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What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service | BBC World Service | YouTubeToText
YouTube Transcript: What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service
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This content explores the transformative potential of Artificial Intelligence (AI) across various sectors, featuring insights from leading AI engineers on its applications in healthcare, robotics, and complex problem-solving, while also touching upon the ethical considerations and future trajectory of AI development.
Hello.
I'm Caroline Steel.
This is the BBC World Service.
And welcome to The Engineers.
This year we're at the Science Specialist University, Imperial College London.
And we're here to focus on the technical revolution
defining our era, artificial intelligence.
I'm joined by a panel of three world leaders in the field and a large,
enthusiastic audience in Imperial's Great Hall.
Already a computer can defeat the world's
greatest player at our most complex strategy game.
The first movie written entirely by AI
has just been released, and AI may have discovered
our first antibiotic in three decades.
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.
He's now founder and CEO of Embodied, which is a company
that builds emotionally intelligent robots to help with child development.
David Silver
is from the UK, where he's principal research scientist at the AI
Research Lab.
Google DeepMind.
He led the team that used AI
to defeat the world's best player at the complicated strategy game Go.
And he's working on artificial general intelligence.
Regina Barzilay is Israeli-American and a distinguished professor for AI
and Health at MIT in the U.S.
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.
So please do join me in welcoming them all.
Regina, let's start with you.
So what is it that made you shift your work to oncology?
Sadly, you were in the perfect position to do that, weren't you?
Yeah.
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.
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.
Neither the diagnostics nor the treatment nor the post-treatment.
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.
And I was thinking, What can I do?
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,
but none of it is actually coming to the hospitals and helping patients.
So after I finished my treatment, I still didn't have my hair.
I started kind of going from doctor
to doctor, asking them, you know, how I can bring AI - I will do it for free.
I'm a professor.
So there were not many takers, but eventually
we found somebody who... it was a doctor called Connie Lehman
who had the idea that we can apply
AI to do early detection of cancer.
Thank you, Regina.
David, you came to AI via the games industry
and you did a PhD in reinforcement learning.
What is reinforcement learning and how did you use it in those early days?
Yeah.
So I guess I started out in the games industry before
I went back to academia and I was working on building computer games.
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.
And I found myself being fundamentally disappointed
by the methods that were being used in those games.
And it felt like
what I really wanted to do was build something that had real AI in it.
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.
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?
Yes, it's a lot like that.
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
that makes the brain actually learn
to do more of the good things and less of the bad things.
And so actually, that's inspired a lot of work in machine
learning to make machines have that same capability.
But a machine doesn't feel heat or isn't rewarded by a cookie.
How can you reward a machine?
Yeah, so for a machine, it's just a number.
So you give it a positive number
if it's done something good and a negative number if it's done
something bad.
And at the end of the day, everything
stems from that one single number.
So this one single number, which we call the reward, contains
enormous power because it's the signal that drives everything.
Paolo, you said your experiences of feeling
alienated in foreign countries made you want to create an imaginary friend.
And I'm sure much of our audience can relate to having an imaginary friend,
but for most of us, they stay imaginary.
How did you go about making a real one?
So unfortunately, there's a lot of people
in need of companionship or therapy,
and there's a massive gap of labour force
that can provide us, as an example, to use numbers from the U.S.
We know the prevalence of things such as autism is growing rapidly.
Ten years ago it was one out of about 200 kids.
Today, it's one out of about 30 kids.
So the experiences I had, which was leaving my family
at a very young age, living abroad in a society that's amazing.
I mean, these are amazing people,
but yet you are different, so you are not going to be embraced.
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.
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
such as eye contact, turn-taking, joint attention and so on,
so that the child has a chance of being successful in their society.
Thank you.
Regina, what can AI do
when it comes to understanding cancer that humans can't?
So I think that in cancer and in many other diseases,
a big question is always, how do you deal with uncertainty?
And unfortunately, today we rely on humans who don't have this capacity
to make predictions.
And as a result, many times people get wrong treatments
or they are diagnosed much later.
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
already had on a tiny small cancer.
Now, for human eye, for radiologists,
it's impossible to diagnose it because it's so, so confusing.
There's so many other spots on your tissue.
So what AI can do, it can do a lot of tasks which humans cannot do.
Take all the data that we have
and remove the guessing out of diagnosis and treatment.
Thank you.
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?
Which sounds more complicated.
So the game of Go is this very beautiful and elegant game
where it seems at first glance like the rules are very simple.
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.
They've used incredible intuition.
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.
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
imagine what the game will pan out like, you know, 300 moves later
with this sort of intuitive sense of where it will go.
And that required some some major breakthroughs.
Paolo, debatably even more complex than Go is human children.
Your human centred robot forms an emotional bond with children.
How can a robot do that?
Well, first of all, it's important
to make clear that the robot is not meant to replace the need for human contact.
It's really almost like training wheels to teach children the social skills
and then be able to practice those in real life.
The way the robot forms
bond is that humans are wired to bonding.
We create connections with inanimate objects all the time.
I mean, with a robot that has eyes, can make eye contact,
can smile back at you and can speak to you and express emotion and empathy.
It's actually not that hard to create a bond there.
And children open up to to these robots very quickly
in ways that they may not even open up to their therapists or parents.
Thank you, Paolo.
Regina, you not only made an impact on oncology, your team at MIT used
AI to discover what could be our first new
antibiotic in three decades.
It seems it can be E. Coli, MRSA, and strains of bacteria
which are currently resistant to all other antibiotics.
So I think we all wish it success.
How did you do that?
So I should say
that, you know, developing antibiotics is not an area
with an immense competition, even though their resistance to
to antibiotics that we have continues to grow.
This happened to be an area where pharmaceutical companies
are not very active because economically it doesn't work for them.
So in some ways we do need to have alternative approaches.
I met a colleague and he was working.
He was from biological engineering, he was working on antibiotics.
And he was describing the big problem of finding new molecules
which are effective against bacteria, drug resistant bacteria.
But at the same time, are not toxic to humans.
They have some molecules screened against, I think E.coli.
We started with that
and then we just gave to the machine, you know, thousands of molecules.
And for each molecule you knew whether it kills a bacteria or not.
It was kind of the first attempt to learn automatically.
How do you look at the structure
of the molecule and predict whether it would have a desired effect?
We found a molecule that didn't look like something human created.
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.
And that's what made it so effective against so many different species.
David, let's go back to you.
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.
Could you explain what artificial general intelligence is?
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,
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.
And that's what we refer to as artificial general intelligence or AGI for short.
And how far off do you think we are from that being a reality?
So I think it's going to be a spectrum over many years.
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
level of intelligence that humans have.
Regina, you've developed AI to better predict cancer,
but it's only employed in a tiny number of cases, right?
Why is AI not used more widely in medicine?
The problem is that we're creating a lot of great technology,
but this technology is not really translated into patient care.
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.
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.
One is the regulation or regulations, in Europe,
UK, US - continue to change.
Another big problem is people don't really know how to bill for AI.
And today the American doctor who uses AI loses money
when they see a patient.
So it's not much of a motivation.
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?
I'm referring to a specific paper.
The way the billing is done
it somehow relates to the time the doctor spends with a patient.
So if you have something that makes it faster, you're actually losing money.
Very depressing.
And Paolo, what about your challenges?
The robot you've developed mimics human behaviour.
What's next?
I'm very hopeful with what we are doing in terms
of creating social emotional AI systems
that can help humanity become its best.
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.
When we think about other vulnerable
areas of our life is when we age.
Social isolation, being lonely, and that leads to mental
health issues, that leads to physical health issues and so on.
We can have the same systems become a companion that help you there.
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,
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
when we are at that age.
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?
I think it is within the next decade.
Oh wow. 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.
But novels, should we really be letting AI sort of take over human culture?
So it's a great question.
I think I wouldn't see it as taking over human culture.
I think what will happen, or the most likely outcome, is that we'll be
providing an incredibly powerful tool to human authors.
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.
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.
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.
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.
So, you know, I'm excited to be in a world where we have much more...
the most amazing novels that we can imagine,
that which go far beyond the ones we have today.
Thank you, David.
Thank you.
This is The Engineers - Intelligent Machines from the BBC World Service.
We've discussed medical AI, emotionally intelligent robots, the goal
of artificial general intelligence and the threats AI might pose.
Has anyone got a question on anything we've discussed so far? Wow.
Okay.
Pretty much everyone has a hand up, so this is going to be tricky.
Could we start with the man in the red shirt?
If you could stand up and say your name and your question.
Thank you very much.
Hello, my name's Simon. The British government wants to make Britain
a leader in AI and sees the way to do this by making it a safe space.
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.
Does the panel think that's the right approach?
So, yes.
David, what do you think about legislation around AI?
I think we need some kind of regulation.
I think it's, you know, an area which clearly is going
to have more and more consequence and impacts on society.
So I think regulation is important.
I think some of the areas which have been agreed
in various summits over the last year - you know, fantastic start.
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.
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.
There's a lot of anxiety about the pace
AI is moving, right?
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.
Regina?
I actually feel quite opposite.
People are really suffering. There are lots of incurable diseases.
It's hard for patients, it's hard for their families.
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
I think we are really making many, many people suffer.
Thank you. Paolo, what about robots?
Are they going to take all our jobs?
Yes. Okay, great. We've got it.
We finally have an answer. Thank you, Paolo.
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
so that no one with bad intentions goes wild.
On the other hand, if you think about it, it's an extremely
potent technology.
It could change everything
in our lives including
being strategically important technology
to master from a national perspective.
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?
Are they going to then have an advantage over us?
So it's a bit of an arms race.
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
international regulation and agreement
across all the powerful nations to say this is how we're going to handle it.
And I haven't seen that work out very well in the history of time.
Fair enough.
Audience, we have time for a couple more questions.
So hands up. If we could go to the man in the white top.
Thank you.
Hello, panel. My name's Rob.
And I'm a business and sports coach.
And I'm wondering whether what you've been talking about
is possible for helping humans to improve their performance
at a sport.
Good question.
Who's our greatest sport enthusiast?
Perhaps this one is for you, David.
Can we be using AI to improve sports performance?
It's a great question.
There's a lot of really amazing research that's happening to try and do just that.
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.
So that's one example.
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
to new discoveries and new ways to do things.
And so it's been really fun actually, just watching that kind of thing unfold.
Very cool. Thank you, David.
I can see we've got lots of young people in the audience, some teenagers even.
Does any of our younger audience have a question?
If so stick your hand up.
There was a young lady who had a hand up here
with a ponytail. Yes.
As AI develops, I reckon
that humans probably depend more on AI and maybe learn less.
Is there anything we could do to maybe make sure that as it
develops, humans still develop and learn?
Ooh, good question.
So will we stop learning, as AI does the learning instead for us?
I'm going to put that to all of you if that's all right. What do you think, David?
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.
Regina, what do you think?
Do you think we're just going to end up relying on
AI for everything?
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
we don't have
and so I make a lot of mistakes when I write.
I see now how I write papers.
Actually, it removes a lot of my pressure in writing
and I can really focus on ideas rather than doing the small things.
So I hope that we will find a symbiosis.
We don't really, you know, remove our basic skills,
but we can just do more and focus on the things that we can do better.
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,
imagine what they would have done and the impact it would have had on the world
if they had access to these tools. In the next five years
potentially, you don't need to code.
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.
So it will make you more prolific.
Thank you.
I think it's fair to say all three of you have given us hope for the future.
Audience, thank you so much for your questions.
I wish we could take more, but I'm afraid we're out of time.
That's it for The Engineers -
Intelligent Machines at Imperial College, London.
I'm Caroline Steel.
On behalf of the BBC World Service
our partners
the Royal Commission for the Exhibition of 1851 and my producer, Charlie Taylor.
Please join me in giving a warm round of applause for our brilliant pioneering
AI engineers Regina Barzilay, Paolo Pirjanian and David Silver
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