Translator: Martin Peniak Reviewer: Ivana Kopisova
Thank you very much.
First of all, I would like to say
that I am really happy to be here
after such a long time.
I am going to tell you a short story
and I hope
that some of it will inspire you.
First of all, I would like to say
that being an optimist and having the ability
to dream big is what keeps me motivated.
I believe that each of us
has a lot of potential hidden inside
and each of us has their dreams.
It is often difficult to be yourself
and fight for these dreams.
I believe that we can achieve anything,
providing we work hard
and do not give up easily.
As a little boy, I was dreaming
that one day I will be working with robots.
Back then, this dream seemed unrealistic
but I was fascinated by technology.
As a little kid,
I was taking everything apart
in our household.
I pierced all the speakers that we had,
took apart all the radios, etc.
Every time I was sick,
my granddad would bring me
a random device that I would
investigate and take apart.
I have been a curious person and I think
that something like this is inside each of us.
If we follow this curiosity,
listen to our true self,
to what keeps us motivated,
then we can really achieve a lot.
As time passed by,
I was 19 years old,
eager to move on with my life,
so I decided to go to UK.
I did not speak much English
I only brought with me one bongo.
(Laughter)
(Applause)
Previously, I used to make bongo drums
and thought I would bring one
just in case, you know...
As time passed by...
At the beginning, I slept at a bus station.
I did not have much money.
I even had to sell that bongo.
(Laughter)
But on my 20th birthday,
I met one professor
who advised me
to study at a university.
Up until that point,
I had no interest in studying whatsoever.
But then I thought:
"Okay, I am going to give it a try,
at least the first year.
Hopefully things will work out.
It would be awesome if I passed
the first year."
I found a job
and begun working full-time at night
while studying full-time during the day.
I passed every single year,
received some awards and things like that
that does not matter but then...
(Laughter)
(Applause)
After that I started doing a PhD
in artificial intelligence.
(Laughter)
(Applause)
I am saying this just because
I am really happy with that.
But mainly because...
(Laughter)
I have the chance to motivate others.
We can achieve a lot when we are confident
and when we fight for our dreams.
In the past we were dreaming
about travelling to the Moon.
About things that seemed impossible.
Now everyone knows
that we have been to the Moon,
we have sent rovers to Mars, etc.
Likewise, our current dreams
might seem impossible,
but human diligence is awesome,
and I think that
with so many intelligent people
on this planet,
we can make anything happen.
On this slide, you can see various robots.
This is showing us
our imagination
of the technological future,
which got reflected
in Hollywood movies.
What's interesting is that these robots currently exist
only in our imagination.
I believe that it is this imagination of the future
that drives our present actions
and thus shapes the actual future.
These movies have created a gap
between the reality and our dreams
of the technological future.
The sad reality
is that our current robots do not even reach
the level of intelligence of a cockroach.
Something in the past went wrong.
Artificial intelligence pioneers claimed
that we will develop intelligent robots
within a decade or two.
They were overly optimistic.
Now we know that these ideas
have not come true.
And this was more than 60 years ago.
So something went wrong.
We cannot really say wrong but the method
that we chose at the beginning
was not the right one.
It was not going anywhere.
We created robots
that could only do the tasks
they were programmed for.
One of the examples is the IBM computer
Deep Blue that beat the master of chess.
The question is:
"Is this computer really intelligent?
Would it be able to play for example Tetris?"
Because this is what characterises
human intelligence.
That we are adaptive and can apply
our experience in new areas.
This classical approach for developing AI
was not so bad because it gave us robots
that make our everyday life easier
by doing the hard work in factories,
monotonous and dirty work
that we would not want to be doing.
So it was definitely good for something.
But not so good for reaching our dream
about developing artificial intelligence
and creating robots reaching or exceeding
the level of human intelligence.
And we can ask ourselves
whether we believe
that robots will one day achieve this.
And then the second question is:
"Would something like this be good?
Would it be such a great achievement
if they exceeded human intelligence?"
Next generation of scientists
were looking for a new methodology
for reaching this goal.
Many of them took the inspiration
from biology,
neuroscience,
psychology
as well as linguistics.
On the left side, you can see a picture
of neural networks in the brain.
It is said that the human brain
has as many neurons
as there are stars in our galaxy.
The latest research suggests
that it is 86 billion neurons.
Each of them can have thousands
of connections with other neurons.
This is an immensely complex system
and we are trying to model it mathematically.
by creating artificial neural networks
that have the ability to learn.
The power of neural networks
lies in their ability to generalise
previous knowledge to novel scenarios.
Now I am going to show you
my research with the iCub
and how I created neural networks
that allowed him to learn to develop skills
as well as understand language.
Afterwards, I will tell you about the technology
that made all this possible.
Here you can see myself and the iCub
as well as some graphs with the neural networks.
The goal was to teach the iCub
to manipulate objects
so that he could recognize various objects
and also
so that he could learn something
and then generalize what he learnt
in new circumstances.
If he learns to lift a cube here
then he can also lift it there.
If he learns to do some action
with a particular object then he can also
perform this action on different objects.
He is flexible and not only
preprogrammed to do one thing.
Here you can see
how I was teaching the iCub
by demonstration.
I showed him actions I wanted him to learn.
I showed him different movements.
And then I was training neural networks.
This is my first experiment,
which showed that these neural networks
were able to learn complex actions
and the robot learned to lift that cube
regardless of its position.
This experiment was a proof of concept
but I was not really interested
because it was not very interesting.
(Laughter)
(Applause)
What was interesting
was that I was realizing
that these neural networks were rather limited.
I wanted to increase the number of neurons
so that I can train them with more capabilities.
I cannot train a neural network
with three neurons to do amazing things.
I started training the iCub
more complex tasks
so that he could understand commands
and perform more complex
and elaborated motor actions.
(Video) Touch the ball.
Slide left the ball.
Slide right the ball.
Point the ball.
(Laughter) (Applause)
Pull the ball.
Lift the ball.
Martin Peniak: He did it.
He was able to learn all this because
I used a different computing technology
that evolved from graphics cards.
Graphics cards need to be processing
millions of pixels every second and therefore
they have been optimised
to process information in parallel.
On the other hand, the CPU
in your mobile device
or computer,
does one thing at a time.
The following video explains this clearly
and everyone will understand what I mean.
(Video) Alright, I introduce to you Leonardo
and he is going to paint a picture,
for you guys,
in a way that a CPU might do it.
As a series of discrete actions
performed sequentially.
One after the other.
Three
two
one.
Let me speed it up.
(Applause)
Ladies and gentlemen,
Leonardo 2.0.
(Applause)
21 hundred gallons of air
goes through these accumulators
out these valves
into all 1100 of these tubes,
which are all exactly the same length,
into these tubes.
In which the bottom of is a paintball
Each of those paint balls will fly
across 7 feet of space
and in 80 milliseconds reach its target.
GPU painting demonstration.
10...
9...
8...
7...
6...
5...
4...
3...
2...
1...
(Laughter)
(Whistling)
(Applause)
Ladies and gentlemen.
Science class is now over.
MP: So here you could see the difference
between a CPU and a GPU.
(Laughter)
(Applause)
Because I have used this parallel processor
for training neural networks
I achieved approximately 50x speedup.
Typically, training neural networks
is a very time-consuming task.
Even with a GPU, sometimes the training lasts
for a day or two.
The CPU might need a month or more
so this is the difference.
I think that adopting this parallel
computing technology advances
the field of artificial intelligence
and brings us one step closer
to reaching our dream of developing
intelligent robots.
Not only robotics but also other fields
in science and industry achieved
great speedups with GPUs.
Astrophysics,
neuroscience,
medicine,
cancer research, etc.
This technology is amazing
and I am really happy
that I could talk about it.
I would like to finish this talk
the same way as I have started it.
Do not forget about your dreams
and believe that with hard work
you can achieve anything.
(Applause)
Thanks.