Translator: Beth Hine Reviewer: eric vautier
Artificial Intelligence - AI...
Please, anyone who has heard of it in this room, raise your hand.
Wow! The whole room almost!
Does it frighten some of you?
Almost half...
Science fiction has largely contributed to this.
Remember George Orwell's book '1984',
'Big brother is watching you!'
or the film Minority Report
where mutant humans were able to predict crimes
before they even took place,
or even the film 'Her'
in which the hero falls in love with a machine.
We all believed that it was impossible,
that science would never be able to create such systems.
Artificial Intelligence
puzzles, fascinates, but also worries.
Hardly a week goes by without us hearing:
'What will become of human beings?'
'Will machines surpass us?' etc.
We hear everything and its opposite.
Let's go back to the pictures we saw at the beginning of this presentation,
here, Alpha Go.
In 2016, this machine managed to defeat
the world's best player of Go.
Yet the game of Go is rather simple:
there are only nine rules.
But the number of possible combinations
exceeds the number of atoms known in the universe.
Remarkable feats have also taken place in the medical field
where systems can detect cancerous tumours
much more efficiently than experienced doctors.
Here, in the industry, predictive maintenance
can detect anomalies before they become critical.
And then, finally, in our daily lives,
virtual assistants help us find information,
and they perform tasks
that are more or less useful, I grant you that.
'Hey Magic Assistant!
could you please add raclette cheese to the shopping list?'
But this science is not new.
In 1956, scientists met together.
They dreamed of a new world,
a world where we could recreate all human cognitive functions
inside a machine.
Behind artificial intelligence,
hides the hidden will to create tools and high-performance systems,
but above all to unravel the mysteries of intelligence.
Yes, to unravel intelligence's mysteries!
Intelligence... It is difficult to define this complex concept.
The Latin word 'intelligere' means the ability to make connections.
With artificial intelligence, we want a machine to become smart.
Now you're probably wondering:
how does a machine obtain information,
make links, make sense of it,
and then generate new knowledge?
Yes, that's simple: it must be able to learn.
One of the ways a machine learns
is by using statistical artificial intelligence based on data.
The words 'automated learning' or 'machine learning'
are probably familiar to you.
So let's start with an example.
Imagine you are a real estate agent
and you have collected a lot of information
on properties around an area.
For each property,
you have collected information
such as the size of the garden, the surface area of the lounge,
the number of rooms, the sun exposure,
the geographical location, etc., a lot of information,
and then its price.
By providing all of that data to a machine,
the machine will be able to learn how to predict the price of a property.
How?
By performing statistical calculations,
and by creating links between all this data.
At no time are you going to step in
and tell it exactly how to accomplish this task.
However,
your role in providing it with data is paramount.
If you don't give it enough representative examples,
the machine will not be able to predict all cases.
Today,
artificial intelligence performs only simple tasks
that are specialized and repetitive.
And as we have seen in some cases,
AI can perform tasks better than us, humans.
That's called 'narrow AI'
because, at this point,
machines do not know how to generalise their learning processes yet.
The challenge of tomorrow's AI will be to generate new knowledge,
to go from a 'narrow artificial intelligence',
to an 'Artificial General Intelligence'.
Since the 2000's,
statistical artificial intelligence is booming.
Its prowess is the result of three factors:
firstly, data that are increasingly present and accessible;
secondly, computing capacities increasingly efficient;
and thirdly,
algorithms that solve increasingly complex tasks.
Many of these systems use artificial neural networks,
networks which contain neurons, a bit like in our brains.
An artificial neuron is a mathematical unit of simple calculations.
What is unique is that all the calculation results of one layer
are used as input for the calculation of the other layers.
That way, each layer enables
the understanding of the problem to be progressively deepened.
This technic is called 'deep learning',
It has led to major advances
in voice recognition for example -
you all dictate orders to your phones -
or in computer vision -
some systems, some cars anticipate braking
as soon as they detect pedestrians.
All these artificial neural networks
are not copied from our biological neural networks.
They are inspired by them.
Yes! They are bio-inspired.
Now, let's talk about our brains for a minute,
especially airplane pilots' brains.
I'm going to tell you an incredible but true story.
A pilot and his co-pilot are on board.
They want to land.
But there is a problem: a plane is blocking the runway.
The pilot and co-pilot are totally focused on this event.
Simultaneously, in the cockpit, a strident auditory alarm blares,
but they just ignore it.
They are so focused on the plane that is still on the runway.
The plane takes off and they begin their landing.
The alarm they totally ignored was to warn them
that they did not lowered the landing gear.
No worries - I can feel you're anxious -
they are fine!
But you see, their brains did betray them.
We all know that neuroscientists use helmets with sensors
or medical imaging equipment to measure our brain waves.
That way, they collect a large amount of data
and with this data, they can better understand how our brain works.
In the previous example of the airplane pilots
who suffered in this case of 'inattentional deafness',
the goal of neuroscientists
is to understand the brain mechanisms that are set up in such situations.
Their long-term objective is to create smarter cockpits
that could adjust to the pilot's state.
Now how do neuroscientists do to analyse all these data?
Well, they simply use tools
such as computer science, mathematics, or artificial intelligence.
This is what we call 'computational neurosciences'.
Now, why do we speak
of artificial intelligence and neurosciences at the same time?
Well, it is simply because in both situations,
neurons are at the core of all concerns.
In order to keep on improving, artificial intelligence needs
to be inspired by discoveries made in neuroscience.
This will enable AI to create even smarter systems
based on neural networks even more efficient.
Conversely, neurosciences need artificial intelligence
to continue assisting it in testing and formalising new theories.
Finally,
we are already living in the 'century of the brain'.
We have already entered in a 'neurocentric' world,
as evidenced by the advent of new neurodisciplines
such as neuroeconomics, neuroergonomics, neuromarketing, and even neuroeducation.
We already live in the era of the fourth industrial revolution,
that of the technological revolution.
No revolution has been as fast as this one,
changing not only our way of living and working,
but also our relationship with others.
Even before being technological,
the transformation of artificial intelligence
is human, cultural and societal.
In the employment sector, all branches of activity are affected,
whether it be health, agriculture, justice, education or transportation,
but also all professions,
thus upsetting companies' organisation.
One of the main objectives of artificial intelligence
will be to make everyone everywhere accept these new tools
which help, optimise or replace
tasks hitherto performed by humans.
You, your children, your grandchildren, your great-grandchildren
will live tomorrow in a world where human being and machines
will undoubtedly coexist in an increasingly 'smarter' environment.
I believe this human-machine association will give birth
to a more effective collaboration in which people will have more time
and will be able to devote themselves to tasks more motivating and rewarding.
You will not have to all become experts in AI and neuroscience.
However, you will all need to understand
the strategic and economic issues of these new tools.
And this will enable you to realise
what are the potentials and the limits of these technologies.
So, with artificial intelligence and the neurosciences,
humans are still rediscovering themselves.
Thank you for your attention.
(Applause)