Speaker 1: This is Bloomberg Business Week with Carol Messer and Bloomberg
Speaker 1: Quick Takes Tim Stenovic on Bloomberg Radio. Kai Fu Lee
Speaker 1: has spent nearly forty years at the forefront of artificial intelligence.
Speaker 1: He's the chairman, CEO, and co founder of sign Ovation Ventures.
Speaker 1: It's a venture capital firm with about two point five
Speaker 1: billion dollars in assets. It focuses on AI, healthcare, and
Speaker 1: other high tech and has made more than three hundred investments.
Speaker 1: It's one of the first Chinese VC firms to have
Speaker 1: a presence in the US, and he's also the co
Speaker 1: chair of the Artificial Intelligence Council at the World Economic Forum.
Speaker 1: He's the former president of Google China and before that
Speaker 1: was an executive at Microsoft and at Apple. His newest
Speaker 1: book is AI Ten Visions for Our Future, and unlike
Speaker 1: his last book, it's a work of fiction. He and
Speaker 1: his co author, the sci fi novelist chen Cho Fan
Speaker 1: right ten stories. They're all set twenty years from now,
Speaker 1: in ten forty one now. These stories illustrate a world
Speaker 1: that's layered with artificial intel agents. They cover themes like
Speaker 1: AI displacing workers, deep fakes, and how a I will
Speaker 1: reimagine education of course, also imagines what could go wrong.
Speaker 1: Kai fu Ley joins us now from Beijing. Kai ful,
Speaker 1: great to chat with you. Thanks so much for taking
Speaker 1: the time. Why are these stories? Why is sci fi?
Speaker 1: Why is it the best way to communicate what you
Speaker 1: envision AI doing to society in twenty years. Hi great
Speaker 1: to have to be a here. AI is such an
Speaker 1: important technology, perhaps the most important in the history of mankind,
Speaker 1: and yet it's sometimes viewed as rocket science, very hard
Speaker 1: to understand. So I wanted to see if there's a
Speaker 1: way to make it accessible to people and that it's
Speaker 1: actually not only understandable, but also fun to read. That's
Speaker 1: why I uh found a science fiction writer to work
Speaker 1: with me. He writes the stories, I write the analysis,
Speaker 1: and then we make sure that the stories are realistic
Speaker 1: within the twenty year time frame, so that the stories
Speaker 1: you read will be roughly what life will be like
Speaker 1: twenty years from now. Each story has an introduction, it
Speaker 1: also has an analysis after the fact. What were you
Speaker 1: trying to do here? I wanted to explain the how
Speaker 1: the technology works and what it's good for, why it's
Speaker 1: why it's good at what it's good at, and also
Speaker 1: why it is almost always a double ex short. What
Speaker 1: makes it incredibly powerful also creates some downside. For example,
Speaker 1: AI is great to help Facebook and Amazon make a
Speaker 1: lot of money by showing us things that we will
Speaker 1: watch and buying, But then are those things good for
Speaker 1: us because that's not being considered in the AI run
Speaker 1: by Facebook and Amazon. So there is a flip side
Speaker 1: to this, and the story is really not just tell
Speaker 1: the technologies but also bringing up issues that they anology
Speaker 1: causes and also suggests possible solutions. Do you think the
Speaker 1: companies that deploy AI today that are working on solving
Speaker 1: these issues, do you think they're taking into account the
Speaker 1: pitfalls the perils that you associate with AI. I think
Speaker 1: they are trying. But because it's a new technology, so uh,
Speaker 1: they're the tools to combat the problems aren't there yet,
Speaker 1: and the engineers building the products weren't aware of the problems.
Speaker 1: None of the problems were intentionally created, you know, issues
Speaker 1: with respect to personal data and explainability and what happens
Speaker 1: when AI makes a mistake and accountability uh, and also bias.
Speaker 1: So these were not intentional by the companies or the
Speaker 1: engineers who build it. But now that we know these
Speaker 1: are real problems, we need to put the training regulations
Speaker 1: UH and investigating new technologies to come back techno oology problems.
Speaker 1: Do you think the incentives, though, are aligned for companies
Speaker 1: that do work on this type of technology to create
Speaker 1: solutions or are they mainly concerned with us getting to
Speaker 1: to buy more, to engage more, to to read more,
Speaker 1: to spend more time on the platform without being concerned
Speaker 1: about the consequences or while you really hit the problem
Speaker 1: on the head that it is exactly because the incentives
Speaker 1: are not aligned. So I think the only way to
Speaker 1: fix it is to realign the incentives. And in the
Speaker 1: short term, the internet companies could have an incentive realignment
Speaker 1: if there were either regulations or certain parties that may
Speaker 1: do an AI audit, or maybe there would be a
Speaker 1: watchdog functions that are estimating percentage of fake news and
Speaker 1: things like that that forces them before their brand and
Speaker 1: reputation to consider that because if they lose their brand
Speaker 1: and have a some kind of a media disaster, they
Speaker 1: will lose customers. So that would is one way to
Speaker 1: externally force an approximation to an alignment, but there are
Speaker 1: also other ways. Why can't we not encourage building of
Speaker 1: applications and products that help us in the long term.
Speaker 1: So if there were a product that could make me
Speaker 1: more intelligent, more knowledgeable, more wealthy, m more happier, then
Speaker 1: that would be a product I would want to buy
Speaker 1: more than I would want the Facebook news speed, um
Speaker 1: or YouTube. So I think there are ways to align
Speaker 1: user interests. I think people just um the company's just
Speaker 1: felt like they got such a good AI tool. It
Speaker 1: can make money directly, so use it for that singular purpose,
Speaker 1: um and forget really that companies should be focused on
Speaker 1: user needs first, and users want more than um just
Speaker 1: watching some videos and and reading some blogs. There are
Speaker 1: long term goals that we have, and when we have
Speaker 1: such a powerful AI tool that can optimize our long
Speaker 1: term goals, it would be such a unfortunate, um events
Speaker 1: if nobody tried to build something that that's for our
Speaker 1: long term interests. So let's stick with Facebook because it's
Speaker 1: one of the companies that you mentioned. So who should
Speaker 1: regulate Facebook? Should it be the U S Government or
Speaker 1: should there be some sort of international body that takes
Speaker 1: these difficult questions into account to come up with some
Speaker 1: regulatory framework. After all, for all intents and purposes, Facebook
Speaker 1: is a global company, right right. I'm not an expert
Speaker 1: in regulation, and I think regulation, in my opinion, is
Speaker 1: just usually the last resort because, especially given the tools
Speaker 1: that the US has generally to use, such as breaking
Speaker 1: a company up, that's just too brittle. So I think
Speaker 1: there needs to be ways to push the company into
Speaker 1: an alignment with the goals that we the users have,
Speaker 1: and I mentioned a few of them. Uh. You know,
Speaker 1: when I work for Google, there were third parties that
Speaker 1: measured the relevance quality of Google Search Engine, and Google
Speaker 1: paid a lot of attention to that. So if there
Speaker 1: could be similar third party watchdog measures of level of
Speaker 1: fake news, number of deep faith, and uh, how much
Speaker 1: spam uh, and then it's measured externally, that would put
Speaker 1: a pressure on the engineering team to figure out at
Speaker 1: least to spend part of their time to make the
Speaker 1: content higher quality, uh, instead of just getting us to
Speaker 1: click more and more. So, okay, I feel let's take
Speaker 1: a step back and just think about how we interact
Speaker 1: with AI ine just just take us through, because I
Speaker 1: think that there are a lot of people who don't
Speaker 1: necessarily think about it. Each and every day that we
Speaker 1: are interacting with this technology, we are aariensing this technology
Speaker 1: so so what does it look like for in our
Speaker 1: daily lives globally. Sure, I'll give a couple of examples.
Speaker 1: So one example is that um Ai language capabilities to
Speaker 1: converse with us will grow significantly in many cases indistrestinguishable
Speaker 1: from human human communication. And that means Ai will become
Speaker 1: a companion. UH. And that companion can learn our interests,
Speaker 1: can try to help us improve ourselves. That would be
Speaker 1: an educational companion or to figure out how how we
Speaker 1: might be healthier. That it might be a more of
Speaker 1: a doctor or psychiatrist companion, or it could just be
Speaker 1: a friend, a power for for a young kid. And
Speaker 1: they take and take shapes in UH, both in the
Speaker 1: in the phone and the computer of course, but but
Speaker 1: also v R and a R will be really realistic
Speaker 1: by then that if we put on something like glasses,
Speaker 1: we will see figures and avatars superimposed on the glasses
Speaker 1: and and then there there there is as though they
Speaker 1: were in the actual environment. So it would be um
Speaker 1: really virtual reality that's indistinguishable from the real reality. UH.
Speaker 1: In transportation, autonomous vehicles should just about work by then
Speaker 1: get all the kings worked out. Of course, throughout the
Speaker 1: way we will see incrementally robot buses and shuttles and
Speaker 1: trucks and so on. But truly, the steering feel wheel
Speaker 1: free car that we can either share right or buy
Speaker 1: will be just about fifteen to twenty years from now. UM.
Speaker 1: That would dramatically change our habits because we will have
Speaker 1: um something like ten hours a week freed up that
Speaker 1: when we're in the car we can educate and entertain, communicate,
Speaker 1: no longer have to drive or look at the road um.
Speaker 1: And also cars can be on demand available um. And
Speaker 1: they will be smart, they will talk to each other,
Speaker 1: they will avoid accidents, they will miss each other by
Speaker 1: one's enemy there in a very calculated way. They will
Speaker 1: drive more safely, probably eventually reducing fatalities. So it will
Speaker 1: be more efficient, lower costs, save us time, more comfortable
Speaker 1: um and and also safer uh. And finally, in healthcare,
Speaker 1: a I will be able to with a very high
Speaker 1: likelihood predict the next pandemic because it could be watching
Speaker 1: a lot of data all over the world. And AI
Speaker 1: will be used to alongside with scientists invent new drugs,
Speaker 1: and that the cost of new drugs probably could be
Speaker 1: as little as ten of new drugs inventions today and
Speaker 1: what that means is today's pharmaceuticals can only go after
Speaker 1: blockbuster drugs for common diseases. But in the future, when
Speaker 1: the cost is slashed because AI is proposing treatments that
Speaker 1: are highly accurate working with scientists reducing the cost, that
Speaker 1: means rare diseases for which is not economical for pharmaceuticals
Speaker 1: to book go after will become possible. And and also
Speaker 1: AI will be keeping us healthy by taking all of
Speaker 1: our m R I and gene sequencing and blood to
Speaker 1: regularly monitor our health and give us hints on how
Speaker 1: to be healthier and live longer, and both of which
Speaker 1: will happen in twenty years. You you paint a picture
Speaker 1: that I think is really optimistic when it comes to
Speaker 1: this type of technology. But I'm wondering what your biggest
Speaker 1: fears are regarding AI. Yeah, AI, like any technology, is neutral,
Speaker 1: and it's good or bad because of the people who
Speaker 1: program it have good or bad intentions. And the examples
Speaker 1: that gave earlier are all constructive, but there are definitely
Speaker 1: cases where AI can can do terrible things. AI can
Speaker 1: be used by UM companies to change the way we think.
Speaker 1: We already saw that to influence the way we think,
Speaker 1: and that's extremely dangerous. AI can be used in uh.
Speaker 1: The most disastrous situation is autonomous weapons. Imagine a thousand
Speaker 1: dollar drone combined with recognition of individuals with a bullet
Speaker 1: connected to it, and that becomes an assassination tool, and
Speaker 1: it's deployed in the tens of thousands, it becomes a
Speaker 1: tool for genocide. So that essentially makes terrorist groups that
Speaker 1: have a few million dollars they can almost um uh
Speaker 1: do what countries do to create havoc can damage without
Speaker 1: risking any terrorist lives. So that's something that's um I think,
Speaker 1: extremely dangerous. And and also unlike nuclear weapons, which uh
Speaker 1: offers a unique property that is mutually destructive, so people
Speaker 1: don't launch it before fear of retaliation, but autonomous weapons
Speaker 1: are not totally destructive and not easily detectable, So that's
Speaker 1: another serious problem. And and lastly, we already saw the facts,
Speaker 1: but that's really just the beginning. AI will be able
Speaker 1: to fake many things. It will be able to make
Speaker 1: videos that look really real when it's off fabricated UM.
Speaker 1: It will be able to talk like someone quote quote
Speaker 1: room evidence. UH may maybe maybe at the point where
Speaker 1: people can't tell if it's fake or real anymore, not
Speaker 1: to mention rumors on the and false rumors on the Internet.
Speaker 1: And also AI will be able to fake emotion, so
Speaker 1: you know the scenario we saw in the movie Her.
Speaker 1: You know, while AI doesn't have any feelings, it can
Speaker 1: fake feelings to a point that could be used to
Speaker 1: do bad things as well. So I think that interest
Speaker 1: cases are just as significant. But my optimism is based
Speaker 1: on the fact that all technologies that have been incredibly powerful,
Speaker 1: you know, electricity, automobile, internet, PC, UM, they have caused
Speaker 1: problems from time to time, but ultimately they end up
Speaker 1: doing a lot more good than evil because hopefully we
Speaker 1: are learning from experience and have some wisdom to um
Speaker 1: to self managed so that we figure out ways to
Speaker 1: counter the bad guys and encourage the good guys. Well,
Speaker 1: how do we do that? It ends up being all
Speaker 1: constructed because I can't help, but think that those do
Speaker 1: we counter Yeah, yeah, because they can't help, But think
Speaker 1: that those problems are of a different magnitude than problems
Speaker 1: created by other types of modernization. It really makes me
Speaker 1: think of the sci fi series Black Mirror and the
Speaker 1: way that that show really lays out so so many
Speaker 1: of the dystopian futures that you could think about when
Speaker 1: it comes to this type of technology, how do we
Speaker 1: prevent that type of future from overtaking us. Yeah, I'm
Speaker 1: a fan of Black Mirror. It's partly inspired the book,
Speaker 1: and I think in the Black Mirror, though, it's usually
Speaker 1: uh a technology is used in the wrong way by
Speaker 1: some some part bad person and then it leads to
Speaker 1: usually a disastrous ending. In in my book, I try
Speaker 1: to create scenarios where many of the bad endings were
Speaker 1: saved by the by the good guys, and that might
Speaker 1: be saved with some clever technology that combats the bad technology,
Speaker 1: or with some smart tools prevents the back technology from propagating,
Speaker 1: or maybe there's some new regulations like the AI audit.
Speaker 1: So what I wanted to propose in the book is
Speaker 1: that yes, AI and like any other great technology, will
Speaker 1: create a lot of problems, but many of the problems
Speaker 1: can be greatly reduced if we let technologies work on
Speaker 1: solutions that address the problems created by technologies themselves. I
Speaker 1: gotta ask about your interpretation of of what's been happening
Speaker 1: in China and your interpretation specifically of President She's cracked
Speaker 1: down on technology I'm wondering how you interpret Beijing's measures
Speaker 1: against d D, Shu Shing or Jack Moll for example. Oh,
Speaker 1: these are not really technology firms per se. And we
Speaker 1: invest in technology, invest in robots and AI and healthcare
Speaker 1: and you know, new cures and new drugs. Those are
Speaker 1: actually heavily encouraged. UM. What those companies are d D
Speaker 1: and Ali Baba and Tensent and others are really I
Speaker 1: think the issue is that they have a lot of
Speaker 1: power and a lot of days done, and that gives
Speaker 1: them even more power, and that power needed to be checked.
Speaker 1: So not at all, you know, consistent with what US
Speaker 1: and EU are doing to rein in Google, Facebook, UM
Speaker 1: and Amazon. Given what's happened in China over the last year,
Speaker 1: when it comes to reigning in these tech firms, has
Speaker 1: it changed the way that you're thinking about investing in
Speaker 1: Chinese companies? Does it make you think differently about how
Speaker 1: to deploy your money. Well, actually, we've always been investors
Speaker 1: in deep tech, so now it's just a better time
Speaker 1: than ever to keep doing what we have been doing.
Speaker 1: UM deep tech hasn't been as favored. They haven't seen
Speaker 1: as much growth five or ten years ago compared to
Speaker 1: the Internet giants. But I think going forward, deep tech
Speaker 1: um is going to generate the best returns, probably globally,
Speaker 1: certainly in China. You told Bloomberg a few years ago
Speaker 1: back in of your money was invested in in Chinese companies.
Speaker 1: Is that consistent roughly yes, still the case, even more
Speaker 1: given COVID. How so what do you mean, Well, we're
Speaker 1: unable to travel, so we can't really see a lot
Speaker 1: of companies outside China, and you think you need to
Speaker 1: see companies in order to know whether or not to
Speaker 1: invest in them. That would help to build trust, to
Speaker 1: see the people, to understand them. So I think generally
Speaker 1: the answer is yes, we have made one investment in
Speaker 1: the French company without seeing them, but that detainitely the exception,
Speaker 1: not the rule. So that brings me to the next
Speaker 1: question is how do you think the pandemic will will
Speaker 1: affect China, the tech industry and in China's place in
Speaker 1: the world. The pandemic has really in China pushed further
Speaker 1: the adoption of automation and robotics. Initially it may have
Speaker 1: been for reducing social contact UH by having robots in
Speaker 1: UH factories and restaurants and even homes, I get my
Speaker 1: um takeoff delivered by by robot nowadays, So I think
Speaker 1: the robotic revolution is probably the big big step forward
Speaker 1: that will be helpful to push China's technologies ahead. Um.
Speaker 1: On the flip side, UM, the other big thing that's
Speaker 1: happened globally is the work from home and and that
Speaker 1: has really streamlined work for a lot of companies and
Speaker 1: also enabled the use of uh ai and cloud technologies.
Speaker 1: And that's why where I think US has really done
Speaker 1: by far the best job, partly because the US has
Speaker 1: always had stronger enterprise technologies, but also partly because Americans
Speaker 1: were um working from home longer than the Chinese. So
Speaker 1: I think each country has got some benefit um um
Speaker 1: out of COVID, even though it's terrible disaster for for
Speaker 1: the whole world, I feel things have changed just quite
Speaker 1: a bit since you picked your computer science as your
Speaker 1: college major years ago. It was of course before you
Speaker 1: went to get your PhD from Carnegie Mellon. So if
Speaker 1: you could go back in time, what advice would you
Speaker 1: give your eighteen year old self, what advice would you
Speaker 1: give to college students today? Um, I would say back
Speaker 1: then I going or today, following your heart, do what
Speaker 1: you really love to do. Is It's the same advice
Speaker 1: I give to myself back then and also to a
Speaker 1: young person today, perhaps even more important today because um,
Speaker 1: a young person is not just competing with other young people,
Speaker 1: but also AI is taking away routine jobs. So it
Speaker 1: pushes us to do things that a I cannot do,
Speaker 1: whether it's creativity or compassion, and it forces us to
Speaker 1: be the best we can be. And the best we
Speaker 1: can be is doing things that we're good at and
Speaker 1: the things we love. CAIFOOLEI thank you so much for
Speaker 1: taking the time and joining us in Bloomberg business. We
Speaker 1: really appreciate it.