Transcriber: Virginie Labeye Reviewer: Denise RQ
How would you feel in a world
where half of the doctors, lawyers, and counselors would be machines?
How would you feel in a world where half of the caretakers
of your elderly people or young children would be robots?
How would you feel in a world where 'Her' would be a computer?
Those questions are not mine, they surround us, they fascinate us,
and sometimes they scare us
because those are questions about ourselves,
those are questions about how different we are
and how different we feel from the machines that we build.
Those are questions about a race,
a race between artificial intelligence and human intelligence.
As a control scientist,
I'm studying the control mechanisms that enable intelligence
both in machines and in nature.
And today, I would like to share with you
a control mechanism that is different in the brain and in the computer.
This difference is so simple
that it's almost embarrassing to talk about it.
And yet, I think it tells us a great deal
about how differently we compute from the machines that we build.
But let's start from the beginning.
The race between the computer and the brain started in 1950 in Cambridge
when Alan Turing,
one of the greatest scientists of the last century,
invented a game: the imitation game.
A modern version of the game is as follows:
you receive an invitation on Facebook,
but you don't know whether your new friend is a machine or a human,
so you engage in a conversation with her,
trying to figure out whether she has a brain.
And if you do figure out, you win, if you don't, you loose.
Turing predicted that by the year 2000,
machines would win the imitation game most of the time.
Turing's paper set a fundamental link between intelligence and computation,
and the imitation game launched
one of the richest dialogues of contemporary science.
For the last 60 years, the brain has been a model of the computer,
and the computer has been a model of the brain.
And here we are, 60 years later.
Our pocket machines solve our daily calculations,
correct our spelling mistakes, complete our sentences, translate our texts,
solve our quizzes, teach us how to play chess.
Machines solve those complicated computations
by decomposing them into a sequence of very simple operations.
So they need memory to store the intermediate steps,
and they need processors to execute the operations.
Computer technology has developed steadily to a point where those pocket machines
can store huge amounts of memory and execute operations
at a speed that literally, approaches the speed of light.
This makes computers incredibly fast
at solving computations that would be formidable for our brains.
So the victory of computer technology is really humbling for the brain,
to the point where you wonder whether there's still anything
our brains can do better than computers when it comes to computing.
Well, I think there is at least one thing
our brains still do much better than computers.
We forget what we memorize, we disregard what we see,
and often we don't listen to what we hear.
That might have been a problem at school.
Perhaps you consider this as a further weakness of your brain
compared to the computer
because computers do not forget what they memorize,
machines do not disregard what they see,
but forgetting and disregarding
are the basis of discerning.
Our brains keep discerning.
Machines are quite bad at discerning,
and this makes our brain fast, in fact much faster than computers
at zooming in and zooming out on the relevant information.
A few weeks ago, I was hiking with my wife in a part of Turkey called Lycia,
in the Mediterranean Sea.
One afternoon, we decided to go down 500 meters below our hiking path
to explore a beautiful little beach.
It was already 2 p.m., so we were a bit in a hurry,
and as we were running down this very rocky terrain
and looking for these small yellow and red signs
that would lead us to the beach,
I kept thinking how challenging it would be for a machine
to do what was great fun for us
because we are quite good at detecting those small signs,
just because they matter to us.
But then, relaxing on the beach and recovering from the descent,
I remember all these awareness experiments designed by neuroscientists,
I remember missing a gorilla in the team of basket players
just because my mind was focusing on the ball passes.
This is amazing. I think it will take a lot of time
before computers can miss a gorilla among a team of basket players.
This ability of detecting very small things
or ignoring very big things depending on our state of mind,
this ability of focalizing our computations
in a very specific window in time and space,
then modulating this window very quickly,
I would call 'plastic localization' or 'multiresolution.'
Brains are superfast at executing, but they are rather slow at localizing,
because they are rigid, they are monoresolution.
Brains instead are rather slow at executing,
but they are incredibly fast at localizing and at modulating the resolution.
They are plastic, they are multiresolution.
But why is that? Why can't we make machines plastically localized?
To me, localization has not much to do with how fast you compute
or how much you can store,
it has to do with a control mechanism,
which brings me to the second part of my talk.
Two years before Turing's paper,
Norbert Wiener, a respected control engineer at the MIT,
published a book where he mostly emphasized
the fundamental role of feedback to intelligence.
A feedback processor is a processor that feeds the output back at the input.
This processor does not process the input signal you intend to process.
Instead, it processes a comparison
between that intended signal and the one that is actually seen at the output.
You are all very familiar with feedback
because we all know how important it is to learning.
How could we learn without errors?
How could we form errors without comparing intended actions to actual actions?
But there are two types of feedback: negative feedback and positive feedback.
Negative feedback is how your thermostat works.
The input to your heating system is not the desired temperature,
it is a comparison
between the desired temperature and the actual temperature.
The processor tries to make the input and output alike,
so it is a regulating device.
We are surrounded by negative feedback.
Negative feedback is what regulates the temperature in this room,
the temperature of your body,
the speed of your car, the speed of your heart.
Positive feedback is the opposite of negative feedback.
Here, you add the output to the input.
So if, there is a change of the input, your further amplify the change.
As a result of this instability,
you get an all-or-none device, you get a switching device.
Positive feedback is everywhere around us.
Positive feedback switches off and on the light in your living room,
but also the genes in your cells, and the transistors in your computer.
So plenty of negative feedback, plenty of positive feedback.
Now, let's imagine a device that would have two feedback loops,
one for positive, the other one for negative,
a mix of switching and regulation.
If you ever raise a child or a pet,
you might think that this is not so bad as a policy.
Positive feedback is a switch between reward and punishment,
something stimulating,
which you'd like to balance with a regulation of emotions,
which is negative feedback.
So it seems that intelligence promotes the mixed feedback policy.
But an engineer would probably tell you that this device is not optimal
because every feedback loop has a cost.
So you might convince an engineer that you need one feedback loop,
either for regulating or for switching,
but you will have a hard time convincing an engineer
that you need a second feedback loop
to undo what the first feedback loop is doing.
And this is one of the many reasons
why you will not find that device in technology,
it has not survived the technological evolution.
And that was my control education: plenty of positive feedback,
plenty of negative feedback, never a mix.
And that picture changed
when I started studying feedback mechanisms in nature,
first as a hobby more than as a profession.
I wasn't surprised
to find lots of positive feedback and negative feedback in nature,
but I was increasingly surprised
to see that every single device in nature seems to have two-feedback loops,
hardwired at twice the cost of a single-feedback loop.
So either natural evolution obeys different rules
than technological evolution
or engineers have missed something here.
So here comes that different mechanism.
Plenty of negative feedback, plenty of positive feedback everywhere,
always separated in technology, never separated in nature.
This difference is so simple that it is almost embarrassing to talk about it,
but today my research is entirely about exploring that difference
because I believe
that the mixed-feedback principle is the essence of plastic localization,
of multiresolution, this property our brains do not share with machines.
The mixed-feedback processor is plastic, this is a long story,
but here is a cartoon:
on the right, you have a strong positive feedback,
so the output is all or none, a switching device.
On the left, you have a lazy regulator, input is the same as the output,
a regulating device.
But in the center, you have this balanced processor,
and the balanced processor can be turned into a switching or regulating device
by a mere modulation of the balance
between the positive and negative feedback.
This processor is highly localized.
It activates in a very narrow range.
But this localization is not just in range, it is also in time,
because if you make the positive feedback a bit faster than the negative feedback,
your device will first switch, then regulate.
This is called an 'excitable behavior,'
and this behavior is the behavior of every single one of your neurons.
And you can also localize in space
because you can make the positive feedback short ranged
and the negative feedback longer ranged.
And by localizing in time and space, you will build a wave
with a certain wavelength in space and time.
And once you build one wave, you can start modulating the frequency,
the spatial and temporal frequency of that wave
by relying on the collective activity of many such balanced processors.
And nature has found an incredible amount of different ways
to modulate the resolution.
It always seems that there is
this very simple feedback principle at work:
never a positive feedback without the negative counterpart.
At the core of the computer, we have transistors.
And transistors are switches. This is a positive feedback technology.
At the core of the brain, we have neurons that emit action potentials,
which are produced by the combination of a positive and negative feedback,
a mixed-feedback technology.
And your tens of billions neurons collaborate in many different ways
to change the wavelength of those localized signals.
I wish to think that machines will not win the next stage of the imitation game
until they will have incorporated a mixed-feedback principle at their core.
So whether you enjoy scientific predictions
or whether you are scared by scientific predictions,
you should acknowledge that imitation game
and artificial intelligence have changed our world indeed,
but that the machine hasn't eradicated the human yet.
Instead, perhaps artificial intelligence has improved
our understanding of ourselves
because it has improved our understanding of how different we are from machines.
And this is perhaps something of interest to all of us,
because we all like to play the imitation game,
we all like to assess how much 'Her' is different from a machine.
So next time you play the imitation game, you may want to remember
that your best advantage is perhaps not fast computing or quiz solving,
your best advantage is perhaps your ability to balance
your positive and negative feedback at many different scales.
Your best advantage is perhaps to discern,
at least for a while.
Thank you very much.
(Applause)