoveries you like the fraction of your false discoveries among your discoveries to be small that's a different criterion than accuracy or precision or recall or sensitivity and specificity it's it's a different quantity those latter ones are almost all of them um have more of a frequencies flavor they say given the truth is that the null hypothesis is true here's what accuracy i would get or given that the alternative is true here's what i would get so it's kind of going forward from the state of nature to the data the bayesian goes the other direction from the data back to the state of nature and that's actually what false discovery rate is it says given you made a discovery okay that's condition on your data what's the probability of the hypothesis it's going the other direction and so um the classical frequency look at that so i can't know that there's some priors needed in that and the empirical bayesian goes ahead and plows forward and starts writing down these formulas and realizes at some point some of those things can actually be estimated in a reasonable way oh and so it's kind of it's a beautiful set of ideas so i i this kind of line of argument has come out it's not certainly mine but it it sort of came out from robin's around 1960. uh brad ephron has written beautifully about this in various papers and books and uh and the fdr is you know ben yamini in israel um john storey did this bayesian interpretation and so on so i've just absorbed these things over the years and find it a very healthy way to think about statistics let me ask you about intelligence to jump slightly back out into philosophy perhaps you said that uh maybe you can elaborate but uh you said that defining just even the question of what is intelligence is a word is as a very difficult question is that a useful question do you think we'll one day understand the fundamentals of human intelligence and what it means you know have good uh benchmarks for general intelligence that we put before our machines so i don't work on these topics so much you're really asking a question for a psychologist really and i just studied some but i don't consider myself at least an expert at this point you know a psychologist aims to understand human intelligence right and i think many psychologists i know are fairly humble about this they they might try and understand how a baby understands you know whether something's a solid or liquid or uh whether something's hidden or not and um maybe how you know a child starts to learn the meaning of certain words what's a verb what's a noun and also you know slowly but surely trying to figure out things um but human's ability to take a really complicated environment reason about it abstract about it find the right abstractions communicate about it interact and so on is just you know really staggeringly rich and complicated um and so you know i think in all humidity we don't think we're kind of aiming for that in the near future certainly psychologists doing experiments with babies in the lab or with people talking is is has a much more limited aspiration and you know conor mcversky would look at our reasoning patterns and they're they're not deeply understanding all the how we do our reasoning but they're sort of saying here's some here's some oddities about the reasoning and some things you should you need to think about it but also i as i emphasize and things some things i've been writing about um you know ai the revolution hasn't happened yet yeah um great blog post i've i've been emphasizing that you know if you step back and look at uh intelligent systems of any kind whatever you mean by intelligence it's not just the humans or the animals or you know the plants or whatever you know so a market that brings goods into a city you know food to restaurants or something every day uh is a system it's a decentralized set of decisions looking at it from far enough away it's just like a collection of neurons everyone every neuron is making its own little decisions presumably in some way and if you step back enough every little part of an economic system is making us all of its decisions and just like with the brain who knows what the individual neuron doesn't know what the overall goal is right but something happens at some aggregate level same thing with the economy people eat in a city and it's robust it works at all scales small villages to big cities it's been working for thousands of years uh it works rain or shine so it's adaptive um so all kind of you know those are adjeeves one tends to apply to intelligent systems robust adaptive you know you don't need to keep adjusting it it's self self healing whatever plus not perfect you know intelligences are never perfect and markets are not perfect um but i do not believe in this area that you cannot that you can say well our computers our humans are smart but you know no markets are not more markets are so they are intelligent uh now um we humans didn't evolve to be markets we've been participating in them right but we are not ourselves a market per se um the neurons could be viewed as the market you can't there's economic you know neuroscience kind of perspectives that's interesting to pursue all that the point though is is that if you were to study humans and really be the world's best psychologist study for thousands of years and come up with the theory of human intelligence you might have never discovered principles of markets you know spy demand curves and you know matching and auctions and all that uh those are real principles and they lead to a form of intelligence that's not maybe human intelligence it's arguably another kind of intelligence there probably are third kinds of intelligence or fourth that none of us are really thinking too much about right now so if you really and then all those are relevant to computer systems in the future certainly the market one is relevant right now whereas understand human intelligence is not so clear that it's relevant right now probably not um so if you want general intelligence whatever one means by that or you know understand the intelligence in a deep sense and all that it is definitely has to be not just human intelligence it's got to be this broader thing and that's not a mystery markets are intelligent so you know it's definitely not just a philosophical stance to say we gotta move beyond and tell who intelligence that sounds ridiculous yeah but it's not and in that blog post you define different kinds of like intelligent infrastructure iii which i really like that's some of the concept you've just been describing do you see ourselves if we see earth human civilization is a single organism do you think the intelligence of that organism when you think from the perspective of markets and intelligence infrastructure is increasing is it increasing linearly is it increasing exponentially what do you think the future of that intelligence i don't know i don't tend to think i don't tend to answer questions like that because you know that's science fiction hoping to catch you off guard well again because you said it's so far in the future it's fun to ask and you'll probably you know like you said predicting the future is really nearly impossible but say as an axiom one day we create a human level superhuman level intelligent not the scale of markets but the scale of an individual what do you think is is what do you think it would take to do that or maybe to ask another question is how would that system be different than the biological human beings that we see around us today is it possible to say anything interesting to that question or is it just a stupid question it's not stupid question but it's science fiction science fiction and so i'm totally happy to read science fiction and think about it from time my own life i loved there was this like brain in a vat kind of you know little thing that people were talking about when i was a student i remember you know imagine that uh um you know between your brain and your body there's you know there's a bunch of wires right and suppose that every one of them was replaced with a uh uh literal wire and then suppose that wire was turning actually a little wireless you know there's a receiver and sender so the brain has got all the senders and receiver you know on all of its exiting uh you know axons and all the dendrites down the body have replaced with syndrome receivers now you could move the body off somewhere and put the brain in a vat right and then you could do things like start killing off those centers of receivers one by one and after you've killed off all of them where is that person you know they thought they were out in the body walking around the world and they moved on so those are science fiction things those are fun to think about it's just intriguing about where's what is thought where is it and all that and i think every 18 year old it's to take philosophy classes and think about these things and i think that everyone should think about what could happen in society that's kind of bad and all that but i really don't think that's the right thing for most of us that are my age group to be doing and thinking about i really think that we have so many more present you know first challenges and dangers and real things to build and all that um such that uh you know uh spending too much time on science fiction at least in public fora like this i think is is not what we should be doing maybe over beers in private that's right i'm well welcome welcome i'm not gonna broadcast where i have beers because this is gonna go on facebook a lot of people showing up there but um yeah i'll uh i love facebook twitter amazon youtube i have i'm optimistic and hopeful but uh maybe maybe i don't have grounds for such optimism and hope let me ask term you've mentored some of the brightest sort of some of the seminal figures in the field can you uh give advice to people who undergraduates today what does it take to take you know advice on their journey if they're interested in machine learning and ai in in the ideas of markets from economics and psychology and all the kinds of things that you're exploring what what what steps should they take on that journey well yeah first of all the door is open and second it's a journey i like your language there uh it is not that you're so brilliant and you have great brilliant ideas and therefore that's that's just you know that's how you have success or that's how you enter into the field uh it's that you apprentice yourself you you spend a lot of time you work on hard things you try and pull back and you be as broad as you can you talk lots of people um and it's like entering any kind of a creative community there's um years that are needed and uh human connections are critical to it so you know i think about you know being a musician or being an artist or something you don't just you know immediately from day one you know you you're a genius and therefore you do it no you um you know practice really really hard on basics and you uh be humble about where you are and then and you realize you'll never be an expert on everything so you kind of pick and there's a lot of randomness and a lot of kind of luck but luck just kind of picks out which branch of the tree go down but you'll go down some branch um so yeah it's it's a community so the graduate school is i still think is one of the wonderful phenomena that we have in our in our world it's it's very much about apprenticeship with an advisor it's very much about a group of people you belong to it's a four or five year process so it's plenty of time to start from kind of nothing to come up to something you know more expertise and then start to have your own creativity start to flower even surprise into your own self um and it's a very cooperative endeavor it's i think a lot of people uh think of science as highly competitive and i think in some other fields it might be more so here it's way more cooperative than you might imagine and people are always teaching each other something and people are always more than happy to uh be clear that so i i feel i'm an expert on certain kind of things but i'm very much not expert on lots of other things and a lot of them are relevant and a lot of them are i should know but it should in some sense i you know you don't so um i'm always willing to reveal my ignorance to people around me so they can teach me things and uh i think a lot of us feel that way about our field so it's very cooperative uh i might add it's also very international because it's so cooperative we see no barriers and uh so that the nationalism that you see especially in the current era and everything is just at odds with the way that most of us think about what we're doing here where this is a human endeavor and we we cooperate and are very much trying to do it together for the you know the benefit of everybody so last question where and how and why did you learn french and which language is more beautiful english or french um great question so um first of all i think italian's actually more beautiful than french and english and i also speak that so i'm i'm i'm married to an italian and i have kids and we speak italian um anyway though all kidding aside that every language allows you to express things a bit differently um and it is one of the great fun things to do in life is to explore those things so in fact when i kids or you know teens or uh college students ask me what they just study i say well do what your heart where your heart is certainly do a lot of math math is good for everybody but do some poetry and do some history and do some language too um you know throughout your life you'll want to be a thinking person you'll want to have done that um for me uh yeah french i learned when i was i'd say a late teen um i was living in the middle of the country in kansas and uh not much was going on in kansas with all due respect to kansas but uh and so my parents happen to have some french books on the shelf and just in my boredom i pulled them down and i found this is fun and i kind of learned the language by reading and when i first heard it spoken i had no idea what was being spoken but i realized i somehow knew it from some previous life and so i made the connection um but then you know i traveled and just i i love to go beyond my own barriers and uh my own comfort or whatever and i found myself in you know on trains in france next to say older people who would you know live the whole life of their own and the ability to communicate with them was was you know special and uh ability to also see myself in other people's shoes and have empathy and kind of work on that language as part of that um so um so after that kind of experience um and also embedding myself in french culture which is you know quite quite amazing you know languages are rich not just because there's something inherently beautiful about it but it's all the creativity that went into it so i learned a lot of songs read poems read books um and then i was here actually at mit where we're doing the podcast today and uh young professor um you know not yet married and uh um you know not having a lot of friends in the area so i just didn't have i was getting kind of a bored person i said i heard a lot of italians around there's happened to be a lot of italians at mit behind professor for some reason and so i was kind of vaguely understanding what they were talking about i said well i should learn this language too so i i did and then later met my spouse and uh you know wow italian became a more important part of my life but um but i go to china a lot these days i go to asia i go to europe and um every time i go i kind of uh i'm amazed by the richness of human experience and the the people don't have any idea if you haven't traveled kind of how i'm you know amazingly rich and i love the diversity it's not just a buzzword to me it really means something i love the you know you know embed myself with other people's experiences and uh so yeah learning language is a big part of that i think i've said in some interview at some point that if i had you know millions of dollars on the infinite time whatever what would you really work on if you really wanted to do ai and for me that is natural language and really done right you know deep understanding of language um that's to me an amazingly interesting scientific challenge and uh when we're very far away one we're very far away but good natural language people are kind of really invested then i think a lot of them see that's where the core of ai is that if you understand that you really help human communication you understand something about the human mind the semantics that come out of the human mind and i agree i think that will be such a long time so i didn't do that in my career just because i kind of i was behind in the early days i didn't kind of know enough of that stuff i was at mit i didn't learn much language and it was too late at some point to kind of spend a whole career doing that but i admire that field and uh um and so in my little way by learning language you know kind of that part of my brain has um has been trained up jan was right you truly are the miles davis and machine learning i don't think there's a better place than it was mike is a huge honor talking to you today merci beaucoup all right it's been my pleasure thank you thanks for listening to this conversation with michael i jordan and thank you to our presenting sponsor cash app download it use code lex podcast you'll get ten dollars and ten dollars will go to first an organization that inspires and educates young minds to become science and technology innovators of tomorrow if you enjoy this podcast subscribe on youtube give it five stars on apple podcast support it on patreon or simply connect with me on twitter at lex friedman and now let me leave you with some words of wisdom from michael i jordan from his blog post titled artificial intelligence the revolution hasn't happened yet calling for broadening the scope of the ai field we should embrace the fact that what we are witnessing is the creation of a new branch of engineering the term engineering is often invoked in a narrow sense in academia and beyond with overtones of cold effectless machinery and negative connotations of loss of control by humans but an engineering discipline can be what we want it to be in the current era we have a real opportunity to conceive of something historically new a human-centric engineering discipline i'll resist giving this emerging discipline a name but if the acronym ai continues to be used let's be aware of the very real limitations of this placeholder let's broaden our scope tone down the hype and recognize the serious challenges ahead thank you for listening and hope to see you next time you

Channel: Lex Fridman

Views: 143,387

Keywords: michael i. jordan, machine learning, statistics, artificial intelligence, agi, ai, ai podcast, artificial intelligence podcast, lex fridman, lex podcast, lex mit, lex ai, lex jre, mit ai

Id: EYIKy_FM9x0

Length: 105min 49sec (6349 seconds)

Published: Mon Feb 24 2020