back to indexKai-Fu Lee: AI Superpowers - China and Silicon Valley | Lex Fridman Podcast #27
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The following is a conversation with Kai Fu Li.
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He's the chairman and CEO of Sinovation Ventures
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that manages a $2 billion dual currency investment fund
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with a focus on developing the next generation
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of Chinese high tech companies.
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He's the former president of Google China
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and the founder of what is now called Microsoft Research
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Asia, an institute that trained many
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of the artificial intelligence leaders in China,
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including CTOs or AI execs at Baidu, Tencent, Alibaba,
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Lenovo, and Huawei.
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He was named one of the 100 most influential people
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in the world by Time Magazine.
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He's the author of seven bestselling books in Chinese
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and most recently, the New York Times bestseller called
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AI Superpowers, China, Silicon Valley,
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and the New World Order.
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He has unparalleled experience in working across major tech
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companies and governments on applications of AI.
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And so he has a unique perspective
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on global innovation in the future of AI
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that I think is important to listen to and think about.
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This is the Artificial Intelligence Podcast.
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If you enjoy it, subscribe on YouTube and iTunes,
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support it on Patreon, or simply connect with me on Twitter
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And now, here's my conversation with Kaifu Li.
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I immigrated from Russia to US when I was 13.
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You immigrated to US at about the same age.
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The Russian people, the American people, the Chinese people,
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each have a certain soul, a spirit,
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that permeates throughout the generations.
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So maybe it's a little bit of a poetic question,
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but could you describe your sense of what
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defines the Chinese soul?
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I think the Chinese soul of people today, right,
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we're talking about people who have had centuries of burden
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because of the poverty that the country has gone through
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and suddenly shined with hope of prosperity
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in the past 40 years as China opened up
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and embraced market economy.
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And undoubtedly, there are two sets of pressures
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on the people, that of the tradition,
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that of facing difficult situations,
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and that of hope of wanting to be the first
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to become successful and wealthy,
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so that it's a very strong hunger and strong desire
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and strong work ethic that drives China forward.
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And is there roots to not just this generation,
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but before, that's deeper than just
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the new economic developments?
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Is there something that's unique to China
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that you could speak to that's in the people?
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Well, the Chinese tradition is about excellence,
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dedication, and results.
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And the Chinese exams and study subjects in schools
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have traditionally started from memorizing 10,000 characters,
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not an easy task to start with.
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And further by memorizing historic philosophers,
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literature, poetry.
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So it really is probably the strongest road
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learning mechanism created to make sure people had good memory
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and remembered things extremely well.
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That, I think, at the same time suppresses
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the breakthrough innovation.
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And also enhances the speed execution get results.
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And that, I think, characterizes the historic basis of China.
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That's interesting, because there's echoes of that
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in Russian education as well as rote memorization.
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So you memorize a lot of poetry.
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I mean, there's just an emphasis on perfection in all forms
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that's not conducive to perhaps what you're speaking to,
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which is creativity.
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But you think that kind of education
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holds back the innovative spirit that you
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might see in the United States?
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Well, it holds back the breakthrough innovative spirit
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that we see in the United States.
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But it does not hold back the valuable execution oriented,
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result oriented value creating engines, which we see China
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being very successful.
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So is there a difference between a Chinese AI engineer
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today and an American AI engineer perhaps rooted
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in the culture that we just talked about or the education
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or the very soul of the people or no?
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And what would your advice be to each
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if there's a difference?
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Well, there's a lot that's similar,
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because AI is about mastering sciences,
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about using known technologies and trying new things.
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But it's also about picking from many parts of possible networks
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to use and different types of parameters to tune.
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And that part is somewhat rote.
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And it is also, as anyone who's built AI products,
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can tell you a lot about cleansing the data.
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Because AI runs better with more data.
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And data is generally unstructured, errorful,
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And the effort to clean the data is immense.
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So I think the better part of the American AI engineering
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process is to try new things, to do things people haven't done
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before, and to use technology to solve most, if not all,
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So to make the algorithm work despite not so great data,
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find error tolerant ways to deal with the data.
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The Chinese way would be to basically enumerate,
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to the fullest extent, all the possible ways
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by a lot of machines, try lots of different ways
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to get it to work, and spend a lot of resources and money
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and time cleaning up data.
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That means the AI engineer may be writing data cleansing
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algorithms, working with thousands of people
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who label or correct or do things with the data.
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That is the incredible hard work that
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might lead to better results.
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So the Chinese engineer would rely on and ask for more and more
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data and find ways to cleanse them and make them work
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in the system, and probably less time thinking
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about new algorithms that can overcome data or other issues.
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So where's your intuition?
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What do you think the biggest impact the next 10 years
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Is it in some breakthrough algorithms?
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Or is it in just this at scale rigor, a rigorous approach
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to data, cleaning data, organizing data
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onto the same algorithms?
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What do you think the big impact in the applied world is?
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Well, if you're really in the company
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and you have to deliver results, using known techniques
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and enhancing data seems like the more expedient approach
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that's very low risk and likely to generate
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better and better results.
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And that's why the Chinese approach has done quite well.
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Now, there are a lot of more challenging startups
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and problems, such as autonomous vehicles,
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medical diagnosis, that existing algorithms probably
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And that would put the Chinese approach more challenged
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and give them more breakthrough innovation approach, more
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of an edge on those kinds of problems.
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So let me talk to that a little more.
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So my intuition, personally, is that data
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can take us extremely far.
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So you brought up autonomous vehicles and medical diagnosis.
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So your intuition is that huge amounts of data
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might not be able to completely help us solve that problem.
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So breaking that down further, autonomous vehicle,
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I think huge amounts of data probably
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will solve trucks driving on highways, which
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will deliver significant value.
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And China will probably lead in that.
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And full L5 autonomous is likely to require new technologies
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we don't yet know.
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And that might require academia and great industrial research,
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both innovating and working together.
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And in that case, US has an advantage.
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So the interesting question there is,
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I don't know if you're familiar on the autonomous vehicle
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space and the developments with Tesla and Elon Musk,
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where they are, in fact, a full steam ahead
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into this mysterious, complex world of full autonomy, L5,
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And they're trying to solve that purely with data.
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So the same kind of thing that you're saying
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is just for highway, which is what a lot of people
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share your intuition, they're trying to solve with data.
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It's just to linger on that moment further.
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Do you think possible for them to achieve success
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with simply just a huge amount of this training
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on edge cases, on difficult cases in urban environments,
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not just highway and so on?
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I think they'll be very hard.
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One could characterize Tesla's approach as kind
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of a Chinese strength approach, gather all the data you can,
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and hope that will overcome the problems.
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But in autonomous driving, clearly a lot of the decisions
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aren't merely solved by aggregating data
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and having feedback loop.
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There are things that are more akin to human thinking.
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And how would those be integrated and built?
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There has not yet been a lot of success
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integrating human intelligence or, you know,
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colored expert systems, if you will,
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even though that's a taboo word with the machine learning.
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And the integration of the two types of thinking
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hasn't yet been demonstrated.
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And the question is, how much can you
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push a purely machine learning approach?
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And of course, Tesla also has an additional constraint
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that they don't have all the sensors.
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I know that they think it's foolish to use LIDARS,
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but that's clearly a one less, very valuable and reliable
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source of input that they're foregoing, which
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may also have consequences.
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I think the advantage, of course,
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is capturing data that no one has ever seen before.
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And in some cases, such as computer vision and speech
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recognition, I have seen Chinese companies accumulate data
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that's not seen anywhere in the Western world,
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and they have delivered superior results.
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But then speech recognition and object recognition
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are relatively suitable problems for deep learning
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and don't have the potentially need for the human intelligence
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analytical planning elements.
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And the same on the speech recognition side,
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your intuition that speech recognition and the machine
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learning approaches to speech recognition
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won't take us to a conversational system that
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can pass the Turing test, which is maybe akin to what
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So it needs to have something more than just simply simple
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language understanding, simple language generation.
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Roughly right, I would say that based on purely machine
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learning approaches, it's hard to imagine.
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It could lead to a full conversational experience
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across arbitrary domains, which is akin to L5.
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I'm a little hesitant to use the word Turing test,
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because the original definition was probably too easy.
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We probably do that.
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The spirit of the Turing test is what I was referring to.
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So you've had major leadership research positions
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at Apple, Microsoft, Google.
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So continuing on the discussion of America, Russia, Chinese soul
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and culture and so on, what is the culture of Silicon
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Valley in contrast to China and maybe US broadly?
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And what is the unique culture of each of these three
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major companies, in your view?
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I think in aggregate, Silicon Valley companies,
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we could probably include Microsoft in that,
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even though they're not in the Valley,
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is really dream big and have visionary goals
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and believe that technology will conquer all
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and also the self confidence and the self entitlement
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that whatever they produce, the whole world should use
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And those are historically important, I think.
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Steve Jobs's famous quote that he doesn't do focus groups.
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He looks in the mirror and asks the person in the mirror,
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And that really is an inspirational comment
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that says the great company shouldn't just ask users
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what they want, but develop something
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that users will know they want when they see it,
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but they could never come up with themselves.
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I think that is probably the most exhilarating description
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of what the essence of Silicon Valley is,
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that this brilliant idea could cause you to build something
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that couldn't come out of the focus groups or A.B. tests.
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And iPhone would be an example of that.
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No one in the age of BlackBerry would write down
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they want an iPhone or multi touch, a browser,
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might be another example.
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No one would say they want that in the days of FTP,
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but once they see it, they want it.
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So I think that is what Silicon Valley is best at.
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But it also came with a lot of success.
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These products became global platforms,
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and there were basically no competitors anywhere.
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And that has also led to a belief
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that these are the only things that one should do,
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that companies should not tread on other companies territory,
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so that a Groupon and a Yelp and an OpenTable
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and the Grubhub would each feel,
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okay, I'm not going to do the other companies business
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because that would not be the pride of innovating
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what each of these four companies have innovated.
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But I think the Chinese approach is do whatever it takes to win.
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And it's a winner take all market.
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And in fact, in the internet space,
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the market leader will get predominantly all the value
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extracted out of the system.
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And the system isn't just defined as one narrow category,
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but gets broader and broader.
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So it's amazing ambition for success and domination
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of increasingly larger product categories
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leading to clear market winner status
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and the opportunity to extract tremendous value.
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And that develops a practical, result oriented,
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ultra ambitious winner take all gladiatorial mentality.
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And if what it takes is to build what the competitors built,
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essentially a copycat, that can be done without infringing laws.
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If what it takes is to satisfy a foreign country's need
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by forking the code base and building something
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that looks really ugly and different, they'll do it.
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So it's contrasted very sharply with the Silicon Valley approach.
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And I think the flexibility and the speed and execution
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has helped the Chinese approach.
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And I think the Silicon Valley approach
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is potentially challenged if every Chinese entrepreneur is
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learning from the whole world, US and China,
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and the American entrepreneurs only look internally
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and write off China as a copycat.
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And the second part of your question about the three
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The unique elements of the three companies, perhaps.
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I think Apple represents, while the user, please the user,
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and the essence of design and brand,
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and it's the one company and perhaps the only tech company
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that draws people with a strong, serious desire
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for the product and the willingness to pay a premium
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because of the halo effect of the brand, which
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came from the attention to detail and great respect
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Microsoft represents a platform approach
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that builds giant products that become very strong modes
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that others can't do because it's
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well architected at the bottom level
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and the work is efficiently delegated to individuals
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and then the whole product is built
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by adding small parts that sum together.
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So it's probably the most effective high tech assembly
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line that builds a very difficult product
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that the whole process of doing that
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is kind of a differentiation and something competitors
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can't easily repeat.
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Are there elements of the Chinese approach
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in the way Microsoft went about assembling those little pieces
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and essentially dominating the market for a long time?
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Or do you see those as distinct?
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I think there are elements that are the same.
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I think the three American companies
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that had or have Chinese characteristics,
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and obviously as well as American characteristics,
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are Microsoft, Facebook, and Amazon.
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Yes, that's right, Amazon.
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Because these are companies that will tenaciously
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go after adjacent markets, build up strong product offering,
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and find ways to extract greater value from a sphere that's
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And they understand the value of the platforms.
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So that's the similarity.
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And then with Google, I think it's a genuinely value oriented
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company that does have a heart and soul
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and that wants to do great things for the world
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by connecting information and that has also
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very strong technology genes and wants to use technology
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and has found out of the box ways to use technology
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to deliver incredible value to the end user.
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We can look at Google, for example.
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You mentioned heart and soul.
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There seems to be an element where Google
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is after making the world better.
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There's a more positive view.
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I mean, they used to have the slogan, don't be evil.
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And Facebook a little bit more has a negative tend to it,
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at least in the perception of privacy and so on.
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Do you have a sense of how these different companies can
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achieve, because you've talked about how much
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we can make the world better in all these kinds of ways
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with AI, what is it about a company that can make,
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give it a heart and soul, gain the trust of the public,
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and just actually just not be evil and do good for the world?
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And I think Google has struggled with that.
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First, they don't do evil.
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Mantra is very dangerous, because every employee's
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definition of evil is different.
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And that has led to some difficult employee situations
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So I don't necessarily think that's a good value statement.
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But just watching the kinds of things
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Google or its parent company Alphabet does in new areas
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like health care, like eradicating mosquitoes,
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things that are really not in the business
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of a internet tech company, I think
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that shows that there is a heart and soul
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and desire to do good and willingness to put in the resources
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to do something when they see it's good, they will pursue it.
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That doesn't necessarily mean it has
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all the trust of the users.
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I realize while most people would view Facebook
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as the primary target of their recent unhappiness
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about Silicon Valley companies, many would put Google
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And some have named Google's business practices
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as predatory also.
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So it's kind of difficult to have the two parts of a body.
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The brain wants to do what it's supposed to do for a shareholder,
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And then the heart and soul wants
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to do good things that may run against what the brain wants to do.
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So in this complex balancing that these companies have to do,
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you've mentioned that you're concerned about a future where
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too few companies like Google, Facebook, Amazon
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are controlling our data or are controlling too much
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of our digital lives.
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Can you elaborate on this concern?
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Perhaps do you have a better way forward?
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I think I'm hardly the most vocal complainer of this.
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There are a lot louder complainers out there.
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I do observe that having a lot of data
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does perpetuate their strength and limits
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competition in many spaces.
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But I also believe AI is much broader than the internet space.
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So the entrepreneurial opportunities
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still exists in using AI to empower
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financial, retail, manufacturing, education,
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So I don't think it's quite a case of full monopolistic dominance
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that totally stifles innovation.
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But I do believe in their areas of strength
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it's hard to dislodge them.
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I don't know if I have a good solution.
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Probably the best solution is let the entrepreneurial VC
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ecosystem work well and find all the places that
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can create the next Google, the next Facebook.
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So there will always be increasing number of challengers.
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In some sense, that has happened a little bit.
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You see Uber, Airbnb having emerged despite the strength
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And I think China as an environment
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may be more interesting for the emergence.
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Because if you look at companies between, let's say,
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$50 to $300 billion, China has emerged more of such companies
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than the US in the last three to four years.
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Because of the larger marketplace,
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because of the more fearless nature of the entrepreneurs.
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And the Chinese giants are just as powerful as American ones.
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Tencent Alibaba are very strong.
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But Bytes Dance has emerged worth $75 billion.
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And financial, while it's Alibaba affiliated,
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it's nevertheless independent and worth $150 billion.
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And so I do think if we start to extend
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to traditional businesses, we will see very valuable companies.
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So it's probably not the case that in five or 10 years,
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we'll still see the whole world with these five companies
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having such dominance.
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So you've mentioned a couple of times
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this fascinating world of entrepreneurship
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in China of the fearless nature of the entrepreneurs.
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So can you maybe talk a little bit
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about what it takes to be an entrepreneur in China?
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What are the strategies that are undertaken?
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What are the ways that you success?
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What is the dynamic of VCF funding,
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of the way the government helps companies, and so on?
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What are the interesting aspects here that are distinct from,
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that are different from the Silicon Valley world
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of entrepreneurship?
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Well, many of the listeners probably
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still would brand Chinese entrepreneur as copycats.
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And no doubt, 10 years ago, that would not
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be an inaccurate description.
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Back 10 years ago, an entrepreneur probably
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could not get funding if he or she could not
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describe what product he or she is copying from the US.
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The first question is, who has proven this business model,
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which is a nice way of asking, who are you copying?
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And that reason is understandable,
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because China had a much lower internet penetration
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and didn't have enough indigenous experience
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to build innovative products.
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And secondly, internet was emerging.
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Link startup was the way to do things,
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building a first minimally viable product,
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and then expanding was the right way to go.
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And the American successes have given a shortcut
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that if you build your minimally viable product based
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on an American product, it's guaranteed
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to be a decent starting point.
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Then you tweak it afterwards.
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So as long as there are no IP infringement, which,
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as far as I know, there hasn't been in the mobile and AI
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spaces, that's a much better shortcut.
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And I think Silicon Valley would view that as still not
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very honorable, because that's not your own idea to start with.
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But you can't really, at the same time,
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believe every idea must be your own
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and believe in the link startup methodology,
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because link startup is intended to try many, many things
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and then converge when that works.
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And it's meant to be iterated and changed.
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So finding a decent starting point without legal violations,
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there should be nothing morally dishonorable about that.
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So just a quick pause on that.
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It's fascinating that that's why is that not honorable, right?
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It's exactly as you formulated.
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It seems like a perfect start for business
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is to take a look at Amazon and say, OK,
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we'll do exactly what Amazon is doing.
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Let's start there in this particular market.
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And then let's out innovate them from that starting point.
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Yes. Come up with new ways.
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I mean, is it wrong to be, except the word copycat just
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sounds bad, but is it wrong to be a copycat?
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It just seems like a smart strategy.
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But yes, doesn't have a heroic nature to it
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that Steve Jobs, Elon Musk, sort of in something completely
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coming up with something completely new.
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Yeah, I like the way you describe it.
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It's a nonheroic, acceptable way to start the company.
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And maybe more expedient.
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So that's, I think, a baggage for Silicon Valley,
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that if it doesn't let go, then it
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may limit the ultimate ceiling of the company.
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Take Snapchat as an example.
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I think Evan's brilliant.
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He built a great product.
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But he's very proud that he wants
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to build his own features, not copy others.
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While Facebook was more willing to copy his features,
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and you see what happens in the competition.
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So I think putting that handcuff on the company
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would limit its ability to reach the maximum potential.
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So back to the Chinese environment,
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copying was merely a way to learn from the American masters.
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Just like if we learned to play piano or painting,
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you start by copying.
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You don't start by innovating when
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you don't have the basic skill sets.
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So very amazingly, the Chinese entrepreneurs
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about six years ago started to branch off
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with these lean startups built on American ideas
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to build better products than American products.
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But they did start from the American idea.
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And today, WeChat is better than WhatsApp.
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Weibo is better than Twitter.
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Zihu is better than Quora and so on.
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So that, I think, is Chinese entrepreneurs
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going to step two.
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And then step three is once these entrepreneurs have
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done one or two of these companies,
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they now look at the Chinese market and the opportunities
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and come up with ideas that didn't exist elsewhere.
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So products like and financial under which includes Alipay,
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which is mobile payments, and also the financial products
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for loans built on that, and also in education, VIP kid,
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and in social video, social network, TikTok,
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and in social eCommerce, Pinduoduo,
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and then in ride sharing, Mobike.
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These are all Chinese innovative products
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that now are being copied elsewhere.
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So an additional interesting observation
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is some of these products are built on unique Chinese
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demographics, which may not work in the US,
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but may work very well in Southeast Asia, Africa,
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and other developing worlds that are a few years behind China.
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And a few of these products maybe are universal
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and are getting traction even in the United States,
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So this whole ecosystem is supported by VCs
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as a virtuous cycle, because a large market
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with innovative entrepreneurs will draw a lot of money
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and then invest in these companies.
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As the market gets larger and larger,
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China market is easily three, four times larger than the US.
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They will create greater value and greater returns
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for the VCs, thereby raising even more money.
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So at Sinovation Ventures, our first fund was $15 million.
link |
Our last fund was $500 million.
link |
So it reflects the valuation of the companies
link |
and our us going multi stage and things like that.
link |
It also has government support, but not
link |
in the way most Americans would think of it.
link |
The government actually leaves the entrepreneurial space
link |
as a private enterprise, so the self regulating.
link |
And the government would build infrastructures
link |
that would around it to make it work better.
link |
For example, the mass entrepreneur mass innovation
link |
plan builds 8,000 incubators.
link |
So the pipeline is very strong to the VCs
link |
for autonomous vehicles.
link |
The Chinese government is building smart highways
link |
with sensors, smart cities that separate pedestrians
link |
from cars that may allow initially an inferior autonomous
link |
vehicle company to launch a car without increasing,
link |
with lower casualty, because the roads or the city is smart.
link |
And the Chinese government at local levels
link |
would have these guiding funds acting as LPs,
link |
passive LPs to funds.
link |
And when the fund makes money, part of the money made
link |
is given back to the GPs and potentially other LPs
link |
to increase everybody's return at the expense
link |
of the government's return.
link |
So that's an interesting incentive
link |
that entrusts the task of choosing entrepreneurs to VCs
link |
who are better at it than the government
link |
by letting some of the profits move that way.
link |
So this is really fascinating, right?
link |
So I look at the Russian government as a case study
link |
where, let me put it this way, there
link |
is no such government driven, large scale
link |
support of entrepreneurship.
link |
And probably the same is true in the United States.
link |
But the entrepreneurs themselves kind of find a way.
link |
So maybe in a form of advice or explanation,
link |
how did the Chinese government arrive to be this way,
link |
so supportive on entrepreneurship,
link |
to be in this particular way so forward thinking
link |
at such a large scale?
link |
And also perhaps, how can we copy it in other countries?
link |
How can we encourage other governments,
link |
like even the United States government,
link |
to support infrastructure for autonomous vehicles
link |
in that same kind of way, perhaps?
link |
So these techniques are the result of several key things,
link |
some of which may be learnable, some of which
link |
One is just trial and error and watching
link |
what everyone else is doing.
link |
I think it's important to be humble and not
link |
feel like you know all the answers.
link |
The guiding funds idea came from Singapore,
link |
which came from Israel.
link |
And China made a few tweaks and turned it into a,
link |
because the Chinese cities and government officials kind
link |
of compete with each other.
link |
Because they all want to make their city more successful,
link |
so they can get the next level in their political career.
link |
And it's somewhat competitive.
link |
So the central government made it a bit of a competition.
link |
Everybody has a budget.
link |
They can put it on AI, or they can put it on bio,
link |
or they can put it on energy.
link |
And then whoever gets the results, the city shines,
link |
the people are better off, the mayor gets a promotion.
link |
So the tools is kind of almost like an entrepreneurial
link |
environment for local governments
link |
to see who can do a better job.
link |
And also, many of them tried different experiments.
link |
Some have given award to very smart researchers,
link |
just give them money and hope they'll start a company.
link |
Some have given money to academic research labs,
link |
maybe government research labs, to see
link |
if they can spin off some companies from the science
link |
lab or something like that.
link |
Some have tried to recruit overseas Chinese
link |
to come back and start companies.
link |
And they've had mixed results.
link |
The one that worked the best was the guiding funds.
link |
So it's almost like a lean startup idea
link |
where people try different things in what works, sticks,
link |
and everybody copies.
link |
So now every city has a guiding fund.
link |
So that's how that came about.
link |
The autonomous vehicle and the massive spending
link |
in highways and smart cities, that's a Chinese way.
link |
It's about building infrastructure to facilitate.
link |
It's a clear division of the government's responsibility
link |
The market should do everything in a private freeway.
link |
But there are things the market can't afford to do,
link |
like infrastructure.
link |
So the government always appropriates
link |
large amounts of money for infrastructure building.
link |
This happens with not only autonomous vehicle and AI,
link |
but happened with the 3G and 4G.
link |
You'll find that the Chinese wireless reception
link |
is better than the US, because massive spending that
link |
tries to cover the whole country.
link |
Whereas in the US, it may be a little spotty.
link |
It's a government driven, because I think
link |
they view the coverage of cell access and 3G, 4G access
link |
to be a governmental infrastructure spending,
link |
as opposed to capitalistic.
link |
So of course, the state or enterprise
link |
is also publicly traded, but they also
link |
carry a government responsibility
link |
to deliver infrastructure to all.
link |
So it's a different way of thinking
link |
that may be very hard to inject into Western countries
link |
to say starting tomorrow, bandwidth infrastructure
link |
and highways are going to be governmental spending
link |
with some characteristics.
link |
What's your sense, and sorry to interrupt,
link |
but because it's such a fascinating point,
link |
do you think on the autonomous vehicle space
link |
it's possible to solve the problem of full autonomy
link |
without significant investment in infrastructure?
link |
Well, that's really hard to speculate.
link |
I think it's not a yes, no question,
link |
but how long does it take question?
link |
15 years, 30 years, 45 years.
link |
Clearly with infrastructure augmentation,
link |
where there's road, the city, or whole city planning,
link |
building a new city, I'm sure that will accelerate
link |
the day of the L5.
link |
I'm not knowledgeable enough, and it's
link |
hard to predict even when we're knowledgeable,
link |
because a lot of it is speculative.
link |
But in the US, I don't think people
link |
would consider building a new city the size of Chicago
link |
to make it the AI slash autonomous city.
link |
There are smaller ones being built, I'm aware of that.
link |
But is infrastructure spend really
link |
impossible for US or Western countries?
link |
The US highway system was built.
link |
Was that during President Eisenhower or Kennedy?
link |
So maybe historians can study how the President Eisenhower
link |
get the resources to build this massive infrastructure that
link |
surely gave US a tremendous amount of prosperity
link |
over the next decade, if not century.
link |
If I may comment on that, then, it
link |
takes us to artificial intelligence
link |
a little bit, because in order to build infrastructure,
link |
it creates a lot of jobs.
link |
So I'll be actually interested if you
link |
would say that you're talking in your book about all kinds
link |
of jobs that could and could not be automated.
link |
I wonder if building infrastructure
link |
is one of the jobs that would not be easily automated,
link |
something you can think about, because I think you've mentioned
link |
somewhere in a talk, or that there
link |
might be, as jobs are being automated,
link |
a role for government to create jobs that can't be automated.
link |
Yes, I think that's a possibility.
link |
Back in the last financial crisis,
link |
China put a lot of money to basically give this economy
link |
a boost, and a lot of it went into infrastructure building.
link |
And I think that's a legitimate way, at the government level,
link |
to deal with the employment issues as well as build out
link |
the infrastructure, as long as the infrastructures are truly
link |
needed, and as long as there is an employment problem, which
link |
So maybe taking a little step back,
link |
if you've been a leader and a researcher in AI
link |
for several decades, at least 30 years,
link |
so how has AI changed in the West and the East
link |
as you've observed, as you've been deep in it
link |
over the past 30 years?
link |
Well, AI began as the pursuit of understanding
link |
human intelligence, and the term itself represents that.
link |
But it kind of drifted into the one subarea that
link |
worked extremely well, which is machine intelligence.
link |
And that's actually more using pattern recognition techniques
link |
to basically do incredibly well on a limited domain,
link |
large amount of data, but relatively simple kinds
link |
of planning, tasks, and not very creative.
link |
So we didn't end up building human intelligence.
link |
We built a different machine that
link |
was a lot better than us, some problems,
link |
but nowhere close to us on other problems.
link |
So today, I think a lot of people still
link |
misunderstand when we say artificial intelligence
link |
and what various products can do.
link |
People still think it's about replicating human intelligence.
link |
But the products out there really
link |
are closer to having invented the internet or the spreadsheet
link |
or the database and getting broader adoption.
link |
And speaking further to the fears, near term fears
link |
that people have about AI, so you're commenting
link |
on the general intelligence that people
link |
in the popular culture from sci fi movies
link |
have a sense about AI, but there's practical fears
link |
about AI, the kind of narrow AI that you're talking about
link |
of automating particular kinds of jobs,
link |
and you talk about them in the book.
link |
So what are the kinds of jobs in your view
link |
that you see in the next five, 10 years beginning
link |
to be automated by AI systems algorithms?
link |
Yes, this is also maybe a little bit counterintuitive
link |
because it's the routine jobs that
link |
will be displaced the soonest.
link |
And they may not be displaced entirely, maybe 50%, 80%
link |
of a job, but when the workload drops by that much,
link |
employment will come down.
link |
And also another part of misunderstanding
link |
is most people think of AI replacing routine jobs,
link |
then they think of the assembly line, the workers.
link |
Well, that will have some effects,
link |
but it's actually the routine white collar workers that's
link |
easiest to replace because to replace a white collar worker,
link |
you just need software.
link |
To replace a blue collar worker,
link |
you need robotics, mechanical excellence,
link |
and the ability to deal with dexterity,
link |
and maybe even unknown environments, very, very difficult.
link |
So if we were to categorize the most dangerous white collar
link |
jobs, they would be things like back office,
link |
people who copy and paste and deal with simple computer
link |
programs and data, and maybe paper and OCR,
link |
and they don't make strategic decisions,
link |
they basically facilitate the process.
link |
These software and paper systems don't work,
link |
so you have people dealing with new employee orientation,
link |
searching for past lawsuits and financial documents,
link |
and doing reference check, so basic searching and management
link |
of data that's the most in danger of being lost.
link |
In addition to the white collar repetitive work,
link |
a lot of simple interaction work can also
link |
be taken care of, such as tele sales, telemarketing,
link |
customer service, as well as many physical jobs
link |
that are in the same location and don't
link |
require a high degree of dexterity,
link |
so fruit picking, dishwashing, assembly line, inspection,
link |
our jobs in that category.
link |
So altogether, back office is a big part,
link |
and the other, the blue collar may be smaller initially,
link |
but over time, AI will get better.
link |
And when we start to get to over the next 15, 20 years,
link |
the ability to actually have the dexterity
link |
of doing assembly line, that's a huge chunk of jobs.
link |
And when autonomous vehicles start
link |
to work initially starting with truck drivers,
link |
but eventually to all drivers, that's
link |
another huge group of workers.
link |
So I see modest numbers in the next five years,
link |
but increasing rapidly after that.
link |
On the worry of the jobs that are in danger
link |
and the gradual loss of jobs, I'm not
link |
sure if you're familiar with Andrew Yang.
link |
So there's a candidate for president of the United States
link |
whose platform, Andrew Yang, is based around, in part,
link |
around job loss due to automation,
link |
and also, in addition, the need, perhaps,
link |
of universal basic income to support jobs that are folks who
link |
lose their job due to automation and so on,
link |
and in general, support people under complex,
link |
unstable job market.
link |
So what are your thoughts about his concerns,
link |
him as a candidate, his ideas in general?
link |
I think his thinking is generally in the right direction,
link |
but his approach as a presidential candidate
link |
may be a little bit ahead at the time.
link |
I think the displacements will happen,
link |
but will they happen soon enough for people
link |
to agree to vote for him?
link |
The unemployment numbers are not very high yet.
link |
And I think he and I have the same challenge.
link |
If I want to theoretically convince people this is an issue
link |
and he wants to become the president,
link |
people have to see how can this be the case when
link |
unemployment numbers are low.
link |
So that is the challenge.
link |
And I think I do agree with him on the displacement issue,
link |
on universal basic income, at a very vanilla level.
link |
I don't agree with it because I think the main issue
link |
So people need to be incented not by just giving a monthly
link |
$2,000 check or $1,000 check and do whatever they want
link |
because they don't have the know how
link |
to know what to retrain to go into what type of a job
link |
and guidance is needed.
link |
And retraining is needed because historically
link |
in technology revolutions, when routine jobs were displaced,
link |
new routine jobs came up.
link |
So there was always room for that.
link |
But with AI and automation, the whole point
link |
is replacing all routine jobs eventually.
link |
So there will be fewer and fewer routine jobs.
link |
And AI will create jobs, but it won't create routine jobs
link |
because if it creates routine jobs,
link |
why wouldn't AI just do it?
link |
So therefore, the people who are losing the jobs
link |
are losing routine jobs.
link |
The jobs that are becoming available are nonroutine jobs.
link |
So the social stipend needs to be put in place
link |
is for the routine workers who lost their jobs
link |
to be retrained maybe in six months, maybe in three years.
link |
Takes a while to retrain on the nonroutine job
link |
and then take on a job that will last
link |
for that person's lifetime.
link |
Now, having said that, if you look deeply
link |
into Andrew's document, he does cater for that.
link |
So I'm not disagreeing with what he's trying to do.
link |
But for simplification, sometimes he just says UBI,
link |
but simple UBI wouldn't work.
link |
And I think you've mentioned elsewhere
link |
that the goal isn't necessarily to give people enough money
link |
to survive or live or even to prosper.
link |
The point is to give them a job that gives them meaning.
link |
That meaning is extremely important.
link |
That our employment, at least in the United States
link |
and perhaps it cares across the world,
link |
provides something that's, forgive me for saying,
link |
greater than money, it provides meaning.
link |
So now what kind of jobs do you think can't be automated?
link |
You talk a little bit about creativity
link |
and compassion in your book.
link |
What aspects do you think it's difficult
link |
to automate for an AI system?
link |
Because an AI system is currently merely optimizing.
link |
It's not able to reason, plan,
link |
or think creatively or strategically.
link |
It's not able to deal with complex problems.
link |
It can't come up with a new problem and solve it.
link |
A human needs to find the problem
link |
and pose it as an optimization problem,
link |
then have the AI work at it.
link |
So an AI would have a very hard time
link |
discovering a new drug
link |
or discovering a new style of painting
link |
or dealing with complex tasks
link |
such as managing a company
link |
that isn't just about optimizing the bottom line,
link |
but also about employee satisfaction, corporate brand,
link |
and many, many other things.
link |
So that is one category of things.
link |
And because these things are challenging, creative, complex,
link |
doing them creates a higher degree of satisfaction
link |
and therefore appealing to our desire for working,
link |
which isn't just to make the money,
link |
make the ends meet,
link |
but also that we've accomplished something
link |
that others maybe can't do or can't do as well.
link |
Another type of job that is much numerous
link |
would be compassionate jobs,
link |
jobs that require compassion, empathy, human touch, human trust.
link |
AI can't do that because AI is cold, calculating,
link |
and even if it can fake that to some extent,
link |
it will make errors and that will make it look very silly.
link |
And also, I think even if AI did okay,
link |
people would want to interact with another person,
link |
whether it's for some kind of a service or a teacher or a doctor
link |
or a concierge or a masseuse or bartender.
link |
There are so many jobs where people just don't want to interact
link |
with a cold robot or software.
link |
I've had an entrepreneur who built an elderly care robot
link |
and they found that the elderly really only use it for customer service.
link |
But not to service the product,
link |
but they click on customer service and the video of a person comes up
link |
and then the person says,
link |
how come my daughter didn't call me? Let me show you a picture of her grandkids.
link |
So people earn for that, people people interaction.
link |
So even if robots improved, people just don't want it.
link |
And those jobs are going to be increasing
link |
because AI will create a lot of value,
link |
$16 trillion to the world in next 11 years according to PWC
link |
and that will give people money to enjoy services,
link |
whether it's eating a gourmet meal or tourism and traveling
link |
or having concierge services.
link |
The services revolving around, you know,
link |
every dollar of that $16 trillion will be tremendous.
link |
It will create more opportunities to service the people who did well
link |
through AI with things.
link |
But even at the same time, the entire society is very much short
link |
in need of many service oriented, compassionate oriented jobs.
link |
The best example is probably in healthcare services.
link |
There's going to be 2 million new jobs, not counting replacement,
link |
just brand new incremental jobs in the next six years in healthcare services.
link |
That includes nurses orderly in the hospital,
link |
elderly care and also at home care.
link |
It's particularly lacking.
link |
And those jobs are not likely to be filled.
link |
So there's likely to be a shortage.
link |
And the reason they're not filled is simply because they don't pay very well
link |
and that the social status of these jobs are not very good.
link |
So they pay about half as much as a heavy equipment operator,
link |
which will be replaced a lot sooner.
link |
And they pay probably comparably to someone on the assembly line.
link |
And so if we're ignoring all the other issues
link |
and just think about satisfaction from one's job,
link |
someone repetitively doing the same manual action at an assembly line,
link |
that can't create a lot of job satisfaction.
link |
But someone taking care of a sick person
link |
and getting a hug and thank you from that person and the family,
link |
I think is quite satisfying.
link |
So if only we could fix the pay for service jobs,
link |
there are plenty of jobs that require some training or a lot of training
link |
for the people coming off the routine jobs to take.
link |
We can easily imagine someone who was maybe a cashier at the grocery store,
link |
at stores become automated, learns to become a nurse or at home care.
link |
Also, I do want to point out the blue collar jobs are going to stay around a bit longer,
link |
some of them quite a bit longer.
link |
AI cannot be told, go clean an arbitrary home.
link |
That's incredibly hard.
link |
Arguably is an L5 level of difficulty.
link |
And then AI cannot be a good plumber,
link |
because plumber is almost like a mini detective
link |
that has to figure out where the leak came from.
link |
So yet AI probably can be an assembly line and auto mechanic and so on.
link |
So one has to study which blue collar jobs are going away
link |
and facilitate retraining for the people to go into the ones that won't go away
link |
or maybe even will increase.
link |
I mean, it is fascinating that it's easier to build a world champion chess player
link |
than it is to build a mediocre plumber.
link |
And to AI, and that goes counterintuitive to a lot of people's understanding
link |
of what artificial intelligence is.
link |
So it sounds, I mean, you're painting a pretty optimistic picture
link |
about retraining, about the number of jobs
link |
and actually the meaningful nature of those jobs once we automate repetitive tasks.
link |
So overall, are you optimistic about the future
link |
where much of the repetitive tasks are automated,
link |
that there is a lot of room for humans, for the compassionate,
link |
for the creative input that only humans can provide?
link |
I am optimistic if we start to take action.
link |
If we have no action in the next five years,
link |
I think it's going to be hard to deal with the devastating losses that will emerge.
link |
So if we start thinking about retraining, maybe with the low hanging fruits,
link |
explaining to vocational schools why they should train more plumbers than auto mechanics,
link |
maybe starting with some government subsidy for corporations to have more training positions.
link |
We start to explain to people why retraining is important.
link |
We start to think about what the future of education,
link |
how that needs to be tweaked for the era of AI.
link |
If we start to make incremental progress,
link |
and the greater number of people understand,
link |
then there's no reason to think we can't deal with this,
link |
because this technological revolution is arguably similar to
link |
what electricity, industrial revolutions, and internet brought about.
link |
Do you think there's a role for policy, for governments to step in
link |
to help with policy to create a better world?
link |
Absolutely, and the governments don't have to believe
link |
that unemployment will go up, and they don't have to believe automation will be this fast to do something.
link |
Revamping vocational school would be one example.
link |
Another is if there's a big gap in healthcare service employment,
link |
and we know that a country's population is growing older and more longevity living older,
link |
because people over 80 require five times as much care as those under 80,
link |
then it is a good time to incent training programs for elderly care,
link |
to find ways to improve the pay.
link |
Maybe one way would be to offer as part of Medicare or the equivalent program
link |
for people over 80 to be entitled to a few hours of elderly care at home,
link |
and then that might be reimbursable,
link |
and that will stimulate the service industry around the policy.
link |
Do you have concerns about large entities,
link |
whether it's governments or companies, controlling the future of AI development in general?
link |
So we talked about companies.
link |
Do you have a better sense that governments can better represent the interest of the people
link |
than companies, or do you believe companies are better at representing the interest of the people?
link |
Or is there no easy answer?
link |
I don't think there's an easy answer because it's a double edged sword.
link |
The companies and governments can provide better services with more access to data and more access to AI,
link |
but that also leads to greater power, which can lead to uncontrollable problems,
link |
whether it's monopoly or corruption in the government.
link |
So I think one has to be careful to look at how much data that companies and governments have,
link |
and some kind of checks and balances would be helpful.
link |
So again, I come from Russia.
link |
There's something called the Cold War.
link |
So let me ask a difficult question here, looking at conflict.
link |
Steven Pinker wrote a great book that conflict all over the world is decreasing in general.
link |
But do you have a sense that having written the book AI Superpowers,
link |
do you see a major international conflict potentially arising between major nations,
link |
whatever they are, whether it's Russia, China, European nations, United States,
link |
or others in the next 10, 20, 50 years around AI, around the digital space, cyber space?
link |
Do you worry about that?
link |
Is that something we need to think about and try to alleviate or prevent?
link |
I believe in greater engagement.
link |
A lot of the worries about more powerful AI are based on an arms race metaphor.
link |
And when you extrapolate into military kinds of scenarios,
link |
AI can automate autonomous weapons that needs to be controlled somehow.
link |
And autonomous decision making can lead to not enough time to fix international crises.
link |
So I actually believe a Cold War mentality would be very dangerous
link |
because should two countries rely on AI to make certain decisions
link |
and they don't even talk to each other, they do their own scenario planning,
link |
then something could easily go wrong.
link |
I think engagement, interaction, some protocols to avoid inadvertent disasters is actually needed.
link |
So it's natural for each country to want to be the best,
link |
whether it's in nuclear technologies or AI or bio.
link |
But I think it's important to realize if each country has a black box AI
link |
and don't talk to each other, that probably presents greater challenges to humanity
link |
than if they interacted.
link |
I think there can still be competition, but with some degree of protocol for interaction.
link |
Just like when there was a nuclear competition,
link |
there were some protocol for deterrence among US, Russia, and China.
link |
And I think that engagement is needed.
link |
So of course, we're still far from AI presenting that kind of danger.
link |
But what I worry the most about is the level of engagement seems to be coming down.
link |
The level of distrust seems to be going up,
link |
especially from the US towards other large countries such as China and Russia.
link |
Is there a way to make that better?
link |
So that's beautifully put, level of engagement and even just basic trust and communication
link |
as opposed to making artificial enemies out of particular countries.
link |
Do you have a sense how we can make it better, actionable items that as a society we can take on?
link |
I'm not an expert at geopolitics, but I would say that we look pretty foolish as humankind
link |
when we are faced with the opportunity to create $16 trillion for humanity.
link |
And yet we're not solving fundamental problems with parts of the world still in poverty.
link |
And for the first time, we have the resources to overcome poverty and hunger.
link |
We're not using it on that, but we're fueling competition among superpowers.
link |
And that's a very unfortunate thing.
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If we become utopian for a moment, imagine a benevolent world government that has this $16 trillion
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and maybe some AI to figure out how to use it to deal with diseases and problems and hate and things like that.
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World would be a lot better off.
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So what is wrong with the current world?
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I think the people with more skill than I should think about this.
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And then the geopolitics issue with superpower competition is one side of the issue.
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There's another side which I worry maybe even more, which is as the $16 trillion all gets made by U.S. and China
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and a few of the other developed countries, the poorer country will get nothing
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because they don't have technology and the wealth disparity and inequality will increase.
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So a poorer country with a large population will not only benefit from the AI boom or other technology booms
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but they will have their workers who previously had hoped they could do the China model and do outsource manufacturing
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or the India model so they could do the outsource process or call center
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while all those jobs are going to be gone in 10 or 15 years.
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So the individual citizen may be a net liability, I mean financially speaking, to a poorer country
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and not an asset to claw itself out of poverty.
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So in that kind of situation, these large countries with not much tech are going to be facing a downward spiral
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and it's unclear what could be done and then when we look back and say there's $16 trillion being created
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and it's all being kept by U.S. China and other developed countries, it just doesn't feel right.
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So I hope people who know about geopolitics can find solutions that's beyond my expertise.
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So different countries that we've talked about have different value systems.
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If you look at the United States to an almost extreme degree, there is an absolute desire for freedom of speech.
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If you look at a country where I was raised, that desire just amongst the people is not as elevated as it is to basically fundamental level
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to the essence of what it means to be America, right?
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And the same is true with China, there's different value systems.
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There is some censorship of internet content that China and Russia and many other countries undertake.
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Do you see that having effects on innovation, other aspects of some of the tech stuff, AI development we talked about
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and maybe from another angle, do you see that changing in different ways over the next 10 years, 20 years, 50 years as China continues to grow
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as it does now in its tech innovation?
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There's a common belief that full freedom of speech and expression is correlated with creativity, which is correlated with entrepreneurial success.
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I think empirically we have seen that is not true and China has been successful.
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That's not to say the fundamental values are not right or not the best, but it's just that perfect correlation isn't there.
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It's hard to read the tea leaves on opening up or not in any country and I've not been very good at that in my past predictions.
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But I do believe every country shares some fundamental value, a lot of fundamental values for the long term.
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So, you know, China is drafting its privacy policy for individual citizens and they don't look that different from the American or European ones.
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So, people do want to protect their privacy and have the opportunity to express and I think the fundamental values are there.
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The question is in the execution and timing, how soon or when will that start to open up?
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So, as long as each government knows, ultimately people want that kind of protection, there should be a plan to move towards that.
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As to when or how, again, I'm not an expert.
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On the point of privacy to me, it's really interesting.
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So, AI needs data to create a personalized awesome experience.
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I'm just speaking generally in terms of products.
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And then we have currently, depending on the age and depending on the demographics of who we're talking about,
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some people are more or less concerned about the amount of data they hand over.
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So, in your view, how do we get this balance right?
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That we provide an amazing experience to people that use products.
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You look at Facebook, you know, the more Facebook knows about you, yes, it's scary to say.
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The better it can probably, a better experience it can probably create.
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So, in your view, how do we get that balance right?
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Yes, I think 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.
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So, you can deny them access to further data and still enjoy the services we have.
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If we take back all the data, all the services will give us nonsense.
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We'll no longer be able to use products that function well in terms of, you know, right ranking, right products, right user experience.
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So, yet I do understand we don't want to permit misuse of the data.
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From legal policy standpoint, I think there can be severe punishment for those who have egregious misuse of the data.
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That's, I think, a good first step.
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Actually, China on this aspect has very strong laws about people who sell or give data to other companies.
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And that over the past few years, since that law came into effect, pretty much eradicated the illegal distribution sharing of data.
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Additionally, I think giving, I think technology is often a very good way to solve technology misuse.
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So, can we come up with new technologies that will let us have our cake and eat it too?
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People are looking into homomorphic encryption, which is letting you keep the data, have it encrypted and train encrypted data.
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Of course, we haven't solved that one yet, but that kind of direction may be worth pursuing.
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Also federated learning, which would allow one hospital to train on its hospitals patient data fully because they have a license for that.
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And then hospitals would then share their models, not data, but models to create a supra AI.
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And that also maybe has some promise.
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So I would want to encourage us to be open minded and think of this as not just the policy binary yes no,
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but letting the technologists try to find solutions to let us have our cake and eat it too, or have most of our cake and eat most of it too.
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Finally, I think giving each end user a choice is important and having transparency is important.
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Also, I think that's universal, but the choice you give to the user should not be at a granular level that the user cannot understand.
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GDPR today causes all these pop ups of yes, no, will you give this site this right to use this part of your data?
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I don't think any user understands what they're saying yes or no to, and I suspect most are just saying yes because they don't understand it.
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So while GDPR in its current implementation has lived up to its promise of transparency and user choice,
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it implemented it in such a way that really didn't deliver the spirit of GDPR.
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It fit the letter, but not the spirit.
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So again, I think we need to think about is there a way to fit the spirit of GDPR by using some kind of technology?
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Can we have a slider?
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That's an AI trying to figure out how much you want to slide between perfect protection security of your personal data
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versus high degree of convenience with some risks of not having full privacy.
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Each user should have some preference and that gives you the user choice,
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but maybe we should turn the problem on its head and ask can there be an AI algorithm that can customize this
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because we can understand the slider, but we sure cannot understand every pop up question.
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And I think getting that right requires getting the balance between what we talked about earlier,
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which is heart and soul versus profit driven decisions and strategy.
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I think from my perspective, the best way to make a lot of money in the long term is to keep your heart and soul intact.
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I think getting that slider right in the short term may feel like you'll be sacrificing profit,
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but in the long term, you'll be getting user trust and providing a great experience.
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Do you share that kind of view in general?
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Yes, absolutely. I sure would hope there is a way we can do long term projects that really do the right thing.
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I think a lot of people who embrace GDPR, their hearts in the right place.
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I think they just need to figure out how to build a solution.
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I've heard utopians talk about solutions that get me excited,
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but not sure how in the current funding environment they can get started, right?
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People talk about, imagine this crowdsourced data collection that we all trust,
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and then we have these agents that we ask them to ask the trusted agent.
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That agent only, that platform.
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A trusted joint platform that we all believe is trustworthy that can give us all the close loop personal suggestions
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by the new social network, new search engine, new ecommerce engine
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that has access to even more of our data, but not directly but indirectly.
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I think that general concept of licensing to some trusted engine
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and finding a way to trust that engine seems like a great idea,
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but if you think how long it's going to take to implement and tweak and develop it right,
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as well as to collect all the trust and the data from the people,
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it's beyond the current cycle of venture capital.
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How do you do that is a big question.
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You've recently had a fight with cancer, stage 4 lymphoma,
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and in a sort of deep personal level, what did it feel like in the darker moments to face your own mortality?
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Well, I've been the workaholic my whole life,
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and I've basically worked 9.96, 9am to 9pm, 6 days a week, roughly.
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And I didn't really pay a lot of attention to my family, friends, and people who loved me,
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and my life revolved around optimizing for work.
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While my work was not routine, my optimization really made my life basically a very mechanical process.
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But I got a lot of highs out of it because of accomplishments that I thought were really important and dear and the highest priority to me.
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But when I faced mortality and the possible death in matter of months,
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I suddenly realized that this really meant nothing to me,
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that I didn't feel like working for another minute,
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that if I had 6 months left in my life, I would spend it all with my loved ones.
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And thanking them, giving them love back, and apologizing to them that I lived my life the wrong way.
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So that moment of reckoning caused me to really rethink that why we exist in this world
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is something that we might be too much shaped by the society to think that success and accomplishments is why we live.
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And while that can get you periodic successes and satisfaction,
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it's really in them facing death, you see what's truly important to you.
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So as a result of going through the challenges with cancer,
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I've resolved to live a more balanced lifestyle.
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I'm now in remission, knock on wood,
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and I'm spending more time with my family.
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My wife travels with me.
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When my kids need me, I spend more time with them.
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And before, I used to prioritize everything around work.
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When I had a little bit of time, I would dole it out to my family.
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Now, when my family needs something, really needs something,
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I drop everything at work and go to them.
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And then in the time remaining, I allocate to work.
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But one's family is very understanding.
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It's not like they will take 50 hours a week from me.
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So I'm actually able to still work pretty hard,
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maybe 10 hours less per week.
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So I realize the most important thing in my life is really love and the people I love.
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And I give that the highest priority.
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It isn't the only thing I do.
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But when that is needed, I put that at the top priority.
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And I feel much better and I feel much more balanced.
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And I think this also gives a hint as to a life of routine work,
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a life of pursuit of numbers.
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While my job was not routine, it wasn't pursuit of numbers.
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Pursuit of, can I make more money?
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Can I fund more great companies?
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Can I raise more money?
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Can I make sure our VC is ranked higher and higher every year?
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This competitive nature of driving for bigger numbers and better numbers
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became an endless pursuit of that's mechanical.
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And bigger numbers really didn't make me happier.
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And faced with death, I realized bigger numbers really meant nothing.
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And what was important is that people who have given their heart and their love to me
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deserve for me to do the same.
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So there's deep profound truth in that, that everyone should hear and internalize.
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And that's really powerful for you to say that.
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I have to ask sort of a difficult question here.
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So I've competed in sports my whole life, looking historically.
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I'd like to challenge some aspect of that a little bit on the point of hard work.
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That it feels that there are certain aspects that is the greatest,
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the most beautiful aspects of human nature, is the ability to become obsessed,
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of becoming extremely passionate to the point where, yes, flaws are revealed
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and just giving yourself fully to a task.
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That is, in another sense, you mentioned love being important,
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but in another sense, this kind of obsession, this pure exhibition of passion and hard work
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is truly what it means to be human.
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What lessons should we take that's deeper?
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Because you've accomplished incredible things.
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Like chasing numbers.
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But really, there's some incredible work there.
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So how do you think about that when you look back in your 20s, your 30s?
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What would you do differently?
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Would you really take back some of the incredible hard work?
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But it's in percentages, right?
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We're both now computer scientists.
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So I think when one balances one's life, when one is younger,
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you might give a smaller percentage to family, but you would still give them high priority.
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And when you get older, you would give a larger percentage to them and still the high priority.
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And when you're near retirement, you give most of it to them and the highest priority.
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So I think the key point is not that we would work 20 hours less for the whole life
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and just spend it aimlessly with the family, but that when the family has a need,
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when your wife is having a baby, when your daughter has a birthday,
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or when they're depressed, or when they're celebrating something,
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or when they have a get together, or when we have family time,
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that is important for us to put down our phone and PC and be 100% with them.
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And that priority on the things that really matter isn't going to be so taxing
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that it would eliminate or even dramatically reduce our accomplishments.
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It might have some impact, but it might also have other impact
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because if you have a happier family, maybe you fight less.
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If you fight less, you don't spend time taking care of all the aftermath of a fight.
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And I'm sure that it would take more time.
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And if it did, I'd be willing to take that reduction.
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And it's not a dramatic number, but it's a number
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that I think would give me a greater degree of happiness
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and knowing that I've done the right thing
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and still have plenty of hours to get the success that I want to get.
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So given the many successful companies that you've launched
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and much success throughout your career,
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what advice would you give to young people today looking,
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or it doesn't have to be young, but people today looking to launch
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and to create the next $1 billion tech startup, or even AI based startup?
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I would suggest that people understand technology waves move quickly.
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What worked two years ago may not work today.
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And that is very much a case in point for AI.
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I think two years ago, or maybe three years ago,
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you certainly could say I have a couple of super smart PhDs
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and we're not sure what we're going to do,
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but here's how we're going to start and get funding for a very high valuation.
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Those days are over because AI is going from rocket science towards mainstream.
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Not yet commodity, but more mainstream.
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So first, the creation of any company to eventual capitalist
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has to be creation of business value and monetary value.
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And when you have a very scarce commodity,
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VCs may be willing to accept greater uncertainty.
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But now the number of people who have the equivalent of PhD three years ago
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because that can be learned more quickly.
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Platforms are emerging.
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The cost to become an AI engineer is much lower and there are many more AI engineers.
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So the market is different.
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So I would suggest someone who wants to build an AI company
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be thinking about the normal business questions.
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What customer cases are you trying to address?
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What kind of pain are you trying to address?
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How does that translate to value?
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How will you extract value and get paid through what channel?
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And how much business value will get created?
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That today needs to be thought about much earlier up front than it did three years ago.
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The scarcity question of AI talent has changed.
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The number of AI talent has changed.
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So now you need not just AI but also understanding of business customer and the marketplace.
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So I also think you should have a more reasonable evaluation expectation
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and growth expectation.
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There's going to be more competition.
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But the good news though is that AI technologies are now more available in open source.
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TensorFlow, PyTorch and such tools are much easier to use.
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So you should be able to experiment and get results iteratively faster than before.
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So take more of a business mindset to this.
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Think less of this as a laboratory taken into a company because we've gone beyond that stage.
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The only exception is if you truly have a breakthrough in some technology that really no one has,
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then the old way still works.
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But I think that's harder and harder now.
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So I know you believe as many do that we're far from creating an artificial general intelligence system.
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But say once we do and you get to ask her one question, what would that question be?
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What is it that differentiates you and me?
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Beautifully put, Kaifu, thank you so much for your time today.