'AI Superpowers': A Conversation With Kai-Fu Lee

thank you very much it's it's great to be here I'm gonna start off with a with a kind of an echo of Tom and confess that when so I'm coming up on 50 now and I'm getting to that age where friends are writing books and they're very often you know painful to read and you have to sort of like Express you know your enthusiasm and in in in a kind of a manufactured way but I have the same reactions Tom I love this book I thought it was great it was a lot of distilled wisdom and in some ways I feel like I'm kind of like your target demographic you should just say as a matter of personal history yes I feel like this is this is a kind of like a military unit buddy that I'm getting back together with because the process of we both work together on getting Google into China that was I have to say one of the most I think one of the most difficult business problems that I've ever seen teams tackle it was incredibly stressful and difficult and then you just left me there that's true I mean I don't know you get that you get the home leave you take the home leave but so I'm gonna take this conversation a little bit out of the order of the book and what I want to do for the for the benefit of the audience in particular is to start off with a basic understanding of what we mean by AR let's talk about the technology and a particular can you help us understand what it was about 2006 2007 2008 what is the big thing that changed sure that has made this technology that you've been working on basically your entire adult life now a significant force not sure what's really real pleasure to be here I've been working on AI for 38 years since my sophomore year the first 33 of which ended up with absolutely basically no output because because AI was premature and and two things happen in the last five to ten years one was the invention of a technology called deep learning and what deep learning is is a away for a electronic brain can take huge amounts of data and given objective functions that is you tell it to do something it's humanly created objective functions it can train itself on massive amounts of data and reach superhuman performance but with a couple of caveats one you have to have huge amounts of data and two it works only in a single domain and can across domains and three you have to tell it what the right answer is so that it can learn so feed it a billion faces and tell how the system each one who is Andrew John and so on or feed it a lot of speech or feed it a lot of Amazon clicks and which ones led to buying which ones didn't or feed it a lot of video taken from a car and have it learned when you should turn left or right so it's outcome based tagging that gives deep learning huge amount of feedback for it to learn too do something for for the environment for a situation that it hasn't seen before so unlike what you would into it the human does not go in and say okay recognizing Andrews he's got these glasses they're black and they're round the shape like this you don't do that at all you just show enough pictures of Andrew and let the system figure it out so the magical thing is the thing kind of programs itself of course you need some AI scientists to tweak things and that it achieves superhuman accuracy so we we've seen this applied to alphago beating the human world champion it's we've seen it apply to a face recognition face track engine systems can recognize three million people none of us can probably do three thousand speech recognition is now exceeding human recognition accuracy and then recognize recognizing a cancer certain types of cancer MRIs better than radiologists so this will continue but keep in mind it only works in a single domain cannot cross domains so so one big thing that changed that enabled what we now understand deep learning to be able to do is there's a lot of computer power computing power in the world that is networked and very cheap we're a second thing is that computing power and the proliferation of sensors in our phones in our lives in many different ways produced oceans of data yeah a third ingredient though is a technique known as a mural net you helped us understand how a neural networks and what it is that was new and innovative about it yeah so neural nets are the center of deep learning deep learning is basically a way to train neural nets so think of neural nets as having it's a gigantic of it as a gigantic spreadsheet like thing and you feed a tons of data and then you click a button an answer comes out okay so just like you do your you know quarterly reports you feed it all the salaries and income and profit and all that click your EPS comes out the difference is you actually have to program your Excel spreadsheet this deep learning says this is an Excel spreadsheet that requires no programming just feed it enough data and tell us each case what the answer should be and it figures out all the middle magic and deep learning is deep in the sense that it has thousands of layers and there's a mathematical algorithm that will teach the system to train itself and optimize the outcome so if we want to do a face recognition system we will feed it a lot of faces with tags this is Andrew this is John and then the system will continue to iterate until it feels like it can do no better and when it's done it it's basically it's optimizing on the objective function maximize human face recognition rate so it's directly maximizing something that the human can tell it to do given the tagging also I want to comment on the data and the computation I want to give an excuse why my PhD thesis didn't change the world I did my PhD thesis in 1988 it was by far that at the time the best speech recognition system that existed it was so good that people didn't believe me I had to publish the open source code and all the data to prove that it was actually real however and also I had a huge amount of data I had the world's largest database in 1986 and it was a hundred megabytes your five songs right but back then that costs $100,000 for my advisor I mean that was worth two houses in Pittsburgh where I did my thesis so now you can imagine how much more day that we have and my speech recognition companies probably use a hundred t so now that's a million times more computation similarly a million times more storage similarly computation probably went up just as much and and it was really the combination of a lot of compute a lot of data and this deep learning algorithm that made this breakthrough possible invented about ten years ago starting to see traction about five years ago and now it is the hottest thing everywhere so let's take the example of a bank loan officer let's talk about how AI might replicate or supplant that function so a bank loan officer gets a loan application and then using human judgment with probably a rule book tries to make a decision about whether this is a good candidate for a loan yeah I call that situation actually we invest in as such a company so I'll describe the company the company also takes a lot of inputs but the input is a lot more than what's filled on the forms it requires a form your address name I associate security number but it but in the case of the company we funded it's an app for a loan and you download the app you fill out the things that you would have filled out for the loan app and it also asks for your permission to transmit up information from your phone but but not to worry too much it's at the same level that Android allows Facebook snapchat Google YouTube to take nothing more nothing more than that so it takes all that information and and it's fed into a network that was trained basically millions of people who've previously used the app and borrowed the money and there was an app rather than teaching abstract concepts on this is trustworthy it is trained on the very facts whether you return the money or not so millions of people let's say a million people borrowed money let's say 900,000 returned the money then the system learns we want to lend money to more like people like those 900,000 the 100,000 who didn't return the money defaulted ran away because it's just an app it train don't lend more money to people like that and then the system basically determines trains itself to minimize default rates so one of the interesting things about the way AI works in that context is that factors which to a human would be ludicrous to take into consideration how much battery life is left on your phone let's say what day of the week it is what a time of day you applied what altitude you're at who knows things which are absolutely not causally related to the outcome that we want might actually prove to have what you refer to as weak features in other words there are strong indicators weak indicators and what the AI can do that no human can could do is make effective use of those weak indicators exactly I mean we think they're not causally related but actually they are otherwise AI would have ignored them so all the things you transmit up the phone are fed to the deep learning and when you have a million pieces of data the system will learn what's causally read and what's not what we think is irrelevant so as an example the day of the month is quite important and we I can explain to you why the reason is if it's close to a pay day then then it is likely to be okay because you're getting paid soon to pay back if it is just after a pay day it's likely to prove to be problematic because users are out pay why do you need a long so when there's your pay day well it can infer your pay day based on all kinds of things the type of job you have where you live and things like that so it's all magically in the in the network it's not absolutely right it's wrong some of the time but these week features and then my favorite one is the battery life you would think it has absolutely no bearing on the person's trustworthiness but apparently it does if you think about it it conceivably I don't know if this is why conceivably people who keep running out of batteries are not as conscientious and trustworthy but also keep in mind there are 3000 features battery life is probably one of the least important ones but nevertheless the system takes into account 3000 things and how they correlate with each other to come up with the maximum result so it has strong things like income rental savings whether your own properties and so on and then has all these weak ones it adds it all together no human can possibly do that I just like to point out that my battery is it's 69% ok ok somewhere so here here are some oh yeah thank you very much I will definitely pay back tomorrow actually only in the United States can I do this in China we have no cash maybe well yeah if you just wanted well you want to get me Chad me yeah Olly pay me that's ok we can get to that they're coming up or should I explain that we're gonna get to the China stuff in just a second ok I want to do one more follow around so we're sort of setting the table here like what AI is but crucially part of your book is about what AI is not and so one of the you know fun things about growing up in the 70s and 80s was this you know incredible diet of science fiction and we got to grow up alongside and obviously Silicon Valley people are never prone to kind of like fads and groupthink we're all very individual thinkers with our own ideas but it does seem to have been the case that lots of people in the valley in recent years and I won't name names but Elon Musk is one of them have painted a very dire picture of the implications if we take the kind of AI that you just described the neural nets that sort of like bind together massive datasets to derive correlations and be able to answer questions if we project that forward a couple more technical generations of financial investment growth and advancement they talk about either an event horizon where artificial intelligence overwhelm human intelligence a singularity where computers will become smarter than people your book argues that that is a flawed conception why why is that not likely to happen right the idea of singularity is basically based on when you read newspapers are we not seeing rapidly more and more news about AI doing go doing solving cancer beating humans at speech recognition the you know the past year has been accelerating news and that if you believe that accelerating news are correlated to accelerating technology breakthroughs then singularity means exponentially one day you'll suddenly wake up and realize you know a year ago there was one breakthrough half a year ago there were 10 breakthroughs last month there were 100 breakthroughs today I wake up and I'm controlled by by by machines but that idea is well I think you know it's but that's the nature of Exponential's but then they the the flaw in that argument is that there's fundamental technology as well as applications exponentially growing but actually the underlying technology is just deep learning and they're being exponentially or rapidly increasingly adapted for different domains so we're seeing news like that but underlying is just one technology is like if I were to invent let's say we didn't have electricity and suddenly magic happened we had an electrical grid and you're gonna see you know electrical iron an electrical computer electrical cars go up like that but it doesn't mean electricity is gonna take over the world so technology wise deep learning is the single huge breakthrough in the fifty sixty two years of our history and it was invented ten years ago and there hasn't been another invention of equivalent impact so I think to project such exponential increase is not consistent with the data that that we've seen and also just if I if you go and read all the papers now no engineer would have any idea how to build solve any of the problems that the systems have today I mentioned it's limited in that it's only a single domain and it has to be objective answer so things like common sense cross domain thinking strategic thinking creativity are definitely absence the ability to do complex planning is absent and certainly self-awareness and emotion where know where to how to program that and each of these will require one or two or three breakthroughs so where many breakthroughs away I don't I don't exclude the future possibility there might be a day when machines exceed us but we need to see a lot more progress and we're nowhere near I think we're I think I'm pretty confident in saying in 20 years 30 years there would not be singularity free more on behalf of the audience so let's turn them to the China part of the story so your your book makes one factual observation that I think is very important for Americans to get it will not be news to many of the people in this room but let's talk about it and then it also makes a claim so the factual observation is that China is no longer a copycat economy it is no longer a country that grows its technology sector by looking at the rest of the world and then doing kind of pale knockoff versions for China the claim is that what China has become is a place uniquely well positioned to be able to lead in at least three of the four areas of AI yeah and generally to drive the world forward from the perspective of AI and so let's talk about those two things in sequence so first of all China's internet sector is my it you refer to it as kind of like a unique ecosystem a distinct ecosystem different from the United States and no longer driven by copycat innovation could you tell us a little bit more about what it's like there yeah sure I I think the stigmatism about copycatting is causing america's to not see that you can begin a copycat and become an innovator just because in the u.s. that never happens it doesn't mean it can't happen after all when we learn music and art don't we start by copycatting and then with more practice we come up with our own ideas that's what happened in China 10-15 years ago there were absolute copycats there is the you know Google of China Amazon of China and so on but as the market rapidly expanded and the massive capital flowed into China because everyone saw in this very fast market where mobile usage was increasing it's a great place for investment and in fact the VC funds like ours have produced outsized returns so with massive amount of money feeding into entrepreneurs who used to be copycats imagine what happens right you have maybe let's say you have another great idea let's say Groupon this happened about eight years ago and a bunch of Chinese entrepreneurs said well let's do a Chinese Groupon and then but but we've learned a lot we can innovate new ideas and make it better on top of Groupon so in order for you to succeed there are the you are in a class of five thousand Groupon copycats so that's what happened eight years ago and there's really only one way to win and that is to make your version of Groupon uncopyable and that from that evolved China's formula for innovation entrepreneurship and value creation so how does that work I'll give you an example the American group are now worth a couple of billion dollars in market cap maybe a lot less in enterprise value but but the Chinese version of Groupon is now 55 billion dollar IPO and what happened it's not just them how large the market is is what the entrepreneur has chosen to do when you're surrounded in environment of tenacious entrepreneur who are not afraid to copy you must build a very high wall that no one can climb over so matron CEO Wang Qing managed to to basically change the way Chinese people eat so it's a lot more daring than a Yelp or a group on those nicely added to the way we eat but one Qing this matron CEO decided he would change the way Chinese people eat he wants people to do food delivery food ordering from home and instead of cooking or going out so he found out what it would take to do that if if you could deliver to let's say 500 million of the 800 million Chinese Internet users within 30 minutes from the time of order including cooking the hot food to the home and that delivery cost no more than 70 cents then he could break even so that's what he was faced up against most entrepreneurs that's crazy in the 70 cents how even with Chinese salaries an urban density how do you do it well how did he do it he just chipped away at it over five years every month chipped away one or two cents three or five cents eventually got to the 70 cents and the way he ended up was he had to manage a an army of 600,000 electrical moped riders and he had to because they're cheap well 70 cents per delivery so the salaries have to be low yet the service has to be good so how do you train these these people and then also the mopeds run out of battery how do you replace the battery and then there's a routing algorithm at AI of course so building this kind and also you're burning VC money right all of this stuff is burning VC money when I told you earlier about the loan app of the million applicants and the hundred thousand defaulted on the loan so how do you get that data VC giving them a money and it gets lost and when you get trading data then for the next million you don't lose so much so that's what once she did he raised several billion dollars to perfect this system and eventually he built the amazing uber like delivery network for food to the home and he shaved it down to 70 cents after years and years of trying what was the downside and risk if he haven't succeeded suppose he could only get down to one point 700 dollar seventy per order well he has 25 million orders a day so he'd be losing 25 million dollars a day had he not done that so so this is the Chinese entrepreneurism it is the tenacity the work ethic the competitiveness the winner-take-all and do whatever it takes to erect such a high wall that your profit becomes uncopyable now you could say that's not innovative but actually if I offer the service to you you take it right you say wow that's amazing I didn't never realize I could get the service so it is an innovative product at the end but they wasn't derived by a lightbulb going off it was derived by incredible hardwork risk-taking tenacity you know entirely apart from the AI parts of your book actually the chapters which describe the kind of innovation economy in in China make this book you know worth reading in and of themselves I love that part of the book the M the contrast that you draw is between Silicon Valley companies that valorize sort of originality of idea frown upon copying in any way shape or form and where the VC is keep their eyes especially attuned to companies that will have tremendous amounts of leverage in other words the thinnest possible company that will apply a technology to solve an information problem and create a ton of value so uber for example an app with a back-end and some customer support and no cars right by contrast you show how DeeDee in China you know is buying gas stations and buying repair facilities where the company that does short-term apartment listings in which I'm forgetting is providing services like cleaning and restocking and so in China the hyper competitive atmosphere means that and different labor dynamics mean that to build a mode around your business you do the gritty grungy brick and mortar business type activities in order to build a full vertical stack right Silicon Valley companies don't do that and so very often when they come to China trying to replicate the American model that's worked over here they get crushed we've seen that with Amazon with eBay with Yahoo with Google to some extent with Microsoft and so one of the things then that I think is interesting is you draw a contrast between market driven entrepreneurship and China mission driven entrepreneurship in the u.s. you say a little bit about like the roots of that kind of culturally and why it is the Chinese entrepreneurs are so good at that yeah well China is a market where user need becomes becomes the source of working hard in innovation solving the user problem and and the Chinese entrepreneurs come from a background imagine single child even though that policies change but people in their thirties are likely to be a single child who has huge expectations from his or her two parents and four grandparents that he or she will take care of the six of them and that they tell him you're the first generation in the last ten or twenty generations that has a chance because you went to college or you went to us and got a PhD now you've got to go do this you got to be the jack ma you got it you got to do this there's a pressure there's also the potential pain about 40 years ago basically opened up China for market economy and he said let's some people get rich first right that was in sharp contrast with the system before and now you can imagine 1.3 billion people all want to be that some people who get reversed fearing that if you fell behind you might not be the fortunate so it's under those conditions that's people have the work I think they have the tenacity they have and also the Chinese education is more about rote learning so a Steve Jobs like let's say someone had Steve Jobs DNA what was born in China is likely that they would not be given the freedom to think different and drop out of college and start the company instead he might become an outlier so it is a completely different culture and background that led to this this type of entrepreneurism so in China then we've got these these market driven entrepreneurs these are some of the ingredients you lay out another key one is mobile-first users so basically the country had desktops and laptops but in very small numbers relative to the population the smartphone especially the cheap Android smartphone really becomes the default computing device you have a single super app WeChat that pulls together many different services provides an in ecosystem dense cities cheap labor a mobile payment system a government-backed culture shift that you just described so all these ingredients put together then make China a place that has entrepreneurs and incredible amounts of data and building on that is a Chinese advantage in artificial intelligence exactly so we talked all about entred the book we talk I talk about the entrepreneurship and the companies build and the culture because to build a great AI company at this point think about all the things I told you about AI what do you need well you need a lot of data and China has so much data so in the age of AI often talk about data is the new oil in China's the new Saudi Arabia so China has all the data not only more people for more depth because so many services are digitized you're writing a shared bicycle ordering food and so on and so forth so a lot of data China has entrepreneurs and AI gets better with data so the entrepreneur process is you start with an idea you try it out if it doesn't work you pivot but with AI you pivot with the data so if it works you got you get more data then it works better you get even more data so that's what happened the long example I gave right first they have some handwritten rules that were had 20 percent default rate lost a bunch of money then they train the system that had 15 percent default rate lost a bunch more money then they trained up another system based on that data then it has 10 percent default rate and now the default rate is 3 percent and now it's making massive money and it comes from iteration of not only the product to be a better user fit but also the gathering iterative gathering of data to make the AI more robust similarly if you were to do autonomous vehicles if you launch faster you collect more data that's arguably to your favor so the Chinese entrepreneurs are tenacious their work very hard they are very fast they collect a lot of data they get going they don't need a final vision they first get something going and collect more data and let it blossom so a last point I'll make is that because deep learning is the technology it's been invented for 10 years people there are a lot of people who know how to use it it's no longer in the laboratories is no longer held in the minds of few like in the days of Manhattan Project in Enrico Fermi he had on here only he and a small number of people I can do this ai is known by millions of people throughout the world and China has so many engineers and students eager to get in the entry barrier is not as high as you think especially unless you're doing autonomous vehicle or something really really fancy so China so AI has shifted from the era of discovery and invention to the era of implementation one of the nice I think analogies of you that you use to that shift is to electricity so electricity harnessing electricity is a major discovery yeah and then the economic growth for about a hundred years is people finding ways to stick electricity and every single part of human life right and that's not Nobel prize-winning scientists in your terms of the book those are tinker's that's right an AI is faster than electricity because you don't need to build an electrical grid there's already cloud Amazon Cloud Google Cloud Alibaba cloud that have a I on it so tinkerers can start to tinker right now even only after two or three years after the technologies become available on the clock so the data is an advantage the people that China has and they're there sort of work ethic orientation training and so forth are an advantage another sort of third pillar of advantage that you call out is that for some of the kinds of benefits that we want to be able to derive from AI a top-down government that is willing to absorb risk with a long term view they'll have an advantage and so for example in the case of autonomous vehicles in this country people freaked out when there's an accident and you see a lot of reaction and there's a both a legal culture and maybe a kind of like a a risk-averse maturity in our in our culture that says hey I thought of us vehicles are gonna have to be like perfect before we're gonna let them on our streets whereas in China the kinds of infrastructure investments that could enable AI can be both dictated by the government and short-term risk can be absorbed in the interest of what is clearly going to be a safer Street when autonomous vehicles predominate right absolutely I I think if one wants autonomous vehicles to be perfect before they can be launched then there will never be autonomous vehicles because as I described data is gathered beautifully so more data improves the algorithms gives feedback to the scientists who build a better products it's got to be an iterative loop now we obviously have some minimal requirement whether it's in u.s. or China if the if the vehicle is drives worse than people we should not let it out on the streets but if it's better than people well I mean one could debate whether it needs to be one percent better than people or ten percent that's a reasonable discussion but if you want it to be perfect then that country that demands perfect autonomous vehicle will lose in the technology technological race because it's the iterative approach that will work but there's needs to be some degree of minimal responsibility that's kind of first point but the other way is I think Chinese government's actually let's the private enterprises invest in companies to build the products but Chinese government will go in to do public infrastructure investments then no private company can do for example China is building a new city called Xuan which is the size of Chicago designed for autonomous vehicle the sent the downtown of Xuan has two layers pedestrians pets and bicycles around top and vehicles on the bottom so that will eliminate the likelihood of car hitting pedestrian the worst kind of accidents and George young province is building a highway with sensors designed to hint to autonomous vehicles to avoid accidents the city of Suzhou this came out after my book came out with a two-layer ten square kilometer space where on top is are the human drivers bottom is the autonomous driver it's kind of cleverly designed because you know one of the issues including the Tesla accident was a result of lighting you know AI with this camera may not see things in the right color because of lighting so if you put the essentially put the autonomous driver in a basement that's consistently lit then there's less variation you can get it going so almost you're almost seeing well first we're seeing Chinese putting huge amount of dollars into this infrastructure building and secondly we're sort of seeing local governments are sort of like entrepreneurs trying new ideas well-informed entrepreneurs we don't know which one will be the best but I think that's the kind of spirit we'll see moving this forward there are some people who would still say is this government giving help to dissident to advantage China against other countries I would argue it's not dissimilar from President Eisenhower who put in the interstate highway which is the public infrastructure that pushed America forward you know as I feel compelled as an American to at least point out that there is like it to take the Jinyoung example you know there is some risk which your book acknowledges by the way that you know for example if jae-joong builds a sensor Laden radio frequency enabled highway will it be the right standards will the sensors be out of date in two years you know who knows as many of the companies absorbed and adopted or not there are some risks to talk down yes let me just challenge you on on sort of one thing and then I want to reserve a couple of minutes to talk about the jobs threat that you that you identify but let me let me press you on one point Chinese companies have been notoriously bad at succeeding outside China and one of the things that your book points out is that that the the core advantages that it has to produce better AI produce better products produce better and faster more efficient uses of AI to serve people is it possible that that the advantages that China has over American companies operating in China will be handicaps when those Chinese companies try to for example understand a Belgian loan market is there something about AI that makes it so synthetic and abstract that it's easily exportable or do you think Chinese companies will have to really up their game struggle learn how to succeed in Brazil or China because the things that they've learned how to do in China are just as inapplicable as Brazil as you know you know eBay was in China ten years ago right so first China is such a large market most Chinese companies just focus on the Chinese market it's too much extra work and opportunity costs to go to external markets so so far we'll largely see China just in China and the US companies go elsewhere and some markets accept US technology some develop their own but I will also say this is changing because after all the China China market saturation is closed so top Chinese companies are looking at how to expand but they've looked a bit but but they've studied the American cases where the American single platform approach didn't quite provide the right perfect answer for all the markets and China experienced it itself and it also tried to take you know WeChat abroad Baidu search abroad not with a lot of success so I think the collective recognition in China is that the large giant Alibaba Tencent Baidu DD hotel they would not necessarily expand themselves overseas but partner overseas so Alibaba is investing externally Southeast Asia India and Dede is actually investing and when they invest they also inject technology for example there are some countries which have local come local share writing apps that are losing to uber but when Dede investing in them they also gave them the app technology the look and feel the route finding and the AI behind it to help the so-called local insurgents have a chance against the American leaders so I think that's going to be one approach another approach is there are still some purely digital services that don't require local partnership and companies like by 10 sort OTL are expanding to other markets so I'm seeing an increasing number of Chinese companies whether through investing in injecting technology or going directly or actually copy from China that is Chinese companies are awkward when they try to copy an American technology to Brazil but copying a Chinese technology by another company to Brazil is a lot easier so because it's well known as inherent use it every day so generally speaking I think going five years in the future I think America will continue to dominate you gave Belgian his example I think US will continue to dominate Europe basically developed countries US Europe english-speaking countries Japan I think China is going to have a really good shot at Southeast Asia possibly India probably Islamic countries include special e Middle East and definitely Africa so we're going to sort of see the world in two parallel universes with Chinese expanding beyond mainland China but China's expansion to other countries will not be as strong and ownership as Google or Facebook has in in Europe it would be through some investments partnerships technology injections so your book ends on a on a fairly like dire note I mean a hopeful note but a but a fairly dire note about the challenges that is about to hit and that challenge is that AI products are going to be able to do lots and lots of things that humans have been doing better than those humans cheaper more reliably more accurately and you cite a range of estimates for how many decades we've got before the big disruptions hid and how many people's job categories will be replaceable you're on the more pessimistic or optimistic I'm not even sure which way to say it depending on whether I'm speaking for the Star Trek computer or myself but you believe that that that much of the analysis has focused on tasks specific jobs and figuring out what AI will be able to do better you believe that we are under estimating the industry-wide disruptions that can happen where a new technique like the loan company can kind of completely replace the way that an industry has been doing business by applying a and so there's probably a lot to say about that I want to save some time for questions but part of the way that you analyze that problem is different because of your personal experience with lymphoma you know and I wonder if you could just tell us a little bit of the story of lye show how that hits you and what that taught you about how we can do that sure so first let me talk about the why I think there is an issue and then how we might solve it with my lymphoma story in the middle so why why do i why am i more pessimistic that more jobs will get this place because as we discussed AI is about a single task intelligence system that can take care of one task a repetitive routine task but if we think about not people in this room but on the whole earth what percentage of people do single repetitive tasks or scripts of multiple tasks each of which is repetitive and routine will probably come to the conclusion that's a fairly large number and I'm not that this believer in human may I symbiosis I think there are creative jobs professional jobs jobs such as scientists colonists writers lawyers that will become better because of AI the AI plus the creative or the professional will make do things better than either can do I'm a huge believer in that but after all that is definitely not most of the world's population so a number of jobs that are now created non-strategic and repetitive routine is significant and as AI gets to do them business people will be forced to make the trade-off do I retain my employees and risk getting killed by my competitor or do I automate and then deal with the workforce reduction so that is the the premise and then where with this 50% or 40% of the people go what jobs will they take on is the question that's the first part then the Lymphoma story was five years ago I have been sort of a machine workaholic working extremely hard maybe my job is not so routine but it's kind of machine-like and repetitive because I wanted to make a big difference to the world and I became a workaholic and I would wake up twice a night to answer email when you were emailing me from Mountain View don't think I didn't notice I would wake up automatically at 2:00 a.m. and 5:00 a.m. almost every night and answer all my email so that my American colleagues were feeling I was responsive and then my Chinese employees would feel that they have to work hard too that that is the Chinese work ethic we talked about that's leading to China's rise in technology and AI that's I think what a lot of us in the room as well as a lot of routing job workers feel about alcoholism that the meaning of our lives is equated with our job and it was after that basically the week after I found I had lymphoma after I've had my going through the face of denial why me making my deal with God writing my will then I came to realize that my life was had my priorities all upside down that in the whatever remaining days that I had continuing to work was no longer something I wanted to do that much more important was loving the people I love giving back love to the people who loved me and pursuing things that I'm passionate about it's not about working harder and making more money becoming more famous and and it was that realization and that process that got me to think so when all the aren't these routine job workers in the same state that I am in that we were all brainwashed by the Industrial Revolution value that our work equals the meaning of our life and perhaps AI is a wake-up call for us to realize that there is something else maybe it's about love compassion empathy human to human relations and that if I could imagine our maker could be very frustrated with us that after thousands of years of evolution we're still stuck here like rats running on the wheel doing the routine same jobs every day and not spending time on what we're passionate about spending more time with the people we love thinking about the meaning of life but just thinking it's all work work work so maybe our maker is so frustrated that he threw a eye at us that to take away all the routine jobs so we have time to think and to love and that also gave me a possible resolution to the job losses that is are there possibly enough jobs that are compassionate or empathetic or people-to-people interaction so as to retrain and absorb the workforce that might be displaced so if we think about jobs like elderly care nurses nannies these are the perfect compassionate jobs empathetic jobs and we sure need a lot more of them think about elderly care people over 80 need five times as much as much care a lot of AI scientists are trying to amend robots to take care of older people but think think how how mean that is would would you when you get older or your parents really want that I have an entrepreneur who built a robot to take care of elderly and then the only function that was used was customer service the person would click on and say how come my kids aren't here you know or let me tell you about my grandkids so elderly people don't want a robot they who hunts people and then there are a million elderly care jobs not filled in the US for the simple reason that it's not paid well so if we believe that AI will generate all this wealth and some Silicon Valley people which think we should give $20,000 to everybody and be done with it is much too simplistic I think why don't we not give it to everybody why don't we take that wealth whether it's taxed or however generated and subsidized and subsidized elderly care subsidized teachers to be to increase the student-teacher ratio as AI takes over the routine parts of a teacher's job as AI starts to diagnose for the doctor they can be more empathetic compassionate they might need a different kind of training we can afford to have 10 times more teachers ten times more doctors and there would be a lot of jobs you might think well how could someone do a routine job be trained to be a doctor well in 20 or 30 years where the diagnosis is all done by the AI the doctor is really just the human interface of dealing with the patient teasing out the issues family history making the patient feel better and giving the patient the confidence and for that you don't need a 10 year training maybe more like a nurse practitioner so if we think that way and also teachers why can't we actually are there will be a 60 minute segment on our work with AI and education in either next and there at the Sunday after that it's about a lot of the teacher's job is routine grading homework or giving exams giving the same lecture over and over again if we take that out and let AI or Mook take care of that maybe teachers can become more mentors on one on one and giving help and if that happens we can have a lot more teachers and if that happens maybe we should pay people parents would choose to home-school their kids so these are probably a lot more meaningful ways to spend all the wealth that we collectively make in AI to make the empathetic jobs more meaningful more better paid and also to help retrain that displaced routine workers who can move on to those well [Applause] [Music] firk addendum let's let's take some questions for the audience and while we're raising hands and delivering a mic to somebody I'll just note that the policy wonk and meat you know my ears perk up when I hear you say that because to meet what that says is we're going to be we're going to need to do steeply progressive taxation of people that are accumulating an ever-increasing share of wealth in order to subsidize socially good activities for which there is not currently a viable private employer which is very interesting yeah so uh so let's just go with a hand that I see here through the lights there's a mic coming for you right here if you will pronounce your question into the mic yes you say that there's nothing to fear about computers taking over because you're there it hasn't been a breakthrough what I say is that we don't need the breakthrough what will happen is the computers will evolve we already use genetic algorithms and probably more powerful algorithms I'm not a computer scientist but the way life evolved by just simple chemicals developing ways of self reproducing the computers eventually we'll be able to do that it just requires more computer power and then take advantage of the facts of how addictive humans are just look at the street how people are totally unaware and the way food companies develop food products to addicting eventually computers will be able to do that without an additional breakthrough they will create their own breakthrough by evolution what do you think ok I I don't agree I well the current computer algorithms are just dumb tools that mean they're really smart in that they're better than us in making loans and all that stuff but we give it the objective function on which they optimize I think you can be imaginative and say can there possibly be some day that they can evolve or self replicate but that day hasn't come and it will be a breakthrough where someone can cross the line and say this program is now reinventing itself in getting smarter and smarter you know media isn't helping this at all you've probably some of you probably read you know Facebook Labs came up with a new human language because the robots are talking to each other and they're evolving but but that's just a bug so it's second I'm sorry second question right down in the front row those of us who remember Star Trek we used to say that space is the next frontier but for we Earthlings the next frontier is mapping the brain and that's what's going on today beyond the behind-the-scenes money is being committed to it on a long-term basis and the interplay between artificial intelligence and mapping the brain and what we will discover as possible which we know very little about today presents us with a child an ethical challenge a cultural challenge an institutional challenge which will go to supplement a lot of the problems with artificial intelligence but which will but will cause more problems because we haven't got the guidelines and we won't do that for a long period of time but there are people in this country a young the 23 euros the 30 year olds the early 40 year olds who are working progressively on this issue of mapping the brain and they're coming up with not very much at this point as you did 20 years ago or 25 you but it's there and it's going to be done and it's gonna have the greatest effect on what we did just as we did with our blood cells and our genome this is going to be the next frontier and we'll challenge the interplay between the human being and the machine and has already problems that are discovered that I could discuss on that basis at this time but that's your next frontier and it's dramatic and it's going to be consequential thank you I think that is a possible scenario but as you said we don't yet know how mapping to bring an AI will or will not need to break through so to the extent it does there are big issues personally I would say CRISPR is probably a bigger concern that's similar and there are many other areas I understand yeah I think AI plus other things can lead to all kinds of danger I was talking to Andrew when I talked Andrew earlier I was referring to this super intelligent cyborg emerging through singularity as an unlikely or impossible event I do think regulating the combinations of you know brain breakthroughs CRISPR breakthroughs or if someone comes up with what the previous gentleman said self writing programs when those breakthroughs happen they may raise other threats or challenges also think a security is a big problem people could hack into autonomous vehicles and turn them into a killer weapons so I think there are lots of dangers beyond the job one I think they haven't quite materialized but I think we should get ahead of the game and start to study them okay so the enterprise computer not likely to happen c-3po and r2d2 still a long ways off is AI gonna help me get my jetpack okay you're my jetpack I still don't have the jetpack that I grew up expecting let's do a question over here on the left but you know there's already holodeck and all that stuff right I mean when I go to work my face opens the door right and then we're talking to alexa more and more so a lot of those non super intelligent AI things in Star Trek are happening autonomous vehicles are happening so I think they will you mentioned the complimentary sort of capabilities of the US and China in AI discovery is American the American Forte implementation is Chinese are the prospects of cooperation in the current environment are sort of you know it's unthinkable but if we set aside the politics just from the perspective of sort of you know the theory of comparative advantage I mean is there a are there some industries and sectors where the two countries can sort of you know leverage their complementary skills and cooperate so not military but say healthcare or some of these other areas you talked about these qualities of compassion and empathy and understanding I mean is there a scenario of the two taking advantage of their complementary strengths and cooperating yes I agree I think either at the theoretical level it's extremely complementary if we pick some really hard problem I think the simple prop honestly I think for the simple AI problems the bank loans and the big day that the internet the Chinese companies and run really fast they don't need much help but for the tougher problems you know cancer autonomous vehicle and things like that if somehow we could construct the best of the American top researcher and the top fastest of the Chinese implementer we can deliver solution is much faster but that's theoretical in practice you know export control Sophia's and so on I would probably make that an impossibility but I perhaps we can have hope that in healthcare and education the two countries can look beyond whatever differences there are in the trade dispute and find a way to work together I think in terms of not working together but just watching each other I think is another way to build complementarity part of the reason for writing this book is to show the Americans who want to build a company but aren't Steve Jobs this is really saying there is another formula another way to build a company and study it right and I think in terms of the compassionate jobs I think all countries can study each other you know Korea in gifted education in we can study Japan and Switzerland for craftsmen as a profession Canada and the Netherlands in volunteerism so I think in terms of how to create the compassionate professions all countries can love from I can learn from each other and not necessary to build one entity it's probably enough to just kind of watch watch and learn let me just pick up the moderators prerogative and press you on one thing one of the so that that optimistic scenario the one where you know the different approaches of the different nations can can be brought together countries can can work together much of the I think wave of fear that people have about AI has something to do with the sense that people in positions of power on the world really have not proven themselves able to able to be trusted with the kind of power that these technologies can bring you know thuggish nacinda scungy zero-sum game mentality xenophobia racism misogyny people with those kinds of values given the power of AI to make decisions about others leaves you fairly fearful I've been reading a lot recently about kind of military uses of AI and there's the thousand drone swarm scenario that you know under any radars ability to detect can send a force into a country and destroy a base missile silo a plane assassinate people do you see some reason for hope in in in a eyes or maybe it's a this lump somewhat differently people's ability and our ability to educate people in values like love and commonality of purpose and and loyalty and so forth can we get the world into a position faster than a I can get into a position to do good with AI faster than AI will get into a position to be misused i I think it's the only thing we can do but I'm not sure it's less sufficient to to avoid any of the problems that you mentioned I think doing all those things certainly will help and I think countries I think actually we almost should view as a crowdsourcing problem because the problems are too many and it's not just a I say I and crisper and cognitive science and all these other things the problems are too many we should almost crowdsource and let all the countries try different things and then learn from each other I think you know obviously UN is not powerful enough to regulate the whole world in each country every country is now somewhat aware of some of these problems and you know for example Europe has come up with GDP our for privacy protection while I don't think it's a very good solution it's at least a step forward then we can learn from it I don't really have any good answer to this one if I did I put it in the book maybe maybe it's my next book all right baby we write the next one together okay thank you guys we look at three things that a I cannot do know if you mentioned earlier cross-domain complex planning self-awareness emotion as AI begins to approach these capabilities do you think the US and China would continue to advance in the way they've in the past the u.s. taking the discovery about China taking the implementation route or do you think the rows might get switched along the way or shared okay so first the premise is that we would make breakthrough on these areas and I think in some of them I'm somewhat optimistic in cross-domain thinking strategy planning I do think will make progress things like self-awareness and emotion I think it becomes a philosophical problem whether we believe the sanctity of our soul or that human can be complete completely replicated so we're not going to get into that discussion here but but anyway answering your question about the advances I would fully expect us to say at the forefront of research for the foreseeable future and I think if there were more funding if there were more open immigration for top minds to the US that advantage is going to be very hard for any country to surpass because it's a combination of already existing great universities and research systems it's a country with the culture of free thinking and it's a magnet that attracts a disproportionate number of high IQ people to come to the US and many of whom will stay so it's a self-sustaining ecosystem but in order for this to work that the pipeline that people coming in can cannot be cut off I think that's kind of the long-term danger there might be but it but even if small mistakes happen I think us is bound to be ahead in the area of discovery for you know one or two decades to come if not longer does anybody have a question that can be answered in three minutes all right let's go to the just because of the way the lighting here is I'm going to go with the hands I can see a few nice to see you again I wonder if you could talk briefly about there's a lot of political noise obviously between US and China now with tariffs and other things and president Trump has said that China 2025 is a threat to America I think you do a very good job of translating the differences in between in China and and the u.s. in business could you translate that politically because I think last I checked you actually have more Twitter followers than the President of the United States oh no not anymore I never never did and never never did but yeah so about the China various China state plans I think there is a belief that the China States the China is one is the Chinese government is one entity and it just puts a lot of money in Chinese companies and gives them an unfair advantage against American companies and on top of theirs their IP issues but I want to clarify that I didn't study the 2025 plan in a lot of detail I study the AI plan 2030 plan in a lot of detail it's fairly well written it sets audacious goals but it in itself didn't give money to any companies China's AI so I can only speak on AI again China's entire success so far in AI has been privately funded we've created five unicorns none of them have received government funding I think there may be a new round that some of them may have it but I think up to now they've had their joint total market valuation is 21 billion dollar US dollars and there's no it's all privately funded in fact it's largely funded by American money you know our our LPS are American repentant funds and then we take that money and when you fund them so we're making lots of money for you guys but but but but what is the role of the government the government plays I think three very important roles the first is that those documents especially the AI plan sets a tone so once the tone is set a bank is much more likely to acquire to buy AI software and pay for it a local government is much more likely to build those two layer roads so that tone is important but it's not ordered down each city and each bank can make its own decisions the second thing they do to Chinese government does very effectively is infrastructure building which I've covered like in the smart highways and the third area I think is the general approach to what in the book I call techno utilitarian policy which means a little bit contrary to the gentleman in the front the Chinese approaches let these new technologies blossom we will watch them closely and when things don't look right we'll regulate it if things really don't look right we'll stop it and that is something I think a strong government can do as an example I was going to get to why I have no money in China China permitted to large companies to basically eradicate cash and credit cards over the last three years it's phenomenal in America there would probably be all kinds of lobbying disagreeing concerns about where the software companies can be trusted with the money or can only banks and credit card companies all that so government basically said hmm new technology looks like a good thing let's watch it six months later okay still looks good keep going then it took over and then there's no cash and no credit cards but it doesn't mean the government doesn't interfere in the cryptocurrency the government so that watches for a while say whoa wait a minute there are these village ladies buying icos that's not gonna work so they so the government banned cryptocurrency and ICS so that's the technical utilitarian approach I'm not arguing it's good or bad right and wrong I'm just describing it as an alternate way of policy and it seems effective mobile Internet and yeah but it's time for us to wrap up and then wrap up with just one question for for you to maybe speculate about so one of the I've invested in a couple of companies recently that have that are trying to use AI to try to persuade people to vote for Democrats and it's you know it's it's it's something that I think I need to do but I'm not thrilled about its implications for democracy broadly in other words the science of persuasion is built on a notion that we have kind of open deliberative discourse and that people can go through reasoned arguments I'm not sure that the kind of technology that uses AI to figure out how to precisely target messages based on non demographic characteristics like loyalty or a belief in equality or fairness or whatever I'm not sure that's so great could you speculate if the country wanted foster like kind of a healthy society a healthy public domain healthy public discourse how would that country be putting AI to work today gosh by the way a two-minute question ok I I think I for a country to think about using AI tools I think the country is better start first with using AI to listen to gather input because that seems a lot more innocuous than using AI to brainwash essentially kind of education uses of AI can produce people that can't just get better grades but also can be like better citizens is there some reason to think that those two things could be could be correlated yeah so we actually invest in a lot in AI in education and we haven't gone to the depth of using AI to promote creativity or breakthrough thinking what we have done is use AI to make students learn their normal courseware much faster so they have you know people students in China are very poor they spend a lot more time studying it's the same 996 crazy studying hours to get crammed into the desk school so the AI use has letting let them get good grades without spending you know until midnight every night studying so that gave them some time back so I think it's up to the parents who need to wisely give that few hours given back to the students to guide their kids into critical thinking alright and on that note let me say thank you to the Asia Society and you showed me a Thank You typhoon [Applause]

Channel: Asia Society

Views: 53,853

Rating: 4.888 out of 5

Keywords: video, asia society new york, Kai-Fu Lee, andrew mclaughlin, artificial intelligence, technolog, books, u.s.-china relations

Id: oNAFI3Lh97Y

Length: 71min 54sec (4314 seconds)

Published: Tue Oct 02 2018

Reddit Comments

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Author: https://en.wikipedia.org/wiki/Kai-Fu_Lee

Hosting organization: https://en.wikipedia.org/wiki/Asia_Society

Some reviews of the book, not completely positive:

• https://www.economist.com/books-and-arts/2018/09/27/in-the-struggle-for-ai-supremacy-china-will-prevail

• https://www.foreignaffairs.com/reviews/review-essay/2018-11-16/beyond-ai-arms-race

• https://www.washingtonpost.com/outlook/in-the-race-for-supremacy-in-artificial-intelligence-its-us-innovation-vs-chinese-ambition/2018/11/02/013e0030-b08c-11e8-aed9-001309990777_story.html

• https://supchina.com/2018/09/18/james-mcgregor-reviews-kai-fu-lee-ai-superpowers/

The book's theses:

1. AI dominance in the coming yeas depends on four factors: access to abundant data for AI to work with/train on, intensity of private sector AI-related entrepreneurship, quality of human AI researchers, and supportive government policy.

2. China has a clear advantage in 3 of these. The US has better AI researchers.

3. But even this advantage matters less if one assumes that near-term advances are mostly about implementation rather than breakthroughs. Lee spends some time explaining arguing that deep learning, the most recent big breakthrough, is big enough to sustain a lot of incremental innovation for the foreseeable future.

4. Lee downplays "existential" AI risks (contra Elon Musk, Bill Gates, Stephen Hawking, Nick Bostrom, etc.) and instead focuses on risks related to employment/inequality. He is firmly in "AI is coming for our jobs" camp--i.e., a pessimist on technological unemployment. He acknowledges that this is inconsistent with mainstream economic thought, and argues that the scale, speed, and skill-bias of AI separates it from previous big tech disruptions like those in agricultural productivity and electrification.

5. He offers policy suggestions tailored to four different categories of jobs, depending on how intensely AI complements and/or substitutes for the relevant labor tasks. He sees UBI as a palliative rather than a cure, but isn't necessarily against it as long as it isn't the only thing governments do.

6. He talks a lot about how AI has the potential to engender more love, empathy, and care in the labor landscape and society as a whole, and thinks policy can help make this happen. It's a bit vague in my opinion.

Obviously there are many books predicting the future course of AI, China, US-China relations, et cetera. Lee is aware of the diversity of views here and addresses common objections to his own.

It's a valuable introduction for anyone who wants a solid introduction to this point of view. It shouldn't be the only thing you read about it.

Also: It's fine to be bearish on China's economic rise or AI progress, but don't let that be an excuse for complacency. This is a huge issue, and liberal governments should take the possibility of technological leadership by a non-liberal state as a serious challenge.