Translator: Tanya Cushman Reviewer: Peter van de Ven
So, the brain is a marvelous machine; it can do lots of things.
It's responsible for all the thoughts,
emotions and complex decisions that you're making,
and everyone in this room has one, and only one.
Okay?
So with this marvelous machine,
we've created AI, which is getting more and more powerful every day,
and now we're spending a lot of time
thinking about when AI might exceed us and begin to threaten us,
and also we have the challenge of controlling AI
so that it can benefit human society.
So the question arises
as to whether we can use our brain in AI to understand ourselves,
because in order to understand the questions
about the relationship between humans and AI,
not only do we have to understand how the AI is created, what it can do,
but we also have to understand
who we are and what we are capable of doing.
My main argument today
is that we know far more about AI than about the human brain.
That's why I chose this image.
Because if you look at this image carefully -
which was painted by my favorite artist,
René Magritte -
you can see that the mirror shows you an accurate reflection of the book,
a small book,
but it's not what you would expect to see
for the image of the person standing in front of the mirror,
and symbolically and figuratively,
I'd like to argue that AI is like that small book
and we still don't know exactly who we are,
even if you use the most powerful scientific engine
that we have developed in our history.
And to illustrate that,
I'd like to point out several interesting features of our human brain,
what the brains do well,
which we might be somewhat counterintuitive;
this might be different
from what you always assume what the brain really does.
As a first illustration,
I want you to focus on the red dot at the center of this image
and try not to move your eyes.
Try to fixate the red dot,
and notice the fact that while you're doing that,
probably all of you can see
three other colored dots in your peripheral vision:
yellow, green and white dot, okay?
But you'll notice that those dots will disappear from your vision
once the moon begins to rotate,
and please raise your hand -
so that I have a sense as to whether this is working or not -
when the dots begin to disappear.
Thank you.
So this is a very famous illusion in psychology
called "emotion-induced blindness."
It's been confusing and baffling to many psychologists
because it's not that easy to explain as to why this happens.
But at a higher level, this is quite expected
because this happens because our brain is a predictive machine.
A lot of us think that the brain gives us
a very accurate and reliable representation of the outside world,
like the library, where you can save information for countless books
and you can retrieve the information you want from a particular page,
or a theater,
where you can see the movements of all the actors accurately.
But that's not what the brain is good at.
What the brain is trying to do is always trying to predict what's going to happen.
It's tuned and constantly looking for things that are unpredictable,
that you did not predict,
and then tries to minimize the errors or discrepancies
between what you predicted
and what you observed in the outside world.
And the reason why these dots disappear after a few seconds
is because those dots are completely predictable,
and therefore, the brain loses interest quickly,
and therefore, it begins to erase it from your mental activity.
This kind of explanation
can deal with lots of other complex phenomena in the brain.
For example, if you present any sensory stimulus,
such as a flash or sound,
and then maintain it for while and then turn it off,
the neurons in the brain do not follow that pattern precisely;
instead, what they tend to do
is that they show vigorous response, a huge response,
at the onset of a sensory stimulus,
and then quickly reduce their activity,
and then when the stimulus is removed, it briefly suppresses activity.
The reason why they do that - one explanation for that -
is because these neurons
are constantly looking for surprising, unpredictable events.
So when the stimulus is turned on unpredictably, neurons respond vigorously,
and then when it is turned off unexpectedly,
they briefly suppress activity.
But when there are no changes in the environment,
neurons actually are not that interested.
In another example that's consistent with this kind of explanation
is this illusion called rotating snakes.
If you fixate any point in this image and do not move your eyes,
you can confirm that this image is completely static;
there is nothing in this screen that's moving around.
But as soon as you begin to move your eyes around,
you'll notice that a lot of these circles briefly rotate.
And again, this is very confusing.
Why do we see motion that's not there in the physical world?
An interesting study that was recently published,
that if you build a deep-learning network -
this is a cutting-edge AI model of our human condition -
and then train that network to try to predict
what's going to be the next frame
in many different movies that you use to train this network,
those kinds of deep-learning networks will also see the illusory motion
that you see with this rotating-snake illusion.
Again, suggesting that the reason why you see these illusory motions
is because your brain is more interested
in predicting what's going to happen in the next screen
rather than analyzing what's currently on the screen.
Okay, so that's one important feature of our brain
that we may not be aware of all the time,
that our brain is a predictive machine.
So we might be able to predict
how the difference between AI and humans will change,
but it might be harder for us to understand
exactly what human brains are
and what the difference between AI and the human brain is.
Second important feature of the human brain and mind
that I'd like to point out
is that we are very efficient in categorizing things.
So when I show you an image like this,
everyone in this room can immediately recognize they're all apples
and there are green apples and red apples,
and, of course, there are many other features in this image
that you instantaneously ignore,
such as subtle changes in the colors
and subtle changes in the size of the apples,
but we can effortlessly classify and categorize different objects that we see,
and this is very hard for AI to do, okay?
In fact,
many AI algorithms kind of show off their performance by showing
that they can categorize different images into cats and dogs and so on,
but humans are really, really good at this.
But this comes at a cost as well
because as soon as you start categorizing things into discrete domains,
you start to ignore the subtle differences between them.
So the question is,
Why are we so good at categorizing things,
and why, also,
so good at ignoring rich variations among different individual objects?
So the reason why we are so good at ignoring different features
is because the brain evolved
not to accurately represent the outside world
but to choose the most beneficial action for us.
And that requires different kinds of functions, of properties.
So for example, imagine that you're both tired and hungry.
So you need to sleep and eat at almost the same time.
And imagine that you are, like, indecisive
and you're trying to switch between these two activities constantly;
you're going back and forth between the bed and the kitchen.
You will not accomplish either of these objectives.
So in order to deal with these kinds of conflicting situations,
what the brain has to do is build networks that have these kinds of structures,
where neurons that are responsible for selecting sleeping behavior,
once they get chosen,
that they have to maintain their activity
and then suppress the activities of competing neurons
that might be promoting eating
so that while you're sleeping
you don't get up constantly to go to the kitchen and vice-versa.
So these two functions are often referred to as a self-excitation
that maintains the activity of the neuron persistently
and then mutual inhibition
because you don't want the competing actions to interrupt the ongoing activity.
And this type of action selection
might be the the reason why even when we're trying to evaluate
different objects we see in the sensory world,
we tend to categorize things right away.
So, third feature of our mind and brain is the fact that we're extremely social,
and therefore our brain is also extremely social.
And the reason is because social activity
is more important than individual decision-making in everyday life,
in almost every moment in our lives,
and also this requires very complicated computations.
Because unlike when you are making decisions individually,
when you're acting in a social context,
what's most important
is for you to accurately predict what other people will do
because success and failures of action
really depends upon your ability to predict what other people will do.
But this gets complicated very quickly
because both you and I have similar hardware, our brains,
and it has similar objectives,
and therefore, just like I'm trying to predict what you're going to do,
you are also trying to predict what I am going to do.
Therefore, for me to be able to predict what you're going to do, accurately,
I also need to be able to predict
what you're going to predict that I'm going to do.
And this can go on many, many times and creates a very complicated loop.
Therefore, it creates lots of room for errors,
and a lot of mental disabilities that we have
actually correspond to the failures in this type of mental simulations
in imagining what other people might be thinking and so on.
So that's the cost of having an extremely successful social brain
that deals with these complicated social situations.
One last feature that I'd like to point out
is that our brains are analog computers,
and this is important to keep in mind
because in order to understand
the best strategies that we have to adopt when we're dealing with AI,
rapidly developing AI technology,
is that we like to understand
the difference between human brains and digital computers
and often tend to think
that a lot of the features that digital computers have
might be applicable to human brains.
But at the fundamental level, the human brain is an analog computer.
It processes analog signals rather than processing bits and zeros and ones,
you know, binary number scheme.
And one good example
is that there are elements in both human brains and supercomputers
that work like a switch,
whose changes have a huge effect on the ongoing activities of the system
by small changes in the input.
In the computer, it's called a transistor that does the work of the switching.
In the human brain, there are elements called synapses,
which is where two different neurons meet
and where the information exchange occurs.
And when you magnify these structures, transistors and synapses,
you can see that, physically,
the transistor has a much simpler structure.
Synapses are much more complicated,
and they can process analog information in a much more complicated way
than a computer transistor.
So, and this also can explain
some additional similarities between human brains and analog computers
because just like the human brain,
analog computers can perform different functions
depending upon the connections that you make between different parts.
And similarly in the human brain, the brain is programmed;
it's not by entering some computer codes,
but instead, altering the connections between different neurons.
So that's another similarity that makes a lot of sense
when you begin to realize that the human brain
is more like an analog computer than a digital computer.
So understanding these differences, and also similarities,
between human brains and computers and AI and robots
can actually help us solve some difficult, ethical challenges
that we face ahead.
For example, when you watch a sci-fi movie where robots act very much like humans,
it's very difficult to withhold some empathy for these robots
and also allow these robots to have some rights,
which I think is very dangerous,
and I think it could follow from some of the tendencies that our brain has,
as I've already explained.
Personally, I think that the reason why we feel empathy
for the robots that look like us and behave like us
is not that different from the emotional response that we might show
when somebody, for example,
tries to destroy some physical objects we value.
For example, if you're a musical fan
and if you're attached to objects like a piano
and then somebody smashes them,
it's actually very painful to watch them,
even though that piano has no mental activity,
has no emotion and so on.
I think those are due to the similar properties
of our human brain and human mind.
And one last thing I'd like to point out
is that getting complete self-knowledge, even using marvelous scientific tools
like the tools that are currently developed in neuroscience,
actually is really, really hard.
To, in a way, to get complete self-knowledge or self-intelligence,
you'll probably have to record all the neurons in our brain
and then analyze them and extract all the meaningful patterns from them,
which is not only physically impossible
but also has a logical paradox in it as well,
which has been noticed
by many philosophers and mathematicians and logicians in the last century,
which has been adopted in many science-fiction movies.
So, for example, in this particular Star Trek episode,
Captain Kirk and his crew were visiting a planet;
in fact, they were trapped in a planet which was dominated by androids.
And in order to escape from them,
Captain Kirk told this android that "I'm lying,"
which includes reference to himself.
And therefore, when this robot begins to analyze the truth of this statement,
it got overheated and burned itself,
and therefore, it allowed Captain Kirk to escape that planet very safely.
So, in closing, I'd like to actually do a short experiment.
I have asked the camera crew, earlier,
to aim the camera to the screen as I'm exiting the stage,
which is kind of to give feedback,
which has been noted almost 60 years ago,
when people started connecting the video camera to the TV screen,
and in a way, you can see that as an analogy to a simple physical device,
like the computer camera and a screen trying to understand itself,
and you can see that it's -
even for a camera, which is much simpler than our human brains,
this is a very complicated process.
So I hope that you enjoy this video.
Thank you.
(Applause)