Fry: The idea of creating
artificial creatures
has obsessed us
for millennia,
but for the purposes of this exercise
before we go any further,
I’m going to ask you to purge any of
the following thoughts from your head.
Checklist:
Hephaestus expelled from Olympus
and then built
two servant robots,
the Bhuta Vahana Yanta
or spirit movement machines
of 12th Century India made to protect
the relics of Buddha,
the chess playing mechanical Turk,
just a bloke in a box pulling levers.
Mary Shelley’s Frankenstein -
he walks, he talks, not a lot else.
Kubrick’s HAL “Don’t Call Me Dave,”
C3P0, R2D2, canine, NS2,
I mean seriously these are
just numbers and letters.
Robbie the Robot,
robot lovers, RoboCop.
Feeling better? Okay. Let’s get going.
I’m Hannah Fry, I’m an Associate
Professor in Mathematics,
and I am AI curious,
and this is DeepMind,
the podcast series
where we look
at the fast-moving story
of artificial intelligence.
We’ve been talking
to the scientists,
researchers and engineers
based at DeepMind in London.
We’re looking at how they’re
approaching the science of AI.
And some of the tricky decisions
the whole field is wrestling
with at the moment.
So whether you just want
to know more
or want to be inspired
on your own AI journey,
then this is the place to be.
You see the thing is - robots sell.
We’ve long lusted after the idea
of upending the natural order
with human ingenuity.
We just can’t seem
to leave it alone,
and in this episode we are
looking at AI and robotics.
Murray Shanahan is the Senior
Scientist at DeepMind -
he’s also a professor
of cognitive robotics
at Imperial College London,
and growing up,
Murray was utterly mesmerized
by science fiction,
so you can picture his face when
the Hollywood film director Alex Garland
approached him following the publication
of his book
Embodiment
and the Inner Life.
Shanahan: Alex contacted me and said:
“Oh I’m writing a script for ah film
about AI and consciousness
and I read your book
and it you know helped
to crystallize some ideas
and would you like to chat about
ah chat about it?
And ah and so of course it was you know
it was a great opportunity
ah to get involved
in the science fiction film and then
and then to my great good fortune
it turns out to be an absolute cracker.
Fry: And that is how Murray
became scientific advisor
on the Oscar-winning film
Ex Machina.
I met with Murray
to get a potted history of AI.
Murray, people tend to think of AI
as this this this very new thing,
a very a very modern invention,
but it’s actually been around
for quite a long time.
Shanahan: It has. The idea
of artificial intelligence,
the idea of making artificial creatures
dates back to Greek mythology
but the sort of modern conception of AI
perhaps really dates back to Alan
Turing’s paper published in the 1950s
where
he first kind of asked the question -
could a machine think?
And gave a number of kind of refutations
for counter arguments
to the idea that a machine could think.
Fry: This is where the Turing test
comes from.
Shanahan: And this was this famous paper
inaugurated the so-called
Turing test because
Turing didn’t call it
the Turing test [Hannah laughs]
which is the idea that we should subject
a machine
to a test
to see whether it’s basically -
whether it’s indistinguishable
from a human in dialogue.
The term “artificial intelligence”
was actually coined by John McCarthy,
a Stanford professor -
he was at MIT at the time,
and John McCarthy organized
a conference in 1956
bringing together a lot of leading
thinkers in maths
to try and scope out the idea
of building a thinking machine
and he coined the term
artificial intelligence.
Fry: What did they describe it
as at that time -
how did they see
artificial intelligence?
Shanahan: John McCarthy in particular -
his idea of artificial intelligence
that he had in mind was a kind of system
that would answer questions really
and be able to engage in dialogue
with humans, so…
Fry: Something that you are
actually just talking to -
Shanahan: So something that you’re that
you’re talking to
although of course in those days
it wouldn’t have been through speech,
it would have been by typing in
at the keyboard,
and it was very much a disembodied
notion of artificial intelligence,
so this system didn’t have a body
and interact with the physical world
in the way that we do or animals do
or indeed that robots do
so they weren’t really thinking
about robotics at that point.
Fry: I’m sort of imagining something
like um HAL in “2001 Space Odyssey,”
except typing in rather
than speaking to -
Shanahan: Yeah, and a kind of nice
version, you know.
That their approach
to artificial intelligence
was to build systems
that reasoned in logic
and we now think of
that whole sort of approach
to artificial intelligence
of using logic and reasoning
as so-called good old
fashioned artificial intelligence,
or Gofi, or classical AI.
Fry: Now I know, I know I did
a bad thing there.
I mentioned HAL but nobody ever said
this was going to be easy.
But to recap - in classical AI,
you have to write down
a complete list of rules
for how you want your agent to think.
If this happens, then do this.
If that happens, then do that.
It’s a nice idea in theory,
but if your agent is going to know
how to handle every possible scenario
you could throw at it,
it’s going to need
to be a long, long list.
Shanahan: There was a project
called Psych
which attempted to write out
all the rules of common sense
to build an enormous encyclopedic
database of common sense.
Fry: Can you remember any of them?
Shanahan: Well I mean
there would be things like -
if you’ve got a container,
and you put something in that container
and then move that container
somewhere else,
then the thing that was in it
gets moved as well.
You know -
Fry: Common sense!
Shanahan: Yeah, stuff like
that you know,
and if you buy something and pay for it,
you’ll have less money
than you had in the first place.
So do I think it’s impossible
to do that?
I think it’s impossible in practice.
Because it turns out
that the sheer number of rules
that you would have to write
is absolutely enormous.
Fry: We might have moved away
from this long list of rules by now
as a way to teach our artificial
intelligence in favor of agents
that can learn the rules for themselves.
But the skills that we want our AI
to have like good
old fashioned common sense
are just as important now
as they ever were.
Imagine one day long into the future
a wealthy computer scientist
builds an AI
to manage his stamp collection.
He plugs it into the internet,
gives it access to his bank account
and sets up the challenge
to buy as many stamps as possible.
At first the agent acts
as its creator intended,
signing up to eBay, bidding on stamps.
But after a while he gets another idea.
More money equals more stamps,
so why not start trading on
the stock market to make more money?
And it soon realizes
it can get the stamps cheaper
if it can get them at source.
So the agent buys up a factory,
converts its manufacturing process
to stamp-making,
and goes on with achieving its goal.
But of course the limiting factor
here is paper - more paper, more stamps.
So it starts commanding forests
to be felled, the wood to be processed,
all to feed its single
minded ambition more stamps.
Now there’s no denying that the AI
is doing what it was told,
but it’s doing so at any cost,
and any agent
without some kind of commons
sense will be at risk of taking
our instructions a bit too literally.
This might be a bit of an
extreme example, but Victoria Krakovna,
a research scientist a DeepMind
working on AI safety
is already seeing agents
that aren’t exactly behaving
in the way their designers intended.
Krakovna: A reinforcement agent
that was playing a boat racing game
and the intended behavior
there was to go around the race track
and finish the race as soon as possible,
and the agent was encouraged to do this
by having these little green squares
along the track
that would give it rewards,
and then what the agent
figured out is that
instead of actually playing the game
it could be going around in circles
and heading the same green squares over
and over again to rack up more points,
and then you have this this whole
situation with the boat going in circles
and crashing into everything
and catching on fire
and still getting more points
than to otherwise
get this this kind of situations
are quite common.
Fry: But because you haven’t ever
stopped the AI from doing that,
or told the AI
that you don’t want it to do that,
that’s a perfectly sensible solution
for it to come across.
Krakovna: Yeah, from the perspective
of the AI
it can’t really tell
that the solution is a cheat.
It’s just something
that gives it a lot of rewards,
so it can’t necessarily distinguish
between the general solution
and an overly creative solution
that humans haven’t foreseen.
Fry: There are plenty of examples
like this.
One team of researchers
created an agent
inside a very simple
two-dimension computer game
and tasked it
with building itself a body
to get itself from the start line
to the finish line.
Quite quickly it worked out
that it could just build itself taller
and taller and taller
until it was as high
as the track was long,
and then just flopped forwards
to cross the line.
And there was the agent
playing the game of Tetris
which realized it could just pause
the game forever and never lose.
But there is a balancing act here.
We don’t want our AI misbehaving,
but we also don’t want
to restrict our agents too much.
Krakovna: And this is part of what’s
tricky about achieving safe behaviors
that we don’t want to hamper
the systems’ ability
to come up with really interesting
and innovative solutions
that we have not seen -
so we don’t just want human imitation.
We want super human capabilities
but without unsafe behavior.
Fry: There is a very fine line
between a naughty algorithm
and one that’s finding
innovative solutions to problems
that humans haven’t been
able to solve.
The AI doesn’t know the difference
between the two -
it doesn’t understand
what’s really important to us,
it doesn’t have any common sense -
and that means you have to be
very careful
when you’re setting up incentives
and rewards for your agents.
Krakovna: Part of the reason that this
is so difficult is this general affect
that’s called
Goodhart’s law in economics -
what Goodhart’s law comes
when a metric becomes a target,
it ceases to be a good metric.
Fry: A classic example of Goodhart’s Law
comes from British India,
when authorities offered cash rewards
for dead cobras
as a way to decrease
the population of snakes.
Unbeknownst to the British,
the locals started to breed cobras
in order to take advantage
of the reward.
As soon as the authorities
found this out,
they scrapped the scheme altogether
and revoked the rewards.
But now there were these snake farms
everywhere filled with worthless cobras,
so what did the locals do?
Release the cobras into the country,
resulting in an increase
in the cobra population.
This is what the scientists
call a specification problem -
when your specified objective fails
to bring about the intended behavior.
This is generally quite likely to happen
because human preferences
tend to be quite complex
and whenever we try to distill them
into some kind of specification
or something that we say we want
it’s going to be a lot simpler
than our real preferences are
and it wouldn’t necessarily capture
everything that’s important to us.
Let us imagine we’re living
in the future
where robot butlers are commonplace.
You clearly specify
the objective for your robot -
it should serve you at all times.
Now, how is that agent going to feel
about its own off switch.
Leike: Your robot always has an
incentive to preserve its own function.
If it gets turned off,
it can’t vacuum the floors anymore.
It can’t bring you coffee. It want
to disable its off switch for example.
Fry: Jan Leike is a Senior Research
Scientist at DeepMind
also working on AI safety.
Leike: If you turn it off,
then it can’t vacuum the floors.
So if it understands how the off switch
the off switch mechanism works,
it would want to disable it.
What we want is you want our systems
to actually do something
that is good for us -
ready to do something that we actually
wanted, not just what we said we wanted.
Fry: But how do you get around
these kinds of problems?
Well we already know that writing a long
list of dos and dont’s won’t work.
However long your list gets you’re
always going to forget something.
The new breed of learning agents
is going to need a different approach.
Leike: So one direction that we are
pursuing is learning what functions
for reinforcement learning agents.
And you can kind of think of this
as learning what your systems
should be doing from human feedback.
So for example in in one work
that we did together with Open AI
is we trained a simulated robot
to do a backflip,
and um the way this works
is like the robot does some movement
and then you watch a video of that
movement and you or you say two videos
and then you can compare which of those
looks more like a backflip.
Fry: Jan’s experiment has a human
sitting and watching a screen,
looking at an AI
attempting to do a backflip.
Each time the human will feedback
on whether the attempt was good enough,
slowly nudging the agent
in the right direction.
Here is the key idea -
with constant human feedback,
the human can communicate
their preferences
without having
to actually specify them,
and risk oversimplifying things
in the process.
Leike: And after like a few hundred
rounds of feedback,
the robot can actually
perform a backflip.
It’s kind of learn
what the objective should be -
that the objective should be a backflip,
and what a backflip is.
This is difficult to specify
precisely what a backflip would be
in so in my case
like I can’t do a backflip, right?
And but I can see
if the systems do a backflip,
and in some ways like this
allows us to get super human capability.
Fry: The AI then is essentially
being rewarded
by pleasing the human -
in a way?
Leike: Exactly. And so we can
we can do a little experiment.
Fry: Okay.
Leike: I’ll try to teach you
to make a sound.
Fry: Okay.
Leike: By giving feedback.
So you’ll make two sounds.
Fry: Mm-hmm.
Leike: And I’ll tell you which
of the two sounds is closer
to what I have in mind.
Fry: Okay.
Leike: Yeah, does that sound good?
Fry: Let’s do it, let’s do it!
So I’m being the AI here?
Leike: Yeah you’re being the AI
and I’m being the human teacher.
Fry: And you’re training me essentially
with reinforcement learning,
and my reward function
is getting you to be happy.
Leike: You know that’s like -
so the reward function here
is like something
is in my mind, right?
And I’m trying to teach you,
so you can’t directly see the reward -
you can only see like my feedback.
Fry: But my objective is to get you to
say you like the sound that I’m making.
Leike: Exactly.
Fry: Okay, let’s think of two sounds.
Let’s go for: Meep!
And: Meeeeep!
Leike: The first one.
[Hannah laughs]
Fry: Okay.
Have you actually got something
in your mind or are you just making -
Fry: This went on for a while.
Beep Beep and [more noises here].
But with Jan’s feedback,
we got Beep and Beep -
Leike: I’d go with the first one -
Fry: Absolutely nowhere!
[Jan laughs]
Leike: This is really hard because of
the expression problem actually.
Fry: I mean ultimately -
but if I was an actual AI
I’d have I’d have gone through
10,000 iterations by now.
Leike: well there’s you have the,
have a human loop
that reviews all of that
so it is it is kind of slow um
but this is actually a problem
that we have in our systems is that,
like in order to give useful feedback,
you have to have useful examples, right?
In this case you produced sounds
that are very similar,
and like the sound I had in mind
was like very different
Fry: Totally different
Leike: So I, I don't really like have
the opportunity
to give you
a very useful feedback, right?
Fry: Damn My optimization strategy
was terrible here, okay [laughs]
Leike: But this is like basically
this is the same thing would come up
with a backflip right like if your robot
just like lies on the floor
in like two different ways,
like what are you going to do?
Fry: But you would hope though after
I’d have maybe a hundred go’s at it,
I’d end up with something that began
to approach what you had in mind.
Leike: yeah, definitely, definitely.
Fry: Is it like - oh no,
I’m not allowed to ask any questions
am I, because I’m an AI.
Leike: I mean ideally
this is at some point
this is what we want our assistants
to be able to do, right?
Or just like describe the sound
to you in actual language
and then you could just do it,
that would be really cool, right?
Fry: Yeah.
Leike: So this is the kind of research
that we want to do in the future,
that’s like the kind of systems
that we want to figure out how to build.
Fry: Oh, okay, actually. Everything that
I have done so far has been like voices.
You didn’t say that it had to be
a vocal thing, did you?
Leike: Yeah -
Fry: Okay, hang on.
Okay, alright, how about this,
how about this.
[clicks tongue].
And [whistles].
Leike: The first one.
Fry: Ooh.
Leike: The first one is actually
what I had in mind.
[Laughs]. This is great.
Fry: I was so far away.
The only problem with setting up
reinforcement
learning with a human standing over it
offering feedback at every stage
is that it is monumentally slow.
Now it would take an agent months if not
years to master a game like Atari.
And even then, you’d need to hire
a pretty large group of poor students
to do the boring job
of supplying feedback in real-time.
But there is an alternative.
You can rustle up a slightly more
sophisticated learning partnership.
Leike: So what we do when we actually
built these systems is
that we don’t literally do
the experiment that we do now.
But instead we have -
we train a neural network -
a second neural network that learns
how I would give feedback as a human
and then the neural network
can teach you
because it can just oversee
all of the things that we are doing.
Fry: By now neural networks have become
really good at spotting patterns.
Dog, not dog.
Backflip, not backflip.
Perhaps you don’t always need a human
in the loop laboriously giving feedback.
Why not have two agents -
one attempting the task,
and the other deciding if it succeeded.
Leike: The reason why this works
very well
is because evaluating the objective
is an easier task than producing
the behavior that achieves it.
So you can have something
like the for example
the and the back-flipping robot
all the neural network has to do
is like look at what the robot is doing
and see whether it’s a backflip.
Fry: You’re listening to DeepMind
the podcast, a window into AI research.
But of course where this stuff
really comes alive
is where you take the ideas -
take them out of a computer simulation
and allow your imagination to roam
into the world of embodied AI.
Robots that learn how to cook, robots
that learn how to pack fruit in crates,
tuck you into bed and perform backflips.
It’s time to visit
the DeepMind robotics laboratory.
Kay: we are standing right
outside of the DeepMind robotics lab.
Fry: This is your lab?
This is where you spend your days?
Well it’s not just my lab,
but yes, this is our lab.
Kay: Well it’s not just my lab, but yes.
It is our lab.
Fry: Can we go inside?
Kay: Yes. We’ll get our badge . . .
Fry: This is Jackie Kay,
a software engineer.
Packed to the rafters with robots.
Kay: Yes.
Fry: It’s very cool!
Kay: We’re basically
running out of space.
Fry: It’s quite noisy in here.
Kay: Yeah, we have a lot going on today.
Fry: This place isn’t quite the high
security basement laboratory
you’d imagine.
There are half assembled
robot arms and machine parts
scattered all around the place.
Some look like mechanical hands,
others quite a lot like red kitchen
aide style food mixers.
And curiously,
there is a SpongeBob Squarepants
mascot hanging from the ceiling.
SpongeBob: Hey kids, check it out!
[Jackie laughs]
Kay: I think we use it as kind of
a punishment of somebody
like leaves a tool lying around
when they are supposed to put it back,
we’ll like put a SpongeBob
in his space or something
[Jackie laughs] you know.
Fry: That seems like a fair punishment.
Much of the work in this room
focusses on getting robotics arm
to learn how to do simple tasks.
Each robot is bolted to the floor
in its own cordoned off area,
and as we come in the door,
there’s one that’s caught my eye.
You know that game that you play
when you’re a kid um
and you’re you have a tennis bat
and a ball attached to it
and then you kind of play tennis
on your own,
it sort of looks like
a robotic version of that.
Kay: It’s called the ball in a cup.
Fry: Oh, so it actually is!
Kay: Yes. It is exactly that.
It is trying to swing the ball
into that little basket. [Fry: Oh!]
So you can see it’s swinging
the ball around a little,
and it’s actually checking position
of the yellow ball um
and it’s trying to minimize
the distance
from that ball to the area
inside the basket, um -
Fry: Quick! it did it!
Kay: Oh - it got it -Yes!
Fry: Not exactly a smooth delivery
of the ball into the cup -
more a slightly clumsy, lucky flip.
Of course if you’re ultimate goal
was to make a perfect cup
and ball playing robot,
you could directly program
a machine to do that without fail.
You can build robots that perform simple
tasks in much more elegant ways.
But that’s not really the point.
Here’s Raia Hadsell, Senior Research
Scientist at DeepMind.
Hadsell: A robot is a machine
that can take over a task.
One can say that
in the broadest sense
that your dishwasher at home
and your vacuum cleaner are both robots
because they do something complex,
they run on their own,
they have some autonomy
to them of course
we also like to think about robots
about having some intelligence
and then you get more into
the realm of a robot with AI.
And I’m particularly
interested in saying
how can we take
this AI technology
and make it work on robots
so that we have embodied AI.
Fry: And that is
an important distinction.
The focus of the research
in this robotics lab
isn’t to create robots
that are told what to do,
but have them learn
their own skills
much in the same way
that other agents do here.
The robots here are
what’s known as embodied AI.
Kay: So let’s say yeah, the task is
a robot that’s trying to lift a box um
I as the programmer in the kind
of traditional setting
would say go to box,
open hand,
move hand um you know 30 centimeters
towards the box, close hand.
Fry: But this is different.
Kay: Yeah, this is sort of taking
what we want
and then figuring out
how to accomplish what we want.
Fry: Every few seconds
this particular robot
will have an attempt of getting
at ball into the cup
before pausing, resetting,
and having another go.
And every now and then, by chance,
the ball lands in the cup
and the robot is rewarded
with a positive score.
Just like if it was playing
a computer game.
Kay: The reset in between
the training episodes
where it untangles itself
or flips the ball out of the cup,
those are scripted but then when it
actually tries to accomplish the task,
that is a policy which it has
taught itself through experience.
Fry: Over time from everything it learns
from the schools it receives,
the robot builds a picture
of how the ball moves,
and how this relates
to the robot’s own movement.
Fry: So had we come in right at the very
beginning bit, what would we see?
Kay: Just completely random noise,
probably very chaotic
swinging around, ah - oh!
Fry: Oh it did it in one!
It did it in one!
Kay: That was quite - that was wow!
That was like in three seconds.
Fry: So will it know now that that
movement gave it a successful result?
Kay: Yes.
Fry: And so the next time
that we see this do we expect it
to be better
than when we walked in?
Kay: Yeah, umm it will try
similar actions
ah that gave it a positive reward
or a success.
Fry: It’s just sort of sitting there
quite smugly right now.
Kay: Yes.
Fry: There is a big advantage to getting
the AI to figure out tasks for itself.
You’ll end up with a robot that is
much more flexible out of the box.
It doesn’t matter what you want it
to do - tie a knot,
stack some bricks, peel a banana.
Just as long as you can clearly
communicate your objective,
you don’t need to specify a long list
of instructions for these robots.
And perhaps they’ll come up
with a new way of banana peeling
that you haven’t thought of.
But there is also a big drawback
in training AI
that has a physical body -
they are much slower to learn
than all of the disembodied agents
you’ll find in this building.
The ones that only exist
inside a computer.
Kay: Other researchers are able
to take advantage of parallelism -
that is they run their environments
in simulation, on computers,
and they can run them in parallel
on hundreds of compute -
different computers.
We're all gathering data
about this environment
they’re trying to learn
something about.
We only have let’s see
there’s 4 robots here
and they are frequently not running
the same experiment,
so it might just be
one robot collecting data.
And that means our training
will be orders of magnitudes slower
for comparable tasks,
compared to ones that are running
in simulation on computers.
Fry: Progress is slower in this room,
and you can tell.
The agents here
are a little less accomplished.
In another corner another robot
is trying to pick up a Lego brick
with a hooked gripping device,
kind of like a claw,
and there’s a rather ominous box
of mangled Lego bricks next to it.
Kay: In the exploration phases
of training
it will sort of randomly open
and close its gripper,
and I think this one has some shaping
to ah close its gripper
when it detects it’s close to the brick.
Fry: Ah - close!
Kay: And then it also has a fixed
training time [Fry: ah!]
so after some number of seconds,
it just will give up
and go back to the start
and then try again.
Lego is sort of this building block
to general purpose manipulation.
If you know if we can stack you know
two bricks together,
we can then do kind of arbitrarily
Fry: It’s going to break it!
[Jackie laughs]
Kay: Oh it’s fine.
Fry: sorry I got distracted by the robot
smashing up the Lego.
Fry: There is real potential here,
and so Jackie and the team
are constantly trying to find ways
of speeding up that learning process.
Kay: One technique
ah that we are looking into in order to
in that some of the researchers here
have done some really cool work on
in the past is something we call
Sim to reel -
or simulation to reality transfer
which is where you take a simulation
on a computer that models your robot -
and we can learn kind of in broad
strokes what the robot is like,
how its actions affects its environment
and how it can do something similar to a
task it’s trying to learn in real life.
So once we figure out
all of that in simulation,
without even touching the real robot,
we can transfer the data
it’s collected onto real hardware.
Fry: So you’re -
you can cheat basically.
Kay: Yeah.
Fry: You can cheat by imagining
the real robot within a computer
calculating all the physics
that would happen in real life
and then use the same techniques.
You used that army of computers
to give you a bit of a head start
before you even apply it
to the real physical robot.
Kay: Exactly.
Fry: So the real robots end up acting
the same way as the simulated robots do.
Kay: Well they started out
acting the same way
as the simulated robots do
and then as they train more,
they might start behaving
slightly differently or better
when they go into reality.
Fry: Using simulation might give you
a head start on reality
but it’s never going
to match precisely.
The real robot has to contend with grip,
friction, gravity, wear and tear.
All of which play important roles
in the real world,
but none of which will be perfectly
represented inside the computer.
And all of that means, well -
I think science fiction
may have set some false expectations.
Fry: One thing um that I am a little bit
surprised about being in here
um don’t take this the wrong way,
but these robots are a bit rubbish
Kay: Yes, it’s true, I mean,
we’ve got a lot of work to do.
Fry: But as with so much
of what happens here at
DeepMind, it’s not so much
about these exact agents,
it’s not about cup and ball
or Lego stacking, it’s about the type
of intelligence being acquired,
and how that fits
into the bigger picture.
Kay: we want to demonstrate general
purpose physical intelligence.
Fry: Physical intelligence?
Kay: Right.
So contrasting that with kind of
an intelligence that’s not embodied,
ah which maybe it can learn
to play games,
or maybe even understand language,
physical intelligence is looking
at how the physical actions
of your body affect the real world
ah so we want to take
a wide variety of tasks,
playing with objects, using tools,
ah maybe walking around
or running in the future
and we want to show that robots
can teach themselves
those tasks- how to do those tasks.
Fry: In the physical world?
Kay: In the physical world, yes.
Fry: But physical intelligence is
of course
only one type of intelligence -
one string to the robot’s bow,
and DeepMind
as we have seen dares to dream big.
Here’s Murray
Shanahan with the big finish.
Shanahan: The holy grail of AI research
is to build artificial
general intelligence
so to build AI that is as good at doing
an enormous variety of tasks
as we humans are,
so we’re not specialists
in that kind of way,
you know a young adult human can learn
to do a huge number of things.
You can learn to make food,
you can learn to um make a company,
you can learn to build things,
to fix things, you can do
so many things to have conversations,
to rear children, so all of those things
- and we really
want to be able to build AI
that has the same level
of generality as that.
Fry: If you want to know more about
robotics and technical AI safety,
then head over to the show notes
where you can also explore the world of
AI research beyond DeepMind,
and we’d welcome your feedback
or your questions on any aspects
of artificial intelligence
that we’re covering in this series.
So if you want to join in the discussion
or point us to stories or resources
that you think other listeners would
find helpful, then please let us know.
You can message us on Twitter
or you can email us -
podcasts@deepmind.com
