This year we're at the Science Specialist
University, Imperial College London.
今年我們在倫敦帝國理工學院，這所科學專業大學。
00:26
And we're here to focus
on the technical revolution defining our era, artificial intelligence.
我們在這裡關注定義我們時代的技術革命——人工智慧。
00:33
I'm joined by a panel of three world
leaders in the field and a large, enthusiastic audience in Imperial's
Great Hall.
我請到三位世界領域領袖和帝國理工學院大廳裡的一大批熱情觀眾。
00:44
Already a computer can defeat the world's greatest player at our most complex
strategy game.
現在，電腦已經能戰勝世界上最大的玩家在我們最複的策略遊戲中。
00:50
The first movie written entirely by AI has just been released,
and AI may have discovered our first antibiotic in three decades.
第一部完全由AI撰寫的電影剛剛發，AI可能已經發現了我們三十年來的第一種抗生素。
00:59
Together
with our partners, Royal Commission 1851, we've brought together three engineers
at the cutting edge of this field to discuss their work
and what it means for us humans.
Paolo Pirjanian is Armenian, but he was born in Iran
and started his career working on Mars rovers for NASA.
保羅·皮爾尼安是亞美尼亞人，但他出生在伊朗，並在NASA的火星探測車項目上開始了他的職業生。
01:16
He's now founder and CEO of Embodied,
which is a company that builds emotionally intelligent robots
to help with child development.
他現在是Embodied的創始人和CEO，該公司製造情感智慧機器人，以助兒童發展。
01:24
David Silver is from the UK, where he's
principal research scientist at the AI Research Lab.
大衛·西爾弗來自英國，他是Google DeepMind AI研究實驗室的首席研究科學家。
01:30
Google DeepMind.
Google DeepMind。
01:31
He led the team that used AI to defeat the world's best player
at the complicated strategy game Go.
他領導了使用AI戰勝世界上最好的玩家在複策略遊戲Go的團隊。
01:38
And he's working on artificial general
intelligence.
他現在正在研究人工一般智慧。
01:41
Regina Barzilay is Israeli-American
and a distinguished professor for AI and Health at MIT in the U.S.
吉娜·巴勒是以色列-美國人，美國麻省理工學院的AI和健康學區域的出教授。
01:48
She created a major breakthrough
in detecting early stage breast cancer and also led the team that used AI to discover what is hoped to be
a brand new antibiotic.
在早期乳的檢測方面取得了重大突破，並領導了使用AI發現可能是全新的抗生素的團隊。
01:58
So please do join me in
welcoming them all.
和我一起歡迎他們所有人。
02:10
Regina, let's start with you.
吉娜，讓我們從你開始。
02:13
So what is it that made
you shift your work to oncology?
那麼，是什麼讓你將工作重心轉移到學？
02:16
Sadly, you were in the perfect position
to do that, weren't you?
可，你當時處於最佳位置，可以做到這一點，不是嗎？
02:20
Yeah.
是的。
02:20
So actually,
I started my work at MIT in 2003 as a faculty and I was working on
natural language processing and AI And in 2014,
I was diagnosed with breast cancer
and I was treated
in one of the best hospitals in the United States,
Massachusetts General Hospital.
而我在美國最好的醫院之一——麻省總醫院接受了治療。
02:39
And what I discovered going through
the treatment that there was really no AI or not even basic information technology
as part of the treatment.
在接受治療的過程中，我發現治療中幾乎沒有使用AI，甚至連基本的資訊技術都沒有。
02:48
Neither the diagnostics
nor the treatment nor the post-treatment.
無論是診斷、治療還是術後照，都沒有。
02:53
And, you know, after I was treated,
I just was totally confused as to what I want to do, because it was the first time
I realised that, you know, my life is finite
and I've seen a lot of very sick people
there surrounding me.
而我周圍有很多病人。
03:06
And I was thinking, What can I do?
我在想，我能做什麼？
03:08
And MGH - this hospital at MIT -
is just one subway stop away, they are separated by a bridge I’m saying, how come we have
all this great technology at MIT,
So there were not many takers,
but eventually we found somebody who...
謝，雷吉娜。
03:37
it was
a doctor called Connie Lehman who had the idea that we can apply AI to do early detection of cancer.
大衛，你是通過遊戲業進入AI領域的，並且獲得了強化學習的博士學位。
03:46
Thank you, Regina.
什麼是強化學習，你是如何在早期應用它的？
03:48
David,
you came to AI via the games industry and you did a PhD in reinforcement
learning.
大衛，你透過遊戲業進入AI領域，並獲得了強化學習博士學位。
03:54
What is reinforcement learning and
how did you use it in those early days?
化學習是什麼，你在早期是如何使用它的？
03:59
Yeah.
對啊。
03:59
So I guess I started out in the games
industry before I went back to academia and I was working
on building computer games.
那么，我想我一開始是在遊戲業工作，後來才回到學術界，我當時正在從事建造電腦遊戲的工作。
04:07
And as a big part of building
computer games is building the AI for those games, that kind of makes
all of the characters move around.
而建造電腦遊戲的一個重要部分，就是為遊戲建造人工智慧（AI），使得所有角色都能在遊戲中移動。
04:14
And I found myself
being fundamentally disappointed by the methods that were being used
in those games.
但我發現自己對於當時遊戲中使用的方法感到非常失望。
04:21
And it felt like what I really wanted to do was build
something that had real AI in it.
而我真正想做的是建造一個擁有真正人工智慧的東西。
04:25
I discovered this idea of reinforcement
learning, which is basically a method very much like those that animals
and humans use, where the system is able to learn
for itself from experience, from trial
and error, from trying things out
and seeing what works and what doesn't.
從試驗和錯誤中學習，嘗試各種方法，然後看看什麼有效，什麼無效。
04:39
So is it sort of like when we learn to not touch fire because at some point
we try it and it really hurts and we learn don't do that in the future
because the consequences aren't appealing.
Is it sort of like how humans
learn reinforcement learning?
這是不是類似於人類如何學習強化學習？
04:53
Yes, it's a lot like that.
是的，很像。
04:56
So in fact humans are believed to have,
you know, a major part of the brain which is devoted to providing a signal,
giving feedback that
事實上，人類被認為有一個大腦部分專門提供信號，給予反饋，讓大腦學習做更多好事，少做壞事。
05:04
that makes the brain actually learn to do more of the good things
and less of the bad things.
這個信號使大腦真正學習做更多好事，少做壞事。
05:09
And so actually, that's inspired
a lot of work in machine learning to make machines
have that same capability.
而這實際上激發了很多機器學習的工作，讓機器也具有這種能力。
05:15
But a machine doesn't feel heat
or isn't rewarded by a cookie.
但是機器不會感受到熱，也不會因為得到一塊餅乾而感到獎勵。
05:19
How can you reward a machine?
你如何獎勵一台機器？
05:22
Yeah, so for a machine, it's just a number.
對於機器來說，只是一個數字。
05:24
So you give it a positive number if it's done something good
and a negative number if it's done something bad.
所以如果機器做了好事，你就給它一個正數，如果做了壞事，就給它一個負數。
05:31
And at the end of the day,
everything stems from that one single number.
最終，一切都歸結於這個單一的數字。
05:35
So this one single number,
which we call the reward, contains enormous power because it's the signal
that drives everything.
這個單一的數字，我們稱之為獎勵，包含了巨大的力量，因為它是驅動一切的信號。
05:41
Paolo, you said your experiences of feeling alienated in foreign countries made
you want to create an imaginary friend.
保羅，你說你在外國感到孤獨的經驗，讓你想要創造一個虛擬朋友。
05:48
And I'm sure much of our audience
can relate to having an imaginary friend, but for most of us, they stay imaginary.
而我相信我們的觀眾中有很多人都曾經有過想象中的朋友，但對於大多數人來說，這些朋友始終停留在想象中。
05:56
How did you go about making a real one?
你是如何創造出一個真實的朋友的？
05:59
So unfortunately, there's a lot of people in need of companionship or therapy, and there's a massive gap of labour force
可的是，現在有很多人需要伴或治療，但能提供這些服務的動力卻重短缺，
06:10
that can provide us, as an example,
to use numbers from the U.S.
以美國為例，
06:14
We know the prevalence of things
such as autism is growing rapidly.
我們知道自閉症等疾病的發生率正在迅速增加。
06:18
Ten years ago
it was one out of about 200 kids.
十年前，自閉症的發生率大約是每200個孩子中有1個。
06:21
Today, it's one out of about 30 kids.
今天，這個數字已經增加到每30個孩子中就有1個。
06:23
So the experiences I had,
which was leaving my family at a very young age,
living abroad in a society that's amazing.
我曾經有過的經歷，例如在很小的時候就離開了家人，獨自在一個生的社會中生活。
06:32
I mean, these are amazing people, but yet you are different,
so you are not going to be embraced.
我的意思是，那些人非常好，但你仍然是不同的，所以你不會被完全接受。
06:38
So this is not too dissimilar
from a child on the autism spectrum that has a hard time expressing themselves
or reading emotions from other people.
與自閉症兒童的經歷有些相似，他們很難表達自己的情感或從他人身上讀懂情感。
06:47
And that was the genesis of creating
a robot companion that understands human emotions,
can create a deep relationship with a child, and it'll help them
exercise and practice social skills
就是創造一個能理解人類情感的機器人伴的起源，能與孩子建立深厚的關係，並助他們練習社交技能，
07:00
such as eye contact,
turn-taking, joint attention and so on, so that the child has a chance
of being successful in their society.
如眼神接、輪流、共同注意力等，讓孩子有機會在自己的社會中成功。
07:08
Thank you.
謝。
07:09
Regina, what can AI do when it comes to understanding
cancer that humans can't?
Regina，人工智慧在了解症方面可以做些什麼，而人類做不到的？
07:16
So I think
that in cancer and in many other diseases, a big question is always,
how do you deal with uncertainty?
我認為在症和許多其他疾病中，總是存在一個問題，那就是如何處理不確定性。
07:23
And unfortunately, today we rely on humans
who don't have this capacity to make predictions.
可的是，現在我們依的人類缺預測的能力。
07:30
And as a result, many times
people get wrong treatments or they are diagnosed much later.
結果，很多時候，病人會接受錯誤的治療，或者是在很晚才被診斷出來。
07:36
And one question that really troubled me
is, you know, how late I was diagnosed and when we already developed a model,
I came back to my own mammograms and rediscovered the mammograms
two years earlier
Now, for human eye, for radiologists, it's impossible to diagnose it
because it's so, so confusing.
現在，對於人類的眼睛，對於放射科醫生來說，診斷出這種症是非常困難的，因為它太小了，太容易被忽略。
08:00
There's
so many other spots on your tissue.
你身體的組織上還有很多其他的東西。
08:03
So what AI can do, it can do a lot of tasks
which humans cannot do.
所以人工智慧可以做很多事情，人類無法做到。
08:06
Take all the data that we have and remove the guessing
out of diagnosis and treatment.
所有的數據收集起來，從診斷和治療中去除猜測。
08:12
Thank you.
謝。
08:14
David, AI had already defeated the reigning
grandmaster, Garry Kasparov, at chess well before you started your project AlphaGo and the rules of Go
sound quite simple.
Basically, on each turn,
a player puts down a counter on the board and you gain territory
by connecting your counters and the player with the most territory
at the end of the game wins.
So why is it harder for a computer
to beat a human at Go than at chess?
那麼，為什麼電腦比在西洋棋上擊敗人類更難在圍棋上擊敗人類？
08:43
Which sounds more complicated.
一個聽起來更複。
08:45
So the game of Go
is this very beautiful and elegant game where it seems at first glance
like the rules are very simple.
棋是一個非常美麗和優雅的遊戲，一看似乎規則很簡單。
08:52
But once you start to understand it
a bit like unpeeling an onion, you discover more
and more layers of complexity and what's amazing is that when humans
play this game, they basically...
If you ask them to describe how they did
something, they really don't know.
如果你問他們如何做某事，他們真的不知道。
09:06
They've used incredible intuition.
他們使用了不可思議的直覺。
09:08
And so these amazing professional players
who've devoted their entire lives to this game have built this incredible
intuition and creativity and intuition and creativity
are two traits which were previously
considered to be very human
and very hard to build into machines.
被認為是非常人性化和很難在機器中建立。
09:23
So while chess, it was possible to succeed
just with tactical look ahead in the game of Go, that wasn't enough
because, you know, early on in the game you just have this handful of stones
on the board and you really just have to
Coli, MRSA,
and strains of bacteria which are currently resistant
to all other antibiotics.
所以我想我們都希望它成功。
10:59
So I think we all wish it success.
你是如何做到的？
11:03
How did you do that?
我應該說，開發抗生素並不是一個競爭激烈的領域，管細菌對我們現有的抗生素的耐藥性仍在不斷增強。
11:05
So I should say that, you know, developing
antibiotics is not an area with an immense competition,
even though their resistance to
但是這好是藥廠不太活的領域，因為從經濟角度來看，這對他們不划算。
11:14
to antibiotics
that we have continues to grow.
所以在某種程度上，我們確實需要有替代的方法。
11:17
This happened to be an area
where pharmaceutical companies are not very active because economically
it doesn't work for them.
我遇到了一位同事，他正在生物工程領域工作，正在研究抗生素。
11:25
So in some ways we do need to have
alternative approaches.
他正在描述尋找能有效對抗耐藥細菌的新分子的大問題。
11:29
I met a colleague and he was working.
他描述了這個問題，說明了開發新抗生素的困難。
11:31
He was from biological engineering,
he was working on antibiotics.
他是生物工程領域的，正在研究抗生素。
11:37
And he was describing the big problem
of finding new molecules which are effective against bacteria, drug
resistant bacteria.
他正在描述一個很大的問題，那就是尋找對細菌、尤其是耐藥性細菌有效的新分子。
11:45
But at the same time, are not toxic to humans.
但同時，它們也不會對人類造成毒害。
11:47
They have some molecules screened against,
I think E.coli.
它們有一些分子是經過篩選的，我想是針對大腸桿菌（E.coli）。
11:53
We started with that and then we just gave to the machine,
you know, thousands of molecules.
我們從那開始，然後只要給機器幾千個分子。
11:58
And for each molecule you knew
whether it kills a bacteria or not.
而對於每個分子，你都知道它是否能殺死細菌。
12:01
It was kind of the first attempt
to learn automatically.
這是第一次嘗試自動學習。
12:04
How do you look at the structure of the molecule and predict
whether it would have a desired effect?
你如何觀察分子的結構並預測它是否會產生所需的效果？
12:09
We found a molecule that didn't look like
something human created.
我們發現了一個看起來不像人工創造的分子。
12:13
And it turns out in the lab
that it was able to kill using a different mechanism of action,
kill it in a different way.
而實驗結果表明，它能夠使用不同的作用機制殺死細菌，且殺死的方式不同。
12:20
And that's what made it so effective
against so many different species.
這就是它對許多不同物種都有效的原因。
12:25
David, let's go back to you.
大衛，讓我們回到你。
12:27
So, so far we've been talking
about systems designed to perform one task - that's known as narrow AI, but you're working towards
artificial general intelligence.
到目前為止，我們一直在談論設計用於執行單一任務的系統——這被稱為狹義人工智慧（narrow AI），但你正在朝著人工一般智慧（artificial general intelligence）努力。
12:37
Could you explain
what artificial general intelligence is?
你能解釋什麼是人工一般智慧嗎？
12:40
So if you think about humans
and human intelligence, it's this wonderful and beautiful thing
where we're able to learn skills which are incredibly diverse, where,
如果你思考人類和人類智慧，它是一件美妙而美麗的事情，我們能夠學習各種各樣的技能，
12:50
you know, one person might choose to
specialise in learning how to play tennis and another person might specialise in becoming an amazing chef
and another person, a pianist
and another person, a scientist
and so when we want to build artificial intelligence, we want systems
which not only solve a single problem but in a similar manner to humans,
are able to approach
any number of problems
with intelligence, and that's capable of doing amazing things
in each of those different areas.
並且在每個領域都能做出令人驚奇的事情。
13:20
And that's what we refer to as artificial
general intelligence or AGI for short.
這就是我們所指的人工一般智慧，或簡稱AGI。
13:25
And how far off do you think we are from
that being a reality?
你認為我們離那個目標還有多遠？
13:29
So I think it's going to be a spectrum
over many years.
所以我認為這將是一個跨越多年的過程。
13:31
And I also think it's likely
or at least plausible that there are many breakthroughs
that are still required before we can really crack,
you know, the same kind of
而且我也認為，在我們真正能夠解決人工一般智慧之前，可能還需要許多突破。
13:41
level of intelligence that humans have.
人類所有的智慧水平。
13:43
Regina, you've developed
AI to better predict cancer, but it's only employed
in a tiny number of cases, right?
Regina，你開發了人工智慧來更好地預測症，但它只在極少數情況下被採用，是嗎？
13:50
Why is AI not used
more widely in medicine?
為什麼人工智慧在醫學領域中沒有被更廣泛地使用？
13:53
The problem is that
we're creating a lot of great technology, but this technology is
not really translated into patient care.
題在於，我們創造了很多很棒的技術，但這些技術並沒有真正地轉化為病人護理。
13:59
And if I would ask the audience, you know,
how many of you when you last saw your physician,
have you actually seen any AI, and I'm sure not
many can really attest to it.
如果我問觀眾，你們上次去看醫生時，是否真的看到了任何人工智慧的應用，我相信很少有人能證實。
14:10
So I think the technology for many of the tasks
is really mature, but there are many other questions
which have nothing to do with AI per se.
所以我認為，對於很多任務來說，技術已經非常成熟，但還有很多其他問題與人工智慧本身無關。
14:19
One is the regulation or regulations,
in Europe, UK, US - continue to change.
其中一個問題是法規，在歐洲、英國、美國等地不斷地變化。
14:27
Another big problem is people
don't really know how to bill for AI.
一個大問題是人們不知道如何為人工智慧進行收費。
14:31
And today the American doctor
who uses AI loses money when they see a patient.
而在美國，今天使用人工智慧的醫生在看病人時會錢。
14:37
So it's not much of a motivation.
所以這並不是一個很大的動力。
14:39
So just to clarify that, did
you say that using AI for doctors in the US could actually make them lose money
because it makes treatment more effective?
為了清，你說使用人工智慧的醫生在美國可能會錢，因為它使治療更有效？
14:48
I'm referring
to a specific paper.
我指的是一篇特定的論文。
14:53
The way the billing is done it somehow relates to the time
the doctor spends with a patient.
收費的方式與醫生與病人相處的時間有關。
15:00
So if you have something that makes
it faster, you're actually losing money.
所以，如果你有一些東西可以使治療更快，你其實會錢。
15:04
Very depressing.
真的很令人。
15:05
And Paolo, what about your challenges?
Paolo，你的挑戰是什麼？
15:07
The robot you've developed
mimics human behaviour.
你開發的機器人模仿人類行為。
15:10
What's next?
下一步是什麼？
15:12
I'm very
hopeful with what we are doing in terms of creating social emotional AI systems that can help humanity become its best.
我對於創造可以助人類成為最好的社會情感人工智慧系統感到非常樂觀。
15:21
If we can intervene early with children
for instance on the autism spectrum, they have a chance of really integrating
well with the society and doing really well.
如果我們可以在兒童早期介入，例如在自閉症譜系上，他們就有機會真正地融入社會並做得很好。
15:32
When we think about other vulnerable areas of our life is when we age.
我們思考生活中其他弱的領域時，就是當我們年增長的時候。
15:38
Social isolation, being lonely,
and that leads to mental health issues, that leads
to physical health issues and so on.
社會孤立、孤獨，這會導致心理健康問題，進而導致身體健康問題等等。
15:45
We can have the same systems
become a companion that help you there.
我們可以建立相同的系統，成為你的伴，助你應對這些挑戰。
15:49
Once we figure out the physical task, you can also imagine
that they can give you assistive care, meaning that they can be not only
a social emotional companion for you,
一旦我們解決了身體任務，你也可以想象它們可以提供輔助照護，意味著它們不僅可以成為你的社交情感伴，
15:59
but they can also be a companion
and say, let's cook some food together, let's go for a walk together
and so on, which is going to help a lot with independent
living with dignity
可以成為你的伴，說「讓我們一起做飯」，「讓我們一起散步」等等，這將有助於獨立生活，維護尊，
16:09
when we are at that age.
特別是在我們年長的時候。
16:11
Do you think we'll see robots
helping with assistive care in the future any time soon,
or is this way off in the distance?
你認為我們會在不久的將來看到機器人助輔助照護，還是這個目標還很遠？
16:19
I think it is within the next decade.
我認為這是在未來十年內可以實現的。
16:22
Oh wow.
。
16:22
David, you're working on Gemini,
which is Google's answer to ChatGPT, and you aim for it to be able to do
both tax returns and write a novel.
People would probably be very happy
to have their tax returns done or I would definitely would be.
人們可能會很高興有人忙做務申報，我肯定會很高興。
16:37
But novels, should we really be letting
AI sort of take over human culture?
但是小說，應該讓AI接管人類文化嗎？
16:44
So it's a great question.
是一個很好的問題。
16:46
I think I wouldn't see it as taking over
human culture.
我不認為AI會接管人類文化。
16:49
I think what will happen, or
the most likely outcome, is that we'll be providing an incredibly powerful tool
to human authors.
我認為會發生的事情是，我們會為人類作者提供一個非常強大的工具。
16:57
So we've already seen this
in a number of areas where we've developed technologies
that enable authors of different kinds of media to basically create things
much more powerfully using tools.
我們已經在很多領域看到這種情況，我們開發了技術，讓不同媒體的作者能使用工具創造出更強大的東西。
17:08
So, for example, there's a music
authoring system called Lyria that was released recently,
and there's this wonderful footage of Will.i.am when he's playing with it
for the first time.
例如，最近有一個叫Lyria的音樂創作系統，還有Will.i.am第一次使用它的精彩錄像。
17:18
And he's just so excited
because he says it can kind of speed up his songwriting process
by, you know, 10 to 100 times.
他非常興，因為他說這個系統可以加速他的歌曲創作過程，提高10到100倍。
17:25
So I think, you know what I really hope
we get to is a world in which the AI and the humans kind of work together
to just make everything better.
所以，我希望我們能達到一個AI和人類合作，讓一切變得更好的世界。
17:34
So, you know, I'm excited to be in a world
where we have much more...
所以，我很高興能生活在一個有更多...
17:38
the most amazing novels
that we can imagine, that which go far beyond the ones
we have today.
我們能想象出最令人驚奇的小說，那些遠遠超越我們今天所有的東西。
17:42
Thank you, David.
謝你，David。
17:43
Thank you.
謝。
17:44
This is The Engineers - Intelligent
Machines from the BBC World Service.
是來自BBC世界服務的《工程師》- 能機器。
17:48
We've discussed medical AI,
emotionally intelligent robots, the goal of artificial general intelligence
and the threats AI might pose.
我們討論了醫學AI、情感智能機器人、人工一般智慧的目標以及AI可能帶來的威。
17:56
Has anyone got a question
on anything we've discussed so far?
有人對我們到目前為止討論的內容有疑問嗎？
17:59
Wow.
。
18:00
Okay.
好的。
18:01
Pretty much everyone has a hand up,
so this is going to be tricky.
乎每個人都舉起了手，所以這將會很手。
18:04
Could we start
with the man in the red shirt?
我們可以從穿紅色的男人開始嗎？
18:11
If you could stand up and say your name
and your question.
如果你能站起來說出你的名字和你的問題。
18:13
Thank you very much.
非常感謝。
18:15
Hello, my name's Simon.
你好，我的名字是Simon。
18:17
The British
government wants to make Britain a leader in AI and sees the way to do this
by making it a safe space.
英國政府希望使英國成為AI領域的領導者，並認為實現這一目標的方法是通過創造一個安全的環境。
18:23
They're looking at doing that
by making sure you can't develop AI where you don't understand
the consequences before you develop it.
他們正在研究如何通過確保在開發AI之前了解其後果來實現這一目標。
18:31
Does the panel think that's the right
approach?
小組成員是否認為這是正確的方法？
18:34
So yes.
是的。
18:35
David, what do you think about legislation
around AI?
David，你如何看待AI的立法？
18:39
I think we need some kind of regulation.
我認為我們需要某種形式的監管。
18:42
I think it's, you know, an area
which clearly is going to have more and more consequence
and impacts on society.
我認為這是， 你知道，一個將會對社會產生越來越多影響的領域。
18:49
So I think regulation is important.
因此，我認為監管是很重要的。
18:51
I think some of the areas
which have been agreed in various summits over the last year -
you know, fantastic start.
我認為一些領域已經在過去一年裡的各種峰會上達成共識，你知道，這是一個很好的開始。
19:01
One thing I would say
is I think it's hard sometimes to come up with like a one size
fits all recipe for AI because it's
so different in different areas.
So you know, the regulation
that you might need in medicine might look quite different to the kind of regulation
you might need for, say, a chatbot.
所以，你知道，在醫學領域中可能需要的監管可能與你在聊天機器人領域中需要的監管大不相同。
19:19
So I think, you know,
we have to look at each area separately and make sure that whatever we do,
you know, we really fully understand the consequences of what
the impact of AI will be in that area.
因此，我認為我們必須分別審視每個領域，確保無論我們做什麼，都要充分了解人工智慧在該領域的影響。
19:30
There's a lot of anxiety about the pace AI is moving, right?
有很多人對人工智慧的發展速度感到焦，對吧？
19:34
We have people resigning, leaders
in the field to campaign for more safeguarding
against the threat of misinformation, the threat to jobs,
and even an existential risk to humanity.
我們有領域領袖職，為了推動更多的保障措施，以應對假信息的威、工作位的威，甚至是對人類的生存威。
19:44
Regina?
Regina？
19:45
I actually feel quite opposite.
我其實有相反的感覺。
19:46
People are really suffering.
人們真的在受苦。
19:48
There are lots of incurable diseases.
有很多無法治的疾病。
19:49
It's hard for patients, it's hard
for their families.
病人來說很難，对他们的家人也很難。
19:52
There is a lot of technologies
that is out there and because we cannot get together
to put the regulation in place, make, you know, the payers take part of it
and find a way to bring it
有很多技術可以使用，但因為我們無法合作，制定監管，讓付款方參與，並找到方法將其帶入實。
20:02
I think we are really making many,
many people suffer.
我認為我們真的讓很多人受苦。
20:08
Thank you.
謝。
20:09
Paolo, what about robots?
Paolo，關於機器人呢？
20:11
Are they going to take all our jobs?
們會走我們所有的工作嗎？
20:13
Yes.
是的。
20:14
Yes.
是的。
20:14
Okay, great.
好的，很好。
20:15
We've got it.
我們已經得到了答案。
20:16
We finally have
an answer.
我們終於有了答案。
20:17
Thank you, Paolo.
謝你，Paolo。
20:20
On a serious
note, I feel it's a bit of a conundrum to think about legislation,
because on one hand, yes, there are definitely risks
and you would like to regulate it
正經的，我覺得思考立法是一個很困的問題，因為一方面，的確存在風險，你會希望加以規管，
20:31
so that no one with bad intentions
goes wild.
樣就不會有人意地胡作非為。
20:35
On the other hand,
if you think about it, it's an extremely potent technology.
一方面，如果你認真思考，這是一種極其強大的技術。
20:43
It could change everything in our lives including being strategically important technology
可能會改變我們生活的所有方面，包括從國家層面來看具有戰略重要性的技術。
20:51
to master from a national perspective.
這個角度來看，如果你規管它，假設規管的一部分是減慢它的發展速度，那麼我們的對手會怎麼做？
20:55
And from that perspective,
if you regulate it, let's say part of
the regulation is slowing it down, what are our adversaries going to do?
And I think for that reason, practically speaking,
I think it's going to be very hard to regulate it to that
level unless you can have
而我還沒有看到這種情況在歷史上曾經成功過。
21:20
international regulation and agreement across all the powerful nations to say
this is how we're going to handle it.
是合理的。
21:26
And I haven't seen that work out very well
in the history of time.
眾們，我們還有時間回答幾個問題。
21:31
Fair enough.
所以請舉手。
21:32
Audience, we have time for a couple more questions.
如果我們可以請穿白色上衣的先生發言。
21:34
So hands up.
謝。
21:35
If we could go to the man
in the white top.
你好，專家小組。
21:37
Thank you.
謝謝。
21:38
Hello, panel.
大家好，專家小組。
21:40
My name's Rob.
我的名字是 Rob。
21:42
And I'm a business and sports coach.
而我是一名商業和體育教練。
21:45
And I'm wondering
whether what you've been talking about is possible for helping humans
to improve their performance at a sport.
我在想你們所討論的東西是否有可能助人類改善他們在體育方面的表現。
21:55
Good question.
好問題。
21:57
Who's our greatest sport enthusiast?
是我們最大的體育愛好者？
21:59
Perhaps this one is for you, David.
也許這個問題是為你而設的，David。
22:01
Can we be using AI to improve sports
performance?
我們可以使用人工智慧來改善體育表現嗎？
22:06
It's a great question.
是一個很好的問題。
22:07
There's a lot of really amazing research
that's happening to try and do just that.
目前有很多令人驚的研究正在試做到這一點。
22:11
You know, one thing which we've been doing
at Google DeepMind is actually a collaboration with Liverpool Football
Club to try and help them improve their tactics.
你知道，在 Google DeepMind，我們正在與利物浦足球樂部合作，試助他們改善戰術。
22:22
So that's one example.
是一個例子。
22:24
I think, you know, the amazing thing
about sports is it's become over time so refined in terms of the particular
approaches that people take that actually,
you know, really they're very open
And so it's been really fun actually,
just watching that kind of thing unfold.
真的很。
22:43
Very cool.
謝，David。
22:44
Thank you, David.
我可以看到我們的觀眾中有很多年輕人，甚至還有一些青少年。
22:45
I can see we've got lots of young people
in the audience, some teenagers even.
我們的年輕觀眾中有人有問題嗎？
22:49
Does any of our younger audience
have a question?
如果有，請舉手。
22:52
If so stick your hand up.
如果你有問題，請舉手。
22:53
There was a young lady
who had a hand up here with a ponytail.
裡有一位年輕女士，她有一個馬尾，舉起了手。
22:58
Yes.
是的。
22:59
As AI develops, I reckon that humans probably depend more on AI
and maybe learn less.
著人工智慧的發展，我想人類可能會越來越依人工智慧，同時也可能學得越來越少。
23:06
Is there anything we could do to
maybe make sure that as it develops, humans still develop and learn?
有沒有什麼方法可以確保，當人工智慧發展的同時，人類仍然能發展和學習？
23:14
Ooh, good question.
，很好的問題。
23:15
So will we stop learning, as
AI does the learning instead for us?
那麼，當人工智慧替我們學習的時候，我們是否就不需要學習了？
23:20
I'm going to put that to all of you if that's all right.
我想問問大家的意見，大家覺得呢？
23:22
What do you think, David?
你怎麼想，David？
23:23
I'd like
to imagine a world where we have an AGI which is like a personal friend,
assistant teacher, and everything we do understands
what we want to learn, and it knows
just how to teach us and help us
to learn more and more and more and more.
而且能不斷地學習。
23:37
Regina, what do you think?
Regina，你怎麼想？
23:38
Do you think we're just going to end up
relying on AI for everything?
你覺得我們最終會完全依人工智慧嗎？
23:42
As a non-native speaker of English I remember as a young professor
I spent a humongous amount of time like reading my papers and making sure,
you know, because in my native language
I would like to imagine
if Isaac Newton or Albert Einstein had access to these tools today,
I mean, as prolific as they were at a time
where we didn't even have calculators,
在未來五年內，你可能不需要寫程式。
24:27
imagine what they would have done and
the impact it would have had on the world if they had access to these tools.
想像一下，如果他們能使用今天的這些工具，會做些什麼，又會對世界產生什麼樣的影響。
24:33
In the next five years potentially, you don't need to code.
潛在的未來五年內，你可能不需要編寫程式碼。
24:36
You will just tell your favourite AI system
and say I want a code that does x, y, z and you can accomplish what would
take years of many people in hours today.
