Will AI Enhance or Hack Humanity? - Fei-Fei Li & Yuval Noah Harari in Conversation with Nicholas Thompson

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.

[audience clapping]

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

at Google Cloud.

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.

[audience applause]

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.

[audience laughs]

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.

Yuval.

[Yuval] Yeah.

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.

[all laughing]

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 is good?

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?

What do you go by?

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.

[laughing loudly]

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.

[all laughing]

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, absolutely.

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.

[crowd chatter]

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.

Is love hackable?

Ask Tinder, I don't know.

[crowd and panel laughing]

Dating--

It depends--

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,

then I think yes.

I mean, why not?

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]

Why we do this?

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.

Yeah.

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

and in mind.

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?

[audience applause]

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.

Fei-Fei.

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]

All right so,

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

too much. [laughs]

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 does it mean?

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.

Wow.

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.

Yeah.

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.

Fantastic.

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?

Great question.

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.

That's group A.

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.

Is that a concern?

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.

[Nick] So--

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.

[loud laughing]

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,

take Rob's class.

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,

the game is over.

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]

Okay.

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

in their society.

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.