Futuremakers Podcast: Is China leading the way in AI? (Season 1: Episode 9)

00:46:38
https://www.youtube.com/watch?v=tPTXHHLkb7M

Ringkasan

TLDRThis discussion highlights the extensive ambitions of China to lead in Artificial Intelligence (AI) research and application by 2030, backed by government support and a robust industry-academic collaboration. Experts talk about the differences in cultural attitudes towards technology between China and the West, emphasizing a more accepting view in China. Ethical considerations regarding AI's potential risks and the challenges of privacy in data usage, especially in healthcare, are also discussed. The conversation suggests a need for global cooperation in technological advancements and a balanced view of AI's impacts on society.

Takeaways

  • 🔍 The podcast explores existential risks of AI and technology.
  • 📈 China aims to become a global AI leader by 2030.
  • 🤖 Ethical implications of AI development are critical to address.
  • 🌍 Cooperation between countries is essential to navigate AI risks.
  • 💡 China's culture supports rapid adoption of new technologies.
  • 📊 AI development driven by data availability is a significant advantage.
  • 👨‍💻 Industry and academia collaboration enhances talent development.
  • ❗ Regulatory challenges hinder AI applications in healthcare in the West.
  • 🌏 Different cultural attitudes influence technology acceptance.
  • 🛠️ Automation and AI capabilities can reshape industries globally.

Garis waktu

  • 00:00:00 - 00:05:00

    The speaker introduces a podcast series called 'The End of the World with Josh Clark,' which explores existential risks associated with technological advancements such as AI and mutated viruses. This podcast aims to deliver serious scientific discussions in an immersive audio experience.

  • 00:05:00 - 00:10:00

    The focus shifts to 'Future Makers,' a series discussing AI, featuring experts from Oxford. The discussion highlights China's ambition to become the leading Innovation Center by 2030, aiming for significant GDP boosts from AI. The panel considers the realism of these goals and what can be learned from China's approach.

  • 00:10:00 - 00:15:00

    Experts continue discussing China's AI ambitions, emphasizing the government's long-term vision and prior efforts towards becoming a scientific powerhouse. Previous plans have laid the groundwork for the current focus on AI, indicating a significant commitment to these goals.

  • 00:15:00 - 00:20:00

    The discussion touches on AI's prioritization in China's five-year plans, with AI now seen as a transformative technology crucial for meeting GDP and military goals. The experts note a shift towards greater importance of AI within China's strategic planning.

  • 00:20:00 - 00:25:00

    Historically, China had a minimal presence in AI research, but recent statistics indicate a dramatic rise in Chinese contributions to AI conferences. This marks a clear shift in the global AI research landscape, increasingly dominated by younger researchers in China.

  • 00:25:00 - 00:30:00

    The role of Chinese industries, including tech giants like Baidu and Alibaba, in fostering AI innovation is highlighted. The blend of academic and industrial efforts is seen as crucial for building a skilled workforce necessary for the field's advancement.

  • 00:30:00 - 00:35:00

    The conversation explores the differences in regulatory environments between China and the West, noting that China allows more freedom to innovate without extensive pre-regulation, whereas Western countries are more cautious about tech rollout and its implications on privacy.

  • 00:35:00 - 00:40:00

    A cultural comparison emerges, illustrating how Chinese users are more willing to trade privacy for convenience in technology, while Western skepticism around data privacy and corporate intentions creates a different landscape of tech acceptance and innovation.

  • 00:40:00 - 00:46:38

    Finally, the podcast concludes with reflections on China's rapid advancements in AI and potential global implications. Experts underscore the importance of balancing competition with collaboration in AI development, especially concerning ethical considerations and shared advancements.

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Video Tanya Jawab

  • What is the main focus of the podcast series "The End of the World with Josh Clark"?

    The series discusses existential risks that could threaten humanity due to mismanaged technology.

  • What are China's goals for artificial intelligence by 2030?

    China aims to become the world's primary innovation center, significantly boosting its GDP through AI-related advancements.

  • How does China's approach to AI differ from that of the West?

    China has a more centralized government backing, leading to faster deployment and possibly more acceptance of data sharing.

  • What ethical considerations are raised in AI development?

    The potential risks of deploying technology without adequate regulation and the ethical implications of using AI in areas like healthcare.

  • How is the relationship between academia and industry evolving in China's AI landscape?

    There is increased collaboration between universities and tech companies in China, driving innovation and talent development.

  • What evidence supports the growth of China in AI research?

    Statistics show a rapid increase in AI publications from China, indicating a strong presence and competitiveness in the field.

  • What are the implications of AI's development for global cooperation?

    It's vital to foster international collaboration to address risks associated with AI deployment.

  • How do cultural attitudes toward technology differ between China and the West?

    Chinese users tend to be more accepting of new technologies and willing to trade privacy for convenience.

  • What is the role of data in AI development?

    Data is a crucial driver for AI advancements, and China's large population provides a significant advantage in data availability.

  • What are the challenges faced when using healthcare data for AI in the West?

    Regulatory concerns and privacy issues make it challenging to leverage healthcare data effectively in Western countries.

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    listen to all 10 episodes now and join
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    the conversation on social media with
  • 00:01:34
    hashtag e OTW josh clark welcome to
  • 00:01:42
    future makers your invitation to cutting
  • 00:01:45
    edge debates on our changing society
  • 00:01:47
    with leading researchers at the
  • 00:01:49
    University of Oxford our first series is
  • 00:01:52
    all about artificial intelligence
  • 00:01:54
    I'm Peter Milliken professor of
  • 00:01:57
    philosophy
  • 00:01:57
    thank you for joining us here in the
  • 00:02:00
    Thomas Hobbes room at Harvard College
  • 00:02:02
    today in the penultimate episode of
  • 00:02:05
    series one we're looking at the
  • 00:02:07
    development of AI across the globe
  • 00:02:11
    China has set itself the challenge of
  • 00:02:14
    becoming the world's primary Innovation
  • 00:02:16
    Center by 2030 a move forecast to
  • 00:02:21
    generate a 26 percent boost in GDP from
  • 00:02:25
    AI related benefits alone but how
  • 00:02:28
    realistic is the same and in what ways
  • 00:02:31
    can we learn from what China's doing
  • 00:02:34
    [Music]
  • 00:02:39
    with me to discuss this our mic
  • 00:02:42
    Wooldridge head of Oxford's department
  • 00:02:45
    of computer science showering ding a
  • 00:02:47
    postdoctoral researcher who studied and
  • 00:02:50
    worked at several of China's leading
  • 00:02:52
    universities and companies and Sophie
  • 00:02:55
    Charlotte Fisher a visiting researcher
  • 00:02:58
    that the future of humanity Institute in
  • 00:03:00
    Oxford and working on a doctorate at ETH
  • 00:03:04
    Zurich focusing on the development of AI
  • 00:03:06
    in China and the USA welcome to you all
  • 00:03:10
    great to be here Sharon could you set
  • 00:03:14
    the scene for us what is it that China
  • 00:03:16
    is hoping to achieve by 2030 China seeks
  • 00:03:19
    to becomes the leader in most of the
  • 00:03:22
    area from the base from the cereal to
  • 00:03:25
    the application to the technology into
  • 00:03:27
    the applications usually the central
  • 00:03:30
    government they release the plan and
  • 00:03:32
    most of the local governments like the
  • 00:03:35
    province some of central cities like the
  • 00:03:37
    Beijing Shanghai and Shenzhen they have
  • 00:03:39
    their very formal in the plan to would
  • 00:03:43
    set their own sub plans to support the
  • 00:03:46
    central plans they would attract global
  • 00:03:50
    local talent to build the ecosystem in
  • 00:03:53
    the Naoko area Suresh a lot what do you
  • 00:03:57
    think about this plan well I think it's
  • 00:03:59
    certainly a very ambitious plan that the
  • 00:04:02
    Chinese State Council has published last
  • 00:04:05
    year and I think it's important to
  • 00:04:07
    situate this and the broader context of
  • 00:04:09
    Chinese goals when it comes to science
  • 00:04:11
    and technology generally China is aiming
  • 00:04:14
    at becoming an innovative nation and a
  • 00:04:16
    global powerhouse for science and
  • 00:04:17
    technology and this artificial
  • 00:04:18
    intelligence plan certainly one aspect
  • 00:04:20
    of it and maybe we can go a bit more
  • 00:04:23
    into detail about why they're actually
  • 00:04:25
    setting themselves his goals if we look
  • 00:04:27
    at previous plans we do see for example
  • 00:04:30
    in the 2006 national medium and long
  • 00:04:33
    term plan for the development of science
  • 00:04:34
    and technology that China aims to become
  • 00:04:37
    an innovative nation by 2020 already in
  • 00:04:39
    a global scientific powerhouse by 2050
  • 00:04:42
    so this is across the whole area this is
  • 00:04:45
    across the whole era exactly and this
  • 00:04:47
    was already a goal which was stated in
  • 00:04:49
    this plan by 2002
  • 00:04:51
    six but more recently for example last
  • 00:04:54
    year in October 2017 Chinese President
  • 00:04:57
    Xi Jinping reiterated in his report to
  • 00:04:59
    the 19th Party Congress his dream for
  • 00:05:01
    China to become a science and technology
  • 00:05:03
    superpower so in line with these goals
  • 00:05:05
    that were said earlier and these goals
  • 00:05:07
    are also reflected in the current
  • 00:05:09
    five-year plan the thirteenth five-year
  • 00:05:11
    plan which covers the time period from
  • 00:05:13
    2016 to 2020 and in this plan artificial
  • 00:05:17
    intelligence already plays a role and
  • 00:05:19
    it's considered as the sixth of in total
  • 00:05:21
    69 priorities that the Chinese
  • 00:05:24
    government has so yeah I think it's
  • 00:05:25
    important to see that this plan by the
  • 00:05:27
    state counts so that was published last
  • 00:05:29
    year it's not the first time that the
  • 00:05:31
    government actually started to think
  • 00:05:32
    about artificial intelligence but that
  • 00:05:34
    this was already sort of in previous
  • 00:05:36
    plans a prominent goal and have these
  • 00:05:39
    previous plans actually been delivering
  • 00:05:42
    what we can say about this artificial
  • 00:05:44
    intelligence plan that was published
  • 00:05:46
    last year is that this really is the
  • 00:05:49
    signal and a bundling of all these prior
  • 00:05:52
    plans or really signals the resolve of
  • 00:05:54
    the Chinese government to make this a
  • 00:05:56
    priority which is certainly very
  • 00:05:57
    important in order to really implement
  • 00:06:00
    the goals that Sharon was talking about
  • 00:06:02
    before so I think now we can really
  • 00:06:04
    observe what is happening at the time up
  • 00:06:06
    to 2030 and maybe from their judge again
  • 00:06:10
    whether China's actually delivered on
  • 00:06:12
    these goals that had set itself you said
  • 00:06:14
    that AI was number six out of 69
  • 00:06:17
    priorities now if I heard that some plan
  • 00:06:21
    in Britain was number six priority of
  • 00:06:24
    the government I'd be rather skeptical
  • 00:06:26
    as to whether much is going to happen
  • 00:06:27
    but my guess is that in China it might
  • 00:06:30
    be a little bit different I mean what
  • 00:06:32
    are the top five priorities how big is
  • 00:06:34
    number six with the next-generation
  • 00:06:37
    artificial intelligence development plan
  • 00:06:39
    from last year the prioritization has
  • 00:06:41
    clearly changed so now a is really
  • 00:06:43
    considered as the transformative
  • 00:06:45
    technology that can help China to meet
  • 00:06:48
    its GDP targets and also to for example
  • 00:06:50
    transform the military and to play a
  • 00:06:52
    huge role when it comes to social
  • 00:06:54
    governance in China so I would say we
  • 00:06:56
    shouldn't be too focused on this number
  • 00:06:58
    six in the last five year plan but we
  • 00:07:00
    should really recognize that these
  • 00:07:02
    priorities might also have changed
  • 00:07:04
    that a I has become a lot more important
  • 00:07:06
    over the last two years so it's moving
  • 00:07:08
    up and up exactly yes Mike historically
  • 00:07:12
    how big a role has China played in AI
  • 00:07:15
    I've been an AI researcher for thirty
  • 00:07:17
    years started my PhD in 1989 and that
  • 00:07:21
    time I think it's probably fair to say
  • 00:07:23
    that there was really no Chinese
  • 00:07:25
    presence here's some statistics and when
  • 00:07:27
    I started my PhD there was one
  • 00:07:28
    conference that was the coolest place to
  • 00:07:30
    publish in AI and it's called triple AI
  • 00:07:33
    and it was originally the American
  • 00:07:34
    Association for AI when this conference
  • 00:07:37
    started in 1980 there were no Chinese
  • 00:07:39
    papers at all I mean there was not a
  • 00:07:40
    single paper from China in the in the
  • 00:07:42
    conference if you go forward to 1998
  • 00:07:46
    which has to be the first year that I
  • 00:07:47
    published there there was one paper from
  • 00:07:49
    China and it was from Hong Kong and of
  • 00:07:52
    course Hong Kong had just transitioned
  • 00:07:53
    back to Chinese rule one year before
  • 00:07:55
    that this year there were more Chinese
  • 00:07:58
    papers than US papers for the first time
  • 00:08:00
    and that trajectory has just been
  • 00:08:04
    astonishing I mean witnessing that has
  • 00:08:06
    somebody that's part of this community
  • 00:08:07
    that's seeing the dynamics of science
  • 00:08:10
    and the areas that become fashionable
  • 00:08:12
    and then go out of fashion and so on
  • 00:08:14
    but actually witnessing that has just
  • 00:08:16
    been astonishing and I think if if you
  • 00:08:18
    talk about these sort of national
  • 00:08:20
    ambitions and you want some evidence
  • 00:08:22
    that you know that well on the road to
  • 00:08:25
    achieving those I think there's actually
  • 00:08:26
    some clear evidence there it is it feels
  • 00:08:29
    like we are in the middle of a global
  • 00:08:32
    transition I mean that's genuinely what
  • 00:08:34
    it feels like as somebody that's on the
  • 00:08:35
    the inside of that community it feels
  • 00:08:38
    like the community is changing and China
  • 00:08:40
    is becoming a dominant force the
  • 00:08:42
    contributors to these conferences are
  • 00:08:44
    these typically young people who are
  • 00:08:48
    coming through or are they older people
  • 00:08:50
    who may now be focusing more
  • 00:08:53
    internationally than they were before no
  • 00:08:55
    it's a mixture so I would say I did with
  • 00:08:58
    respect to the Chinese presence I think
  • 00:08:59
    yes it's a very young presence that's
  • 00:09:01
    very very clear but it's the conference
  • 00:09:03
    itself takes a very very broad intake of
  • 00:09:07
    papers from the whole spectrum of of AI
  • 00:09:09
    and for that reason I think it's a
  • 00:09:11
    pretty good bellwether of what's going
  • 00:09:12
    on in the community what is it that's
  • 00:09:14
    bringing this about and what are the
  • 00:09:16
    Chinese
  • 00:09:17
    doing that is encouraging their young
  • 00:09:20
    people to go into research and their
  • 00:09:21
    researchers to produce more papers for
  • 00:09:24
    these international conferences well I
  • 00:09:26
    think what's very interesting about this
  • 00:09:27
    is that a lot of the drive is not just
  • 00:09:30
    academic the if you again if you go back
  • 00:09:33
    to 1980 the vast majority of the
  • 00:09:35
    publications at this conference would
  • 00:09:37
    have been from well they would have been
  • 00:09:38
    from the US there was a tiny presence
  • 00:09:40
    from outside there but it would have
  • 00:09:41
    been from a small clique of US
  • 00:09:43
    universities like Stanford Berkeley MIT
  • 00:09:45
    Carnegie Mellon University who were the
  • 00:09:47
    kind of the dominant AI universities for
  • 00:09:50
    a very very long period of time but
  • 00:09:52
    what's interesting that you're really
  • 00:09:54
    seeing now is a huge presence from the
  • 00:09:57
    kind of companies that we just heard
  • 00:09:59
    referred to let by do Alibaba $0.10 and
  • 00:10:02
    so the drive is not just coming from
  • 00:10:04
    academia it's coming from industry and
  • 00:10:06
    there's an ambition on the part of
  • 00:10:08
    industry to get engaged with this
  • 00:10:11
    community and I think that's a
  • 00:10:13
    recognition that you know the key to all
  • 00:10:15
    of this is skills and you need a very
  • 00:10:18
    very highly skilled workforce to be able
  • 00:10:21
    to operate successfully in that space
  • 00:10:23
    those companies need to be able to
  • 00:10:25
    engage with world leading universities
  • 00:10:27
    to get that kind of talent so I think
  • 00:10:30
    that's been a been an interesting
  • 00:10:32
    dimension to this much more industrial
  • 00:10:34
    presence than you would have seen 3035
  • 00:10:36
    years ago and do you think that the
  • 00:10:38
    collaboration between universities and
  • 00:10:41
    industry in China is significantly
  • 00:10:44
    different from what it's like in Britain
  • 00:10:46
    say or the u.s. the character of the AI
  • 00:10:50
    Drive is interesting because it's
  • 00:10:52
    because it does have this very clear
  • 00:10:54
    national backing the government backing
  • 00:10:57
    and pushing pushing it and that can open
  • 00:11:00
    doors for companies that want to work in
  • 00:11:02
    the space in a way which isn't really
  • 00:11:04
    possible for example in the UK because
  • 00:11:06
    we've got a very different regulatory
  • 00:11:08
    environment in the UK and this I think
  • 00:11:11
    is one of the huge advantages that
  • 00:11:13
    they've got I mean I think the fact that
  • 00:11:15
    you've got national backing in and the
  • 00:11:17
    willingness to make things happen is
  • 00:11:20
    another kind of quite dramatic aspect of
  • 00:11:23
    of what what we're witnessing Sharan for
  • 00:11:27
    people in China how would they
  • 00:11:29
    experience the
  • 00:11:30
    emphasis from the government on movement
  • 00:11:33
    towards AI as is there's one interesting
  • 00:11:37
    you may say that from the the salaries
  • 00:11:41
    of the graduate in the area like ten
  • 00:11:44
    years ago is comparable with other
  • 00:11:46
    engineering but now in China the the
  • 00:11:50
    center if you could program it and your
  • 00:11:52
    work is related about AI it is far more
  • 00:11:55
    higher than any other in other areas I
  • 00:11:59
    have a got a lot of universities turning
  • 00:12:02
    out graduates in computer science and
  • 00:12:05
    artificial intelligence and so forth
  • 00:12:06
    yeah particularly in the recent years
  • 00:12:08
    there is also a lot of graduate program
  • 00:12:11
    and also some new degree programs in
  • 00:12:14
    this area the industries in China would
  • 00:12:16
    they normally be looking to employ
  • 00:12:18
    people who have been educated and
  • 00:12:20
    trained in China I think for the top
  • 00:12:23
    talents the Chinese they don't matter
  • 00:12:25
    where they're from they might recruit
  • 00:12:28
    the International top talents but
  • 00:12:30
    generally for the other traditions they
  • 00:12:34
    would prefer like Chinese graduate
  • 00:12:35
    because of their culture at the language
  • 00:12:37
    one point that is very interesting in
  • 00:12:40
    the next generation artificial
  • 00:12:42
    intelligence development plan is that
  • 00:12:43
    there is a section on remaining
  • 00:12:45
    weaknesses of China with regards to its
  • 00:12:47
    AI capabilities and one of these
  • 00:12:49
    weaknesses is that there is a shortage
  • 00:12:51
    of talent when it comes to top-level AI
  • 00:12:53
    research and China is increasingly
  • 00:12:56
    looking to attract Chinese that went
  • 00:12:59
    abroad to study in the US and maybe also
  • 00:13:02
    went on to work in the u.s. to come back
  • 00:13:04
    to China and there in parently quite
  • 00:13:06
    successful on that because at the moment
  • 00:13:07
    there is this AI boom in China there's a
  • 00:13:10
    lot of capital which is available there
  • 00:13:11
    are very interesting opportunities in
  • 00:13:13
    the industry and there are also more and
  • 00:13:15
    more foreigners who become interested in
  • 00:13:17
    joining Chinese companies and work there
  • 00:13:20
    of course it's still a very small number
  • 00:13:22
    of people or foreigners to work in these
  • 00:13:24
    Chinese AI companies but have met a lot
  • 00:13:26
    of people for example in Shanghai who
  • 00:13:28
    came from they were from the US have
  • 00:13:30
    worked before for a company like Google
  • 00:13:32
    and now want to be part of this AI boom
  • 00:13:34
    which is currently taking place in China
  • 00:13:36
    Mike there's a change in the perception
  • 00:13:40
    that for example China is now seen as a
  • 00:13:42
    place where for example in academia
  • 00:13:44
    where there are many opportunities in AI
  • 00:13:46
    and it's much more attractive for people
  • 00:13:48
    to to go back to China for example you
  • 00:13:52
    go and do your PhD in Stanford or
  • 00:13:53
    something like that again but thirty
  • 00:13:56
    years ago you would probably have tried
  • 00:13:57
    to get a post in a good US university
  • 00:14:00
    because the perception was that the
  • 00:14:02
    opportunities weren't quite the same
  • 00:14:03
    actually that's that's that's changing
  • 00:14:05
    quite dramatically and it's becoming a
  • 00:14:08
    much more attractive a much more
  • 00:14:10
    attractive option for people I think who
  • 00:14:12
    want to pursue a world-class academic
  • 00:14:14
    career in the area and is it attractive
  • 00:14:16
    for people who themselves are not from
  • 00:14:18
    China I mean would you get Europeans
  • 00:14:21
    studying over here or in America and
  • 00:14:23
    then pursuing careers in China these
  • 00:14:25
    days well I think the truth is at the
  • 00:14:27
    moment if you're good in AI then the
  • 00:14:29
    world is your oyster you can go and work
  • 00:14:31
    anywhere so China has to vie with
  • 00:14:33
    everybody else and there are very very
  • 00:14:35
    good options right now in the u.s. in
  • 00:14:38
    mainland Europe in the UK the UK is done
  • 00:14:41
    very well in terms of responding to the
  • 00:14:43
    AI boom so I think the biggest barrier
  • 00:14:46
    there is of course just language and
  • 00:14:48
    cultural barrier but not I think the
  • 00:14:51
    interesting thing is not in terms of the
  • 00:14:53
    perception of the opportunities that it
  • 00:14:55
    will give you that's that's the big
  • 00:14:57
    change I think right and Sophie I'd like
  • 00:14:59
    to pursue something that Mike mentioned
  • 00:15:01
    a bit earlier about the regulatory
  • 00:15:04
    backing in China and I can imagine that
  • 00:15:07
    if you are a researcher it may be that
  • 00:15:10
    working in a country like China where
  • 00:15:12
    the government is fully behind this and
  • 00:15:15
    able presumably to change the regulatory
  • 00:15:18
    framework in a way that might be make
  • 00:15:21
    things easier for researchers than it is
  • 00:15:23
    in Europe would you like to comment on
  • 00:15:25
    that I think on the one hand it might be
  • 00:15:27
    attractive for researchers to go to
  • 00:15:29
    China and work in a relatively Lex
  • 00:15:31
    regulatory environment where you might
  • 00:15:33
    be able to develop a technology get it
  • 00:15:35
    out in the market
  • 00:15:36
    try it out see the effects and maybe
  • 00:15:39
    afterwards if the technology has any
  • 00:15:41
    negative effects the government might
  • 00:15:42
    come in and regulate it so you might
  • 00:15:44
    have more freedom and the process of
  • 00:15:46
    developing the technology and
  • 00:15:47
    implementing in the market at first
  • 00:15:49
    however I think there are also many
  • 00:15:51
    researchers in particular in this AI
  • 00:15:53
    research community who think that it's
  • 00:15:55
    very important that the research they
  • 00:15:57
    are doing
  • 00:15:58
    is aligned with ethical values and to
  • 00:16:02
    conduct this research and institutions
  • 00:16:03
    which are playing which pay attention to
  • 00:16:07
    the safety of the technology so what
  • 00:16:10
    we've seen for example in the u.s. is
  • 00:16:11
    quite an quite extreme case but Google
  • 00:16:14
    has cooperated with the United States
  • 00:16:16
    Department of Defense on a project
  • 00:16:18
    project which is called Project maven
  • 00:16:19
    and Google has been involved in
  • 00:16:21
    automating the analysis of video data
  • 00:16:24
    that was collected by drones and this
  • 00:16:28
    project was in public first but when it
  • 00:16:30
    became public a lot of researchers at
  • 00:16:32
    Google opposed it they collected
  • 00:16:34
    signatures and in the end the pressure
  • 00:16:35
    was so high on Google that it terminated
  • 00:16:38
    the contract with the US Department of
  • 00:16:39
    Defense and also Google set up some well
  • 00:16:43
    regulatory guidelines or some guiding
  • 00:16:45
    principles for its research and
  • 00:16:46
    artificial intelligence in China it's
  • 00:16:48
    possible for products to be released at
  • 00:16:52
    a relatively early stage try them out
  • 00:16:55
    see how they go and then deal with the
  • 00:16:57
    problems afterwards whereas I think here
  • 00:17:01
    there's much more caution to start with
  • 00:17:04
    and you have to show that everything is
  • 00:17:07
    going to be fine before you release it
  • 00:17:08
    now that's not necessarily a matter of
  • 00:17:10
    ethics it's simply a question of how you
  • 00:17:13
    line up the risks against possible
  • 00:17:15
    benefits do you think there's a major
  • 00:17:18
    difference there yes absolutely I think
  • 00:17:20
    there is a major difference in the way
  • 00:17:21
    that we in Europe for example look at
  • 00:17:24
    technological risks and at what stage
  • 00:17:26
    they need to be regulated and
  • 00:17:28
    anticipated and then to the Chinese
  • 00:17:30
    approach so I think this as I would call
  • 00:17:33
    call it real word prototyping approach
  • 00:17:36
    and China works very very differently
  • 00:17:38
    then how we approach these problems in
  • 00:17:42
    Europe but I think also in the United
  • 00:17:43
    States so there certainly is a big
  • 00:17:45
    difference I think there's another
  • 00:17:46
    aspect to it which is kind of attitudes
  • 00:17:48
    to technology and I think there's a
  • 00:17:51
    again this is just purely anecdotal but
  • 00:17:53
    my sense is there's a much more kind of
  • 00:17:56
    embracing attitude and a much more
  • 00:17:57
    excitement about new technology and the
  • 00:18:00
    cool things that new technology can do
  • 00:18:02
    and I think you know whereas whereas I
  • 00:18:03
    think you know we're we are somewhat
  • 00:18:06
    more cynical and Stanback ish a little
  • 00:18:08
    bit and
  • 00:18:09
    we laugh at everybody when we first see
  • 00:18:11
    at an Apple watch you know with what a
  • 00:18:13
    piece of junk you know and that whereas
  • 00:18:15
    I think there's a much more kind of it I
  • 00:18:16
    say embracing kind of attitude go for it
  • 00:18:19
    what an exciting thing what cool things
  • 00:18:21
    can it do and so it seems to move
  • 00:18:24
    differently so I'll give you just
  • 00:18:26
    experience from being in China there's
  • 00:18:28
    an application called WeChat have you
  • 00:18:32
    got WeChat Peter on your phone so it's
  • 00:18:35
    this kind of entire ecosystem developed
  • 00:18:38
    by tens then it's an entire online kind
  • 00:18:40
    of ecosystem it does messaging it does
  • 00:18:41
    ecommerce it does social media it does
  • 00:18:45
    it does everything you can think of and
  • 00:18:47
    it really feels like you can't get by
  • 00:18:50
    without it I'm looking at shower on I
  • 00:18:51
    mean you know if you want to do business
  • 00:18:53
    in China if you want to pay for anything
  • 00:18:54
    you need WeChat on your phone and it's I
  • 00:18:57
    say it's this kind of this product which
  • 00:18:59
    has just been kind of embraced on a
  • 00:19:04
    national scale but that national scale
  • 00:19:06
    means there's more than a billion users
  • 00:19:07
    of this product so I asked my daughter
  • 00:19:09
    my teenage daughter who spends her
  • 00:19:11
    entire life on social media have you
  • 00:19:13
    ever heard of WeChat she and none of her
  • 00:19:15
    friends had ever even heard of it but
  • 00:19:16
    actually in terms of number of users
  • 00:19:18
    it's absolutely up there with Instagram
  • 00:19:21
    and Facebook and all of the rest of it I
  • 00:19:23
    mean it's a global global system in
  • 00:19:26
    terms of the number of people that use
  • 00:19:28
    it and I say it's just become this
  • 00:19:30
    ubiquitous thing that now just it seems
  • 00:19:33
    to everything you do in every time you
  • 00:19:36
    do business in China you need WeChat in
  • 00:19:38
    order to be able to do it that's very
  • 00:19:40
    interesting so we've got two different
  • 00:19:42
    aspects of the environment there the
  • 00:19:43
    you might think would tell in opposite
  • 00:19:45
    directions I mean if you have an
  • 00:19:47
    environment where testing something out
  • 00:19:49
    is gonna mean you've got a billion
  • 00:19:52
    guinea pigs using it you might think
  • 00:19:54
    that would imply more caution from the
  • 00:19:56
    point of view of regulators I think it's
  • 00:19:58
    not necessarily a contradiction I think
  • 00:20:00
    Chinese users are also just more
  • 00:20:02
    accepting of technologies that are not
  • 00:20:04
    completely perfect by the time they're
  • 00:20:06
    released so it's Nicor has said I think
  • 00:20:07
    that is more excited to try out new
  • 00:20:09
    technologies and it's not as important
  • 00:20:11
    as in Europe or the United States for
  • 00:20:13
    example that by the time these products
  • 00:20:15
    are released they are already
  • 00:20:16
    functioning perfectly so I think in
  • 00:20:18
    China people will try them out the
  • 00:20:20
    companies will improve them and it's
  • 00:20:21
    more a process
  • 00:20:23
    well and I think this is yeah this is
  • 00:20:25
    very different in a European in a US
  • 00:20:27
    environment is that related to the issue
  • 00:20:30
    of trust do you think that in the West
  • 00:20:33
    there isn't the same degree of trust in
  • 00:20:37
    should we say the good intentions of
  • 00:20:39
    those who are bringing in these
  • 00:20:42
    applications and so people are prepared
  • 00:20:44
    to go for them without worrying that
  • 00:20:47
    they might be exploited whereas here we
  • 00:20:50
    seem to be very worried all the time
  • 00:20:52
    about new things coming in and is our
  • 00:20:55
    information going to be misused and all
  • 00:20:58
    that sort of thing I think Chinese users
  • 00:21:00
    are definitely looking more at what they
  • 00:21:03
    get when they use an application and
  • 00:21:05
    they are willing to trade some privacy
  • 00:21:07
    or to give data to a company in order to
  • 00:21:09
    get better results from using a certain
  • 00:21:12
    app so for example there are a lot of
  • 00:21:14
    Chinese people who use different online
  • 00:21:16
    shopping platforms and I think they are
  • 00:21:18
    very keen on getting better
  • 00:21:19
    recommendations from these systems by
  • 00:21:22
    providing data well through the daily
  • 00:21:25
    usage of these kind of platforms and I
  • 00:21:27
    think in Europe there seems to be a lot
  • 00:21:29
    of concern about privacy of users so
  • 00:21:32
    users are more and more concerned about
  • 00:21:34
    what is happening to the data that they
  • 00:21:36
    are actually providing companies by
  • 00:21:38
    using certain applications on their
  • 00:21:40
    smartphones but also over the Internet
  • 00:21:42
    and I think this is a little different
  • 00:21:44
    in China although there is also an
  • 00:21:46
    increasing discourse and increasing
  • 00:21:48
    concern about privacy and how this
  • 00:21:51
    basically fits together with the
  • 00:21:53
    increasing use of artificial
  • 00:21:55
    intelligence and machine learning in
  • 00:21:56
    particular Mike do you get the feeling
  • 00:21:59
    over the years that our attitude towards
  • 00:22:02
    innovation has changed in the West that
  • 00:22:04
    we're much more cautious and anxious
  • 00:22:07
    about abuses than we were in the past
  • 00:22:10
    what's interesting particularly about AI
  • 00:22:12
    is that what we've seen is a whole new
  • 00:22:16
    range of opportunities for our rights to
  • 00:22:20
    be abused and I think that's what's kind
  • 00:22:22
    of shocked a sudden taking us aback I
  • 00:22:23
    mean you know said that the Cambridge
  • 00:22:25
    analytic a scandal which horrified
  • 00:22:26
    everybody and rightly so because it just
  • 00:22:29
    didn't occur to us that that our data
  • 00:22:31
    might be used in that way I mean this is
  • 00:22:34
    a much bigger debate than just the
  • 00:22:35
    debate about
  • 00:22:36
    and it goes to the fundamental conundrum
  • 00:22:40
    of living in the digital world which is
  • 00:22:42
    that to get the benefits of living in
  • 00:22:45
    the digital world to be able to meet
  • 00:22:47
    your friends online and so on it seems
  • 00:22:49
    that you have to give up some aspects of
  • 00:22:51
    your privacy you have to hand over your
  • 00:22:53
    data and once your data is is in
  • 00:22:56
    somebody else's hands you've lost
  • 00:22:57
    control of it you don't you know
  • 00:22:59
    whatever regulatory environment you
  • 00:23:01
    might put on top of that ultimately
  • 00:23:02
    you've handed it over to somebody else
  • 00:23:04
    you know that goes to the core of our
  • 00:23:07
    modern world and living in the digital
  • 00:23:08
    age that's to get those benefits you
  • 00:23:11
    have to make some sacrifices we haven't
  • 00:23:13
    yet in the West by any means figured out
  • 00:23:16
    exactly what the final equilibrium there
  • 00:23:19
    is going to be you know what it's going
  • 00:23:21
    to be in terms of our rights to our data
  • 00:23:23
    and so on we're just we are because we
  • 00:23:26
    are experiencing all these new phenomena
  • 00:23:29
    we're finding our way as we go along I'm
  • 00:23:31
    afraid I'm a cynic I feel that actually
  • 00:23:35
    the genie is out of the bottle in terms
  • 00:23:36
    of privacy and data and I don't think
  • 00:23:38
    we're going to be able to put it back in
  • 00:23:40
    in the way that we were used to you know
  • 00:23:42
    when I was a child or I just don't see
  • 00:23:45
    that I mean I think because the benefits
  • 00:23:48
    are too big for us and it is just too
  • 00:23:50
    difficult to kind of regulate on a
  • 00:23:52
    global scale I mean that's just me
  • 00:23:53
    speaking cynically but that's how I feel
  • 00:23:55
    wrong in China do you get the feeling
  • 00:23:58
    that people are worried about handing
  • 00:24:00
    over their data
  • 00:24:01
    I think people they are actually willing
  • 00:24:04
    to trade their privacy data with the
  • 00:24:07
    convenience or the safety just as sophie
  • 00:24:09
    was suggesting yes yeah yeah as a few
  • 00:24:12
    people they have the concerns and indeed
  • 00:24:14
    some it won't happen because of the
  • 00:24:16
    privacy but most people they are still
  • 00:24:19
    willing to contribute it's quite similar
  • 00:24:21
    to the clinical trial in China it is
  • 00:24:24
    quite actually quite easy in China to
  • 00:24:26
    recruit the volunteers for clinical
  • 00:24:27
    trials but in Western countries because
  • 00:24:30
    of the regulations is actually you've
  • 00:24:31
    got a lot of barriers and it's not easy
  • 00:24:34
    to go and Mike if you're right that the
  • 00:24:36
    genie is out of the bottle then might we
  • 00:24:38
    have to learn from China here living
  • 00:24:41
    with it there is a different
  • 00:24:43
    generational attitudes and these things
  • 00:24:44
    you know we've got a generation of kids
  • 00:24:47
    including my own kids who are growing up
  • 00:24:49
    in the digital
  • 00:24:50
    not just the digital age but the online
  • 00:24:52
    age they are just routinely sharing and
  • 00:24:55
    sometimes quite intimate data with each
  • 00:24:57
    other and their entire lives are being
  • 00:25:00
    documented on social media and we didn't
  • 00:25:02
    have that that's a new thing I mean you
  • 00:25:04
    know that I didn't grow up with that you
  • 00:25:06
    didn't grow up with that and so they are
  • 00:25:07
    developing just completely different
  • 00:25:09
    attitudes to to privacy and data and the
  • 00:25:12
    ownership of their data to the ones that
  • 00:25:14
    we had and not necessarily attitudes
  • 00:25:16
    that we would be comfortable with it's
  • 00:25:18
    now an old story but when social media
  • 00:25:19
    first started taking off around about
  • 00:25:22
    2005 2006 there were serious suggestions
  • 00:25:25
    that you would have the right as an
  • 00:25:27
    adult to be able to completely change
  • 00:25:29
    your identity so that you could distance
  • 00:25:31
    yourself from all the stupid things you
  • 00:25:33
    did as a teenager which you documented
  • 00:25:34
    on social media I don't know whether
  • 00:25:36
    that will happen but I say there are
  • 00:25:38
    fundamentally different attitudes I
  • 00:25:40
    think that kids have now people who are
  • 00:25:41
    growing up in the online world we've
  • 00:25:43
    been looking at one aspect in which
  • 00:25:46
    China might benefit compared to the West
  • 00:25:48
    but another that we've alluded to is the
  • 00:25:50
    sheer numbers so how does that make a
  • 00:25:54
    difference in respect of AI research I
  • 00:25:57
    think it's a huge huge huge national
  • 00:26:00
    advantage for China I mean there's
  • 00:26:02
    population of China's currently about
  • 00:26:03
    1.4 billion it's larger than Europe and
  • 00:26:05
    the United States combined 1.4 billion
  • 00:26:08
    people in a single legal regulatory
  • 00:26:10
    system this means that the companies
  • 00:26:13
    like the the big three Baidu Alibaba
  • 00:26:14
    $0.10 when they release an application
  • 00:26:17
    they can have a user base of a billion
  • 00:26:19
    people all working with essentially the
  • 00:26:21
    same language the same regulatory
  • 00:26:24
    environment and the fuel that drives the
  • 00:26:27
    current AI boom is data and if you've
  • 00:26:29
    got a billion users using your app with
  • 00:26:32
    a single language you've just got a huge
  • 00:26:35
    advantage compared to trying to work in
  • 00:26:37
    Europe where any number of different
  • 00:26:39
    languages and different regulatory
  • 00:26:40
    environments so I think this is
  • 00:26:43
    genuinely a big advantage that companies
  • 00:26:45
    have in China compared to those that are
  • 00:26:47
    forming in the West is there some
  • 00:26:49
    particular area where you can see these
  • 00:26:51
    advantages playing out particularly
  • 00:26:53
    strongly for me the exciting one not
  • 00:26:55
    just for China but globally is health
  • 00:26:57
    care so I think this is one area where
  • 00:27:00
    AI looks set to make a dramatic
  • 00:27:03
    difference there are a number of
  • 00:27:05
    different aspects to this but one is
  • 00:27:06
    kind of personalized health care so it's
  • 00:27:09
    quite common now for people to have
  • 00:27:11
    wearable technology like an Apple watch
  • 00:27:13
    or a Fitbit device which is monitoring
  • 00:27:16
    their physiology on a 24/7 basis
  • 00:27:20
    continually monitoring aspects of their
  • 00:27:22
    physiology their heartbeat and that data
  • 00:27:25
    is just fed to the supercomputer in your
  • 00:27:28
    pocket which is your smartphone where AI
  • 00:27:30
    algorithms can analyze it and they can
  • 00:27:33
    do things like I mean it kind of sounds
  • 00:27:35
    trivial but anybody with children will
  • 00:27:36
    realize it isn't trivial they can help
  • 00:27:38
    with things like dealing with sleep
  • 00:27:40
    disorders they can help you to manage
  • 00:27:42
    your sleep and understand how you're
  • 00:27:44
    sleeping and how to sleep better and AI
  • 00:27:47
    can help us do this it's entirely
  • 00:27:49
    plausible that we will have wristwatch
  • 00:27:52
    type devices that are going to be able
  • 00:27:55
    to give us advice on for example when
  • 00:27:57
    we're at risk of a heart attack I mean
  • 00:27:59
    that's actually a genuine possibility
  • 00:28:00
    with current AI technology but to make
  • 00:28:02
    all that work you need data you need
  • 00:28:05
    lots of data you need lots of data using
  • 00:28:07
    these products and you need to be able
  • 00:28:08
    to monitor these people using these
  • 00:28:11
    products over a long period of time and
  • 00:28:13
    there I say scale is a huge huge huge
  • 00:28:15
    advantage so if you're wrong in China do
  • 00:28:18
    a lot of people wear these devices
  • 00:28:20
    already for the house Fitness monitoring
  • 00:28:23
    is quite common for some big companies
  • 00:28:25
    like hallway they produce the rats pen
  • 00:28:28
    that can monitor your heart rate your
  • 00:28:30
    activities and also like the xiaomi band
  • 00:28:33
    there are several big stake holders it
  • 00:28:36
    is quite common for particularly for
  • 00:28:38
    young people they worry to monitor in
  • 00:28:41
    their Fitz's and nowadays also like in
  • 00:28:43
    the health care area as mentioned in the
  • 00:28:46
    development plan house care is a big
  • 00:28:48
    application area so when people wear
  • 00:28:51
    these bands it's not only that the bands
  • 00:28:54
    are providing information to the wearer
  • 00:28:55
    they're also sending information back to
  • 00:28:58
    the companies to fuel their research yes
  • 00:29:01
    I think so because the devices and also
  • 00:29:04
    the algorithms need to be ready to write
  • 00:29:06
    it again again because the algorithm
  • 00:29:09
    needs the data to get evolved now
  • 00:29:12
    there's also directly a research
  • 00:29:14
    direction they combine the personal
  • 00:29:17
    collected data with hospital data to get
  • 00:29:20
    even accurate prediction of their health
  • 00:29:23
    status that linkage of private data with
  • 00:29:26
    hospital data I can imagine would be
  • 00:29:29
    rather difficult here it is and there
  • 00:29:32
    are some examples of it being done
  • 00:29:33
    successfully here but there are also
  • 00:29:35
    some examples where things have gone a
  • 00:29:38
    little bit awry so a nice example of
  • 00:29:41
    where I think it's worked well was
  • 00:29:42
    google deepmind based in london worked
  • 00:29:45
    with Moorfields Eye Hospital and they
  • 00:29:47
    were extremely rigorous about all the
  • 00:29:49
    procedures for handling data for getting
  • 00:29:51
    data and so on and it worked
  • 00:29:53
    tremendously well and they ended up with
  • 00:29:55
    a system which could diagnose eye
  • 00:29:56
    diseases with greater accuracy than a
  • 00:29:59
    typical human expert would be able to
  • 00:30:02
    and they were very careful not to claim
  • 00:30:04
    that this was a product stage but
  • 00:30:06
    actually demonstrated that capability
  • 00:30:07
    but we have the NHS which were very
  • 00:30:10
    proud of and rightly so in this country
  • 00:30:12
    and because we've had a national
  • 00:30:14
    healthcare service since the 1940s we've
  • 00:30:17
    got a huge amount of data going back
  • 00:30:20
    that most of that data is not remotely
  • 00:30:22
    in a form that it could be used for
  • 00:30:23
    machine learning algorithms but
  • 00:30:25
    nevertheless it's it's a huge potential
  • 00:30:27
    resource but using that resource and in
  • 00:30:30
    particular handing that resource over to
  • 00:30:32
    private companies is an incredibly hot
  • 00:30:35
    potato in political terms it's very very
  • 00:30:37
    difficult to be able to do anything
  • 00:30:39
    so the Moorfields example demonstrates
  • 00:30:42
    where it can work on a relatively small
  • 00:30:43
    scale but to be able to get that value
  • 00:30:46
    out of NHS data I think is probably
  • 00:30:48
    quite problematic I think Sophie also
  • 00:30:51
    other countries for example Germany and
  • 00:30:53
    France have realized that it is very
  • 00:30:55
    important to make large data sets
  • 00:30:57
    available to companies but then also to
  • 00:30:59
    researchers who want to develop
  • 00:31:01
    applications based on these large
  • 00:31:03
    amounts of data but of course there are
  • 00:31:06
    very very big questions that are still
  • 00:31:08
    open with regard to how can these large
  • 00:31:11
    amounts of data actually made available
  • 00:31:12
    how can you protect privacy when you
  • 00:31:15
    make these large data sets available and
  • 00:31:17
    in what kind of form so what kind of
  • 00:31:20
    ways do countries such as Germany and
  • 00:31:22
    France find to structure these data and
  • 00:31:24
    to label it so that it's becoming
  • 00:31:26
    accessible to people who want to use
  • 00:31:28
    that for research purposes
  • 00:31:30
    I think China has one more advantage
  • 00:31:32
    when it comes to using these large
  • 00:31:33
    amounts of data Snyder has alluded to
  • 00:31:35
    this data is very difficult to use for
  • 00:31:38
    machine learning when it's just in a raw
  • 00:31:39
    form so it needs to be structured and it
  • 00:31:41
    needs to be labeled for example if we
  • 00:31:43
    have images as a source of data then
  • 00:31:46
    this is only really useful for machine
  • 00:31:48
    learning algorithms if these images are
  • 00:31:51
    labeled so if there is basically
  • 00:31:53
    somebody who says this is a tree this is
  • 00:31:54
    a car this is a house and so on and so
  • 00:31:56
    forth and what is currently happening in
  • 00:31:58
    China is that there is a new industry
  • 00:32:00
    developing around these applications or
  • 00:32:03
    you basically have firms that offer a
  • 00:32:05
    service of labeling all this data for
  • 00:32:08
    you because there is still quite a
  • 00:32:10
    they're quite low wages in China there
  • 00:32:13
    is the possibility to actually have
  • 00:32:15
    people label these images or also other
  • 00:32:18
    sources of data and I think this is a
  • 00:32:20
    huge advantage and actually making this
  • 00:32:22
    data usable for machine learning
  • 00:32:25
    applications so that sounds like a
  • 00:32:27
    wonderful context for them because not
  • 00:32:29
    only do they have lots of data coming in
  • 00:32:31
    they've also got the possibility where
  • 00:32:33
    you need a human to identify things in
  • 00:32:36
    the data in order to train the
  • 00:32:38
    algorithms they've got a huge source of
  • 00:32:40
    cheap labor well I mean you might not
  • 00:32:42
    need this on the long term so hopefully
  • 00:32:44
    at some point you might not need a human
  • 00:32:46
    anymore who's actually labeling this
  • 00:32:47
    data but hopefully it's also possible to
  • 00:32:49
    automate this and so at some point and I
  • 00:32:50
    think it is going to be possible but for
  • 00:32:53
    the moment I think the availability of
  • 00:32:55
    cheap labor is an advantage when you
  • 00:32:57
    really want to make use of these large
  • 00:33:00
    amounts of data yes
  • 00:33:01
    presumably in healthcare in particular
  • 00:33:03
    it's actually rather important to get
  • 00:33:06
    datasets that are from the right
  • 00:33:09
    population because if we say try to
  • 00:33:12
    manage healthcare in Western Europe on
  • 00:33:14
    the basis of data that comes from China
  • 00:33:17
    their health care issues are going to be
  • 00:33:19
    somewhat different from ours yes exactly
  • 00:33:20
    and I also think that something that is
  • 00:33:23
    very important to keep in mind is that
  • 00:33:24
    the healthcare systems in European
  • 00:33:26
    countries for example have a really high
  • 00:33:28
    standard I think in certain areas
  • 00:33:31
    definitely even a higher standard than
  • 00:33:32
    in China and so these healthcare systems
  • 00:33:34
    produce very very valuable data that
  • 00:33:37
    could be used to develop further
  • 00:33:39
    applications and also to develop more
  • 00:33:42
    analyze waste of healthcare so I think
  • 00:33:45
    there it is certainly very important to
  • 00:33:47
    think about how to make this available
  • 00:33:49
    to research us Mike can you give us a
  • 00:33:51
    feel for how a I research and machine
  • 00:33:55
    learning in particular is going on in
  • 00:33:58
    the rest of the world compared with
  • 00:33:59
    China is China already overtaking us
  • 00:34:03
    historically it was all about the u.s.
  • 00:34:06
    AI really started in the u.s. outside
  • 00:34:08
    the US the UK I think it's fair to say
  • 00:34:10
    was was quite genuinely the number two
  • 00:34:14
    country if you go back just sort of 20
  • 00:34:17
    years and I think there's been a much
  • 00:34:18
    broadening out onto onto mainland Europe
  • 00:34:22
    for example Australia Canada also became
  • 00:34:25
    global powers but actually all the big
  • 00:34:29
    innovations all the big headlines
  • 00:34:31
    systems in AI until very recently
  • 00:34:33
    originated in the United States so you
  • 00:34:36
    know the IBM Watson system deep blue
  • 00:34:39
    that'd be Garry Kasparov back in the
  • 00:34:41
    1990s a driverless car the Grand
  • 00:34:43
    Challenge in 2005 that Harold at the era
  • 00:34:45
    of driverless cars that was Stanford
  • 00:34:47
    University and so on and so on and so on
  • 00:34:49
    so the really big developments until
  • 00:34:51
    very recently all came out of the US and
  • 00:34:53
    for the moment at least the bulk of the
  • 00:34:56
    new developments are but what is
  • 00:34:58
    different is the research power base in
  • 00:35:03
    terms of global quality research power
  • 00:35:06
    base
  • 00:35:06
    that's where China is winning I mean
  • 00:35:09
    just in terms of its research power it's
  • 00:35:12
    muscle in this area one could go back
  • 00:35:15
    and talk about the experience in the
  • 00:35:17
    1980s of Japan you will remember there
  • 00:35:21
    was a big move in Japan in the 1980s and
  • 00:35:24
    the sense was that Japan was very
  • 00:35:27
    successful at making products and
  • 00:35:29
    businesses but not necessarily so
  • 00:35:31
    successful at innovating and in
  • 00:35:35
    particular in the area of computer
  • 00:35:36
    software and computer technology so
  • 00:35:38
    there was a massive national investment
  • 00:35:40
    in Japan in what was called the
  • 00:35:42
    fifth-generation computer project they
  • 00:35:44
    bet on the wrong technologies basically
  • 00:35:46
    and one one view of the fifth-generation
  • 00:35:48
    computer project was that it was in some
  • 00:35:51
    sense a failure that didn't deliver the
  • 00:35:53
    global advantage
  • 00:35:55
    that Japan had hoped for but actually
  • 00:35:57
    when I talk to colleagues in Japan their
  • 00:36:00
    view is that what it did is it created
  • 00:36:02
    Japan as a player on the international
  • 00:36:05
    stage in terms of computing and computer
  • 00:36:06
    science research so it didn't deliver
  • 00:36:10
    what I think they wanted but actually it
  • 00:36:12
    delivered something so is a either the
  • 00:36:16
    right technology to bet on I think it's
  • 00:36:18
    a pretty good bet right now for China
  • 00:36:20
    that research muscle even if you can't
  • 00:36:22
    innovate that research muscle that
  • 00:36:25
    research power base is going to deliver
  • 00:36:27
    and would it be fair to say that we live
  • 00:36:29
    in an age at the moment where big
  • 00:36:32
    developments in AI for the next decade
  • 00:36:34
    or two an awful lot of them are going to
  • 00:36:37
    come not from major research innovations
  • 00:36:40
    but rather applications of a technology
  • 00:36:43
    that's already there namely deep
  • 00:36:45
    learning combined with huge amounts of
  • 00:36:47
    data and computing power yes I think
  • 00:36:50
    that's I would say that I think that's
  • 00:36:51
    that's fair to say I mean we've got this
  • 00:36:53
    new technology and what's exciting the
  • 00:36:56
    moment is everybody discovering all the
  • 00:36:58
    amazing things that you can do with it
  • 00:36:59
    and and this is this is why everybody's
  • 00:37:01
    so excited right now about about deep
  • 00:37:04
    learning and machine learning and so I
  • 00:37:06
    think there's an element of truth to
  • 00:37:07
    that if you can just if you can just use
  • 00:37:10
    these technologies then actually you've
  • 00:37:12
    got great scope using them in an
  • 00:37:14
    imaginative way then you've got great
  • 00:37:15
    scope to be able to create new products
  • 00:37:17
    and services and new businesses and I
  • 00:37:19
    think that's that's what we're going to
  • 00:37:20
    see for the next decade
  • 00:37:21
    Sharan looking to the future I would
  • 00:37:24
    have thought if we've got Chinese
  • 00:37:26
    universities turning out lots of very
  • 00:37:28
    well-trained young people we've got
  • 00:37:30
    industry and government giving people
  • 00:37:33
    lots of opportunities for developing
  • 00:37:36
    their skills for well-paid jobs thinking
  • 00:37:40
    about research applications of AI in 10
  • 00:37:44
    20 years time there's going to be a huge
  • 00:37:47
    impetus to keep going either using this
  • 00:37:50
    technology or discovering new ones
  • 00:37:52
    how do you see it changing China well I
  • 00:37:55
    think this is a currently China have big
  • 00:37:58
    advantages in the application or the
  • 00:38:01
    implication of the AI but not the
  • 00:38:04
    discovery so the basic series but
  • 00:38:07
    through this plan
  • 00:38:08
    China have the goal force like to lead
  • 00:38:10
    in in the area of application and then
  • 00:38:12
    it is possible to catch up from the
  • 00:38:15
    theoretical discoveries do their plans
  • 00:38:18
    distinguish quite clearly between
  • 00:38:21
    applications and the theoretical
  • 00:38:24
    scientific innovation yes I think so
  • 00:38:27
    so currently as in China the
  • 00:38:29
    government's really admit that the
  • 00:38:32
    Chinese is not good at the original
  • 00:38:35
    readouts of the AI series but for the
  • 00:38:40
    applications just as Mike mentioned that
  • 00:38:42
    data is a huge advantage and also the
  • 00:38:45
    ecosystem is really good the market the
  • 00:38:48
    applications etc but they are keeping in
  • 00:38:51
    mind that the goal is all kinds from the
  • 00:38:54
    theory to the application Michael over
  • 00:38:57
    the next 20 to 30 years what do you
  • 00:38:59
    think the implications will be of what's
  • 00:39:02
    going on in China now well I think we
  • 00:39:05
    have to take the the AI story is just
  • 00:39:07
    part of a much bigger picture about the
  • 00:39:10
    changes in China and in any any
  • 00:39:13
    statistic that you throw out about China
  • 00:39:15
    is remarkable but I mean here's just the
  • 00:39:17
    most remarkable one that I found what I
  • 00:39:19
    was just doing research for this so over
  • 00:39:20
    the last 30 years China's had an average
  • 00:39:22
    16 percent year-on-year growth in GDP 16
  • 00:39:26
    percent year-on-year for the last 30
  • 00:39:28
    years he's right now by standard
  • 00:39:32
    measures it seems to be the second
  • 00:39:33
    largest economy in the world
  • 00:39:34
    but actually by some measures it's the
  • 00:39:37
    largest if we just continue this
  • 00:39:40
    trajectory and the trajectory that I've
  • 00:39:42
    witnessed as an AI researcher of the
  • 00:39:44
    growth of Chinese AI and just project
  • 00:39:48
    that even a decade into the future if we
  • 00:39:51
    just get that growth continuing a decade
  • 00:39:53
    into the future then actually there's
  • 00:39:55
    good it's a fundamental shift in the
  • 00:39:57
    nature of of our world China will have
  • 00:40:01
    the biggest economy in the world it will
  • 00:40:02
    have a research base not just in AI but
  • 00:40:06
    a research base generally that is the
  • 00:40:09
    largest in the world and it's you just
  • 00:40:11
    have to project that growth a few years
  • 00:40:13
    into the future but AI is only part of
  • 00:40:16
    the story and it's part of the story of
  • 00:40:18
    China over the last 30 years
  • 00:40:21
    opening up engaging with the West
  • 00:40:23
    engaging with business really pushing
  • 00:40:26
    business and developing business and
  • 00:40:27
    products and I think we're gonna see an
  • 00:40:29
    awful lot more of that so in you know my
  • 00:40:31
    grandchildren will have heard of Tenzin
  • 00:40:34
    and Baidu and Alibaba even if my kids
  • 00:40:36
    right now haven't that will be much more
  • 00:40:39
    everyday presence in our lives so it's
  • 00:40:43
    that I think we are we are in a kind of
  • 00:40:45
    a unique period similar to the growth of
  • 00:40:48
    the US in the 20th century and the way
  • 00:40:52
    that the u.s. became by the second half
  • 00:40:54
    of the 20th century became the the
  • 00:40:56
    dominant geopolitical force in the world
  • 00:40:58
    it's very hard not to see that based on
  • 00:41:01
    any kind of statistics that you look at
  • 00:41:03
    about China thank you Sophie yeah I
  • 00:41:07
    think I mean at the moment we still see
  • 00:41:09
    that the u.s. is a leader as measured by
  • 00:41:12
    many artificial intelligence indicators
  • 00:41:14
    for example when it comes to basic
  • 00:41:16
    research or when it comes to hardware
  • 00:41:18
    but China's ambitions should certainly
  • 00:41:21
    not be underestimated in the space given
  • 00:41:23
    the considerable state support for the
  • 00:41:25
    advancement and use of national and
  • 00:41:27
    international AI resources and also the
  • 00:41:30
    enthusiasm of the Chinese population I
  • 00:41:32
    think what is very important is to still
  • 00:41:36
    find a way to cooperate with China on
  • 00:41:38
    many issues especially when it comes to
  • 00:41:41
    risks that will emerge from the
  • 00:41:44
    development of artificial intelligence
  • 00:41:45
    we were talking about safety we were
  • 00:41:47
    talking about ethics question and China
  • 00:41:50
    will certainly play a very very
  • 00:41:52
    important role in steering the
  • 00:41:54
    development but also the implementation
  • 00:41:57
    of these technologies across a number of
  • 00:41:59
    years so I think it's very important to
  • 00:42:02
    find ways for European countries and
  • 00:42:04
    also for the United States to cooperate
  • 00:42:07
    with China for example in research and
  • 00:42:10
    development and to make sure that I mean
  • 00:42:13
    at the moment we're often talking about
  • 00:42:15
    an AI race between China and the United
  • 00:42:18
    States developing and I think it's very
  • 00:42:20
    important to try to make sure that this
  • 00:42:22
    narrative is not becoming dominant and
  • 00:42:25
    that countries are for instance willing
  • 00:42:27
    to compromise safety just for having a
  • 00:42:31
    first mover advantage and a particular
  • 00:42:32
    fear be economics but also the millet
  • 00:42:35
    for example so I think this is very
  • 00:42:36
    important to keep in mind so it's seen
  • 00:42:39
    more as a cooperative enterprise than a
  • 00:42:41
    competitive one well I think at the
  • 00:42:43
    moment it is predominantly framed as a
  • 00:42:45
    competitive enterprise and I think it's
  • 00:42:48
    also framed as a zero-sum game and I
  • 00:42:50
    think this is really going in the wrong
  • 00:42:52
    direction I think it's very important to
  • 00:42:54
    communicate that different countries can
  • 00:42:57
    definitely benefit from this development
  • 00:42:59
    and that there are many ways of
  • 00:43:00
    cooperating and I think decision-makers
  • 00:43:02
    should put more effort into finding
  • 00:43:05
    these areas where there can be fruitful
  • 00:43:08
    cooperation with China you know on the
  • 00:43:10
    side of the United States for example
  • 00:43:11
    we've seen especially over the last year
  • 00:43:13
    there are a lot of initiatives that were
  • 00:43:15
    aiming at sort of isolating the US
  • 00:43:17
    economy in particular also with a focus
  • 00:43:20
    on artificial intelligence so by now we
  • 00:43:22
    have a more strict review procedure for
  • 00:43:24
    foreign direct investments from various
  • 00:43:27
    countries into the United States in
  • 00:43:28
    high-tech industries including
  • 00:43:30
    artificial intelligence now the Commerce
  • 00:43:32
    Department is also thinking about
  • 00:43:34
    expanding its export control so that
  • 00:43:36
    they cover various AI technologies and I
  • 00:43:38
    think it's very important to keep in
  • 00:43:40
    mind really where countries can benefit
  • 00:43:42
    from open trade in these areas from
  • 00:43:45
    exchange in science and that countries
  • 00:43:48
    can really benefit from this development
  • 00:43:50
    and not having it framed as a zero-sum
  • 00:43:52
    game Sharon the Chinese government has
  • 00:43:55
    announced this plan of getting best in
  • 00:43:59
    the world in AI becoming an absolute
  • 00:44:01
    world leader does that have a big impact
  • 00:44:03
    on the people in China the way they
  • 00:44:06
    think about AI the way they plan for the
  • 00:44:09
    future yes I think absolutely the plan
  • 00:44:13
    is a very good beginning for the whole
  • 00:44:15
    countries to get to know more about the
  • 00:44:18
    technology and to develop the technology
  • 00:44:21
    and more importantly actually this is a
  • 00:44:23
    part of the Chinese government's plan to
  • 00:44:27
    develop the innovates Science and
  • 00:44:29
    Technology Innovation and after 30 years
  • 00:44:31
    through this plan I can see the bright
  • 00:44:34
    future this tablet technology can bring
  • 00:44:37
    for the China well that's an excellent
  • 00:44:38
    point to finish on thank you very much
  • 00:44:41
    that's been a very interesting
  • 00:44:42
    discussion I'd heard a lot of talk
  • 00:44:44
    recently about China's M
  • 00:44:47
    in AI and I'd like to thank Mike Sharon
  • 00:44:50
    and Sophie Charlotte for helping me to
  • 00:44:52
    find out more and my thanks to you for
  • 00:44:55
    listening we're nearly at the end of our
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