My name is Rob Reich,
I’m delighted to welcome you here to Stanford University
for an evening of conversation
with Yuval Harari, Fei-Fei Li, and Nick Thompson.
I’m a professor of political science here
and the Faculty Director of
the Stanford Center for Ethics and Society,
which is a co-sponsor of tonight’s event,
along with the Stanford Institute
for Human Centered Artificial Intelligence
and the Stanford Humanities Center.
Our topic tonight is a big one.
We’re going to be thinking together
about the promises and perils of artificial intelligence.
The technology quickly reshaping our economic,
social, and political worlds, for better or for worse.
The questions raised by the emergence of AI
are by now familiar, at least to many people
here in Silicon Valley but, I think it’s fair
to say that their importance is only growing.
What will the future of work look like
when millions of jobs can be automated?
Are we doomed or perhaps blessed to live in a world
where algorithms make decisions instead of humans.
And these are smaller questions in the big scheme of things.
What, might you ask you’re the large ones?
Well, here are three.
What will become of the human species
if machine intelligence approaches
or exceeds that of an ordinary human being?
As a technology that currently relies
on massive centralized pools of data,
does AI favor authoritarian centralized governments
over more decentralized democratic governance?
And are we at the start now of an AI arms race?
And what will happen if powerful systems of AI,
especially when deployed for purposes
like facial recognition, are in the hands
of authoritarian rulers?
These challenges only scratch the surface when it comes
to fully wrestling with the implications of AI,
as the technology continues to improve
and its use cases continue to multiply.
I want to mention the format of the evening event.
First, given the vast areas of expertise
that Yuval and Fei-Fei have,
when you ask questions via Slido,
those questions should pertain
or be limited to the topics under discussion tonight.
So, this web interface that we’re using,
Slido allows people to upvote and downvote questions.
So, you can see them now if you have
an internet communication device.
If you don’t have one, you can take one of these postcards,
which hopefully you got outside
and on the back you can fill in a question you might have
about the evening event and collect it at the end,
and the Stanford Humanities Center
will try to foster some type of conversation
on the basis of those questions.
Couple housekeeping things,
if you didn’t purchase one already,
Yuval’s books are available for sale
outside in the lobby after the event.
A reminder to please turn your cell phone ringers off.
And we will have 90 minutes
for our moderated conversation here
and will end sharp after 90 minutes.
Now, I’m going to leave the stage in just a minute
and allow a really amazing undergraduate student
here at Stanford to introduce our guests.
Her name is Anna-Sofia Lesiv,
let me just tell you a bit about her.
She’s a junior here at Stanford majoring in Economics
with a minor in Computer Science
and outside the classroom, Anna-Sofia is a journalist
whose work has been featured in The Globe and Mail,
Al Jazeera, The Mercury News, The Seattle Times,
and this campuses paper of record, The Stanford Daily.
She’s currently the Executive Editor of The Daily
and her daily magazine article
from earlier in the year called CS Plus Ethics,
examined the history of computer science
and ethics education at Stanford
and it won the student prize for best journalism of 2018.
She continues to publish probing examinations
of the ethical challenges faced by technologists here
and elsewhere so, ladies and gentlemen
I invite you to remember this name
for you’ll be reading about her
or reading her articles, or likely both,
please welcome Stanford junior, Anna-Sofia Lesiv.
Thank you very much for the introduction, Rob.
Well it’s my great honor now,
to introduce our three guests tonight,
Yuval Noah Harari, Fei-Fei Li, and Nicholas Thompson.
Professor Yuval Noah Harari is a historian,
futurist, philosopher, and professor at Hebrew University.
The world also knows him for authoring some of
the most ambitious and influential books of our decade.
Professor Harari’s internationally best-selling books,
which have sold millions of copies worldwide,
have covered a dizzying array of subject matter
from narrativizing the entire history
of the human race in Sapiens,
to predicting the future awaiting humanity,
and even coining a new faith called Dadaism, in Homo Deus.
Professor Harari has become a beloved figure
in Silicon Valley, whose readings are assigned
in Stanford’s classrooms and whose name
is whispered through the hallways
of the comparative literature
and computer science departments, alike.
His most recent book is 21 Lessons for the 21st Century,
which focuses on the technological,
social, political, and ecological challenges
of the present moment.
In this work, Harari cautions
that as technological breakthroughs
continue to accelerate, we will have less
and less time to reflect upon the meaning
and consequences of the changes they bring.
And this urgency, is what charges
Professor Fei-Fei Li’s work everyday,
in her role as the Co-Director of Stanford’s
Human-Centered AI Institute.
This institute is one of the first
to insist that AI is not merely the domain of technologists
but a fundamentally interdisciplinary
and ultimately human issue.
Her fascination with the fundamental questions
of human intelligence is what piqued her interest
in neuroscience, as she eventually became
one of the world’s greatest experts
in the fields of computer vision, machine learning,
and cognitive and computational neuroscience.
She’s published over a hundred scientific articles
in leading journals and has had research supported
by the National Science Foundation, Microsoft,
and the Sloan Foundation.
From 2013 to 2018, Professor Fei-Fei Li served as
the Director of Stanford’s AI lab
and between January, 2017 and September, 2018,
Professor Fei-Fei Li served as Vice President at Google
and Chief Scientist of AI and Machine Learning
Nicholas Thompson is the Editor-In-Chief of Wired magazine,
a position he’s held since January, 2017.
Under Mr. Thompson’s leadership,
the topic of artificial intelligence
has come to hold a special place at the magazine.
Not only has Wired assigned more feature stories
on AI than on any other subject,
but it is the only specific topic
with a full-time reporter assigned to it.
It’s no wonder then, that Professors Harari
and Li are no strangers to its pages.
Mr. Thompson has led discussions
with the world’s leaders in technology and AI,
including Mark Zuckerberg on Facebook and Privacy,
French President, Emmanuel Macron on France’s AI strategy,
and Ray Kurzweil on the ethics and limits of AI.
Mr. Thompson is a Stanford University graduate
who earned his BA, double majoring
in earth systems and political science
and impressively even completed a third degree in economics.
Of course, I would be remiss if I did not mention
that Mr. Thompson cut his journalistic teeth
in the opinions section of the Stanford Daily so,
Nick, that makes both of us.
Like all our guests today, I’m at once fascinated
and worried by the challenges
that artificial intelligence poses for our society.
One of my goals at Stanford has been
to write about and document the challenge
of educating a generation of students whose lives
and workplaces, will eventually be transformed by AI.
Most recently, I published an article
called Complacent Valley, with the Stanford Daily.
In it I critiqued our propensity
to become overly comfortable with the technological
and financial achievements that Silicon Valley
has already reached, lest we become complacent
and lose our ambition and momentum
to tackle the greater challenges the world has in store.
Answering the fundamental questions
of what we should spend our time on,
how we should live our lives,
has become much more difficult,
particularly on the doorstep of the AI revolution.
I believe that the kind of crisis of agency
that Author JD Vance wrote of in Hillbilly Elegy,
for example, is not confined to Appalachia
or the de-industrialized Midwest
but is emerging even at elite institutions like Stanford.
So conversations like hours this evening,
hosting speakers that aim to re-center
the individual at the heart of AI,
will show us how to take responsibility
in a moment when most decisions
can seemingly be made for us, by algorithms.
There are no narratives to guide us through a future
with AI, no ancient myths or stories
that we may rely on to tell us what to do.
At a time when Humanity is facing
its greatest challenge yet,
somehow we cannot be more at a loss for ideas or direction.
It’s this momentous crossroads in human history
that pulls me towards journalism and writing in the future.
And it’s why I’m so eager to hear
our three guests discuss exactly such a future, tonight.
So, please join me in giving them
a very warm welcome this evening.
Wow, thank you so much Anna-Sofia, thank you, Rob.
Thank you, Stanford for inviting us all here.
I’m having a flashback to the last time
I was on a stage at Stanford,
which was playing guitar at the coho
and I didn’t have either Yuval or Fei-Fei with me
so, there were about six people in the audience,
one of whom had her headphones on but, I did meet my wife.
[audience croons] Isn’t that sweet?
All right so, a reminder, housekeeping,
questions are going to come in, in Slido.
You can put them in, you can vote up questions,
we’ve already got several thousand
so please vote up the ones you really like.
If someone can program an AI that can get
a really devastating question in
and stump Yuval, I will get you
a free subscription to Wired.
I want this conversation to kind of have three parts.
First, lay out where we are,
then talk about some of the choices
we have to make now, and last talk about some advice
for all the wonderful people in the halls.
So, those are the three general areas,
I’ll feed in questions as we go.
We may have a specific period for questions
at the end but, let’s get cracking.
So, the last time we talked you said many,
many brilliant things but one that stuck out,
it was a line where you said,
We are not just in a technological crisis,
we are in a philosophical crisis.
So, explain what you meant, explain how it ties to AI,
and let’s get going with a note of existential angst.
Yes, I think what’s happening now
is that the philosophical framework of the modern world
that has been established in the 17th and 18th century,
around ideas like human agency and individual free will,
are being challenged like never before.
Not by philosophical ideas but by practical technologies.
And we see more and more questions,
which used to be, you know, the bread and butter
of the philosophy department, being moved
to the engineering department.
And that’s scary, partly because, unlike philosophers,
who are extremely patient people,
they can discuss something for thousands of years
without reaching any agreement and they are fine with that,
[light audience laughter] the engineers won’t wait
and even if the engineers are willing to wait,
the investors behind the engineers, won’t wait.
So, it means that we don’t have a lot of time
and in order to encapsulate what the crisis is,
I know that, you know engineers,
especially in a place like Silicon Valley,
they like equations so, maybe I
can try to formulate an equation [laughing]
to explain what’s happening.
And the equation is B times C times D equals ah.
Which means, biological knowledge
multiplied by computing power multiplied by data
equals the ability to hack humans.
And the AI revolutional crisis is not just AI,
it’s also biology, it’s biotech.
We haven’t seen anything yet
because the link is not complete.
There is a lot of hype now around AI in computers
but just that it is just half the story.
The other half is the abilities,
the biological knowledge coming from brain science
and biology and once you link that to AI,
what you get is the ability to hack humans.
And maybe I’ll explain what it means,
the ability to hack humans to create an algorithm
that understands me better than I understand myself
and can therefore manipulate me, enhance me, or replace me.
And this something that our philosophical baggage
and all our belief in, you know, human agency,
and free will, and the customer is always right,
and the voter knows best, this just falls apart
once you have this kind of ability.
Once you have this kind of ability
and it’s used to manipulate or replace you,
not if it’s used to enhance you?
Also when it’s used to enhance you,
the question is, who decides what is a good enhancement
and what is a bad enhancement.
So, our immediate fallback position
is to fall back on the traditional humanist ideas
that the customer is always right,
the customers will choose the enhancement,
or the voter is always right.
The voters will vote.
There will be a political decision about enhancement,
or if it feels good, do it.
We’ll just follow our heart, we’ll just listen to ourselves.
None of this works when there is a technology
to hack human on a large scale.
You can’t trust your feelings,
or the voters, or the customers on that.
The easiest people to manipulate
are the people who believe in free will
because they think they cannot be manipulated.
So, how do you decide what to enhance if,
and this a very deep ethical and philosophical question.
Again, it philosophers have been debating
for thousands of years.
What are the good qualities we need to enhance?
So, if you can’t trust the customer,
if you can’t trust the voter,
if you can’t trust your feelings, who do you trust?
All right Fei-Fei, you have a PhD,
you have a CS degree, you’re Professor at Stanford.
Does A times B times C equal H? [laughing]
Is Yuvals theory the right way
to look at where we’re headed?
Wow, what a beginning, thank you, Yuval.
Well, one of the things, I’ve been reading Yuval’s book
for the past couple of years, and talking to you,
and I’m very envious of philosophers now,
because they can propose questions
and crisis but they don’t have to answer them.
Now, as an engineer and scientist,
I feel like we have to now solve the crisis.
So, honestly I think I’m very thankful.
I mean, personally I’ve been reading your book
for two years and I’m very thankful
that Yuval, among other people,
have opened up this really important question
for us and it’s also quite a…
When you said the AI crisis
and I was sitting there thinking,
this a field I loved, and felt passionate about,
and researched for 20 years,
and that was just a scientific curiosity
of a young scientist entering PhD and AI.
What happened, that 20 years later, it has become a crisis?
And it actually speak of the evolution of AI
that got me where I am today
and got my colleagues at Stanford where we are today
with the Human-Center AI,
is that this a transformative technology.
It’s a nascent technology, it’s still a budding science
compared to physics, chemistry, biology but,
with the power of data, computing,
and the kind of diverse impact AI is making,
it is like you said, is touching human lives
and business in broad and deep ways.
And responding to that kind of questions
in crisis that’s facing humanity,
I think one of the proposed solution,
or if not solution at least a try
that Stanford is making an effort about,
is can we reframe the education,
the research, and the dialogue of AI
and technology in general, in a human centered way.
We’re not necessarily gonna find solution today but,
can we involve the humanists, the philosophers,
the historians, the political scientists,
the economists, the ethicist, the legal scholars,
the neuroscientists, the psychologists,
and many more other disciplines,
into the study and development of AI
in the next chapter, in the next phase.
Don’t be so certain we’re not gonna get an answer today.
I’ve got two of the smartest people in the world
glued to their chairs and I’ve got Slido
for 72 minutes so, let’s give it a shot.
But he said we have thousands of years.
But let me go a little bit further in Yuval’s questions.
So, there are lots, or Yuval’s opening statement,
there are a lot of crises about AI
that people talk about, right?
They talk about AI becoming conscious
and what will that mean,
they talk about job displacement,
They talk about biases, when Yuval has very clearly laid out
what he thinks is the most important one,
which is the combination of biology plus
computing plus data leading to hacking.
He’s laid out a very specific concern.
Is that specific concern, what people
who were thinking about AI should be focused on?
So, alien technology humanity has created,
starting from fire, is a double-edged soul.
So, it can bring improvements to life and to work
and to society but it can bring the perils
and AI has the perils, you know?
I wake up every day worried
about the diversity inclusion issue in AI.
We worry about fairness or the lack of fairness,
privacy, the labor market so,
absolutely we need to be concerned
and because of that we need to expand the study,
the research, and the the development of policies,
and the dialogue of AI beyond just the codes
and the products into these human realms,
into these societal issues.
So, I absolutely agree with you on that,
that this the moment to open the dialogue,
to open the research in those issues.
Okay. I would just say that again,
part of my fear is that the dialogue,
I don’t fear AI experts talking with philosophers,
I’m fine with that, historians good,
literary critics wonderful, I fear the moment
you start talking with biologists.
That’s my biggest fear.
When you and the biologist will,
Hey, we actually had a common language
and we can do things together.
And that’s when the really scary things, I think.
Can you elaborate on the what is scaring you
that we talk to biologists?
That’s the moment when you can really hack human beings,
not by collecting data about our search words,
or our purchasing habits, or where do we go about town,
but you can actually start peering inside
and collect data directly
from our hearts and from our brains.
Okay, can I be specific?
First of all, the birth of AI is AI scientist
talking to biologists, specifically neuro scientists.
Right, the birth of AI is very much inspired
by what the brain does.
Fast-forward to sixty years later,
today’s AI is making great improvement in healthcare.
There’s a lot of data from our physiology
and pathology being collected
and using machine learning to help us but,
I feel like you’re talking about something else.
That’s part of it, I mean,
if there wasn’t a great promise in the technology,
there would also be no danger
because nobody would go along that path.
I mean, obviously, there are enormously beneficial things
that AI can do for us, especially
when it is linked with how is biology.
We are about to get the best health care in the world,
in history, and the cheapest,
and available for billions of people via smartphones,
which today they have almost nothing.
And this is why it is almost impossible to resist
the temptation and with all the issue now, of privacy.
If you have a big battle between privacy and health,
health is likely to win hands down.
So, I fully agree with that and, you know,
my job as a historian, as a philosopher,
as a social critic, is to point out the dangers in that
because especially in Silicon Valley,
people are very much familiar with the advantages
but they don’t like to think so much
about the dangers and the big danger
is what happens when you can hack the brain
and that can serve not just your healthcare provider,
that can serve so many things from a crazy dictator, to–
Let’s focus on that, what it means to hack the brain.
Like what, right now in some ways,
my brain is hacked, right?
There’s an allure of this device,
it wants me to check it constantly.
Like, my brain has been a little bit hacked.
Yours hasn’t because you meditate two hours a day
but mine has and probably [laughter]
most of these people have.
But what exactly is the future brain hacking
going to be, that it isn’t today?
Much more of the same, but on a much larger scale.
I mean, the point when for example,
more and more of your personal decisions in lives
are being outsourced to an algorithm
that is just so much better than you.
So, you know we have two distinct dystopias
that kind of mesh together.
We have the dystopia of surveillance capitalism
in which there is no like, Big Brother dictator
but more and more of your decisions
are being made by an algorithm
and it’s not just decisions about what to eat,
or what to shop, but decisions like,
where to work, and where to study, and whom to date,
and whom to marry, and whom to vote for.
It’s the same logic and I would be curious to hear
if you think that there is anything in humans,
which is by definition un-hackable,
that we can’t reach a point when the algorithm
can make that decision better than me.
So, that’s one line of dystopia
which is a bit more familiar in this part of the world
and then you have the full-fledged dystopia
of a totalitarian regime
based on a total surveillance system.
Something like the totalitarian regimes
that we have seen in the twentieth century
but augmented with biometric sensors
and the ability to basically track
each and every individual, 24 hours a day.
And you know, which in the days of,
I don’t know, Stalin or Hitler, was absolutely impossible
because it didn’t have the technology
but maybe, might be possible in 20 years or 30 years.
So, we can choose which dystopia to discuss
but they are very close in–
Let’s choose the liberal democracy dystopia.
Fei-Fei, do you want answer Yuval’s specific question,
which is, is there something in dystopia,
a liberal democracy dystopia, is there something endemic
to humans that cannot be hacked?
So, when you ask me that question just two minutes ago,
the first word that came to my mind is love.
Ask Tinder, I don’t know.
[crowd and panel laughing]
Dating is not the entirety of love, I hope.
The question is which kind of love are you referring to?
If you are referring to this, you know I don’t know,
Greek philosophical love or the loving kindness of Buddhism,
that’s one question,
which I think it’s much more complicated.
If you are referring to the
biological mammalian courtship rituals,
But humans– Why is it different
from anything else that is happening in the body?
But humans are humans because there’s some part of us
that are beyond the mammalian courtship, right?
So, is that part hackable?
That’s the question?
I mean, you know in in most science fiction books
and movies, they give your answer.
When the extra-terrestrial evil robots
are about to conquer planet Earth
and nothing can resist them, resistance is futile,
at the very last moment,
Humans win It’s just one thing,
Because the robots don’t understand love.
Last moment there’s one heroic white dude that saves us.
[audience cheering and applause] [laughter]
No, no, it was a joke, don’t worry.
[audience and panel laughter]
But, okay so, the two dystopia,
I do not have answers to the two dystopias
but I want to keep saying is
this is precisely why this is the moment
that we need to seek for solutions.
This is precisely why this is the moment
that we believe the new chapter of AI needs to be written
by cross-pollinating efforts from humanists,
social scientists, to business leaders,
to civil society, to governments to come at the same table
to have that multilateral and cooperative conversation.
I think you really bring out the urgency
and the importance and the scale of this potential crisis
but I think in the face of that, we need to act.
Yeah, and I agree that we need cooperation,
that we need much closer cooperation
between engineers and philosophers
or engineers and historians
and also from a philosophical perspective,
I think there is something wonderful
about engineers, philosophically.
Thank you. [laughing]
That they you really cut the bullshit.
I mean, philosophers can talk and talk you know,
in cloudy in flowery metaphors
and then the engineers can really focus the question.
Like, I just had a discussion the other day
with an engineer from Google about this
and he said, Okay, I know how to maximize
people’s time on the website.
If somebody comes to me and tells me,
Look, your job is to maximize time on this application.
I know how to do it because I know how to measure it.
But if somebody comes along and tells me,
Well you need to maximize human flourishing
or You need to maximize universal love,
I don’t know what it means.
So, the engineers go back to the philosophers
and ask them, what do you actually mean.
Which, you know, a lot of philosophical theories
collapse around that because they can’t really explain
what and we need this kind of collaboration.
We need a equation for that. In order to move forward.
But then Yuval, is Fei-Fei right?
If we can’t explain and we can’t code love,
can artificial intelligence ever recreate it
or is it something intrinsic to humans
that the machines will never emulate.
I don’t think that machines will feel love
but you don’t necessarily need to feel it
in order to be able to hack it,
to monitor it, to predict it, to manipulate it.
I mean, machines don’t like to play candy crush.
But you think they can– But they can still–
This device, in some future
where it’s infinitely more powerful
than it is right now, could make me fall in love
with somebody in the audience?
Hmm, that goes to the question of consciousness
Let’s go there. I don’t think that we have
the understanding of what consciousness is
to answer the question, whether a non-organic consciousness
is possible or is not possible.
I think we just don’t know but again
the bar for hacking humans is much lower.
The machines don’t need to have consciousness of their own
in order to predict our choices
and manipulate our choices, they just need…
If you accept that something like love is,
in the end, a biological process in the body.
If you think that AI can provide us
with wonderful health care
by being able to monitor and predict
something like the flu or something like cancer,
what’s the essential difference between flu and love?
In the sense of, is this biological
and this something else, which is so separated
from the biological reality of the body,
that even if we have a machine
that is capable of monitoring and predicting flu,
it still lacks something essential
in order to do the same thing with love.
So, I want to make two comments
and this is where my engineering,
you know, personality is speaking.
We’re making two very important assumptions
in this part of the conversation.
One is that AI is so omnipotent
that it’s achieved to a state
that it’s beyond predicting anything physical,
its guarding to the consciousness level
and getting to the, even the ultimate,
the love level of capability
and I do want to make sure that we recognize
that we’re very, very, very far from that.
This technology is still very nascent.
Part of the concern I have about today’s AI
is that super-hyping of its capability so,
I’m not saying that, that’s not a valid question
but I think that part of this conversation
is built upon that assumption that this technology
has become that powerful and there’s,
I don’t even know how many decades we are from that.
Second related assumption, I feel we are,
our conversation is being based on this
that we’re talking about the world or state of the world
that owning that powerful AI exists
or that small group of people
who have produced the powerful AI
and is intended to hack human, are existing.
But in fact our human society is so complex
there’s so many of us, right?
I mean, humanity in its history,
have faced so many technology,
if we left it in the hands of a bad player,
alone without any regulation, multinational collaboration,
rules, laws, moral codes, that technology could have,
maybe not hack human but destroy human
or hurt human in massive ways.
It has happened but by and large,
our society in a historical view
is moving to a more civilized and controlled state.
So, I think it’s important to look at that greater society
and bringing other players and people into this dialogue
so we don’t talk like there is only this omnipotent AI,
you know, deciding it’s gonna hack everything to the end.
And that brings to your topic that in addition
of hacking human at that level that you’re talking about,
there are some very immediate concerns already.
Diversity, privacy, labor, legal changes,
you know, international geopolitics
and I think it’s critical to tackle those now.
I love talking to AI researchers
because five years ago, all the AI researchers were like,
It’s much more powerful than you think and now
they’re all like, It’s not as powerful as you think.
[audience and panel laughter]
Let me ask, It’s because five years ago
you have no idea what AI is,
I’m not saying it’s wrong Now, you’re extrapolating
I didn’t say it was wrong, I just said it was a thing.
I want to go into what you just said
but before you do that I want to take one question here
from the audience because once we move
into the second section, we won’t be able to answer it.
So, the question is, it’s for you Yuval,
this from Mara and Lucini, How can we avoid
the formation of AI power digital dictatorships?
So, how do we avoid dystopia number two?
Let’s answer that and then let’s go Fei-Fei,
into what we can do right now,
not what we can do in the future.
The key issue is how to regulate the ownership of data
because we won’t stop research in biology
and we won’t stop research in computer science and AI.
So, for the three components of biological knowledge,
computing power, and data, I think data is the easiest
and it’s also very difficult but still the easiest,
kind of, to regulate or to protect.
Place some protections there and there are efforts
now being made and they are not just political efforts but,
also philosophical efforts to really conceptualize,
what does it mean to own data
or to regulate the ownership of data
because we have a fairly good understanding
what it means to own land,
we had thousands of years of experience with that,
we have a very poor understanding
of what it actually means to own data
and how to regulate it.
But this the very important front
that we need to focus on in order to prevent
the worst dystopian outcomes
and I agree that AI is not nearly as powerful
as some people imagined but this why,
and again, I think we need to place the bar low
for to reach a critical threshold,
we don’t need the AI to know us perfectly,
which will never happen, we just need the AI
to know us better than we know ourselves.
Which is not so difficult because most people
don’t know themselves very well
and often make [laughter and audience applause]
huge mistakes in critical decisions.
So, whether it’s finance, or career, or love life,
to have this shift in authority
from humans to algorithm, they can still be terrible
but as long as they are a bit less terrible
than us, the authority will shift to them.
Yuv, in your book you tell a very illuminating story
about your own self and your own coming in terms
with you with who you are and how you could be manipulated.
Will you tell that story here,
about coming to terms with your sexuality
and the story you told about Coca-Cola
and your book because I think that will make it clear
what you mean here, very well.
Yes so, I said that I only realized
that I was gay when I was 21.
And I look back at the time when I was,
I don’t know, 15, 17 and it should’ve been so obvious.
And it’s not like a stranger like,
I’m with myself 24 hours a day [laughter]
and I just don’t notice any, of like,
the screaming signs that saying,
There, you were gay and I don’t know how
but the fact is, I missed it.
Now, an AI, even a very stupid AI,
today, will not miss it.
[audience and panel laughing] I’m not so sure.
So imagine, this not like, you know like,
a science fiction scenario of a century from now,
this can happen today, that you can write
all kinds of algorithms that, you know,
they are not perfect but they are still better,
say than the average teenager
and what does it mean to live in a world
in which you learn about something so important
about yourself, from an algorithm.
What happens if the algorithm doesn’t
share the information with you
but it shares the information
with advertisers or with governments?
So, if you want to, and I think we should,
go down from the cloudy heights of,
you know, the extreme scenarios
to the practicalities of day-to-day life,
this a good example because this is already happening.
Yeah, all right well let’s take the elevator
down to the more conceptual level
of this particular shopping mall
that we’re shopping in today
and Fei-Fei, let’s talk about what we can do today
as we think about the risks of AI, the benefits of AI,
and tell us you know, sort of your punch list,
of what you think the most important things
we should be thinking about with AI are.
Wow, boy there are so many things we could do today
and I cannot agree more with Yuval,
that this is such an important topic.
Again I’m gonna try to speak about all the efforts
that’s being made at Stanford
because I think this a good representation
of what we believe, there are so many efforts we can do.
So, in human-centered AI in which,
this the overall theme we believe,
that the next chapter of AI should be, is human-centered.
We believe in three major principles.
One principle is to invest in the next generation
of AI technology that reflects more
of the kind of human intelligence we would like.
I was just thinking about your comment
about AI’s dependence on data and how that the policy
and governance of data should emerge
in order to regulate and govern the AI impact.
Well, we should be developing technology
that can explain AI, in technical field
we call it explainable AI or AI interpretability studies.
We should be focusing on technology that have
the more nuanced understanding of human intelligence.
We should be investing in the development
of less data dependent AI technology
that would take into considerations of intuition, knowledge,
creativity, and other forms of human intelligence.
So, that kind of human intelligence inspired AI
is one of our principles.
The second principle is to, again welcome in
the kind of multidisciplinary study
of AI cross-pollinating with economics,
with ethics, with law, with philosophy,
with history, cognitive science, and so on
because there is so much more we need to understand
in terms of AI’s social, human,
anthropological, ethical impact
and we cannot possibly do this alone as technologists.
Some of us shouldn’t even be doing this,
it’s the ethicist, philosophers should participate
and work with us on these issues.
So, that’s the second principle and the third principle…
Oh, and within this we work with policymakers,
we convene the kind of dialogues
of multilateral stakeholders.
Then the third, the last but not the least,
I think Nick, you said that at the very beginning
of this conversation that we need to promote
that the human enhancing and collaborative
and augmentative aspect of this technology.
You have a point, even there it can become manipulative
but we need to start with that sense of alertness,
understanding, but still promote
that kind of benevolent applications
and design of this technology.
At least these are the three principles
the Stanford’s Human-Centered AI Institute is based on
and I just feel very proud, within a short few months
of the birth of this Institute,
there are more than 200 faculty involved on this campus
in this kind of research dialog, you know,
study education and that number is still growing.
Of those three principles let’s start digging into them.
So, let’s go to number one, explainability,
’cause this a really interesting debate
in artificial intelligence so,
there are some practitioners who say
you should have algorithms that can explain
what they did and the choices they made.
It sounds eminently sensible but how do you do that?
I make all kinds of decisions that I can’t entirely explain
like, why did I hire this person off that person?
I can tell a story about why I did it
but I don’t know for sure.
Like, we don’t know ourselves well enough
to always be able to truthfully
and fully explain what we did.
How can we expect a computer using AI, to do that?
And, if we demand that here in the West
then there are other parts of the world
that don’t demand that, who may be able to move faster.
So, why don’t we start, why don’t I ask you
the first part of that question,
Yuval the second part of that question.
So, the first part is, can we actually get explainability
if it’s super hard even within ourselves?
Well, it’s pretty hard for me to multiply two digits
but you know, computers can do that.
So, the fact that something is hard for humans
doesn’t mean we shouldn’t try to get the machines to do it,
especially, after all, these algorithms
are based on very simple mathematical logic.
Granted, we’re dealing with newer networks these days
of millions of nodes and billions of connections so,
explainability is actually tough, it’s an ongoing research.
But I think this is such a fertile ground
and it’s so critical when it comes to health care decisions,
financial decisions, legal decisions,
there’s so many scenarios where this technology
can be potentially, positively useful
but with that kind of explainable capabilities so,
we’ve gotta try and I’m pretty confident
with a lot of smart minds out there,
this a crackable thing
and on top of that– Got 200 professors on it.
Right, not all of them doing AI algorithms.
On top of that, I think you have a point that
if we have technology that can explain
the decision making process of algorithms,
it makes it harder for it to manipulate and cheat, right?
It’s a technical solution, not the entirety of the solution,
that will contribute to the clarification
of what this technology is doing.
But because the, presumably the AI,
makes decision in a radically different way than humans
then even if the AI explains its logic
the fear is it will make absolutely no sense to most humans.
Most humans, when they are asked to explain a decision
they tell a story in a narrative form,
which may or may not reflect
what is actually happening within them,
in many cases it doesn’t reflect.
It’s just a made-up rationalization and not the real thing.
Now, in AI it could be much better than a human
in telling me like, I applied to the bank for a loan
and the bank says no and I ask why not
and the bank says, Okay, we’ll ask our AI
and the AI gives this extremely long,
statistical analysis based,
not on one or two salient feature of my life
but on 2,517 different data points
which it took into account and gave different weights
and why did you give this, this weight
and why did you give oh, there is another book about that
and most of the data points would seem,
to a human, completely irrelevant.
You applied for a loan on Monday
and not on Wednesday and the AI discovered that
for whatever reason, it’s after the weekend, whatever,
people who apply for loans on a Monday
are 0.075 percent less likely to repay the loan.
So, it goes into the equation
and I get this book of the real explanation,
finally I get a real explanation.
It’s not like sitting with a human banker
that just bullshit’s me [audience laughing]
What do I do with a book? Are you rooting for AI?
Are you saying AI’s good in this case?
In many cases, yes I mean, I think in many cas…
I mean, it’s two sides of the coin.
I think that in many ways the AI in this scenario
will be an improvement over the human banker
because for example, you can really know
what the decision is based on presumably,
but it’s based on something that I,
as a human being, just cannot grasp.
I know how to deal with simple narrative stories.
I didn’t give you a loan because you’re gay,
that’s not good or because you didn’t repay
any of your previous loans.
Okay, I can understand that.
But my mind doesn’t know what to do
with the real explanation that the AI will give,
which is just this crazy statistical thing, which–
Okay so, there are two layers to your comment.
One, is how do you trust
and be able to comprehend AI’s explanation?
Second, is actually, can AI be used
to make humans more trustable
or be more trustable than the human’s?
On the first point, I agree with you.
If AI gives you two thousand dimensions
of potential features with probability,
it’s now human understandable
but the entire history of science in human civilization
is to be able to communicate the result of science
in better and better ways, right?
Like, I just had my annual physical
and the whole bunch of numbers came to my cell phone
and well, first of all, my doctors can,
the expert can help me to explain these numbers.
Now, even Wikipedia can help me
to explain some of these numbers.
But the technological improvements
of explaining these will improve.
It’s our failure as AI technologists
if we just throw two hundred or two thousand dimensions
of probability numbers at you.
But I mean, this the explanation and I think
that the point you raise
is very important but, I see differently.
I think science is getting worse and worse
in explaining its theories and findings to general public.
Which is the reason for things like,
doubting climate change and so forth
and it’s not really even the fault of the scientists
because the science is just getting more
and more complicated and reality is extremely complicated
and the human mind wasn’t adapted
to understanding the dynamics of climate change
or the real reasons for refusing to give somebody a lone.
That’s the point when you have…
Again, let’s put aside the whole question of manipulation
and how can I trust.
Let’s assume the AI is benign
and let’s assume that there are no hidden biases,
everything is okay but, still I can’t understand,
the decision of the AI. That’s why Nick,
people like Nick, the storyteller, says to expla…
What I’m saying, you’re right it’s very complex
but there are people like–
I’m gonna lose my job to computer like, next week
but I’m happy to have your confidence with me.
But that’s the job of the society collectively
to explain the complex science.
I’m not saying we’re doing a great job, at all but,
I’m saying there is hope if we try.
But my fear is that we just really can’t do it
because the human mind is not built
for dealing with these kinds of explanations
and technologies and it’s true for,
I mean, it’s true for the individual customer
who goes to the bank
and the bank refused to give them a loan
and it can even be on the level, I mean,
how many people today on earth
understand the financial system?
[silence followed by light laughter]
How many presidents and prime ministers
understand the financial system?
In this country at zero? [audience laughter and applause]
So, what does it mean to live in a society
where the people who are supposed
to be running the business, and again,
it’s not the fault of a particular politician
it’s just the financial system has become so complicated
and I don’t think that economies
are trying on purpose to hide something for general public,
it’s just extremely complicated.
You had the some of the wisest people in the world
go into the finance industry
and creating these enormously complex models
and tools, which objectively, you just can’t explain it
to most people unless first of all,
they study economics and mathematics
for 10 years or whatever so, I think this a real crisis.
And this again, this part of
the philosophical crisis we started with
and the undermining of human agency.
That’s part of what’s happening,
that we have these extremely intelligent tools
that are able to make, perhaps better decisions
about our health care, about our financial system,
but we can’t understand what they are doing
and why they are doing it and this undermines our autonomy
and our authority and we don’t know
as a society, how to deal with that.
Well, ideally, Fei-Fei’s Institute will help that.
Before we leave this topic though,
I want to move to a very closely related question,
which I think is one of the most interesting,
which is the question of bias in algorithms,
which is something you’ve spoken eloquently about
and let’s stay with the financial systems.
So, you can imagine a loan used by a bank
to determine whether somebody should get a loan
and you can imagine training it on historical data
and historical data is racist and we don’t want that,
so let’s figure out how to make sure the data isn’t racist
and that it gives loans to people regardless of race.
And we probably all, everybody in this room agrees that,
that is a good outcome but let’s say that
analyzing the historical data suggests
that women are more likely to repay their loans than men.
Do we strip that out or do we allow that to stay in?
If you allow it to stay in,
you get a slightly more efficient financial system.
If you strip it out,
you have a little more equality between men and women.
How do you make decisions about
what biases you want to strip
and which ones are okay to keep?
That’s a excellent question Nick, I mean,
I’m not gonna have the answers personally
but I think you touched on the really important question.
It’s, first of all, a machine learning system bias
is a real thing you know, like you said.
It starts with data, it probably starts
with the very moment we’re collecting data
and the type of data were collecting
all the way through the whole pipeline
and then all the way to the application
but biases come in very complex ways.
At Stanford, we have machine learning scientists
studying the technical solutions of bias like,
you know de-biasing data
and normalizing certain decision-making
but we also have humanists debating about what is biased,
what is fairness, when is bias good,
when it’s bias bad so, I think you
just opened up a perfect topic for research
and debate and conversation in this topic
and I also want to point out that Yuval,
you already used a very closely related example,
machine learning algorithm has a potential
to actually expose bias, right?
Like, one of my favorite study was a paper
a couple of years ago analyzing Hollywood movies
and using machine learning face recognition algorithm,
which is a very controversial technology these days,
to recognize Hollywood systematically gives more screen time
to male actors than female actors.
No human being can sit there
and count all the frames of faces
and gender bias and this a perfect example
of using machine learning to expose bias.
So, in general there’s a rich set of issues
we should study and again, bring the humanists,
bring the ethicists, bring the legal scholars,
bring the gender study experts.
Agree though, standing up for humans,
I knew Hollywood was sexist
even before that paper but yes, agreed.
You are a smart human. [light laughter]
Yuval, on that question of the loans,
do you strip out the racist data,
do you strip out the gender data,
what biases do you get rid of,
what biases do you not?
I don’t think there is a one-size-fits-all.
I mean, it’s a question…
we need this day-to-day collaboration
between engineers, and ethicists,
and psychologists, and political scientists–
But not biologists, right?
[laughter] But not biologists? and increasing– [laughter]
And increasingly, also biologists.
It goes back to the question, what should we do?
So, we should teach ethics
to coders as part of their curriculum.
The people today in the world,
that most need a background in ethics
is the people in the computer science departments,
so it should be an integral part of the curriculum
and it’s also in the big corporations,
which are designing these tools,
they should be embedded within the teams,
people with background in things like ethics,
like politics, that they always think
in terms of what biases might we inadvertently
be building into our system.
What could be the cultural or political implications
of what we are building?
It shouldn’t be a kind of afterthought
that you create this neat technical gadget,
it goes into the world, something bad happens,
and then you start thinking,
Oh, we didn’t see this one coming. What do we do now?
From the very beginning, it should be clear
that this is part of the process.
Yep, I do want to give a shout out to Rob Reich
who just introduced this whole event
He and my colleagues, Mehran Sahami
and a few other Stanford professors have opened this course
called Ethics Computation and sorry Rob,
I’m abusing the title of your course
but this exactly the kind of classes it’s…
I think this quarter, the offering
has more than 300 students signed up to that.
I wish the course the existed when I was a student here.
Let me ask an excellent question
from the audience, it ties into this.
This is From Yu Jin Lee;
how do you reconcile the inherent trade-offs
between explainability and efficacy
and accuracy of algorithms?
This question seems to be assuming if you can explain it,
you’re less good or less accurate.
Well, you can imagine that if you require explainability
you lose some level of efficiency,
you’re adding a little bit of complexity to the algorithm.
So okay, first of all,
I don’t necessarily believe in that,
there’s no mathematical logic to this assumption.
Second let’s assume there is a possibility
that an explainable algorithm suffers efficiency.
I think this a societal decision we have to make.
You know, when we put the seatbelt in our car,
driving that’s a little bit of an efficiency loss
’cause I have to do that seatbelt movement
instead of just hopping and drive
but as a society we decided
we can afford that loss of efficiency
because we care more about human safety.
So, I think AI is the same kind of technology
as we make these kind of decisions going forward
in our solutions, in our products,
we have to balance human wellbeing
and societal well-being with efficiency.
So Yuval, let me ask you,
the global consequences of this is something
that a number of people have asked about
in different ways and we’ve touched on
but we haven’t hit head-on.
There are two countries, imaginative country A,
and you have country B.
Country A says all of you AI engineers,
you have to make it explainable,
you have to take ethics classes,
you have to really think about
the consequences of what you’re doing,
you got to have dinner with biologists,
you have to think about love,
and you have to like, read you know, John Locke.
Group B country says just go build some stuff, right?
These two countries, at some point,
are gonna come in conflict and I’m gonna guess
that country B’s technology might be ahead of country A’s.
Yeah, that’s always the concern with arms races,
which become a race to the bottom
in the name of efficiency and domination
and we are in, I mean…
What is extremely problematic or dangerous
about the situation now is, with AI,
is that more and more countries are waking up
to the realization that this could be
the technology of domination in the 21st century.
So, you’re not talking about just any economic competition
between the different textile industries
or even between different oil industries,
like one country decides, we don’t care
about environment at all, we’ll just go full gas ahead
and the other countries is much more environmentally aware.
The situation with AI is potentially much worse
because it could be really, the technology of domination
in the 21st century and those left behind
could be dominated, exploited,
conquered by those who forge ahead.
So, nobody wants to stay behind
and I think the only way to prevent
this kind of catastrophic arms race to the bottom
is greater global cooperation around AI.
Now this sounds utopian because we are now moving
in exactly the opposite direction,
of more and more rivalry and competition
but this is part of, I think, of our job
like with the nuclear arms race,
to make people in different countries realize that
this is an arms race, that whoever wins, humanity loses.
And it’s the same with AI, if AI becomes an arms race
then this is extremely bad news for all the humans
and it’s easy for say, people in the US,
to say we are the good guys in this race,
you should be cheering for us
but this becoming more and more difficult
in a situation when the motto of the day is, America first.
I mean, how can we trust the USA
to be the leader in AI technology
if ultimately it will serve only American interests
in American economic and political domination.
So it’s really, I think most people
when they think arms race in AI,
they think USA versus China
but there are almost 200 other countries in the world
and most of them are far, far behind
and when they look at what is happening
they are increasingly terrified and for a very good reason.
The historical example you’ve made is a little unsettling.
If I heard your answer correctly,
it’s that we need global cooperation
and if we don’t we’re gonna lead to an arms race.
In the actual nuclear arms race
we tried for global cooperation from,
I don’t know, roughly 1945 to 1950
and then we gave up and then we said
we’re going full-throttle the United States
and then why did the Cold War end the way it did?
Who knows, but one argument would be that the United States,
you know, build up and it’s relentless build up
of nuclear weapons helped to keep the peace
until the Soviet Union collapsed.
So, if that is the parallel, then what might happen here
is we’ll try for global cooperation in 2019,
2020, 2021, and then we’ll be off in an arms race.
A, is that likely and, B if it is,
would you say, well then the US,
it needs to really move full-throttle in AI
because it would better for the liberal democracies
to have artificial intelligence than totalitarian states.
Well, I’m afraid it is very likely
that cooperation will break down
and we will find ourselves in an extreme version
of an arms race and in a way,
it’s worse than the nuclear arms race
because with nukes, at least until today,
countries develop them but never use them.
AI will be used all the time.
It’s not something you have on the shelf
for some doomsday war.
It will be used all the time to create
potentially, total surveillance regimes
in extreme totalitarian systems,
in one way or the other.
From this perspective, I think the danger is far greater.
You could say that the nuclear arms race
actually saved democracy, and the free market,
and you know, rock and roll,
and Woodstock, and then the hippies.
They all owe a huge debt to nuclear weapons [smirking]
because if nuclear weapons weren’t invented,
there would have been a conventional arms race
and conventional military buildup
between the Soviet bloc and the American bloc
and that would have meant total mobilization of society.
If the Soviets are having total mobilization
the only way the Americans can compete is to do the same.
Now, what actually happened
was that you had an extreme totalitarian mobilized Society
in the communist bloc but thanks to nuclear weapons
you didn’t have to do it in the United States,
or in western Germany, or in France
because you relied on nukes.
You don’t need millions of conscripts in the army
and with AI it going to be just the opposite
that the technology will not only be developed,
it will be used all the time
and that’s a very scary scenario.
Wait, can I just add one thing?
I don’t know history like you do
but you said AI is different from nuclear technology.
I do want to point out, it is very different
because the same time as you are talking
about these more scarier situation,
this technology has a wide
international scientific collaboration basis
that is being used to make transportation better,
is to improve healthcare, to improve education and,
so it’s a very interesting, new time
that we haven’t seen before because while we have this,
kind of, competition we also have
massive international scientific community collaboration
on these benevolent users
and democratization of this technology.
I just think it’s important to see both side of this.
You’re absolutely right, there also,
as I said, there are also enormous benefits
to this technology.
And in a global collaborative way,
especially among the scientists.
The global aspect is more complicated
because the question is, what happens
if there is a huge gap in abilities
between some countries and most of the world?
Would we have a re-run of the 19th century
Industrial Revolution, when the few industrial powers
conquer, and dominate, and exploit the entire world,
both economically and politically?
What’s to prevent that from repeating?
So, even in terms of, you know,
without this scary war scenario
we might still find ourselves
with a global exploitation regime
in which the benefits, most of the benefits,
go to a small number of countries
at the expense of everybody else.
Have you heard of archive.org?
Archive.org? [light laughs]
So, students in the audience might laugh at this
but we are in a very different scientific research climate
is that the kind of globalization of technology
and technique happens in a way
that the 19th century even 20th century never saw before.
Any paper that is a basic science research paper
in AI today, or technical technique that is produced,
let’s say, this week at Stanford,
it’s easily get globally distributed
through this thing called archive, or GitHub, or repository.
The information is out there, yeah.
Globalization of this scientific technology
travels in a very different way
from the 19th and 20th century.
I mean, I don’t doubt there are,
you know, confined development of this technology,
maybe by regimes but we do have to recognize
that this global, the differences is pretty sharp now
and we might need to take that into consideration
that the scenario you’re describing is harder.
I’m not say impossible, but harder to happen.
So, you think that the way–
Just say that it’s not just the scientific papers.
Yes, the scientific paper’s out there
but if I live in Yemen, or in Nicaragua,
or in the Indonesia, or in Gaza,
yes I can connect to the internet and download the paper.
What will I do with that?
I don’t have the data.
I don’t have the infrastructure.
I mean, you look at
where the big corporations are coming from
that hold all the data of the world,
they are basically coming from just two places.
I mean even Europe is not really in the competition.
There is no European Google,
or European Amazon, or European Baidu,
or European Tencent and if you look beyond Europe,
you think about Central America,
you think about most of Africa,
the Middle East, much of Southeast Asia,
it’s yes, the basic scientific knowledge is out there
but this just one of the components
that go to creating something that can compete
with Amazon or with Tencent or with the abilities
of governments like the US government
or like the Chinese government.
So, I agree that the dissemination of information
and basic scientific knowledge,
we’re at completely different place,
than in the 19th century.
Let me ask you about that
’cause it’s something three or four people
have asked in the questions which is,
it seems like there could be a centralizing force
of artificial intelligence, that it will make
whoever has the data and the best compute,
more powerful and that it could then accentuate
income inequality both within countries
and within the world, right?
You can imagine the countries you’ve just mentioned:
The United States, China, Europe lagging behind,
Canada somewhere behind, way ahead of Central America.
It could accentuate global income inequality.
A, do you think that’s likely
and B, how much does it worry you?
We have about four people who’ve asked a variation on that.
As I said, it’s very, very likely.
It’s already happening and it’s extremely dangerous
because the economic and political consequences
could be catastrophic.
We are talking about the potential collapse
of entire economies and countries.
Countries that depend say, on cheap manual labor
and they just don’t have the educational capital
to compete in a world of AI,
so what are these countries going to do?
I mean if, say you shift back
most production from say, Honduras or Bangladesh,
to the USA into Germany because,
the human salaries are no longer part of the equation
and it’s cheaper to produce the shirt in California
than in Honduras, so what will the people there do?
And you can say, okay but there will be many more jobs
for software engineers but we are not teaching
the kids in Honduras to be software engineers so,
maybe a few of them could somehow immigrate to the US
but most of them won’t and what will they do?
And we at present, we don’t have the economic answers
and the political answers to these questions.
Fei-Fei, you wanna jump in here?
I think that’s fair enough.
I think Yuval definitely has laid out
some of the critical pitfalls of this
and that’s why we need more people to be studying
and thinking about this.
One of the things we over and over noticed,
even in this process of building a community
of human-centered AI and also talking to people,
both internally and externally,
is that there are opportunities
for business around the world
and governments around the world
to I think about their data and AI strategy.
There are still many opportunities
for, you know, outside of the big players
in terms of companies and countries,
to really come to the realization
it’s an important moment for their country,
for their region, for their business,
to transform into this digital age
and I think when you talk about these potential dangers
and lack of data in parts of the world
that hasn’t really caught up
with this digital transformation,
the moment is now and we hope to,
you know, raise that kind of awareness
and then encourage that kind of transformation.
Yeah, I think it’s very urgent.
I mean, what we are seeing at the moment
is on the one hand, what you could call
some kind of data colonization,
that the same model that we saw in the 19th century
that you have the Imperial hub
where they have the advanced technology,
they grow the cotton in India or Egypt,
they send the raw materials to Britain,
they produce the shirts,
the high-tech industry of the 19th century in Manchester,
and they send the shirts back, to sell them in in India
and out-compete the local producers.
And we in a way, might beginning to see the same thing now,
with the data economy, that they harvest the data
in places also like Brazil and Indonesia
but they don’t process the data there.
The data from Brazil and Indonesia
goes to California or goes to Eastern China,
being processed there, later produced
the wonderful new gadgets and technologies,
and sell them back as finished products
to the provinces or to the colonies.
Now, it’s not a one-to-one,
it’s not the same, there are differences
but I think we need to keep this analogy in mind
and another thing that maybe we need to keep in mind
in this respect, I think is re-emergence of stone walls
that I’m kind of, you know…
Originally my specialty was medieval military history.
This how I began my academic career
with the Crusades and castles and knights
and so forth and now I’m doing all these cyborgs
and AI stuff but suddenly there is something
that I know from back then, the walls are coming back.
And I try to kind of, what’s happening here?
I mean, we have virtual realities, we have 3G, AI,
and suddenly the hottest political issue
is building a stone wall.
Like, the most low-tech thing you can imagine [applause]
and what is the significance of a stone wall
in a world of interconnectivity and all that?
And it really frightens me that
there is something very sinister there,
the combination of data is flowing around everywhere
so easily but more and more countries,
and also my home country of Israel, it’s the same thing.
You have the, you know, the startup nation
and then the wall and what does it mean, this combination?
Fei-Fei, you wanna answer that?
[audience and panel laughing]
Maybe you can look at the next question.
You know what, let’s go to the next question
which is tied to that and the next question is,
you have the people there at Stanford
who will help be building these companies,
who will either be furthering the process
of data colonization or reversing it,
or who will be building you know,
the efforts to create a virtual wall.
A world based on artificial intelligence
are being created, or funded at least,
by a Stanford Graduate so,
you have all these students here, in the room,
how do you want them to be thinking
about artificial intelligence
and what do you want them to learn?
Let’s spend the last 10 minutes of this conversation
talking about what everybody here should be doing.
So, if you’re a computer science or engineering student,
If you’re humanists, take my class.
And all of you read Yuval’s books.
Are his books on your syllabus?
Not on mine, sorry.
I teach hard-core, deep learning.
His book doesn’t have equations.
I don’t know B plus C plus D equalls H.
But seriously, you know what I meant to say
is that Stanford students, you have a great opportunity
We have a proud history of bringing this technology to life.
Stanford was at the forefront of the birth of AI,
in fact our very Professor John McCarthy
coined the term artificial intelligence
and came to Stanford in 1963 and started this nation’s,
one of the two oldest AI labs in this country
and since then, Stanford’s AI research
has been at the forefront of every wave of AI changes
and this 2019, we’re also at the forefront
of starting the human-centered AI revolution
or writing of the new AI chapter
and we did all this for the past 60 years, for you guys.
For the people who come through the door
and who will graduate and become practitioners,
leaders, and part of the civil society,
and that’s really what the bottom line is about.
Human-centered AI needs to be written
by the next generation of technologists
who have taken classes like Rob’s class,
to think about the ethical implications,
the human well being and it’s also gonna be written
by those potential future policymakers
who came out of Stanford’s humanity studies
and Business School, who are versed
in the details of the technology,
who understand the implications of this technology,
and who has the capability to communicate
with the technologies.
No matter how we agree and disagree,
that’s the bottom line, is that we need
this kind of multilingual leaders
and thinkers and practitioners and that is
what Stanford’s Human-Center AI Institute is about.
Yuval, how do you wanna answer that question?
Well, on the individual level,
I think it’s important for every individual,
whether in Stanford, whether an engineer or not,
to get to know yourself better
because you are now in a competition.
You know, it’s the all the old advice in the book,
in philosophy, is know yourself.
We’ve heard it from Socrates,
from Confucius, from Buddha, get to know yourself.
But there is a difference,
which is that now, you have competition.
In the day of Socrates or Buddha,
if you didn’t make the effort, so okay,
so you missed on enlightenment but
still the king wasn’t competing with you.
They didn’t have the technology.
Now you have competition, you’re competing
against these giant corporations and governments.
If they get to know you better than you know yourself,
So you need to buy yourself some time
and the first way to buy yourself some time
is to get to know yourself better
and then they have more ground to cover.
For engineers and students I would say,
I’ll focus on engineers maybe,
the two things that I would like
to see coming out from the laboratories
and the engineering departments is first,
tools that inherently work better
in a decentralized system, then in a centralized system.
I don’t know how to do it but if you…
I hope this something that engineers can work with.
I heard this blockchain is like the big promise,
in that area, I don’t know.
But whatever it is, part of when you start designing a tool,
part of the specification of what this tool should be like,
I would say, this tool should work better
in a decentralized system than in a centralized system.
That’s the best defense of democracy.
the second thing that I would like to see coming out–
I don’t want to cut you off
’cause I want you to get to this second thing,
how do you make a tool work better in a democracy than–
I’m not an engineer, I don’t know. [laughter]
All right, well then go to part two.
Take that, someone in this room, figure that out
’cause it’s very important, whatever it means.
I can think about it and then…
I can give you a historical examples
of tools that work better in this way
or in that way but I don’t know how to translate it
into present-day technological terms.
Go to part two ’cause I got a few more questions
to ask from the audience.
Okay so, the other thing that I would like to see coming
is an AI sidekick that serves me
and not some corporation or government.
We can’t stop the progress of this kind of technology
but I would like to see it serving me.
So yes, it can hack me but it hacks me
in order to protect me.
Like, my computer has an anti-virus
but my brain hasn’t, it has a biological antivirus
against the flu or whatever
but not against hackers and fraud and so forth.
So, one project to work on is to create an AI sidekick
which I paid for, maybe a lot of money,
and it belongs to me, and it follows me,
and it monitors me, and what I do,
and my interactions, but everything it learns,
it learns in order to protect me from manipulation
by other AI’s, by other outside influencers.
This something that I think,
with the present day technology,
I would like to see more effort in that direction.
Not to get into too technical terms,
I think you would feel comforted to know that
the budding efforts in this kind of research is happening,
you know, trustworthy AI, explainable AI,
and security motivated,
so I’m not saying we have the solution
but a lot of technologists around the world
are thinking along that line
and trying to make that happen.
It’s not that I want an AI that belongs to Google
or to the government, that I can trust,
I want an AI that I’m its master, it’s serving me,
And it’s powerful, it’s more powerful than my AI
because otherwise my AI could manipulate your AI.
[audience and panel laughter]
It will have the inherent advantage
of knowing me very well, so it might not be able to hack you
but because it follows me around
and it has access to everything I do and so forth,
it gives it an edge in the specific realm of just me.
So, this a kind of counterbalance
to the danger that the people–
But even that would have a lot of challenges
Who is accountable, are you accountable
for your action or your sidekick?
Oh, good question. This is going to be
a more and more difficult question
that we will have to deal with.
The sidekick defense. [light laughter]
All right, Fei-Fei,
let’s go through a couple questions quickly.
We often talk of, this is from Regan Pollock,
we often talk about top-down AI from the big companies,
how should we design personal AI
to help accelerate our lives and careers?
The way I interpret that question is
so much of AI is being done at the big companies.
If you want to have AI at a small company
or personally, can you do that?
So, well first of all, one solution
is what Yuval just said [laughing]
But probably, those things will be built by Facebook.
So, first of all, it’s true
there’s a lot of investment and efforts putting
and resource putting big companies in AI research
and development but it’s not that
all the AI is happening there.
I want to say that academia continue to play a huge role
in AI’s research and development,
especially in the long term exploration of AI
and what is academia?
Academia is a worldwide network
of individual students and professors
thinking very independently and creatively
about different ideas.
So, from that point of view,
it’s a very grassroot kind of effort in AI research
that continues to happen and small businesses
and independent research institutes,
also have a role to play, right?
There are a lot of publicly available data sets,
it’s a global community that is very open about sharing
and disseminating knowledge and technology,
so yes, please, by all means,
we want global participation in this.
All right here’s my favorite question.
This is from anonymous, unfortunately.
If I am in eighth grade, do I still need to study?
[loud laughter and applause]
As a mom, I will tell you yes.
Go back to your homework.
All right Fei-Fei, what do you want
Yuval’s next book to be about?
Wow, I didn’t know this, I need to think about that.
All right well, while you think about that,
Yuval, what area of machine learning
do you want Fei-Fei to pursue next?
The sidekick project. [laughing]
Yeah, I mean, just what I said, an AI,
can we create a kind of AI which can serve individual people
and not some kind of big network?
I mean, is that even possible
or is there something about the nature of AI
which inevitably will always lead back
to some kind of network defect
and winner-takes-all and so forth?
All right, we’re gonna wrap with Fei-Fei,
Okay, his next book is gonna be a science fiction book
between you and your sidekick. [all laughing]
All right, one last question for Yuval
’cause we’ve got two of the top voted questions are this,
without the belief in free will,
what gets you up in the morning?
Without the belief in free will…
I don’t think that the question of, I mean, is very
interesting, or very central.
It has been central in Western civilization
because of some kind of basically,
theological mistake made thousands of years ago [laughing]
but really it’s a misunderstanding of the human condition.
The real question is,
how do you liberate yourself from suffering?
And one of the most important steps in that direction
is to get to know yourself better
and for that, you need to just push aside
this whole, I mean, for me the biggest problem
with the belief in free will is that
it makes people incurious about themselves
and about what is really happening inside themselves
because they basically say, I know everything
I know why I make decisions, this my free will.
And they identify with whatever thought
or emotion pops up in their mind
because ey, this my free will
and this makes them very incurious
about what is really happening inside
and what is also the deep sources
of the misery in their lives.
And so, this what makes me wake up in the morning
to try and understand myself better,
to try and understand the human condition better,
and free will is, it’s just irrelevant for that.
And if we lose it, your sidekick can get you up
in the morning. [light laughter]
Fei-Fei, 75 minutes ago
you said we weren’t gonna reach any conclusions.
Do you think we got somewhere?
Well, we opened a dialogue between the humanist
and the technologists and I want to see more of that.
Great, all right, thank you so much.
Thank you Fei-Fei, thank you Yuval Noah Harari.
It was wonderful to be here, thank you to the audience.