Our world is changing in many ways
and one of the things which is going to have a huge impact on our future
is artificial intelligence - AI,
bringing another industrial revolution.
Previous industrial revolutions expanded human's mechanical power.
This new revolution, this second machine age
is going to expand our cognitive abilities,
our mental power.
Computers are not just going to replace manual labor,
but also mental labor.
So, where do we stand today?
You may have heard about what happened last March
when a machine learning system called AlphaGo
used deep learning to beat the world champion at the game of Go.
Go is an ancient Chinese game
which had been much more difficult for computers to master
than the game of chess.
How did we succeed, now, after decades of AI research?
AlphaGo was trained to play Go.
First, by watching over and over
tens of millions of moves made by very strong human players.
Then, by playing against itself, millions of games.
Machine Learning allows computers to learn from examples.
To learn from data.
Machine learning has turned out to be a key
to cram knowledge into computers.
And this is important
because knowledge is what enables intelligence.
Putting knowledge into computers had been a challenge for previous approaches to AI.
Why?
There are many things which we know intuitively.
So we cannot communicate them verbally.
We do not have conscious access to that intuitive knowledge.
How can we program computers without knowledge?
What's the solution?
The solution is for machines to learn that knowledge by themselves,
just as we do.
And this is important because knowledge is what enables intelligence.
My mission has been to contribute to discover
and understand principles of intelligence through learning.
Whether animal, human or machine learning.
I and others believe that there are a few key principles,
just like the law of physics.
Simple principles which could explain our own intelligence
and help us build intelligent machines.
For example, think about the laws of aerodynamics
which are general enough to explain the flight of both, birds and planes.
Wouldn't it be amazing to discover such simple but powerful principles
that would explain intelligence itself?
Well, we've made some progress.
My collaborators and I have contributed in recent years in a revolution in AI
with our research on neural networks and deep learning,
an approach to machine learning which is inspired by the brain.
It started with speech recognition
on your phones, with neural networks since 2012.
Shortly after, came a breakthrough in computer vision.
Computers can now do a pretty good job of recognizing the content of images.
In fact, they approach human performance on some benchmarks over the last 5 years.
A computer can now get an intuitive understanding
of the visual appearance of a Go-board
that is comparable to that of the best human players.
More recently,
following some discoveries made in my lab,
deep learning has been used to translate from one language to another
and you are going to start seeing this in Google translate.
This is expanding the computer's ability
to understand and generate natural language.
But don't be fooled.
We are still very, very far from a machine
that would be as able as humans
to learn to master many aspects of our world.
So, let's take an example.
Even a two year old child is able to learn things
in a way that computers are not able to do right now.
A two year old child actually masters intuitive physics.
She knows when she drops a ball that it is going to fall down.
When she spills some liquids she expects the resulting mess.
Her parents do not need to teach her
about Newton's laws or differential equations.
She discovers all these things by herself in a unsupervised way.
Unsupervised learning actually remains one of the key challenges for AI.
And it may take several more decades of fundamental research
to crack that knot.
Unsupervised learning is actually trying to discover representations of the data.
Let me show you and example.
Consider a page on the screen that you're seeing with your eyes
or that the computer is seeing as an image, a bunch of pixels.
In order to answer a question about the content of the image
you need to understand its high-level meaning.
This high level meaning corresponds to the highest level of representation
in your brain.
Low down, you have the individual meaning of words
and even lower down, you have characters which make up the words.
Those characters could be rendered in different ways
with different strokes that make up the characters.
And those strokes are made up of edges
and those edges are made up of pixels.
So these are different levels of representation.
But the pixels are not sufficient by themselves
to make sense of the image,
to answer a high level question about the content of the page.
Your brain actually has these different levels of representation
starting with neurons in the first visual area of cortex - V1,
which recognizes edges.
And then, neurons in the second visual area of cortex - V2,
which recognizes strokes and small shapes.
Higher up, you have neurons which detect parts of objects
and then objects and full scenes.
Neural networks, when they're trained with images,
can actually discover these types of levels of representation
that match pretty well what we observe in the brain.
Both, biological neural networks, which are what you have in your brain
and the deep neural networks that we train on our machines
can learn to transform from one level of representation to the next,
with the high levels corresponding to more abstract notions.
For example the abstract notion of the character A
can be rendered in many different ways at the lowest levels
as many different configurations of pixels
depending on the position, rotation, font and so on.
So, how do we learn these high levels of representations?
One thing that has been very successful up to now
in the applications of deep learning,
is what we call supervised learning.
With supervised learning, the computer needs to be taken by the hand
and humans have to tell the computer the answer to many questions.
For example, on millions and millions of images, humans have to tell the machine
well... for this image, it is a cat.
For this image, it is a dog.
For this image, it is a laptop.
For this image, it is a keyboard, And so on, and so on millions of times.
This is very painful and we use crowdsourcing to manage to do that.
Although, this is very powerful
and we are able to solve many interesting problems,
humans are much stronger
and they can learn over many more different aspects of the world
in a much more autonomous way,
just as we've seen with the child learning about intuitive physics.
Unsupervised learning could also help us deal with self-driving cars.
Let me explain what I mean:
Unsupervised learning allows computers to project themselves into the future
to generate plausible futures conditioned on the current situation.
And that allows computers to reason and to plan ahead.
Even for circumstances they have not been trained on.
This is important
because if we use supervised learning we would have to tell the computers
about all the circumstances where the car could be
and how humans would react in that situation.
How did I learn to avoid dangerous driving behavior?
Did I have to die a thousand times in an accident?
(Laughter)
Well, that's the way we train machines right now.
So, it's not going to fly or at least not to drive.
(Laughter)
So, what we need is to train our models
to be able to generate plausible images or plausible futures, be creative.
And we are making progress with that.
So, we're training these deep neural networks
to go from high-level meaning to pixels
rather than from pixels to high level meaning,
going into the other direction through the levels of representation.
And this way, the computer can generate images
that are new images different from what the computer has seen
while it was trained,
but are plausible and look like natural images.
We can also use these models to dream up strange,
sometimes scary images,
just like our dreams and nightmares.
Here's some images that were synthesized by the computer
using these deep charted models.
They look like natural images
but if you look closely, you will see they are different
and they're still missing some of the important details
that we would recognize as natural.
About 10 years ago,
unsupervised learning has been a key to the breakthrough
that we obtained discovering deep learning.
This was happening in just few labs, including mine at the time
at a time when neural networks were not popular.
They were almost abandoned by the scientific community.
Now, things have changed a lot.
It has become a very hot field.
There are now hundreds of students every year applying for graduate studies
at my lab with my collaborators.
Montreal has become the largest academic concentration
of deep learning researchers in the world.
We just received a huge research grant of 94 million dollars
to push the boundaries of AI and data science
and also to transfer technology of deep learning and data science to the industry.
Business people stimulated by all this are creating start-ups, industrial labs,
many of which near the universities.
For example,
just a few weeks ago, we announced the launch of a start-up factory
called 'Element AI'
which is going to focus on the deep learning applications.
There are just not enough deep learning experts.
So, they are getting paid crazy salaries,
and many of my former academic colleagues have accepted generous deals
from companies to work in industrial labs.
I, for myself, have chosen to stay in university,
to work for the public good,
to work with students,
to remain independent.
To guide the next generation of deep learning experts.
One thing that we are doing beyond commercial value
is thinking about the social implications of AI.
Many of us are now starting to turn our eyes
towards social value added applications, like health.
We think that we can use deep learning
to improve treatment with personalized medicine.
I believe that in the future,
as we collect more data from millions and billions people around the earth,
we will be able to provide medical advice
to billions of people who don't have access to it right now.
And we can imagine many other applications for social value of AI.
For example, something that will come out of our research
on natural language understanding
is providing all kinds of services
like legal services, to those who can't afford them.
We are now turning our eyes
also towards the social implications of AI in my community.
But it's not just for experts to think about this.
I believe that beyond the math and the jargon,
ordinary people can get the sense
of what goes on under the hood
enough to participate in the important decisions
that will take place, in the next few years and decades about AI.
So please,
set aside your fees and give yourself some space to learn about it.
My collaborators and I have written several introductory papers
and a book entitled "Deep Learning"
to help students and engineers jump into this exciting field.
There are also many online resources: softwares, tutorials, videos..
and many undergraduate students are learning a lot of this
about research in deep learning by themselves,
to later join the ranks of labs like mine.
Ai is going to have a profound impact on our society.
So, it's important to ask: How are we going to use it?
Immense positives may come along with negatives
such as military use
or rapid disruptive changes in the job market.
To make sure the collective choices that will be made about AI
in the next few years,
will be for the benefit of all,
every citizen should take an active role
in defining how AI will shape our future.
Thank you.
(Applause)