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Kai-Fu Lee: AI Superpowers - China and Silicon Valley | Lex Fridman Podcast #27

Lex Fridman and Kai-Fu Lee on kai-Fu Lee on AI, China’s Rise, Jobs, and the Human Heart.

Lex FridmanhostKai-Fu Leeguest
Jul 15, 20191h 26mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 2:41

    Chinese “soul”: hunger, work ethic, and the legacy of tradition

    1. LF

      The following is a conversation with Kai-Fu Lee. He's the chairman and CEO of Sinovation Ventures that manages a $2 billion dual currency investment fund with a focus on developing the next generation of Chinese high-tech companies. He's the former president of Google China, and the founder of what is now called Microsoft Research Asia, an institute that trained many of the artificial intelligence leaders in China, including CTOs or AI execs at Baidu, Tencent, Alibaba, Lenovo, and Huawei. He was named one of the 100 most influential people in the world by Time Magazine. He's the author of seven best-selling books in Chinese, and most recently, the New York Times best-seller called AI Superpowers: China, Silicon Valley, and the New World Order. He has unparalleled experience in working across major tech companies and governments on applications of AI, and so he has a unique perspective on global innovation and the future of AI that I think is important to listen to and think about. This is the Artificial Intelligence podcast. If you enjoy it, subscribe on YouTube and iTunes, support it on Patreon, or simply connect with me on Twitter @lexfridman. And now, here's my conversation with Kai-Fu Lee. I immigrated from Russia to US when I was 13. You immigrated to US at about the same age. The Russian people, the American people, the Chinese people each have a certain soul, a spirit that permeates throughout the generations.

    2. KL

      Mm-hmm.

    3. LF

      So maybe it's a little bit of a poetic question, but could you, uh, describe your sense of what defines the Chinese soul?

    4. KL

      I think the Chinese soul of people today, right, we're talking about, people who have had, um, centuries of burden because of the poverty that the country has gone through, and suddenly shined with hope of prosperity in the past 40 years as China opened up and embraced market economy. And, um, undoubtedly, there are two sets of pressures on the people, that of the tradition, um, that of, um, facing, uh, difficult situations, and that of hope of wanting to be the first to become successful and wealthy, so that, that's a very strong, uh, hunger and a strong desire and strong work ethic that drives China forward.

  2. 2:41 – 4:28

    Rote learning vs. creativity: how education shapes innovation and execution

    1. LF

      And is there roots to not just this generation but before, that's- that's deeper than just the new economic developments? Is there something that's unique to China that you could speak to that's in the people?

    2. KL

      Yeah. Well, the Chinese, um, tradition is about excellence, dedication, and results, and the Chinese exams and, uh, study subjects in schools have traditionally, uh, started from memorizing 10,000 characters. Not an easy task to start with. And further by memorizing his- historic, um, philosophers, literature, poetry. So it really is the- probably the strongest rote learning mechanism created to make sure people had good memory and remembered things extremely well. Um, that, I think at the same time, uh, suppresses the breakthrough innovation, um, and also enhances the speed execution, get results, and that, I think characterizes the historic basis of, uh, China.

    3. LF

      That's interesting 'cause there's echoes of that in Russian education as well is rote memorization.

    4. KL

      Mm-hmm.

    5. LF

      So you have to memorize a lot of poet- I mean there's-

    6. KL

      Mm-hmm.

    7. LF

      ... just the- an emphasis on perfection in all forms.

    8. KL

      Mm-hmm.

    9. LF

      That's not conducive to perhaps what you're speaking to which is creativity. But you- y- and you think that kind of education holds back the innovative spirit that you might see in the United States?

    10. KL

      Well, it holds back the breakthrough innovative spirit that we see in the United States, but it does hold back the valuable execution-oriented, result-oriented, uh, value creating engines which we see China being very successful.

  3. 4:28 – 6:39

    Chinese vs. American AI engineering: algorithms, experimentation, and data cleaning

    1. LF

      So is there a difference between a Chinese AI engineer today and an American AI engineer, perhaps rooted in the culture that we just talked about, or the education, or the very soul of the people? Or no? And what would your advice be to each if there's a difference?

    2. KL

      Well, there's a lot that's similar because AI is about, um, mastering sciences, about using known technologies and trying new things, but it's also about, um, picking from many parts of possible networks to use and different types of parameters to tune, and that part is somewhat rote, and it is also, as anyone who's built AI products can tell you, a lot about cleansing the data, because AI runs better with more data, and data is generally, um, unstructured, error- er- error-full and, um, uh, unclean, and the effort to clean the data is- is immense. So I think the better part of American engineering- AI engineering process is, uh, to try new things, to do things people haven't done before, and, um, to use technology to solve most if not all problems. Um, so to make the algorithm work despite not so great data, find, you know, error-tolerant ways to deal with the data. The Chinese way would be to, um, basically enumerate to the fullest extent all the possible ways by a lot of machines, try lots of different ways to get it to work, and, um, spend a lot of resources and money and time cleaning up data. That mean- that means the AI engineer may be writing data cleansing algorithms working with-... thousands of people who label or correct or, uh, do things with the data. That is the incredible hard work that might lead to better results. So the Chinese engineer would rely on and ask for more and more and more data, and find ways to cleanse them and make them work in the system, and probably less time thinking about new algorithms that can overcome data or other issues.

  4. 6:39 – 8:35

    Where progress comes from: data scale vs. breakthroughs (autonomy & medicine)

    1. LF

      So where's your intuition? Where do you think the biggest impact in the next 10 years lies? Is it in some breakthrough algorithms, or is it in just this s- at scale rigor-

    2. KL

      (clears throat)

    3. LF

      ... a rigorous approach to data, cleaning data, organizing data unto the same algorithms? What do you think the big impact in the applied world is?

    4. KL

      Well, if you're really in the company and you have to deliver results, using known techniques and enhancing data seems like the more expedient approach that's very, uh, low risk and, uh, likely to generate better and better results. And that's why the Chinese approach has done quite well. Now, there are a lot of more challenging startups and problems, such as autonomous vehicles, medical diagnosis, that existing algorithms may- probably won't solve. Um, and that would put the Chinese approach more challenged, and give the more breakthrough innovation approach, um, more- more- more of an edge on those kinds of problems.

    5. LF

      So let me, uh, talk to that a little more. So, you know, my intuition, personally-

    6. KL

      Mm-hmm.

    7. LF

      ... is, uh, that data can take us extremely far. Uh, so you brought up autonomous vehicles and medical diagnosis. So your intuition is that huge amounts of data might not be able to completely help us solve that problem?

    8. KL

      Right. So breaking that down further, in autonomous vehicle, I think huge amounts of data probably will solve trucks driving on highways, which will deliver significant value, and China will probably lead in that. Um, and, um, full L5 autonomous is likely to require new technologies we don't yet know, and that might require academia and great industrial research both innovating and working together. And in that case, US has an advantage.

  5. 8:35 – 11:56

    Tesla’s data-first autonomy and the limits of pure machine learning

    1. LF

      So the interesting question, uh, there is... I don't know if you're familiar on the autonomous vehicle space and the developments with Tesla and Elon Musk-

    2. KL

      I am.

    3. LF

      ... where they are in fact, uh, full steam ahead-

    4. KL

      Mm-hmm.

    5. LF

      ... into this mysterious complex world of full autonomy, L5, L4, L5, and they're trying to solve that purely with data. So the same kind of thing that you're saying is just for highway, which is what a lot of people share your intuition-

    6. KL

      Yeah.

    7. LF

      ... uh, they're trying to solve with data. So just to linger-

    8. KL

      Yup.

    9. LF

      ... on that moment further-

    10. KL

      Yeah.

    11. LF

      ... do you think possible for them-

    12. KL

      Mm.

    13. LF

      ... to achieve success with simply just a huge amount of this, uh, training on edge cases and difficult cases in urban environments, not just highway and so on?

    14. KL

      I think that would be very hard. Uh, one could characterize Tesla's approach as kind of a Chinese strength approach, right?

    15. LF

      Right.

    16. KL

      Gather all the data you can and hope that will overcome the problems. But in autonomous driving, clearly a lot of the decisions, um, aren't merely solved by aggregating data and having feedback loop. There are, um, things that are more akin to human thinking.

    17. LF

      Right.

    18. KL

      And how would those be integrated and built? Uh, there has not yet been a lot of success integrating human intelligence or, you know, call it expert systems if you will-

    19. LF

      Mm-hmm.

    20. KL

      ... even though that's a taboo word, (laughs) uh, with the, uh, machine learning. And the integration of the two types of thinking hasn't yet been demonstrated. And, uh, the question is how much can you push a purely machine learning approach? And of course, Tesla also has an additional constraint that they don't have all the sensors. Um, I know that they think it's foolish to use LIDARS, but that's clearly a one less very valuable and reliable source of input that they're foregoing, which may also have consequences. Um, I think the advantage, of course, is capturing data no one has ever seen before. And, uh, in some cases such as, uh, computer vision and speech recognition, I have seen Chinese companies accumulate data that's not seen anywhere in the Western world, and they have delivered superior results. But then speech recognition and object recognition are relatively suitable problems for deep learning, and don't have the, uh, potentially h- need for the human intelligence analytical planning elements.

    21. LF

      And the same on the speech recognition side, your intuition that speech recognition-

    22. KL

      (clears throat) .

    23. LF

      ... and the machine learning approaches to speech recognition won't take us to a conversational system that can pass the Turing test, which is sort of maybe akin to, uh, dr- what driving is. So it needs to have something more than just simply simple language understanding, simple language-

    24. KL

      Mm-hmm.

    25. LF

      ... generation?

    26. KL

      Roughly right. I would say that based on purely machine learning approaches, it's hard to imagine it could lead to a full conversational, um, experience across arbitrary domains, which is akin to L5. I'm a little hesitant to use the word Turing test because-

    27. LF

      Sure.

    28. KL

      ... the original definition was probably too easy. We prob- probably do that, yeah.

    29. LF

      The spirit of the Turing test-

    30. KL

      That's right.

  6. 11:56 – 16:26

    Silicon Valley culture vs. China’s “win” culture—and what it means for competition

    1. LF

      So you've had major leadership research positions at Apple, Microsoft, Google. So continuing on the discussion of America, Russia, Chinese soul and culture and so on, what is the culture of Silicon Valley in contrast to, uh, China and maybe US broadly, and what is the unique culture of each of these three major companies in your view?

    2. KL

      I think in aggregate, Silicon Valley companies, and we could probably include Microsoft in that, even though they're not in the valley, is, um, really dream big and have visionary goals and, um, believe that technology will conquer all, and, um, also the self-confidence and the self-entitlement that whatever they produce, the whole world should use and must use.

    3. LF

      Mm-hmm.

    4. KL

      And, um, those are historically important. I think, um, you know, Steve Jobs' famous quote that to d- he doesn't do focus groups. He looks in the mirror and asks- (laughs)

    5. LF

      (laughs)

    6. KL

      ... the person in the mirror what they want. And that really is an inspirational comment that says that great companies shouldn't just ask users what they want, but develop something that users will know th- they want when they see it but they could never come up with themselves. I think that is probably the, um, most exhilarating description of what the essence of Silicon Valley is, that this brilliant idea, uh, could cause you to build something, uh, that couldn't come out of focus groups or A/B tests, and a iPhone would be an example of that. No one in the age of BlackBerry would write down they want an iPhone or multi-touch. A browser might be another example. No one would say they want that in the days of FTP, but once they see it, they want it. So, I, I think that is what e- Silicon Valley is best at. But it also comes with, uh, it came with a lot of success. These products became global platforms, and there were basically no competitors anywhere, and that has also led to a belief that these are the only things that one should do, that companies should not tread on other companies' territory. So that a, you know, um, Groupon and a Yelp and an OpenTable and a Grubhub would each feel, "Okay, I'm not gonna do the other company's business," because that would not be the pride of innovating what ev- each of these four companies have innovated.

    7. LF

      Yeah.

    8. KL

      But I think the Ch- Chinese approach is do whatever it takes to win, and it's a winner take all market. And in fact, in the internet space, the market leader will get predominantly all the value extracted out of the system. So... And the, and the, and the system isn't just defined as one narrow category, but gets broader and broader. So, it's amazing ambition for, uh, success and, uh, domination of increasingly larger product categories leading to, um, clear market winner status and the opportunity to extract tremendous value, and that develops a practical result-oriented, ultra ambitious winner take all gladiatorial mentality. And, um, if what it takes is to build what their competitors built, essentially a copycat, that can be done-

    9. LF

      Mm-hmm.

    10. KL

      ... without infringing laws.

    11. LF

      Mm-hmm.

    12. KL

      If what it takes is to satisfy a foreign co- a foreign country's need by forking the code base and building something that looks really ugly and different, they'll do it. So, it's contrasted very sharply with the Silicon Valley approach, and I think the flexibility and the speed and execution has helped the Chinese approach. And I think the Silicon Valley approach, um, is potentially challenged if every Chinese entrepreneur is learning from the whole world, US and China, and the American entrepreneurs only look internally and write off China as a copycat. And the second part of your question about the three companies.

    13. LF

      The unique elements of the three companies, perhaps.

  7. 16:26 – 21:36

    Inside Apple, Microsoft, and Google: design, platforms, and mission-driven tech

    1. KL

      Yeah. I think Apple represents wow the user, please the user, and, um, the essence of design and brand and, um, it's the one company and perhaps the only tech company that draws people with a s- a, um, s- strong serious desire for the product-

    2. LF

      Mm-hmm.

    3. KL

      ... and the ne- and the willingness to pay a premium because of the halo effect of the brand, which came from the attention to detail and great respect for user needs. Microsoft represents a platform approach that builds giant products-

    4. LF

      Mm.

    5. KL

      ... that become very strong moats that others can't do because it's well-architected at the bottom level and the work is efficiently delegated to individuals and then the, the, the whole product is built by adding small parts that sum together. So, it's probably the most, um, effective high-tech assembly line-

    6. LF

      Hmm.

    7. KL

      ... that builds a very difficult product that... and the whole process of doing that is kind of a, um, um, differentiation and something competitors can't easily repeat.

    8. LF

      Are there elements to the, of the Chinese approach in the way Microsoft went about assembling those little pieces and dominating the m- uh, essentially dominating the market for a long time? Or do you see those as distinct?

    9. KL

      I think there are elements that are the same. I think the three American companies that had or have Chinese characteristics-

    10. LF

      Mm.

    11. KL

      ... and obviously as well as American characteristics, are Microsoft, Facebook, and Amazon.

    12. LF

      Yes, that's right, Amazon, yes.

    13. KL

      Because these are companies that will, um, uh, tenaciously go after adjacent markets, build up strong, um, product offering, and, and find ways to, uh-... to extract greater value from a, uh, sphere that's ever increasing. And, uh, they understand the value of the platforms, so that's the similarity. And then with Google, I think it's a, uh, genuinely value-oriented company that does have a heart and soul, and that wants to do great things for the world e- by connecting information, and that has, um, uh, also very strong technology genes and, um, wants to use t- technology and has found, uh, out of the box ways to use technology to deliver inc- incredible value to the, uh, end user.

    14. LF

      If we can look at Google, for example, you mentioned heart and soul, uh, there seems to be an element where Google is after making the world better. There's a more positive view, I mean-

    15. KL

      Yeah.

    16. LF

      ... they used to have the slogan, "Don't be evil."

    17. KL

      Yeah.

    18. LF

      And, uh, and Facebook a little bit more has a negative tint to it, at least in the perception of privacy and so on.

    19. KL

      Mm-hmm.

    20. LF

      Do you have a sense of, um, how these different companies can achieve? Because you've talked about how much we can make the world better in all these kinds of ways with AI, what is it about a company that can make... give it a heart and soul, uh, gain the trust of the public, and just, actually just not be evil and do good for the world?

    21. KL

      It's really hard and I think Google has, uh, struggled with that. Um, first the don't do evil, um, mantra is very dangerous because every employee's definition of evil is different.

    22. LF

      Right.

    23. KL

      And that has led to some difficult employee situations for them, so I don't necessarily think that's a good, um, value statement. But just watching the kinds of things Google or its parent company Alphabet does, uh, in new areas like healthcare, like, you know, eradicating mosquitoes, (laughs) things that are really not in the business of a internet tech company-

    24. LF

      (laughs)

    25. KL

      ... I, I think that shows that there is a heart and soul and desire to do good and a willingness to, um, uh, put in the resources to do something when they see it's good, they will pursue it. Uh, that doesn't necessarily mean it has all the trust of the users.

    26. LF

      Mm-hmm.

    27. KL

      I realize while most people would view Facebook as the primary target of their recent, uh, unhappiness about Silicon Valley companies-

    28. LF

      Mm-hmm.

    29. KL

      ... many would put Google in that category and some have named Google's business practices as, um, uh, predatory also.

    30. LF

      Yes.

  8. 21:36 – 24:55

    Big Tech power, data moats, and whether monopolies can be displaced

    1. LF

      So in this complex balancing that these companies have to do, you've mentioned that you're concerned about, uh, a future where too few companies like Google, Facebook, Amazon, are controlling our data, uh, or controlling too much of, uh, our digital lives. Can you elaborate on this concern and perhaps do you have a better way forward?

    2. KL

      I think I'm hardly the most vocal (laughs) complainer of this-

    3. LF

      Sure. Of course.

    4. KL

      ... be- uh, there are lou- louder complainers out there. I do observe that, um, having a lot of data does perpetuate their strength and, um, limit competition in s- many spaces. Uh, but I also believe AI is much broader than the internet space. So the entrepreneurial opportunities still exist in using AI to empower, um, financial, retail, manufacturing, education applications. So I don't think it's quite a case of, um, full monopolistic dominance that makes... that totally, uh, stifles innovation. But I do believe in their areas of strength, it's hard to, uh, to dislodge them. I don't know if I have a good solution. Probably the best solution is let the entrepreneurial VC ecosystem work well and find all the places that can create the next Google, the next Facebook, so there will always be increasing number of, uh, challengers. In some sense that has happened a little bit. You see Uber, Airbnb having emerged despite the strength of the, the, the big three. Um, and, um, and I think China as an environment may be more interesting for the emergence because if you look at companies between, uh, let's say 50 to, um, $300 billion, uh, China has emerged more of such companies than the US in the, in the last three to four years because of the larger marketplace, because of the more fearless nature (laughs) of the entrepreneurs, um, that... And, and the Chinese giants are just as powerful as American ones. Tencent, Alibaba are very strong, but ByteDance has emerged worth 75 billion, uh, and financial, while it's Alibaba affiliated, it's nevertheless independent and worth 150 billion. And so I, I, I do think if we start to extend to traditional businesses, we will see value- very valuable companies. So it's probably not the case that in, uh, five or 10 years we'll still see the whole world w- with these five companies having such dominance.

    5. LF

      So you've mentioned a couple times, uh, this fascinating world of entrepreneurship in China-

    6. KL

      Yeah.

    7. LF

      ... uh, uh, of the fearless nature of the entrepreneurs. So can you maybe talk a little bit about what it takes to be an entrepreneur in China? What are the strategies that are undertaken, what are-... the ways to achieve success, what is the dynamic of VCF funding, of the way the government helps companies, and so on.

    8. KL

      Mm-hmm.

    9. LF

      What are the interesting aspects here that are distinct from or different from the Silicon Valley world of entrepreneurship?

  9. 24:55 – 30:17

    Entrepreneurship in China: from copying to out-innovating to exporting new models

    1. KL

      Um, many of the listeners probably still would brand Chinese entrepreneur as copycats, and no doubt 10 years ago that would not be an inaccurate (laughs) description. Uh, back 10 years ago, an entrepreneur probably could not get funding if he or she could not describe what product he or she is copying from the US.

    2. LF

      Mm.

    3. KL

      Um, the first question is, who has proven this business model? Which is a nice way of asking, who are you copying? And, and that reason is understandable because, uh, China had a much lower internet penetration and, um, and, uh, didn't have enough, um, indigenous experience to build innovative products. And secondly, internet was emerging. Lean startup was the way to do things. Building a first, um, minimally viable product and then expanding was the right way to go. And the American successes have given the shortcuts, that if you took your ... If you built your minimally viable product based on American product, it's guaranteed to be a decent starting point. Then you tweak it afterwards. So, as long as there are no IP infringement, which as far I know there hasn't been in the, in the mobile and, um, AI spaces, um, that's a m- a much better shortcut. And I think Silicon Valley would view that as still not very honorable, because that's not your own idea to start with. But you can't really, at the same time, believe every idea must be your own and believe in the lean startup methodology, because lean startup is intended to try many, many things and then converge one that, uh, works, and it is meant to be iterated and changed. So, finding a decent starting point without legal violations, there should be nothing, um, morally, uh, dishonorable about that.

    4. LF

      Yeah, so just a quick pause on that. I- it's fascinating that that's, uh, is why is that not honorable, right? Is exactly as you formulated, is it seems like a perfect start for a business, uh-

    5. KL

      Yes.

    6. LF

      ... is to, uh, to, uh, take, you know, look at Amazon and say, "Okay, we'll, we'll do exactly what Amazon is doing." Let's start there-

    7. KL

      Yeah. Yeah.

    8. LF

      ... in this particular market, and then let's out-innovate them from that starting point.

    9. KL

      Yes.

    10. LF

      Come up with new ways. I mean, is it wrong to be, uh, uh, except the word copycat just sounds bad, but is it wrong to be a copycat? It just seems like a smart strategy. But yes, doesn't have a heroic nature to it.

    11. KL

      Yeah.

    12. LF

      That, uh, like a s- uh, like a s- a Steve Jobs, um, Elon Musk sort of in something completely ... Coming up with something completely new.

    13. KL

      Yeah, I like the way you describe it. It's a non-heroic, uh, acceptable way to start a company, and, uh, maybe more expedient. So that's the, that's, I think, eh, um, a baggage for Silicon Valley. That if it doesn't let go, then it may limit the ultimate ceiling of the company. Take Snapchat as an example. I think, um, you know, Evan's brilliant. He built a great product, but he's very proud that he wants to build his own features, not copy others.

    14. LF

      Mm-hmm.

    15. KL

      While Facebook was more willing to, uh, copy his features.

    16. LF

      Yeah.

    17. KL

      And you see what happens in the competition. So, I think putting that handcuff on the company would limit its ability to reach the maximum potential. So, back to the Chinese environment.

    18. LF

      Mm-hmm.

    19. KL

      Uh, copying was merely a way to learn from the American masters, just like we, if you would, we learn cl- uh, to play piano or painting. You start by copying. You don't start by innovating when you don't have the basic skillsets. So, very amazingly, the Chinese entrepreneurs about f- uh, six years ago started to branch off with these, uh, lean startups built on American ideas to build better products than American products.

    20. LF

      Mm-hmm.

    21. KL

      But they did start from the American idea. And, uh, today, Wee- WeChat is better than WhatsApp, Weibo is better than Twitter, Zhihu is better than Quora, and so on. So that, I think, is, um, um, Chinese, um, entrepreneurs going to step two. And then step three is once these entrepreneurs have done one or two of these companies, they, they now look at a Chinese market and the opportunities and come up with ideas that didn't exist elsewhere. So, products like Ant Financial, under which includes Alipay, which is mobile payments, and also the, um, financial products, uh, for, for loans built on that, and also, um, in education, VIPKid, and, um, in, uh, social, so- video social network-

    22. LF

      Mm-hmm.

    23. KL

      ... uh, TikTok, and in social e-commerce, Pinduoduo, and then in, uh, ride-sharing, Mobike. These are all Chinese, um, innovated products that now are being copied elsewhere. So, and an int- and an additional interesting observation is some of these products are built on unique Chinese demographics-

    24. LF

      Mm-hmm.

  10. 30:17 – 40:05

    VC + government infrastructure: incubators, guiding funds, and smart cities for AVs

    1. KL

      ... which may not work in the US, but may work very well in Southeast Asia, Africa, and other developing worlds that are a few years behind China. And a few of these products maybe are universal and are getting traction even in the United States, such as TikTok. So, this whole ecosystem is supported by VCs-

    2. LF

      Mm-hmm.

    3. KL

      ... as a virtuous cycle, because a large market with, with, uh, innovative entrepreneurs will draw a lot of money and then invest in these companies. As the market gets larger and larger, US mark- China market is easily three, four times larger than the US, um, they will create greater value and greater returns for the VCs, thereby raising even more money. Um, so at Sinovation Ventures, our first fund was 15 million. Our last fund was 500 million. So the, it reflects, uh, the valuation of the companies, and our, us going multi-stage and things like that. It also has government support, uh, but not in the way most Americans would think of it. The government actually leaves the entrepreneurial space as a private enterprise, sort of self-regulating, and the government would build infrastructures that would, um, around it to make it work better. For example, the Mass Entrepreneur, Mass Innovation Plan, uh, built 8,000 incubators. So the pipeline is very strong (laughs) to the VCs. Uh, for autonomous vehicles, the Chinese government is building-

    4. LF

      Mm-hmm.

    5. KL

      ... uh, smart highways with sensors, smart cities that separate pedestrians from cars that may allow initially an inferior autonomous vehicle company to launch a car without increasing... with lower casualty, uh, because the roads or the city is, uh, is smart. And the Chinese government at local levels would have these guiding funds acting as LPs, passive LPs to funds, and when the fund makes money, part of the money made is given back to the GPs and potentially other LPs to ret-, increase everybody's return at the expense of the government's return. So that's, uh, interesting incentive that entrusts the task of choosing entrepreneurs to VCs who are better at it than the government by letting some of the profit, uh, move that way.

    6. LF

      So this is really fascinating, right? So I, I look at the Russian government as a case study-

    7. KL

      Mm-hmm.

    8. LF

      ... where, let me put it this way, there's no such government-driven large scale support of entrepreneurship, and probably the same is true in the United States, but the entrepreneurs themselves kind of, uh, find a way.

    9. KL

      Yeah.

    10. LF

      So, uh, maybe in a form of advice or explanation, how did the Chinese government, uh, arrive to be this way, so supportive on entrepreneurship to be in this particular way so forward-thinking at such a large scale? And also perhaps how can we copy it in other countries-

    11. KL

      Yeah.

    12. LF

      ... uh, that c- how can we encourage other governments-

    13. KL

      Yeah.

    14. LF

      ... like even the United States government to support infrastructure for autonomous vehicles in that same kind of way, perhaps?

    15. KL

      Yes. So these, um, techniques are the result of several key things, some of which may be learnable, some of which may be very hard.

    16. LF

      (laughs)

    17. KL

      Uh, one is just trial and error and watching what everyone else is doing. I think it's important to be humble and not feel like you know all the answers. The guiding funds idea came from Singapore, which came from Israel, and China made a few tweaks and, um, turned it into a, uh... Because the Chinese cities and government officials kind of compete with each other-

    18. LF

      Mm.

    19. KL

      ... 'cause they all want to make their city more successful so they can get the next level, um, in their care- you know, polit- in their political career.

    20. LF

      That's right.

    21. KL

      And, um, it's somewhat competitive, so the central government made it a bit of a competition. Everybody has a budget. They can put it on AI or they can put it on bio or they can put it on energy, and then whoever gets the results, the city shines, the people are better off, the mayor gets a promotion. So the tools, this is kind of almost like an entrepreneurial environment for s- uh, local governments to see who can do a better job, and also, uh, many of them try different experiments. Uh, some have given award to very smart, um, uh, researchers, just give them money and hope they'll start a company. Some have given money to academic, um, uh, research labs, maybe government research labs, to see if they can spin off some companies-

    22. LF

      Mm-hmm.

    23. KL

      ... from the science lab or something like that. Uh, some have tried to recruit overseas Chinese to come back and start companies, and they've had mixed results. The one that worked the best was the guiding funds.

    24. LF

      Mm-hmm.

    25. KL

      So it's almost like a lean startup idea (laughs) where people try different things and what works sticks and everybody copies.

    26. LF

      Mm-hmm.

    27. KL

      So now every city has a guiding fund. So that's how that came about. Uh, the autonomous vehicle, uh, and the massive spending in highways and smart cities, that's a Chinese way. It's about building infrastructure, uh, to facilitate. It's a clear division of the government's responsibility from the, uh, market. The market should do everything, uh, in a private, uh, free way, but there are things the market can't afford to do, like infrastructure. So the government, um, always, um, appropriates large amounts of money for infrastructure building. This happened, um, happens with not only, uh, autonomous vehicle and AI, but happened with the, uh, 3G and 4G. Uh, you'll find that the Chinese, uh, uh, uh, wireless, uh, reception is better than the US because massive spending that tries to cover the whole country. Uh, whereas in the US it may be a little spotty. Um, it's a government driven because I think they view the, uh, coverage of s- of, um, of, uh, cell access and 3G, 4G access to be a governmental infrastructure spending.... uh, as opposed to, as opposed to capitalistic. So that's, of course they're state-owned enterprises also publicly traded, but they also carry a government responsibility to deliver infrastructure to all. So it's a different way of thinking that may be very hard to inject into Western countries, to say, "Starting tomorrow, bandwidth infrastructure and highways are gonna be, um, governmental spending, uh, with some characteristics."

    28. LF

      What's your sense, and sorry to interrupt but, uh-

    29. KL

      No.

    30. LF

      ... because it's such a fascinating point, do you think on the autonomous vehicle space, uh, it's possible to solve the problem of full autonomy without significant investment in infrastructure?

  11. 40:05 – 41:35

    What AI really is today: machine intelligence, not human-level general intelligence

    1. LF

      So maybe taking a little step back, you've, you've been a, a leader and a, a researcher in AI for several decades, at least 30 years, uh, so how has AI changed in the West and the East as you've observed, as you've been deep in it over the past 30 years?

    2. KL

      Well, AI began as the pursuit of understanding human intelligence, and the term itself, uh, represents that. But it kind of drifted into the one sub-area that worked extremely well, which is machine intelligence. And that's actually more using pattern recognition techniques to, um, basically do incredibly well on a limited dome- domain, large amount of data, but relatively simple kinds of, um, planning tasks and not very creative. So, so we didn't end up building human intelligence, (laughs) we built a different machine that was a lot better than us on some problems, but nowhere close to us on, on other problems. So today I think a lot of people still misunderstand when we say artificial intelligence and what various products can do, people still think it's about replicating human intelligence. But the products out there really are closer to having invented the internet or the spreadsheet or the database and getting broader adoption.

  12. 41:35 – 57:28

    Automation and jobs: routine work falls first, meaning comes from compassion and creativity

    1. LF

      And peaking further to the fears, near term fears that people have about AI, so you're commenting on the sort of, the general intelligence that people in the popular culture from sci-fi movies have a sense about AI, but there's practical fears about AI, the kind, the narrow AI that you're talking about of automating particular kinds of jobs, and you talk about them, uh, in the book. So what are the kinds of jobs in your view that you see in the next five, 10 years beginning to be automated by AI systems algorithms?

    2. KL

      Yes. This is, um, also maybe a little bit counterintuitive because it's the routine jobs that will be displaced, uh, the soonest. And they may not be s- displaced entirely, maybe 50%, 80% of a job, but when the workload drops by that much, employment will come down. And also another part of misunderstanding is most people think of AI replacing routine jobs than they think of the assembly line, the workers. Well, that will have some effect but it's actually the routing white-collar workers that's easiest to replace because...Re- to replace a white-collar worker, you just need software. To replace a blue-collar worker, you need, um, robotics, mechanical excellence, and the ability to deal with, um, uh, uh, dexterity and maybe even unknown environments, very, very difficult. So if we were to categorize the most dangerous, um, white-collar jobs, uh, they would be things like back office, people who copy and paste and deal with, uh, simple computer, um, programs and data, and, uh, maybe paper and OCR, and, uh, they don't make strategic decisions. They basically facilitate the process. The softwares and paper systems don't work, so you have people dealing with new employee orientation, searching for past lawsuits and financial documents, and, uh, doing reference check, uh-

    3. LF

      So basic searching and management of data.

    4. KL

      ... of data.

    5. LF

      That's the most in danger of being lost?

    6. KL

      In addition to the, uh, white-collar repetitive work, a lot of, um, simple interaction work can also be taken care of such as telesales, telemarketing-

    7. LF

      Mm-hmm.

    8. KL

      ... customer service, uh, as well as many physical jobs that are in the same location and don't require a high degree of dexterity. So, uh, fruit picking, dishwashing, assembly line inspection, are jobs th- in that category. So altogether, back office is a big part, and, um, the other, uh, the, the, the blue collar may be smaller initially but over time, AI will get better. And when we start to get to over the next 15, 20 years the ability to actually have the dexterity of doing assembly line, that's a huge chunk of jobs, and, and when autonomous vehicles start to work, initially starting with truck drivers but eventually to all drivers, that's another huge group of workers. So I see modest numbers in the next five years, but increasing rapidly after that.

    9. LF

      On the worry of the jobs that are in danger and the gradual loss of jobs, uh, I'm not sure if you're familiar with Andrew Yang?

    10. KL

      Yes, I am.

    11. LF

      Uh, so there's a candidate for President of the United States whose platform, Andrew Yang, is based, uh, around- in part around job loss due to automation, and also in addition, the need perhaps of universal basic income to support, uh, jobs that our, uh, folks who lose their job due to automation, and so on, and in general support people under complex, unstable job market. So what are your thoughts about his concerns, him as a candidate, his ideas in general?

    12. KL

      I think his thinking is generally in the right direction... but his, uh, approach as a presidential candidate may be a little bit ahead of the time.

    13. LF

      Mm.

    14. KL

      Um, I think the displacements will happen, um, but will they happen soon enough for people to agree to vote for him? Uh, the unemployment nu- numbers are not very high yet, and I think, you know, he and I have the same challenge. If I want to, uh, theoretically convince people this is an issue, and he wants to become the president, um, people have to see, um, how can this be the case when unemployment numbers are low, so that is the challenge. And I think, um, I think we do- I do agree with him on the displacement issue. Uh, on universal basic income, um, at- at a very vanilla level, I don't agree with it because I think the main issue is retraining. So people need to be incented, uh, not by just giving a monthly $2,000 check or $1,000 check and do whatever they want because they don't have the knowhow to know what to retrain to go into what, um, type of a job, and guidance is needed. And retraining is needed because historically in technology revolutions when routine jobs were displaced, new routine jobs came up-

    15. LF

      Mm-hmm.

    16. KL

      ... so they- there was always room for that. But with AI and automation, the whole point is replacing all routine jobs eventually so there will be fewer and fewer routine jobs. And, and AI will create jobs but it won't create routine jobs because if it creates routine jobs, why wouldn't AI just do it? So therefore, the people who are losing the jobs are losing routine jobs. The jobs that are becoming available are non-routine jobs, so the social stipend needs to be put in place is for the routine workers who lost their jobs to be retrained maybe in six months, maybe in three years, uh, it takes a while to retrain on a non-routine job, and then take on a job that will last for that person's lifetime. Now, uh, having said that, if you look deeply into Andrew's document, he does cater for that, so I'm not-

    17. LF

      Mm-hmm.

    18. KL

      ... um, disagreeing with, uh, where- what he's trying to do, but for simplification sometimes-

    19. LF

      Yeah.

    20. KL

      ... he just says UBI, but simple UBI wouldn't work.

    21. LF

      And I think you've- you've mentioned elsewhere that, I mean, the goal isn't necessarily to give people enough money to survive, or live, or even to prosper. The point is to, uh, give them a job that gives them meaning, that meaning is extremely important, uh-

    22. KL

      Yes.

    23. LF

      ... uh, that our employment, at least in the United States and perhaps it carries across the world, provides something that's, forgive me for saying, greater than money. It provides meaning. So now, what kind of jobs do you think can't be automated? You talk a little bit about creativity and compassion in your book. What aspects do you think it's difficult to automate for an AI system?

    24. KL

      Because an AI system is, um, currently merely optimizing. It's not able to reason, plan, or think creatively or strategically, it's not able to deal with complex problems, it can't come up with a new problem and solve it. A human needs to find the problem and, uh, pose it as an optimization problem, then have the AI work at it. So an AI would have a very hard time discovering a new drug, or discovering a new style of painting, or dealing with complex tasks that... such as managing a company, that isn't just about optimizing the bottom line, but also about employee satisfaction, c- corporate brand, and many, many other things. So, that is one category of things. And because these things are challenging, creative, complex, doing them creates a higher- high degree of satisfaction, and therefore appealing to our desire for working, which isn't just to make the money, make the ends meet, but also that we've accomplished something that others maybe can't do or can do as well. Um, another type of job that is much numerous would be compassionate jobs, jobs that require compassion, empathy, human touch, human trust. AI can't do that because AI is cold, calculating. And, um, even if it can fake that to some extent, um, it will make errors and that will make it look very silly. And also, I think even if AI did okay, people would want to interact with a peop- another person, whether it's for some kind of a service, or a teacher, or a doctor, or a concierge, or a masseuse, or a bartender. Uh, there are so many jobs where people just don't want to interact with a cold robot or software. Um, I've had an entrepreneur who built an elderly care robot, and they found that the elderly really only used it for customer service.

    25. LF

      (laughs)

    26. KL

      And not... but not to service the product, but they click on v- customer service and the video of a person comes up, and then the person says, "Uh, h- how come my daughter didn't call me?"

    27. LF

      (laughs)

    28. KL

      "Let me show you a picture of her grandkids." So, people yearn for that people-to-people interaction.

    29. LF

      Yeah.

    30. KL

      So, even if robots improved, people just don't want it. And those jobs are going to be increasing because AI will create a lot of value, $16 trillion to the world in the next 11 years according to PwC, and that will give people money to enjoy ser- services, whether it's, um, eating a gourmet meal, or tourism and traveling, or having concierge services. The, the services revolving around, you know, every dollar of that $16 trillion will be tremendous. It will create more opportunities that are to, to service the people who did well through AI, um, with, with, with things. But even... at the same time, the entire society is, uh, very much short in need of many service-oriented, compassionate-oriented jobs. The best example is probably in healthcare services.

  13. 57:28 – 1:26:10

    Governance, privacy, geopolitics—and Kai-Fu’s personal transformation after cancer

    1. LF

      Do you have concerns about large entities, whether it's governments or companies, controlling the future of AI development in general? So we talked about companies, do you have a better sense that governments can better represent the interest of the people than companies, or do you believe companies are better at representing the interests of the people, or is there no easy answer?

    2. KL

      I don't think there's an easy answer because it's a double-edged sword. The companies and governments can provide better services with more access to data and more access to AI, but that also leads to greater power which can lead to, um, uncontrollable problems whether it's monopoly or, um, corruption in the government. So I, I think one has to be careful to look at how much data that companies and governments have and, um, and some kind of checks and balances would, would be helpful.

    3. LF

      So again, I come from Russia.

    4. KL

      Mm-hmm.

    5. LF

      Uh, there is something called the Cold War, uh, so let me ask a difficult question here looking at conflict as Steven Pinker wrote in a great book that conflict all over the world is decreasing in, in general. But do you have a sense that, uh, having written the book AI Superpowers, do you see a major international conflict potentially arising between major nations whatever they are, whether it's Russia, China, European nations, uh, United States, or others in the next 10, 20, 50 years around AI, around the digital space, cyberspace? Do you worry about that, uh, that? Is there something, is that something we need to think about and, uh, try to alleviate or prevent?

    6. KL

      I believe in greater engagement. A lot of the worries about, um, more powerful AI are based on a arms race, um-

    7. LF

      Right.

    8. KL

      ... metaphor. And, um, the, when you extrapolate into military kinds of scenarios, AI can automate and, and, and s- you know, au- autonomous weapons, that needs to be controlled somehow, and, uh, autonomous decision-making can lead to not enough time to fix international crises. So I actually believe a cold war mentality would be very dangerous because should two countries rely on AI to make certain decisions and, um, they don't e- talk to each other, they do their own scenario planning, then something could easily go wrong. Um, I think engagement, interaction, some protocols, um, to avoid, um, inadvertent disasters is actually needed. So it's natural for each country to want to be the best whether it's in nuclear technologies or, um, AI or bio, but I think it's important to realize if each country has a black box AI and, uh, don't talk to each other, that probably presents greater challenges to humanity, uh, than if they interacted. Uh, I think there can still be competition, but with some degree of protocol for in- interaction. Just like when there was, um, um, a, a nuclear competition, um, there were some protocol for deterrents among US-... Russia and China, and I think that engagement is needed. So of course, we're still far from AI presenting that kind of danger, but, uh, what I worry the most about is the level of engagement seems to be coming down. The level of distrust seems to be going up, especially from the US towards other large countries such as China (laughs) and of course-

    9. LF

      And Russia.

    10. KL

      ... and Russia, yes.

    11. LF

      Is there a way to make that better? So that's beautifully put. Level of engagement and even just basic trust and communication as opposed to, uh, sort of, um, uh, you know, making artificial enemies, uh, out of particular, um, out of particular countries. Do- do you have a- do you have a sense how we can make it better?

    12. KL

      Mm-hmm. (laughs)

    13. LF

      Actionable items that, as- as a society we can take on?

    14. KL

      I'm not an expert at, uh, geopolitics.

    15. LF

      (laughs)

    16. KL

      But I would say that we look pretty foolish as humankind when we are faced with the opportunity to create $16 trillion, uh, for- for- for- for human- humanity and, um, we- we're i- yet we're not solving fundamental problems with parts of the world still in poverty. And for the first time, we have the resources to overcome poverty and hunger. We're not using it on that, but we're fueling competition among superpowers, and that's a very, uh, unfortunate thing. If we become utopian for a moment, imagine a- a benevolent world government that has this $16 trillion, uh, and maybe some AI to figure out how to use it to deal with diseases and, um, problems and hate and things like that. World would be a lot better off. So what is wrong (laughs) with the current world? I think the people with more skill than- than I should- should- should think about this. And then the geopolitics issue with superpower competition is one side of the issue. There's another side which I worry maybe even- e- even- even more, which is as the $16 trillion all gets made by US and China and a few of the developed, other developed countries, the poorer country will get nothing because they don't have technology, um, and, uh, the- the wealth disparity and ino- inequality will increase. So a poorer country with a large population will not only benefit from the AI boom or other technology booms, but they will have their workers who previously had hoped they could do the China model and do outsource manufacturing, or the India model so they could do the outsource, um, process or call center. Well all those jobs are gonna be gone in 10 or 15 years. So the- the individual citizen may be a net liability, I mean financially speaking, to a poorer country, and not an asset to- to- to claw itself out of poverty. So in that kind of situation, these, um, large countries with- with not much tech are going to be facing a, um, downward spiral.

    17. LF

      Mm-hmm.

    18. KL

      And it's unclear what could be done, um, and- and then when we look back and say there's $16 trillion being created and it's all being kept by US, China, and other developed countries, it just doesn't feel right. So I hope people who know about geopolitics can find solutions. That's beyond my expertise.

    19. LF

      So different countries that we've talked about have different value systems. Uh, if you look at the United States to an almost extreme degree, there is, uh, an absolute desire for freedom of speech. If you look at a country where I was raised, that desire just amongst the people is not that, uh, s- uh, not as elevated as it is in the, uh, to basically fundamental level to the essence of what it means to be America, right? And the same is true with China. There's different value systems. Uh, there's some censorship of internet content that China and Russia and many other countries undertake. Do you see that having effects on innovation, other aspects of some of the tech stuff, AI development we talked about, and maybe from another angle do you see that changing in different ways over the next 10 years, 20 years, 50 years as, uh, China continues to grow as it does now in the, in its tech innovation?

    20. KL

      Uh, there's a common belief that full freedom of speech and expression is correlated with creativity which is correlated with, um, entrepreneurial success. I think empirically we have seen that is not true and China has been successful. That's not to say the fundamental values are not right or not the best, but it's just that- that- that perfect correlation isn't- isn't there. It's hard to read the tea leaves on an opening up or not in any country and I've not been very good at that in my past predictions, but I- I do believe, uh, every country shares some fundamental value, a lot of fundamental values for the long term. Uh, so you know, China is drafting its, uh, privacy policy for individual citizens and they don't look that different from the American or European ones, so people do want to protect their privacy and, uh, have the opportunity to express and um, um, I think the fundamental values are there. The question is in the execution and timing, how soon or when will that start to open up?... so, so a- so as long as each government knows ultimately people want that kind of protection, there should be a plan to move towards that, um, as to when or how, again, I'm not an expert.

    21. LF

      On the point of privacy, to me it's really interesting. Uh, so AI needs data to create a personalized awesome experience.

    22. KL

      Yeah.

    23. LF

      Right? I'm just speaking generally in terms of products. Uh, and then we have currently depending on the age and depending on the demographics of who we're talking about, some people are more or less concerned about the amount of data they hand over. So, in your view, how do we get this balance right that we provide an amazing experience to-

    24. KL

      Mm-hmm.

    25. LF

      ... people that use products? You look at Facebook, uh, you know, the more Facebook knows about you, yes, it's scary to say, the better it can probably, uh, th- a better experience it can probably create. So in your view how do we get that balance right?

    26. KL

      Yes. I think (sighs) a lot of people have a misunderstanding that it's okay and possible to just rip all the data out from a provider and give it back to you so you can deny them access to further data and still enjoy the services we have.

    27. LF

      Right.

    28. KL

      If we take back all the data, all the services will give us nonsense.

    29. LF

      Mm-hmm.

    30. KL

      We'll no longer be able to, uh, use products that function well in terms of, you know, uh, right ranking, right products, right user experience. So, so yet I do understand we- we don't want, uh, to permit misuse of the data.

Episode duration: 1:26:26

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