The A.I. Race (2017) Movie Script

(mystical music)
[Female Robotic voice] Hi, how are you feeling?
I just checked your health data.
Your last meal contributed 60% of your daily nutrients.
And you've completed 11,000 steps towards
your daily fitness goal.
So you can take a seat now.
I've got something for you to watch.
And I'll be watching, too.
Tonight, you're going to see humans take on
the robots that might replace them.
There will always need to be experienced
people on the road.
From truckies to lawyers,
artificial intelligence is coming.
Actually it's already here.
I didn't realize that it would be just be able
to tell you, hey, here's the exact answer
to your question.
We'll challenge your thinking about AI.
[Male Robotic Voice] Same category, 1600.
The AI's going to become like electricity.
Automation isn't going to affect
some workers, it's going to affect every worker.
And we let the generation most affected
take on the exerts.
I think that the younger generations
probably have a better idea of where things
are going than the younger generations.
Tonight, we'll help you get ready
for The AI Race.
(upbeat music)
(mellow music)
Australian truckies often work up to
72 hours a week and are now driving
bigger rigs to try to make ends meet.
I've seen a lot of people go backwards
out of this industry and I'm seeing
a lot of pressures it's caused them,
their family life, especially when you're
paying the rig off.
Now a new and unexpected threat to Frank
and other truck drivers is coming on fast.
Last year this driverless truck in the US
became the first to make an interstate delivery.
it traveled nearly 200 kilometers on the open road
with no one at the wheel, no human that is.
The idea of robot vehicles on the open road
seemed ludicrous to most people just five years ago.
Now just about every major auto and tech company
is developing them.
So what changed?
An explosion in artificial intelligence.
(mellow music)
There's lots of AI already in our lives.
You can already see it on your smartphone
every time you use Siri, every time you ask
Alexa a question.
Every time you actually use your cellphone
navigation, we're using one of these
algorithms you're using some AI that's
recognizing your speech, answering questions,
giving you search results, recommending books
for you to buy on Amazon.
They're the beginnings of AI everywhere in our lives.
(gentle music)
We don't think about electricity.
Electricity powers our planet.
It powers pretty much everything we do.
It's going to be that you walk into a room
and you say, room, lights on.
You walk and you sit in your car and you say,
take me home.
A driverless car is essentially a robot.
It has a computer that takes input from its senses
and produces an output.
The main senses are radar, which can be found
in adaptive cruise control.
Ultrasonic senses, and then there's cameras
that collect images.
And this data is used to control the car,
to slow the car down, to accelerate the car,
to turn the wheels.
There's been an explosion in AI now,
because of the convergence of full exponentials.
The first exponential is Moore's Law.
The fact that every two years we've had a doubling
and computing performance.
The second exponential is that every two years
we've had a doubling of the amount of data that we have.
Because these machine learning algorithms
are very hungry for data.
The third exponential is that well we've been
working on AI for 50 years or so now.
And our algorithm are starting to get better.
And then the fourth exponential, which is
over the last few years, we've had a doubling
every two years of the amount of funding
going into AI.
We now have the compute power.
We now have the data.
We now have the algorithms.
We now have a lot of people working on the problems.
(engine turns)
It could be you just jump into the car.
You assume the car knows where you need to go
because it has access to your calendar, your diary,
where you're meant to be.
And if you did not want the car to go
where you're calendar says you ought to be,
then you need to tell the car, oh, by the way,
don't take me to the meeting that's in my calendar,
take me to the beach.
[Host] But Frank Black won't have a bar of it.
I think it's crazy stuff.
You've got glitches in computers now.
The banks are having glitches with their ATMs
and emails are having glitches.
Who's to say this is going to be perfect
and this is a lot more dangerous if there's
a computer glitch.
There'll always need to be experienced people
on the road, not machines.
Frank is going to explain why he believes
robots can never match human drivers.
(mellow music)
Okay then, let's do it.
But Frank is off to a rocky start,
Driverless trucks in Rio Tinto Mines
in West Australia, show productivity gains
of 15%.
Frank needs to break every five hours
and rest every 12.
Oh and he needs to eat and he expects to be paid
for his work.
Robots don't need a salary.
Trials also indicate that driverless vehicles
save up to 15% on fuel and emissions.
Especially when driving very close together
in a formation called Platooning.
And at first glance, driverless technology
could dramatically reduce road accidents
because it's estimated that 90% of accidents
are due to human error such as fatigue,
or loss of concentration.
Robots don't get tired.
But hang on, Frank's not done.
He's about to launch a comeback using 30 years
of driving experience.
If there's something, say like a group of kids
playing with a ball on the side of the road.
We can see that ball starting to bounce
towards the road.
We anticipate that it would be a strong possibility
that that child will run out in the road,
you know, after that ball.
I can't see how a computer can anticipate
that for a start and even if it did,
what sort of reaction will it take?
Would it say, swerve to the left,
swerve to the right?
Will it just break and bring the vehicle
to a stop?
What about if it can't stop in time?
In fact, right now, a self-driving
vehicle can only react according to its program.
Anything unprogrammed can create problems.
Like when this Tesla drove into roadworks barrier,
after the human driver failed to take back control.
And what if some of the sensors fail?
What happens if something gets on the lens
and people doesn't know where it's going?
It's true, currently heavy rain or fog
or even unclear road signs can bamboozle
driverless technology.
And then there's the most unpredictable
element of all.
Human drivers.
Stupidity always find new forms.
Quite often you see things you've never seen before.
That's why there are no plans to trial
driverless trucks in complex urban settings right now.
They'll initially be limited to predictable
multi-lane highways.
You also still need a human right now
to load and unload a truck.
And a robot truck won't help change your tire.
If someone's in trouble on the road,
you'll usually find that a trucker
will pull over and make sure they're all right.
Finally there are road rules.
Australia requires human hands on the steering wheel
at all times, in every state and territory.
Hey Frank.
You won the race.
One for the human beings.
(upbeat music)
But how long can human drivers
stay on top?
Nearly 400,000 Australians earn their living
from driving, any more when you add part-time drivers.
But the race is on to deliver the first
version of a fully autonomous vehicle
in just four years.
And it might not be hype.
Because AI is getting much better, much faster
every year.
With a version of AI called Machine Learning.
(mellow music)
Machine learning is the little part of AI
that's focused on teaching programs to learn.
If you think about how we got to be intelligent,
we started out not knowing very much
when we were born and most of what we got
is through learning.
And so we write programs that learn
to improve themselves.
They need, at the moment, lots of data.
And they get better and better and in many cases,
for setting narrow focus domains,
we can often actually exceed human level performance.
When AlphaGo beats Lee Sedol last year,
one of the best Go players on the planet,
that was a landmark moment.
So we've always used games as benchmarks,
both between humans and between humans and machines.
And a quarter century ago, chess fell
to the computers.
And at that time, people thought,
well Go is going to be like that.
Because in Go, there are so many more possible moves.
And the best Go players weren't working
by trying all possibilities ahead.
They were working on kind of the gestalt of
what it looked like and working on intuition.
And we didn't have any idea of how to
instill that type of intuition into a computer.
(mesmerizing music)
But what happened is we've got some recent
techniques with deep learning where
we're able to do things like understand
photos, understand speech and so on
and people said, maybe this will be
the key to getting that type of intuition.
Sp, first it started by practicing on
every game that a master had ever played.
You feed them all in and it practices on that.
The key was to get AlphaGo good enough
from training it on past gains by humans
sot hat it could then start playing itself
and improving itself.
And one things that's very interesting
is that the amount of time it took
the total number of person years invested
is a tenth or less than the amount of time
it took for IBM to do the chess playing.
So the rate of learning is going to be exponential.
Something that we, as humans, are not used
to seeing.
We have to learn things painfully ourselves.
And the computers are going to learn
on a planet-wide scale, not on an individual level.
(mesmerizing music)
There is this interesting idea
that the intelligence would just suddenly explode
and take us to what's called the singularity,
where machines now improve themselves
almost without end.
There are lots of reasons to suppose that
maybe that might happen, but if it does happen,
most of my colleagues think it's about
50 years away, maybe even 100.
(robot gasps)
I'm not convinced that how important
intelligence is.
So I think that there's lots of different
attributes and intelligence is only one of them
and there certainly are tasks that having
a lot of intelligence would help.
And being able to compute quickly would help
so if I want to trade stocks then having a computer
that's smarter than anybody else is going to give
me a definite advantage.
But I think if I wanted to solve the Middle East
crisis, I don't think it's not being solved
because nobody's smart enough.
But AI experts believe robot cars
will improve so much that humans will eventually
be banned from driving.
(dramatic music)
Big road blocks remain, not the least
of which is public acceptance.
As we found out after inviting professional
drivers to meet two robot car experts.
How are you doing, Maria?
Straight away the first thing is to be safety.
You definitely have to have safety paramount.
And obviously efficiency.
So the big question, when is it going to happen?
In the next five to 10 years we will see
higher autonomous vehicles on the road.
If you want to drive from city to Canberra,
you drive to the freeway, activate autopilot
or whatever it will be called at the time
and by the time you arrive in Canberra,
the car will ask you to take back control.
There are predictions that in 20 years time,
50% of new vehicles will actually be
completely driverless.
What makes us think that these computers
in these vehicles are going to be fool-proof?
Well we were able to send rockets to the moon
and you know, I think that there are ways
of doing it and you can have backup systems
and you have backups for your backups and,
but I agree.
Reliability is kind of a big question mark.
But we're not talking a phone call dropping out
or an email shutting down, we're talking about
a 60 ton vehicle, in traffic, that's going to
kill people, there will be deaths
if it makes a mistake.
I think we need to accept that there will
still be accidents and a machine can make
a mistake, can shutdown, can fail
and if we reduce accidents by,
say 90%, there will still be 10% of the current
accidents will still occur on the network.
Who said it's going to be 90%?
How do you work that out?
90% is because 90% of the accidents
are because of human error.
And the idea is if we take the human out
we could potentially reduce it by 90%.
Have any of you ever driven a car
available on the market today with all this
technology, autopilot and everything in there?
It's absolutely unbelievable how safe
and comfortable you feel.
I think people will ultimately accept
this technology because we will be going
in steps.
I would say, for me as an Uber driver,
we're providing a passenger service
and those passengers, when they're going to
the airport, a lot of luggage.
If it's an elderly passenger, they need help
to get into the car, they need help
getting out of the car.
The human factor needs to be there.
I would argue that you can offer
a much better service if you're not
also driving.
So the cars taking care of the journey
and you're taking care of the customer.
And improving the customer experience.
And I think that there's a lot of scope for improvement
in the taxi and Uber customer experience.
You could offer tax advice, you could offer
financial advice.
It's unlimited.
Then we go back though, they're not fully driverless
vehicles anymore, we've still got a babysitter
there, a human being to look after the cars.
So what are we gaining with the driverless technology?
Well, the opportunity to do that.
Yeah, but, that's--
Are you trying to reduce cost by not having to
drive in the vehicle?
Well, it depends on what people are paying for, okay?
And if you are in business, you are trying
to get as many customers as possible.
And if you're competitor has autonomous vehicles
and is offering, you know, daycare services
or looking after disabled, you probably
won't be in business very long if they're able to
provide a much better customer experience.
For my personal use, I like to drive my car.
I want to enjoy driving.
Well I think in 50 years there will be
special places for people with vintage cars
and they can go out and drive around.
(all laugh)
(drowned out by laughter) Someday driving
our vintage car when these autonomous vehicles
have got our roads.
I mean, in the future when all the cars
are autonomous, we won't need traffic lights.
Okay, because the cars will just negotiate
between themselves when they come to intersections,
Can I ask you a question?
If we would do a trial
with high automated
platooning of big road trains,
would you like to be involved?
Yes, I would be involved, yeah, yeah,
Why not?
You convinced Frank, yeah.
If you can convince Frank, you can convince anybody.
Do you want to come out with us and I bet
Frank's the same as well, if you want to
come for a drive in the truck and see
exactly what it's like and the little issues
that would never have been thought of,
I mean, my door is always open,
you're more than welcome to come with me.
Oh definitely, I think it's--
It's time for a road trip.
(all laugh)
The drivers aren't the only ones
trying to find their way into the AI future.
(mellow music)
Across town, it's after work drinks
for a group of young and aspiring professionals.
Most have at least one university degree
or studying for one.
Like Christine Maibom.
I think as law students we know now
that it's pretty tough even to like
get your foot in the door.
I think that at the end of the day,
the employment rate for grads is still pretty high.
Tertiary degrees usually shield against
technological upheaval, but this time
AI will automate not just more physical tasks
but thinking ones.
(dramatic music)
Waiting upstairs for Christine is a new
artificial intelligence application.
One that could impact the research typically done
by paralegals.
We invited her to compete against it
in front of her peers.
Adelaide tax lawyer, Adrian Cartland came up with
the idea for the AI called Ailira.
I'm here with Ailira, the Artificial Intelligent
Legal Information Research Assistant.
And you're going to see if you can beat her.
So what we've got here is a tax question.
Adrian explains to Christine what sounds like
a complicated corporate tax question.
Does that make sense?
Yep, yeah.
Very familiar?
All right, ready?
I'm ready.
Okay guys, ready, set, go.
(upbeat music)
And here we have the answer.
So you've got the answer?
We're done.
That's 30 seconds.
Christine, where are you
up to with the search?
I'm at section 44 of the income tax assessment guide.
Maybe it has the answer, I haven't looked for it yet.
You're in the right act, so now do you want
to keep going or do you wanna give some more time?
I can keep going for a little bit, yeah, sure.
(upbeat music)
No pressure, Christine.
We're at one minute.
(laughs) Okay.
Whew, I might need help on this one.
This is, you know, really complex tax law.
Like I've given you a hard question.
You were in the income tax assessment act,
you were just doing research, what is your process?
Normally what I would do is probably
try to find the legislation first and then
I'll probably look to any commentary on the issue.
Find specific keywords so for example,
consolidated group and and accessible income
obviously there.
That's a pretty standard way.
That's what I would approach.
If you put this whole thing into a keyword search,
it's going to breakdown.
Keyword searches breakdown after about
four, five, or seven words.
Whereas this is, you know, 300-400 words.
So all I've done is I've entered in the question here,
copied and pasted it.
I've clicked on submit.
And she's read through, literally, millions
of cases as soon as I pressed search
and then she's come through and she said,
here is the answers.
Oh wow.
She's highlighted in there what she thinks
is the answer.
Yeah, I mean, wow.
I mean even down to the fact that it can
answer those very specific questions,
I didn't realize that it would just be able to
tell you, hey, here's the exact answer
to your question.
It's awesome.
I think obviously, for paralegals,
I think it's particularly scary because I mean
we're already in such a competitive market.
Adrian Cartland believes AI could
blow up lawyers monopoly on basic legal know how.
And he has an astonishing example of that.
My girlfriend is a speech pathologist who has
no idea about law and she used Ailira
to pass the Adelaide University tax law exam.
Oh wow.
Automation is moving up in the world.
Here's Claire, a financial planner.
It's estimated that 15% of an average financial planners
time is spent on tasks that can be done by AI.
What kind of things do you see it ultimately
taking over?
I would say that everything except talking to
your clients.
Here is Simon.
He used to be a secondary school teacher.
One fifth of that job can be done by AI.
Simon's now become a university lecturer,
which is less vulnerable.
I think there's huge potential
for AI and other educational technologies.
Obviously it's a little bit worrying
if we're talking about making a bunch
of people redundant.
And did I mention journalists?
I hope you enjoyed tonight's program.
The percentage figures were calculated
by economist Andrew Charlton and his team
after drilling into Australian workforce
For the first time, we broke the Australian
economy down into 20 billion hours of work.
And we asked, what does every Australian do
with their day?
And how or what they do in their job change
over the next 15 years.
I think the biggest misconception is that
everyone talks about automation as destroying jobs.
The reality is that automation changes every job.
It's not so much about what jobs will we do,
but how will we do our jobs because automation
isn't going to affect some workers,
it's going to affect every worker.
But if there's less to do at work,
that's got to mean less work or less pay
or both, doesn't it?
If Australia embraces automation,
there is a 2.1 trillion dollar opportunity for us
over the next 15 years.
But here's the thing.
We only get that opportunity if we do two things.
Firstly, if we manage the transition
and we ensure that all of that time
that is lost to machines, from the Australian workplace
is redeployed and people are found new jobs
and new tasks.
And condition number two is that we embrace automation
and bring it into our workplace and take advantage
of the benefits of technology and productivity.
But Australia's not doing well at either.
Right now, Australia's lagging.
One in 10 Australian companies is embracing
automation and that is roughly half the rate
of some of our global peers.
Australia hasn't been very good historically
at transitioning workers affected by
big technology shifts.
Over the last 25 years, one in 10 unskilled men
who lost their job, never worked again.
Today, four in 10 unskilled men don't participate
in the labor market.
(dramatic music)
We asked a group of young lawyers
and legal students, how they felt about embracing AI.
The contrasts were stark.
I often get asked, you know, do you feel threatened?
Absolutely not.
I'm confident and I'm excited about opportunities
that AI presents.
I think the real focus will be on not only up-scaling,
but re-skilling and about diversifying your skillset.
I think for me, I still have an underlying concern
about how much of the work is going to be taken away
from someone who's still learning the law
and just wants a job part time where they can
sort of help with some of those less
judgment based high level tasks.
how much software is there out there, AI
for legal firms at the moment?
There's quite a lot.
There's often a few competing in the same space
so there's a few that my law firm has trialed in.
For example, due diligence, which are great for
identifying certain clauses.
So rather than the lawyer sitting there
trying to find an assignment or a change
of control clause, it will pull that out.
How much time do you think using the Ai
cuts down on that kinda, just crunching,
lots of documents, lots of numbers?
Immensely, I would say potentially up to about
20% of our time in terms of going through
and locating those clauses or pulling them out,
extracting them.
Which of course delivers way better value
for our clients which is great.
Well, I think the first reaction was obviously like
very worried, I suppose.
You just see the way that this burns
through these sort of banal tasks that we'd be,
you know, doing at an entry level job,
and yeah, it's quite an intuitive response,
I suppose, that we're just a bit worried.
And also, it just was so easy, like,
it was just copy and paste,
and so it means that anyone could do it, really,
do you don't really need the sort of specialized
skills that are getting taught to us
in our law degrees, it's pretty much
just a press a button job.
AI is like Tony Stark's Iron Man suit,
it takes someone and makes them, you know,
into Superman, makes them fantastic.
So you could suddenly be doing things
that are like 10 times above your level
and providing that, you know,
at much cheaper than anyone else could do it.
Lawyers might, the legal work of the future
might be done by social workers,
psychiatrists, conveyances, tax agents,
accountants, they have that personal skill set
that lawyers sometimes lack.
Yeah, I always wonder just how much law school
should be teaching us about technology
and new ways of working in legal workforce,
because, I mean, a lot of what you guys
are saying, I've heard for the first time.
I certainly agree with that statement.
This is the first time I've heard
the bulk of this, especially hearing
that there is already existing a lot of AI.
Unfortunately, our education system
just isn't keeping up.
Our research shows that right now,
up to 60% of young Australians
currently in education are studying
for jobs that are highly likely to be automated
over the next 30 years.
It's difficult to know what will be hardest first.
But jobs that help young people makes ends meet
are among the most at risk.
Like hospitality workers.
So, the figure that they've given us
is 58% could be done by versions of AI.
What does that make you feel?
Very, very frustrated, that is really scary.
I don't know, I don't know what other
job I could do whilst studying
or that sort of thing.
Or as a fallback career, it's what
all my friends have done, it's what I've done,
it sort of just helps you survive and,
you know, pay for the food that you need to eat each week.
It may take a while to be cost-effective,
but robots can now help take orders,
flip burgers, make coffee, and deliver food.
Young people will be the most affected
by these changes because the types
of roles that young people take
are precisely the type of entry level task
that can be most easily done by machines
and artificial intelligence.
But here this evening,
there's at least one young student
who's a little more confident about the future.
So, Aniruddh, how much of your job as a doctor
do you imagine that AI could do pretty much now?
Not much, maybe five, 10%.
But artificial intelligence is also
moving into healthcare.
What is Sauron.
Sauron is--
What is leg?
Yes, Watson.
What is executor?
What is shoe?
You are right.
Same category, 1600.
So, in the earliest days of
artificial intelligence and machine learning,
it was all around teaching computers to play games.
Yes, Watson.
What is narcolepsy?
But today, with those machine learning algorithms,
we're teaching those algorithms how to learn
the language of medicine.
We invited Aniruddh to hear
about IBM research in cancer treatment
using its AI supercomputer, Watson.
Today, I'm going to take you through
a demonstration of Watson for oncology.
This is a product that brings together
a multitude of disparate data sources
and if able to learn and reason
and generate treatment recommendations.
This patient is a 62 year old patient
that's been diagnosed with breast cancer
and she's presenting to this clinician.
So the clinician has now entered this note in,
and Watson has read and understood that note.
Watson can read natural language,
and when I attach this final bit
of information, the ask Watson button turns green,
and at which stage, we're ready to ask
Watson for treatment recommendations.
Within seconds, Watson
has read through all the patients records
and doctor's notes as well as relevant medical
articles, guidelines and trials.
And what it comes up with is a set
of ranked treatment recommendations.
Down at the bottom, we can see
those in red that Watson is not recommending.
Does it take into account how many
citations a different article might
have used, say, the more citations,
the more it's going to trust it?
So, this is again where we need clinician
input, to be able to make those recommendations.
Natalie, you've shown us this,
and you know, you've said that this
would be a clinician going through this,
but the fields that you've shown,
really, an educated patient
could fill in a lot of these fields
from their own information.
What do think about that approach,
the the patient's essentially
getting their own second opinion
from Watson for themselves?
I see this as a potential tool to do that.
AI's growing expertise
at image recognition is also being
harnessed by IBM to train Watson on retinal scans.
One in three diabetics have associated
eye disease, but only about half of these
diabetics get regular checks.
We know that with diabetes, the majority
of vision loss is actually preventable
if timely treatment is instigates,
and so that if we can tap into that group,
you're already looking at potentially
incredible improvement in quality of life
for those patients.
How could something like that happen?
We could have a situation
where you have a smartphone applications,
you take a retinal selfie if you like.
That then is uploaded to an AI platform,
analyzed instantly, and then you have
a process by which instantly,
you're known to have high risk or low risk disease.
How long does it take to analyze
a single retinal image using the platform?
Very close to real times, it's in a matter of seconds.
I mean, this is obviously very, very early days,
but the hope is that one day,
these sorts of technologies will be widely
available to everyone for this
sort of self-analysis.
Just like law, AI might one day
enable patients to DIY their own expert
diagnosis and treatment recommendations.
Some doctors will absolutely feel
threatened by it, but I'd come back
to the point that you want to think
of it from the patient's perspective,
so if you're an oncologist sitting
in the clinic with your patient,
the sorts of things that you're dealing with
is things like giving bad news to patients,
and I don't think patients want to get
bad news from a machine.
So it's really that ability to have
that intelligent assistant who's up to date
and providing you with the information
that you need and providing it quickly.
We like to use the term, augmented intelligence.
I think one interesting way to think about this
is, I mentioned 50,000 oncology journals a year.
Now, if you're a clinician trying to read
all of those 50,000 oncology journals,
that would mean you'd need about
160 hours a week just to read
the oncology articles that are published today.
Watson's ability to process all of this medical
literature and information and text is immense.
It's 200 million pages of information in seconds.
Need a bit of work on myself then.
IBM is just one of many companies
promoting the promise of AI and healthcare,
but for all these machine learning algorithms
to be effective, they needs lots of data,
lots of our private medical data.
In my conversations with my patients,
and the patient advocates that we've spoken to,
you know, they certainly want the privacy protected,
but I think it's actually a higher priority for them
to see this data being used for the public good.
But once it has all the data,
could this intelligent assistant ultimately
disrupt medicine's centuries old hierarchy.
They should have more general
practitioners and less of the specialty.
So, doctors, they all have more time
to have a better relationship with you,
maybe they'll be talking about your overall
health rather than waiting for you
to come in with symptoms,
and if they do have to, you know,
analyze an x-ray and look for a disease,
they'll have a computer to do that,
they'll check what the computer does,
but they'll be pretty confident
that the computer's gonna do a good job.
(mellow music)
When we first talked to you, Ani,
in Sydney, you said you thought that
in terms of the time spent on tasks
that doctors do, that AI might be able
to handle maybe five, maybe at the outside 10%.
How do you see that now?
Definitely a lot more.
I tell you, it can go up to 40, 50%.
Using it as a took rather than
taking over, I'd say it's gonna happen.
The percentage for doctors
is 21%, but that's likely to grow in the coming
decades as it will for every profession
and every job.
We've been through technological upheaval
before, but this time, it's different.
One of the challenges will be that
the AI revolutions happens probably much quicker
than the industrial revolution.
We don't have to build big steam engines,
we just have to copy code,
and that takes almost no time and no cost.
There is a very serious questions,
whether there will be as many jobs left as before.
(mellow music)
I think the question is, what is the rate of change,
and is that gonna be so fast that it's
a shock to the system that's gonna
be hard to recover from.
I guess I'm worried about whether
people will get frustrated with that
and whether that will lead to inequality
of haves and have nots.
And maybe we needs some additional safety nets
for those who fall through those cracks
and aren't able to be lifted.
We should explore ideas like universal
basic income to to make sure
that everyone has a cushion to try new ideas.
What to do about mass unemployment?
This is going to be a massive social challenge.
And I think ultimately we will have to have
some kind of universal basic income.
I don't think we're gonna have a choice.
I think it's good that we're experimenting
and looking at various things, and you know,
I think we don't know the answer yet
for what's gonna be effective.
The ascent of artificial intelligence
promises spectacular opportunities,
but also many risks.
To kickstart a national conversation,
we brought together the generation
most affected with some of the
experts helping to design the future.
You will have the ability to do jobs
that your parents and grandparents couldn't have dreamed of.
And it's going to require us to constantly
be educating ourself to keep ahead of the machines.
Actually, first of all, I wanted to say
that I think the younger generations
probably have a better idea of where
things are going than the older generations.
We won't take that personally.
Sorry, sorry, but I think--
So where have we got it wrong?
Well, I think the younger people,
they've grown up being digital natives,
and so they know where it's going,
they know what it has a potential to do,
and they can kind of foresee where
it's gonna go in the future.
We all hate that question at a party
of, like, what do you do?
I think in the future, you'll be asked instead,
what did you do today or what did you do this week?
Because I think the, we all think
of jobs as a secure, safe thing,
but if you work one role, one job title
at one company, then you're actually
setting yourself up to be more likely
to be automated in the future.
The technology in the building game
is, is advancing.
Kind of worry if you're a 22 year old carpenter for example.
I think there's often this misconception
that you have to think about a robot
physically replacing you, one robot for one job,
and actually, it's going to be, in many cases,
a lot more subtle than that.
In your case, there'll be a lot more
of the manufacturing of the carpentry
happens off-site.
That happened between the start
of my apprenticeship and when I finished,
it was while moving sort of all the frames
and everything we build off-site,
and brought to you, and you do all
the work that used to take you three weeks
in three days.
I mean, there is one aspect of carpentry
that I think will stay forever, which is
the more artisan side of carpentry.
We will appreciate things that are made
that have been touched by the human hand.
I think there will be a huge impact in retail
in terms of being influenced by automation.
Probably the cashier, you probably don't
need someone there necessarily
to take that consumer's money.
That can be done quite simply, and that's me.
That's what you're doing?
But at the same time, just from having
a job, there is a biological need met there,
which I think we're overlooking a lot.
I think we might not have a great depression
economically, but actually mentally.
AI is clearly going to create a whole new
raft of jobs, so there are the people
who actually build these AI systems.
I mean, if you have a robot at home,
then every now and then, you're gonna
need somebody to swing by your home
to check it out.
There will be people who need to train
these robots and there will be
robot therapists, there will be
obedience school for robots
and other kinds of, so, it's not,
I mean, I'm not joking.
What should these young people do today
or tomorrow to get ready for this?
There really is only one strategy,
and that is to embrace the technology
and to learn about it, and to understand,
as far as possible, what kind of impact it has
on your job and your goals.
I think the key skills that people need
are the skills to work with machines.
Don't think everyone needs to become a coder,
in fact, if artificial intelligence is any good,
machines will be better at writing code
than humans are, but people need
to be able to work with code,
work with the output of those machines
and turn it into valuable commodities and services
that other people want.
I disagree that we're gonna necessarily
have to work with the machines.
The machines are actually gonna understand
us quite well.
So, what are out strengths,
what are human strengths?
Well, those are our creativity,
our adapitability, and our emotional
and social intelligence.
How do people get those skills?
Well, if they're the important skills.
Well, I think the curriculum at schools
and at universities has to change
so that those are the skills that are taught,
those are the skills that are barely taught,
if you look at the current sorts of curriculums,
you see you have to change the curriculum
so that those have become the really important skills.
A lot of these discussions seem
to be skirting around the issue
that really is the core of it, is that the economic
system is really the problem at play here.
It's all about ownership of the AI
and the robotics and the algorithms.
If that ownership was shared
and the wealth was shared, then we'd be
out sharing that wealth.
The trend is going to be toward
big companies like Amazon and Google.
I don't really see fragmentation because
whoever has the data has the power.
Data is considered by many to be the new
oil because as we move to a digital economy,
we can't have automation without data.
What we see as an example is value now
moving from physical assets to data assets.
For example, Facebook.
Today when I looked, the market capitalization
was about 479 billion dollars.
Now, if you contrast that with Qantas
who has a lot of physical assets,
their market capitalization was nine billion dollars.
But you can go a step further, and if you look
at the underlying structure of Qantas,
about five billion dollars can be contributed
to their loyalty program, which is
effectively a datacentric asset that they've created.
So, the jobs of the future will leverage data.
The ownership of data is important because,
you think about Facebook.
Over time, Facebook learns about you,
and over time, the service improves as you use it further,
so whoever gets to scale with these datacentric
businesses has a natural advantage
and natural monopolistic tendency.
In 20 years time, if big corporations like Google
and Facebook aren't broken up,
then I will incredibly worried for our future.
Part of the reason why there are
so many monopolies is because they've
managed to control access to that data.
Breaking them up, I think, will be
one of the things we need to do
to be able to open the day traps so that all
of us can share the prosperity.
But the global economy is not,
is very rich and complex, and so,
you know, you can't just, Australia
can't just say, oh, we're opening the data.
Well, I just also think we're leaving
a section of the population behind,
and some people in our country
can't afford a computer or the internet
or a home to live in.
It'd be a bit crazy to just let it all go,
free market, just go crazy,
because we don't know if everyone is on that
make the world a better place type thing.
I personally don't want to be served
by a computer even if I am buying a coffee
and things like that.
I enjoy that human connection,
and I think that human connection's really
important for isolated people.
And that job might be really important
for that person and creating meaning
in their life and a purpose in their life,
and, you know, they might not be skilled
enough to work in another industry.
My first thought is that if it is about
human interaction, why do you need
to have a, be buying a coffee to have that human
interaction, why not just have the machine do
the transaction and people can focus
simply on having a conversation?
Perhaps part of that is to simply say,
it is a productive role in society
to interact, to have conversations,
and we can remunerate that and make
that part of people's roles in society.
It could be, a lot of things around caring,
interpersonal interactions, the type of conversation
you were talking about, I think they'll become
an increasingly important part of the way
that we interact, the way we find meaning,
and potentially the way we receive remuneration.
I think we all have choices to make,
and amongst those are the degree to which
we allow or want machines to be part
of our emotional engagement.
Will we entrust our children to robot nannies
Algorithms can be taught to interpret
and perceive human emotion.
We can recognize from an image
that a person is smiling, we can see
from a frown that they're angry,
understand the emotion that's set in text
or in speech.
And you combine that together with other data,
then yes, you could get a much more refined
view of what is that emotion, what is being expressed.
But, does an artificial intelligence
algorithm actually understand emotion?
No, not presently.
We're in the early days of emotion detection,
but this could go quite far.
You could certainly see emotional responses
from algorithms, from computer systems
in caring for people, in teaching,
in our workplaces.
And to some extent, that's already happening
right now as people interact with bots online,
ask questions, and actually feel like
oftentimes, they're interacting with a real person.
(calm music)
When Tay was released in the U.S.
with an audience of the 20 to 25 year olds,
the interactions that Tay was having
on the internet included hate speech and trolling.
And it only lasted a day, but it's a really
fascinating lesson in how careful we need to be
in the interaction between artificial intelligence
and its society.
The key thing is, what we teach our AI
that reflects back to us.
First, you know, you'll kind of want
the robot in your home because it's helpful,
next minute you'll need it because
you start to rely on it, and then you can't live without it.
I think it sounds scary, to be honest,
the thought of replacing that human
interaction and even having robots
in your home that you interact daily with
like a member of the family, I think,
yeah, really human interaction
and real empathy can't be replaced,
and at the end of the day,
the robot doesn't genuinely care about you.
Well, I think you certainly can't stop it,
I mean, we're in, there's not way to stop it.
Software systems and robots of course
can empathize, and they can empathize so much
better than people because they will be able
to extract so much more data
and not just about you, but a lot of people
like you around the world.
To go to this question of whether
we can or cannot stop it, we're seeing,
for example, in the United States already,
computers, algorithms being used
to help judges make decisions,
and there, I think, is a line
we probably don't want to cross,
we don't want to wake up and discover
we're in a world where we're locking
people up because of an algorithm.
I realize that it's fraught,
but all of the evidence says that
AI algorithms are much more reliable
than people, people are so flawed
and they make many, you know,
they're very biased, we discriminate,
and that is much more problematic,
and the reason is that people are not transparent
in the same way as in AI algorithm is.
Humans are deeply fallible.
I more veer on the side of saying
that yes, I do not necessarily trust
judges as much as I do well-designed algorithms.
The most important decisions
we make in our society, the most serious
crimes, we do in front of a jury of our peers,
and we've done that for 100s of years,
and that's something I think we should
give up only very lightly.
Well, Nathan, what do you think?
Well, I think ultimately, I don't know
far you want to go with this discussion.
Like, how far into the future, because ultimately
what's gonna end up happening is
that we're gonna become the second intelligent
species on this planet, and if you take it
to that degree, do we actually merge with the AI?
So, we have to merge our brains with AI,
it's the only way forward, it's inevitable.
But we won't be human then,
we'll be something else.
There's a choice.
Do we not value our humanity anymore?
We started off talking about jobs.
But somehow, artificial intelligence
forces us to also thing about what
it means to be human, about what we value
and who controls that.
So, here we are, on the precipice
of another technological transformation.
The last industrial revolution
turned society upside down.
It ultimately delivered greater prosperity
and many more jobs, as well as the
eight hour day and weekends.
But the transition was at times shocking and violent.
The question is, can we do better this time?
We don't realize that the future is not inevitable.
The future is the result of the decisions
we make today.
These technologies are morally neutral,
they can be used for good or for bad.
There's immense good things they can do,
they can eliminate many disease,
they can help eliminate poverty,
they can tackle climate change.
Equally, the technology can be used for lots of bad.
It can be used to increase inequality,
it can be used to transform warfare.
It can be used to make our lives much worse.
We get to make those choices.
(soft music)