All language subtitles for Vivianne program-001

Thank you.

Oh my goodness!

What are those?

that will be the end
of my terrible Spanish.

for the evening,
or possibly we might insert

a little in as we go.

I have been looking at some of the posters

for the festival up around campus,

and also looking at you here and thinking

I might do a little switcheroo on my talk.

it is listed as a talk about

using artificial intelligence
to improve collective intelligence.

How can we all be smarter together
and I'll talk about that.

but I actually thought

just the tagline

of the conference, Artificial Intelligence

and other intelligence

and trying to understand

the difference between how we think

and how current

bleeding edge

of artificial intelligence
thinks would actually be really useful.

Having said all of that,
anyone who's here because they want a deep

technical dive into machine
learning and artificial intelligence,

please stay around
and come talk to me afterwards.

I would be thrilled to answer
any questions about anything I talk about,

or if you happen to have the misfortune
of knowing my research, or me

for that matter. Anything else.

But instead, I wanted

to start
with something different for a minute.

It won't even feel like we're going
to talk about artificial intelligence,

because I think
the most important question

I can ask you

that's relevant

to how I might affect all of our lives

is cut out.

How are you doing?

Are you happy?

And maybe even more importantly,

if you weren't.

What would you do about it?

And the reason
why this is such an important question.

Such a fascinating question,
is because research

about intelligence and happiness

has this rather astounding finding.

Intelligence doesn't predict happiness.

We could look at all of us in this room

here at a grand university.

I'm sure some of us are quite clever.

If we looked across
all of the different people

and we

went and measured all the different ways

people measure intelligence
in the academic world.

IQ, we don't use so much anymore.

But there's a modern version of it
called G

or working memory span.

How many things can you keep in mind
at any given moment?

How many different things
can you attend to?

So all these different ways to measure

our raw cognitive power.

And none of them

correlate in any way with happiness.

What a strange finding.

There are many things to do.

People
that are more resilient in their lives

tend to be happier.

People that have better communication
skills tend to be happier.

People that have a strong sense of purpose
in life.

Meaningfulness are substantially happier
than those that don't.

Why not? Intelligence?

Every one of those things
I just mentioned purpose,

resilience, communication skills.

They're predictive of a wide range of life
outcomes.

How much money you earn in your lifetime,
how far you'll go in your education.

But unlike intelligence,

which is a strong predictor, both of those

they also predict happiness.

What's going on there?

Well, I never be happy
because it's only intelligence.

Or maybe we're measuring intelligence.

Wrong.

Maybe our own understanding of our own
intelligence

has been profoundly misinformed,

and it's affecting our understanding
of this new form of intelligence

that mad scientists like

me are bringing into the world.

So let's
think about how we measure intelligence,

how people are publishing papers
doing this

with new artificial intelligence
like large language

models GPT, ChatGPT, Claude Gemini,

all these others,
but also with our own children.

What do we do?

We give them a test.

And a test has right and wrong answers.

It could be a fairly simple test.

It could be a test specifically designed
to be invariant to language

skills or cultural bias,
like Raven's Progressive Matrices.

But still, these are tests

that have explicitly right
and wrong answers.

And the more right answers
you could give, the smarter you are.

Interestingly, we might

call this kind of a test
this kind of a problem.

It's a well-posed problem.

It's a well-posed question is a question

that has an explicitly right answer.

And so if we're measuring people
and how well they can answer

questions that have right answers,
then we're measuring how

well they can answer well posed questions,
well posed

problems.

In 25 years of my career,

I've never worked on a well-posed problem.

Overwhelmingly,

the problems that are of interest
to society, to America,

to Mexico, to us in our individual
lives are ill posed problems.

Problems that don't have defined

right answers.

If you weren't happy,

what would you do to change that?

That doesn't have a right answer.

It's fact.

It's been well-studied,
and it would be different

for almost everyone in this room.

You would need to know yourself.

You'd need to know your context.

You have to change everything
you plan to do in response

to a very complicated world.

Why does all of this matter?

Because of artificial intelligence

and the way that we have built it to date.

It's fundamentally designed to solve

well-posed questions

by its very nature.

Let's do a little walkthrough.

I think you've gone through
two weeks of festival, but just in case

anyone wants a quick tutorial
on artificial intelligence,

as we talked about earlier today,

it's any autonomous system

that can answer questions
under uncertainty.

So uncertainty doesn't necessarily

mean that a problem is ill posed.

It could mean there's

this little blob on an x ray cancer

or just an artifact of the machine.

It could mean,

in this contract,

how many flaws are there
that an opposing lawyer could exploit?

These, on some level,
are well posed questions

in that
they have explicit answers to them.

You might need an advanced law degree
to read that contract

and see where the flaws are.

on the other hand,

you may not be able to describe
as most doctors can.

What about the blob?

Makes it look like cancer to you

versus this other blob?

but we can take lots of images
of cancerous,

x rays and lots of images of x rays
that don't have cancer in them.

And we can label one as cancer
and the other as not,

and give them to an algorithm

and have it learn and learn
and learn to distinguish between them.

And in that sense, still fundamentally,
this is a well posed question.

We have all the data we need.

We have all the right answers.

We even know the question
that we're asking

is this x ray indicative of cancer?

That's a serious skill to have.

But now we live in a

world where machines can do that skill,

increasingly competitive

to us and beyond competitive.

In a recent paper published, a group,

used large language models.

It didn't matter what one.

So these are massive AIS,

some of the biggest
that have ever existed.

Trillions
and trillions of individual equations

getting computed to produce a next word.

You give it some words
and it predicts the next word.

It's the world's largest autocomplete.

And while it may feel like I'm selling
GPT short, I'm not.

What's astonishing about
it is what that simple mechanism

can do just by producing the next word.

It, for example, can read a contract,

find all of the legal flaws

equitable
to a working professional lawyer,

and instead of taking two

hours, it takes 30s.

If you work

out the math on that
as published in this paper,

the at similar effectiveness

for identifying problems and contracts.

The AI is 99.7%

no 99.97% less expensive

than a human lawyer.

That's sounds scary.

Scary.

If you're in law school.

Scary if you're in med school.

And by the way, that wasn't just GPT.

That could do it.

Every one of the large language models,

including Lamda, the version that's free
and open source out in the world,

could also beat the human lawyers.

so is this the end of the game?

Well, this is why I wanted to talk today

about the differences
between human and machine intelligence.

it's not just about

whether you can answer
well posed questions.

It's about the messy
uncertainty of the real world.

It turns out that's what's

fascinating about us.

So let me take a little detour.

Now that I've framed
where I want to take this,

and say

I love working
with artificial intelligence.

I built my first model very long ago.

At least in the world of AI 25 years ago.

25 years ago, I was an undergrad.

I thought I was going to stick wires
into cat brains

and be a wet neuroscientist
and understand how the brain works.

And I took my first and only programing
course, and the professor in that course

recommended me to work at this place
called the Machine Perception Lab,

where we were building an AI
that would analyze people's

faces in real time
and tell whether or not they were lying.

If you were curious,

we've been given
a bunch of money by the CIA.

so sometimes that's
what happens in your life.

a bunch of spies give you money to make,

superintelligent machines that can tell
if people are lying.

But we were just doing basic
science research.

What could we actually do?

And it was amazing.

I could build a machine that could tell
if someone was smiling or not.

And I thought, what can I do with this?

How could you make a difference
in the world with something like this?

Even if the original money came
from an organization

that not everybody in the world loves,
and for very good reason.

Later in my career,
I had the chance to build take what

I learned building
those original models, and

I built a system for Google Glass.

It could find

someone, read their facial expression,

write the expression in the little heads
up display.

Now, there's all sorts of terrible things
you could do with this.

many bankers have asked me,

could we get this for all of our tellers?

And then whenever anyone comes
in, it would just float everybody's

credit score above their head,
so I'd know whether or not

they would qualify for a loan before
I've ever even talked

to them.

suffice to say,
I'm not interested in that.

We built this for autistic children.

It turned out that when an autistic kid
had the chance to talk face

to face and a natural social interaction,

but they were told
what the expression was,

something they don't get for free.

Autism is complex.

Some kids are better at these than others,
but at its heart, autistic children

often have

incredibly hard times
reading facial expressions.

So if you could build this thing

and while they're interacting
with the people around them,

they could read their facial expression

or get a signal to teach them how.

And that was the amazing thing.

Not only did these kids learn
how to read facial expressions

better than the standard,
which at the time and still is today,

cartoon faces on flashcards,
you just show them a face.

This is happy.

This is sad.

Now they were interacting
with an actual person

that was smiling or frowning.

And they learned even better.

And not only that, they learned

theory of mind, empathy

turned out it's really hard to learn why

other people do what they do
if they're all speaking a secret language.

You did not.

So I got to take artificial intelligence

and help these children understand

what makes other people happy and sad.

Again, fundamentally, a human experience.

Later, I took the work
I had done doing facial modeling,

and did something even bigger.

I built a game,

a really terrible game,

of which I would be so ashamed.

But you need to wait
for the end of the story.

This game was called Sexy Face

and it was online.

And if you went on to the Sexy Face
website,

our promise was pick out the faces
you think are sexy.

It would just pick random people
out of Facebook and Flickr

that dated a little bit.

For those of you
who are old enough to remember that,

it would just pick out these random faces
and put them in.

Then you pick the faces you think are sexy

and for free will find everyone
on Facebook that you think is sexy.

And for $5, we'll find everyone
that thinks you're sexy.

Now, if

I am a horrible person,
and that's still a reasonable thing

to be wondering about, it's
not because of that.

After
you played a couple of rounds, it then

confessed, actually, we're Trojan,

which means we're a secret game.

we're an AI,
and this is a mind reading game.

So not just sexy faces.

Any face come up with any face category

you can think of and, well, guess it.

And so you think, I don't know, Southeast
Asian

with, wispy mustache and a sense of ennui.

The amazing thing is

that our network

actually learned
how to distinguish these faces.

It could really find a face

based on someone's hidden idea

of what it is.

and it turns out

even this game
wasn't the end of the story.

The End of story is a book.

It's a book
with a million photographs in it.

It is a book that is likely filling up
with photographs right now as we speak.

It is the U.N.

Refugee Program's book of orphan refugees.

And when we did this project
now over ten years ago,

it had a million photographs in it of kids

in refugee camps, all around the world,
right there and then.

So if you were fearing the worst

and you hadn't heard
from your sister's family,

and you drove all the way outside
your country to a refugee camp nearby

and you go to them,

they would give you this book
and you'd start leafing through it,

hoping you don't find your niece,
hoping you do find your niece.

We, we took

the AI that was trained by people playing

our very stupid game,

and we used it to further train

an AI
on the faces of all of these children.

And we built a system
that could run on the little tablet,

and it would pull 18

random kids out of the book
and would show their faces,

and you'd pick
the ones that look more like your niece

and then it would pull up

a next set of faces
based on your past responses.

And in about 3 to 5 minutes,
if your niece is in the camp

anywhere in the world, you found it.

I love working with AI,

not because I love geeking out

about the mathematics
of artificial intelligence,

although there
are moments when that is fun,

not because I

have gnarly data problems
or I mean fabulous engineer, I am not.

I am a scientist.

I have never written a line of code
that didn't involve 32 curse words

for every single function word
I put into the code.

I love it because of what it can do.

What we can choose to do with it.

Yeah, we built in the
AI that the CIA wanted to use to tell

if people were lying.

But we also built systems to help

autistic children
learn how to read facial expressions

and to reunite orphaned refugees
with their extended family

through a program called Refugees United.

That's what got me excited
about working in this space.

And over the years, I founded companies
and I've published, research

in theoretical neuroscience
related to our brains

and artificial intelligence, but

still would always really drove me along.

Was the idea that you could change a life

and that that would be valuable?

One of my current companies has the first

ever biological test
for postpartum depression,

and it's predictive.

So before a mom ever even gives birth,

before she ever experiences
the first symptoms,

we can actually see

in your epigenetics the biological signal

for postpartum depression
and begin treatment before it affects you.

This is the most common complication
in pregnancy in the United States.

It is the single most common reason
women die in childbirth

is related to complications in postpartum

depression, including suicide.

And we can predict.

And it would be impossible to do this
without AI,

in this case, a deep neural network
that analyzes all the epigenetic data

and makes, the predictions
that we need to be able to make.

But we didn't stop there.

The large language models, ChatGPT
and others, we built a version of that.

They can also just talk with moms,
talk with them before

they're even pregnant,
just talk with women.

And in those conversations,
we have a completely separate

AI that's analyzing
their speech patterns and,

turns out, can also independently predict
depression and suicide risk.

Being able to work on projects like these

and bring them out to
the world is immensely powerful.

But you'll notice in none of these cases
that I build

something that just kind of got
turned loose on the world.

And there's a reason for that,

and it's a reason
that it's become very salient

in the world of generative AI, which,

if there are any nerds in the room,
is a very frustrating term for me,

because generative modeling already
means something different mathematically.

And so now for some reason,
we're calling this kind of AI

the exact same word
that isn't mathematically applicable.

It drives a nerd like me up the wall.

But I think we all know
what I'm talking about.

The the chat bots that you can now
talk to the

the models
that can create an image out of nothing.

And they're amazing.

They are huge breakthrough advances.

I will not share them. Sell them short.

GPT and Gemini.

I truly can surpass

human medical students
in answering medical questions

and most average doctors as well.

And wouldn't
it be amazing for all of those people

that don't have access
to an immediate doctor to be able

to ask questions and get answers?

But here's the flip side.

Now that we've come full circle

and why I started this story

with this question that didn't

seem to have anything to do
with artificial intelligence.

Are you happy and how do you become happy?

Because we might do one of two things
right now.

We might look at AI being able to do

all of these amazing things and give up.

Just leave it

to jerks like Vivian to invent things,

and they'll do all the work and solve
all the problems.

You definitely shouldn't trust me
to do anything, but that is an option.

A choice that could be made.

Another.

And let me promise you,
there are a hundred thousand startups

trying to replace every job

that universities are currently training
students to do.

It's important to understand that.

But the other big problem

is that we start using AI

now, that it's no longer just the domains
of the mad scientist like me.

Now that it is something
all of us can interact with.

All of us can talk to.

Now, what we're wondering is,

are we going to keep trying
as hard as we should be trying?

Are we willing to

put in the effort in our lives
when we don't have to,

and something else
can put the effort in for us?

So let's go back to this idea
that I brought up earlier

that there are
ill posed problems in the world,

and there are well
posed problems in the world,

and that artificial intelligence

is quickly surpassing human beings

at solving well posed problems

and wonder,

why is that still

how we're educating our children?

Why are we raising generations of kids

to answer routine questions

when all of us in this room,

in our pocket,
have the answers to those questions

for free?

Instead,

we should be building
kids for the unknown.

We should be building them

to solve ill posed questions.

We need a generation of explorers

because the one thing, at least for now,

that natural intelligence does

and artificial intelligence does not,

is explore the unknown.

Now, to be clear, I use

AI regularly to explore the unknown.

I actually had a question.

this may be a question of zero

interest to the majority of you,
but in a way, that's part of the point.

What is the confidence interval
of the product of two regression model

parameters?

It's obviously

not a question that pops up
in most of our day to day lives.

It's not a question

that actually even pops up in the day
to day lives of most scientists.

Even statisticians
might have to think hard about,

well, gosh, what is the equation
to compute the confidence interval?

Well, fortunately we have GPT and Gemini.

So I ask them and they were wrong.

If you want to know

one simple take away lesson
for how you should interact

with large language models like GPT,

they know everything

and they understand nothing.

GPT knows so much more than I do,

despite how much study I have,
despite my fancy degrees,

despite my years of research,
it just knows more than me.

It's encoded
in those trillions of parameters

that are predicting the next word,

but it doesn't understand
anything that it knows.

That isn't a knock on it.

What it can do is amazing.

but when I asked it

for the answer to this problem,
which, by the way, is in fact a well

posed problem, just a very esoteric one,

a very weird and unusual one.

It gave me the wrong answer.

It gave me the answer to another,
more common problem.

It's another thing to understand
about large language models

and generative image models.

They are

the best of the average of the internet.

Turns out, the average of the internet
doesn't typically have to know

what the confidence interval
of the product of two model

parameters is,
and so its first answer was wrong.

It answered a much simpler question,
but I didn't give up on it.

I stuck with it

and we talked it out.

Now I didn't talk it out
like it was a person,

but at the same time I did engage with it
the way I might engage with

one of my graduate students, my graduate
students, whatever they're studying,

they actually know it better than me.

They're invested in it.

I talked to them an hour
a week about this issue.

They're fully invested in it.

This is their dissertation.

This is
their ticket to whatever is coming next.

And they know it so much more than I do,

but they don't understand it.

That's what
I'm bringing to that relationship

as an understanding of the implications
of what they're discovering

and trying to guide them
towards a similar understanding.

When I interact with GPT

or Gemini, I treat it the same way.

He knows everything and understands
nothing.

And so I said, no, you've understood
misunderstood my problem.

You've actually given me

the confidence interval
to the product of two random variables.

I know this is super exciting to everyone,
but that's what it did.

And so I slowly walked it
through the problem.

I didn't know the equation

that I was looking for.

Interestingly, neither did it,

but about

half an hour later we had the actual
equation written out and correct

and the code written out in the programing
language Python.

To execute that on my data,

something I couldn't have done by myself
and nor could it.

That's what excites me about AI isn't

what can I go do by itself?

I'm too busy to write emails
to all of my business acquaintances.

I don't have the time to write an essay.

I don't want to have to think about
the art I'm putting in my presentation.

Let I do it for you.

No, please don't make that choice.

Think about what you could do better

using AI as a tool.

It's not a magic wand.

It is also not intelligent the way we are.

Intelligent again, it's not a knock
is astonishing what it can do,

but it doesn't understand the world.

That's your job.

That's our job.

Instead of building a

generation of students
ready to answer tough questions

that this can do so much more easily,

we should build a generation of students

that know how to find good questions.

A generation of students

that when their first try doesn't work,
they don't give up.

They keep trying
because they have a belief

that their hard work
will actually pay off.

None of that applies
to artificial intelligence today.

This little interlude is just going to be
for the nerds in the room.

There is a famous difference in human
learning

that is identified in both neuroscience
and psychology, called model free

and model based learning and model free
is basically just statistics.

And we in our brains

definitely do a huge amount of model free
learning.

And essentially,
that's what most of AI is.

It's just a bunch of raw

correlations
churned and churned and churned.

Interestingly, on masses of data
that no human could ever interact with,

the, the there's a whole other class

of artificial intelligence
called reinforcement learning models,

the most famous of which would be AlphaGo,

the AI that beat the world's
best human go players.

And since then,

Deep Brain, the Google subsidiary,
has gone on to use AlphaGo

to create AlphaFold,

and to create,

the new system
that can discover all these proteins.

But in all of these cases,

it's just blindly exploring

in a single night,

the reinforcement learning system
from AlphaGo can play something

on the order of 180

years of human experience.

Go in a single night.

It plays so many games against itself.

It is the human being played it with,

you know, only breaking
to sleep for 186 years.

It's not so

surprising that it does better than we do,

but does it truly understand? Go.

I mean, it would keep playing go
if the game was back or at

it would keep playing go
if the house was burning down.

it turns numbers into numbers.

That's a powerful tool.

But it's not the ability
to explore the unknown.

Let me ratchet this up another level.

A lot of research has looked
at human workers

using large language models
to speed up their work.

Published papers,
some of them by OpenAI itself, others

by notable economists at Harvard
and Stanford and beyond.

So, for example,

let's start at what economists

would see
as the low end of the skills ladder.

Call centers.

You're working on call center.

Someone calls up, you take their call,
you essentially have a script,

and you just run through the script
to try and answer their questions.

And what they found was when call center
workers

had a large language model
helping them answer the questions.

They were 40% better and 40%
more productive.

They answered 40% more calls
and had fewer referrals to their manager.

They were better.

Now let's take a step further

and look at programmers.

Maybe I'm not being fair.

Many people go to university
to learn how to program.

Many people learn
how to program completely on their own.

It's a little unclear.

This was certainly
the skill of the future.

Not five years ago,

certainly ten years ago.

Every policy paper in the world

from the UN, from the World
Economic Forum, the white House, McKinsey,

every major country had a policy paper

and it said, we need to teach every kid
how to program.

Well guess what?

We live in a world in which I can program.

and I could have told you that ten years
ago, and I did.

I didn't tell you, but I told everyone
writing these policy papers.

What a crazy thing.

Imagine you had asked me,

what is

the world financial markets
going to do for the next 30 years?

And I said, don't worry about it.

Simply take all of your money.

Buy nothing

but this one stock
and then never change it.

The logic is about the same.

Teach everyone in the world how to program
because that will be a protected skill.

Programing is routine knowledge.

It's a solution to well-posed problems.

People can do creative things
with programing, but the actual core

of programing is routine
and I can do that and do it very well.

So programmers using large language models

to help them program again
about 40% faster

with fewer errors.

and now I will step

up, professional writers
with university educations.

Again, they're faster
and they produce better work.

Boston Consulting Group consultants

with very fancy degrees
from elite universities.

They're asked to analyze
spreadsheets of data

and produce a report, a written report,

and then a set of slides
for a presentation.

And some of them are given GPS

to use and some of them aren't.

And unsurprisingly,

the ones using GPT are faster and produce
higher quality work

just the same as all the other workers.

Well, but there's two

incredibly important missing parts
to this story.

One in every one of these stories,

it wasn't
everyone that received the boost.

Only the least experienced and lowest
skilled workers.

Interestingly, at every skill level,
the people that already knew

how to do the job
didn't actually get benefit from using AI.

That's really interesting,

particularly
if I add in this additional twist

from the world of education where
we've been studying using AI for decades.

If an

AI tutor ever gives the students
the answer.

They never learn anything.

Wait a minute.

Isn't that exactly what's happening
with these workers?

With the new career, workers

are starting their jobs and the
AI is telling them what to do.

I promise you,
they will never learn how to do their job

any better than they're doing it.

The day they show up.

The idea
that we should simply offload our effort

and our work to
I robs us of the chance to grow.

But there's even a bigger issue,

which is in the Boston Consulting Study.

there was one additional test

because their job, their job

doesn't end with analyze the spreadsheet,
which is a well-posed problem,

and write a report
which shouldn't be a well-posed problem.

But I don't know how many of you
have done a business report.

You might as well let an AI write it.

They read exactly the same every time.

so what was after

that is they then had to say,
what should their client

do about the analysis that they just ran?

And so now we've moved from Well-posed
to opposed.

We moved into a world
in which there isn't a right answer.

And in fact, very cleverly,
the researchers set it up

so that GPT would give them
the wrong answer.

And the people using the

AI did worse than the people that weren't

when it became a creative task.

That should scare all of us.

We are uniquely good

for now at exploring the unknown.

But these highly educated,

highly motivated workers,

when given an AI tool,

stopped exploring
and just did what it told them to.

So how do we reverse this trend?

How do we get the benefits
of artificial intelligence?

And again, I can just bragging, Lee,
describe my own work

autistic children that can learn
how to read facial expressions

or from refugees
reunited with their extended family.

I built the first ever system that could,

predict bipolar episodes.

manic episodes and bipolar sufferers.

My company,

has the first test ever
for manic depression.

When my son in 2011
was diagnosed with type one diabetes.

I hacked all of his medical devices.

Broke.

As it turns out, all sorts of U.S.

federal laws.

And I developed the first ever
AI for diabetes just for my son.

It sent updates to my Google Glass, which,
believe it or not,

I wore to a party at the white House.

There is literally a Secret Service policy

at the white House naming me saying,

you can't wear live computers
on your head in the white House anymore.

but I did get to meet President Obama
because of that.

And after he met me and walked

up, he actually walked up to me,
look straight at me,

and just shook his head
like I was such a disappointment.

What are you wearing?

A fluorescent blue computer on your face
at my fancy party?

But then I said,

okay, Google, take a picture.

so that was my voice
activated Google Glass.

It took a picture of the president.

Google has confirmed
it is the only presidential portrait

ever taken by Google Glass.

It is a terrible picture.

it did not improve
his opinion of me, by the way.

but then he said,

why are you wearing this silly thing?

And I said,
because I built an artificial intelligence

that's monitoring my son's diabetes.

He's back in San Francisco.

I'm on the other side of the continent
in Washington, D.C..

I just got a text message from my AI

telling me my son's blood
glucose levels would go dangerously low.

So I sent a message back to my sister
who was watching after him.

She gave him a tracker and his blood
glucose levels didn't go up.

I built myself a superpower,

something that every parent
should have the chance to build.

I truly can do amazing things.

We should have it in our lives,

but we shouldn't use it to substitute

for the things
we can already do for ourselves.

When I use artificial intelligence today.

I use it in a peculiar way.

Now, I'm not talking about the models
I built for my research.

All of those I'm building by hand,
and I'm trying to solve specific problems

and explore things
I don't understand about the world.

But when I use it as a regular person,

I, for example, in

using it to write on a book
I'm working on right now.

The worst thing I could do is say GPT,

write the next chapter of my book.

I have actually tried.

It writes garbage.

It is a terrible writer.

Not it gets the punctuation.

Actually, I wish my students spelling

and punctuation was as good as GPT three.

Human beings
turn out to be terrible at that,

but it's so banal.

It's so substance less.

Instead, I get to do something different
when I engage with it.

I write my own,

my own article.

My own essay.

Of course I do.

In a world where all of the routine
knowledge is available

for free in your pocket.

What's truly left for us

is our unique voice,

the way we see the

world differently than anyone else.

Literally anyone else.

And is your resilience

and your,

courage to use that voice

that is your value
to the people around you.

So how do I use.

In my case, Gemini, Google's version,

I say, Gemini, you are my worst enemy.

You have been hounding me
for my entire career,

finding every floor
and everything I've ever written.

You were the reason
I don't have a Nobel Prize.

Because you keep pointing out
everything I've ever done that's wrong.

Let's just pretend that that's true.

Now read my next essay and find for me

all of the arrows, all of the things

you might criticize.

I'm not speeding up my writing.

I'm slowing it down.

I already went through
and wrote the whole thing

in a way that I was happy with,

and now I have turned the AI

against me to tell me all of the mistakes
I made,

all the flaws that exist, all of the ways
people might disagree with me.

Now, I don't have to believe everything
it says.

I work with it like it's my grad student.

It knows everything.

It doesn't understand everything.

And so I take some of what it says.

I ignore some of what it says
because I don't think it understood.

And it's what we call productive friction.

Rather than using AI to make my life
easier.

I use AI to make myself better.

That's what we should all be doing.

Whether you are interested in building
AI yourself,

or you are interested simply

in making use of it
for your everyday life.

Ask yourself this very specific question

first.

Am I better when I'm using it?

And then think not just AI,
but a lot of technologies.

We might admit we're not really
a better person when we use it.

Feel free to interpret
better person as you want.

But for me, it means

when I'm not so long from now, 65,

will I still be alive?

Well, I've made lots of money.

Well, I have lots of friends.

Well, I be happy.

Things
we might all agree is a better life.

Does this technology, when I'm using it,
give me a better life?

But here's the second question.

And to me it's just as important.

Am I a better person than when I started?

When I turn it off again?

And even amazing

useful technologies
frequently follow that second test.

And I have a whole story
I can share about Google Maps

and automated navigation,
and why it's an astonishing technology

that is increasing dementia,

and everyone around the world.

But I've run out of time
and so you'll have to ask me about it

later
or come chat with me after this event.

It's been a lot of fun.

I guess I never got back around to talking
about collective intelligence, but,

So I'm gonna do this one last little plug.

When the pandemic started,

I have a very strange life.

In addition to my companies
and my academic work.

My real day
job is running a philanthropic lab.

People bring me challenging problems.

And if I think my team
and I can make a meaningful difference,

I pay for everything.

And whatever we invent, we give away.

So it's the stupidest startup idea
anyone's ever come up with.

Endless demand, zero revenue.

But it's also the best job
in the entire world.

And so people write me and they say,
my daughter has a life threatening

egg allergy. Please save her life.

I say our company has bias
in this promotion process.

Help us figure out where it's coming from.

Our country's education system
is failing our students, but we're doing

everything we're supposed to do,
and it still isn't helping.

And again, if I think
I have my unique voice not because

I'm smarter than everyone else,
but because I'm different.

I have become very comfortable
with being different.

Does my unique voice and insight
have something to say about this problem?

And if so, we just do it.

So at the beginning of the pandemic,
I got almost

the identical question
from two separate people.

Except they weren't people.

They were giant multinational companies.

Facebook and Amazon called me up and said,
we just sent everyone home

and we don't know what to do.

And what we

particularly don't want know what to do
is how to be inclusive

and how to innovate through a camera.

If everyone's life
is going through a camera

and we never see one another,
and we never have time to talk

at the,

over beers or at the watercooler,

where does that collective intelligence

and the brilliance of people come from?

And because I'm weird,
I thought that sounds like a reasonable

philanthropic project.

I should help two of the largest,
richest companies in the world

answer that question.

But as long as they're fine with me
telling everyone what I found.

Then we dove in
and I got to see everybody's life

going through those cameras.

All of our interactions around documents.

And we discovered that the smartest teams
had three things in common.

They were relatively small, 3 to 5 people.

They had very flat hierarchies.

There might be a Nobel Prize
winner and undergraduate intern.

There might be the SVP of engineering

and, someone who's just there
for the summer.

It didn't matter
when they were collaborating.

It all disappeared
and everyone took equal turns.

Again, it is you, your unique voice

that has value to this world.

And the last ingredient

is that those teams were diverse.

So I truly am now finally out of time.

But I will simply say this
sounded like a cool thing to build

an artificial intelligence model for,

not one that people would interact with,

but one that we called the matchmaker

and would look through
a social network of people

and find all the matches to make
and break.

Just as importantly,
breaking social connections

to maximize
the collective intelligence of teams.

And what we found is you could double

the innovative output of a team of people

by just shifting around those connections

dynamically, using our AI system.

And and again, it was so exciting
because it's another look

not at how I can replace us,

but how it can make us better

in this case, not better as individuals,

but by finding the community

with which we shared just enough
trust and enough difference

for the sparks to produce
something truly amazing.

So I'm going to stop here.

But again, say, for anyone that's foolish
enough to want to stay around

and ask me questions, I am happy to spend
as much time here afterwards as I can.

Thank you all so very much.

Let's get it.

Thank you very much.

Okay. The.

First video.

Now is.

No more.

Thank you.

Thank you very much.

got all those questions.

So this is easy for you to answer?

the question
in English or Spanish and, okay.

Got translated.

Thank you very much.

It has been an amazing talk.

I think, the use that you see here,
the research the CIA,

the children we saw, you know,

it's amazing.

So I want to

work with children all week away
today. You.

Yeah.

With children, with ADHD

paper.

I can.

Yeah. So,

I think a whole lot of people

like myself have had, diagnoses of, ADHD.

And interestingly,
the comorbidity, the genetic underpinnings

of many of these sort of track together.

So ADHD,

we certainly don't think of it
as being as challenging as autism

or even things like bipolar
or schizophrenia, but

you see similarities genetically
and where it's emerging from.

So, yes.

one of the things I'm looking at, more
in the neurotechnology side of my life,

one of my companies has developed a,

neuromodulator treatment for Alzheimer's.

so the idea is we stimulate the brain

in a way which,

tricks it into thinking it's working very,
very hard.

And this is good for Alzheimer's
because it

it causes the brain,
particularly support structures

in the brain,
glial cells and astrocytes to help clean

up accumulated,

byproducts of, of neural activity.

And that clearly, is helping,

with Alzheimer's.

But it turns out this idea

that you could stimulate the brain
and you can stimulate it.

We're using light.

So strobe ascorbic light at 40Hz, 40 times
per second.

Light is flicking or at eight hertz.

or you can use
electrical stimulation, alternating

current, transcranial stimulation
and some other methodologies.

You can cause these,

this synchronized activity
to, speed up in the brain.

And I've looked at traumatic brain

injury in kids
recovering working memory function.

there's research looking at in autism

at enhancing functioning there
and intra cortical connectivity.

So this seems to be one of the challenges
with autism.

Is one part of the brain
isn't talking to other wide ranging

areas as well as you might in people
that were neurotypical. And

certain kinds
of stimulation seem to help with that.

This is all very early in ADHD.

It's less clear
exactly what the mechanism is,

but it does seem that that 40Hz

stimulation seems to help.

But it's unclear now.

I don't want to totally nerd out
in front of everyone else,

but one of the issues with ADHD
might simply be that

the resting state of your brain
and another state.

So what we call default mode
network versus

executive control network, that those

those two states of your brain are very
distant and people that have difficulty,

different kinds of difficulties,
for example, inhibitory control,

people that commit crimes a lot looks like
these states are very different.

It's hard for them to take to shift
to a control state

when they suddenly have an impulse
to do a thing.

But what it seems like maybe with ADHD

is that the saliency,
the sort of sensory states

they're just very active
and very close to the default state.

And so as soon as you see something,
it kind of takes over and takes

you into a different place.

so all I can really say is

there is some exciting new work
using normal jittery, triggers.

And there's generic
artificial intelligence work

from my lab and others looking at,

what sort of lived experiences,

what sort of educational interventions
are effective across different kids?

So years ago we released a system
called muse, which helps new parents

with young children
for social emotional development.

and we were super excited
and had all sorts of great results,

but it was made for not for average kids,
because the whole point of the AI in

our system
was figure out who this child is

and what experiences are right for them,
not for someone else.

But now we're collaborating
with a group in Vancouver

that specifically wants to take
this explicitly to non neurotypical.

So ADHD and

other let's

call them non-clinical level,
learning challenges.

And I think there's a huge space
of just being able to identify

which behavioral therapies
will actually be effective,

as well as making space
for doing noninvasive

therapies that could also help as well.

And, you know, I don't want to rule out
the possibility of primary

treatment, drug treatments helping.

But I think the first step
in most of these, the way I think of it

as a computational neuroscientist, is

I want to get these different states.

I want to get the default state closer
to the one

we want and farther
from the one that's causing the problem.

And that's a very new way of thinking
about this sort of a problem.

and if you ever want to talk more,
be happy to follow up on it.

But there is work in this space.

I have a
my daughter has been diagnosed with,

we work with,

I wish, you know.

People are genuinely working hard on this,
but I will say as a having a kid

with diabetes, what I have been told
is for the last 50 years,

people have been saying, as soon as,
you know, when my kid got,

diagnosed, they told me
a cure was right around the corner.

And for 50 years we've been waiting.

So I don't want to over promise.

But we are making progress.

Yeah.

And, My vision for this

as we kind of,

as I think please remember

in the speech you mentioned.

and so how can you use, how we teach,

using the use to make people, a little.

So, hopefully

we can get you to use 3 million.

we have been involved in,

in these issues from,

the war and in the post-World War II.

So we have,

approaches
that people can use about people

that take place within the world.

We use in this

been used in school. So,

simplification and this the

how can we go from, in order to world.

There is no doubt

that two of the biggest consumers
of artificial intelligence,

though they will never admit it
publicly, are the U.S

and the Chinese military.

but even aside from that,

OpenAI and Microsoft, Google,

Baidu and China of two countries

and maybe six companies

effectively control the artificial
intelligence infrastructure of the planet.

It's not that there aren't

other people doing interesting things
with the AI in the world,

but if you're going to use
a large language model,

there are only so many
that actually exist, and all of them

are trained on the infrastructure
of either Amazon, Google or Baidu.

So, or the internal infrastructure
of a company like Facebook.

So that's problematic.

I have two answers to your question.

One is an essay.

I just wrote, an op ed

that was published in the Financial Times
of the United Kingdom,

and it had this very provocative opening

as it is being used today,
artificial intelligence

is fundamentally incompatible

with any modern notion of civil rights.

The idea that you might be

denied a job or a loan,

or that you might receive

additional scrutiny from the police,

you have no control over that.

And I know this
because some of that were my companies.

One of my most successful companies
was using AI and hiring my customer

wasn't you.

My customer was Google
and Citibank and Nestlé.

So we built an AI

that figured out who they should hire.

We didn't
build an AI to tell them to hire you.

So, and
my new company's working in health,

working on Alzheimer's,
working on postpartum depression and more.

let's say you go to a different doctor
because you want another diagnosis,

but they're just running the exact same
AI algorithm,

so you get the exact same diagnosis.

And probably if you're in America,

your insurance company
is not going to pay for it anyways.

So what are we going to do?

So the end of that essay, my argument was

just as we have a right to a doctor

or a lawyer acting solely in our interest,

access to AI acting only for us

must be a civil right itself.

Otherwise
civil rights just doesn't make sense.

If groups that you might be concerned
about that

I'm concerned about
are making decisions in 50 milliseconds

in a server farm in Wyoming,

and you never even knew
that decision was being made,

how would you be able
to take action against it?

So in that spirit,
I've tried to do something different

with my company, Denise's health, that's

working in on postpartum
depression, epigenetics.

I made a promise
before we ever founded the company.

At the same time,
we would found an independent

nonprofit, what's called the Data Trust.

And it it not my company

would own all of the data
and run all of the algorithms.

And it sole reason
for existing and fancy language.

It's so fiduciary responsibility is
the mothers from whom that data is coming.

So legally,
we've created a legal framework

in which our eyes sole purpose

is to help the health
outcomes of those moms.

I actually happen to believe it's
actually a good business decision.

If we stop arguing over who owns the data

and instead thought does May I save lives?

Is it actually making people's lives
better?

Is my business
based on having the best product,

or do I have some unique data

set that no one else has that
I stole from someone else?

Anyways?

Well, if we could stop arguing about that,

we could build systems
that were genuinely meant to help people.

Now I realize I'm being a little
idealistic about it here, but

part of what I wanted to

just show is someone could found a company
based on artificial intelligence

and make an explicit commitment

that it would only serve the interests
of the people that we were trying to help.

We're going to make lots of money.

We have the only test in the world,
and it's a test

for a condition that saves lives.

so if we can't make a business
out of that,

we should stop pretending to be business
people.

I don't need to also sell people's data.

I don't need to.

Also abuse the data
and trick them into doing things.

So this is this is hard.

This knowledge of how to build an
AI is out there in the world,

and the ability to do it
at massive scale is controlled

by a very small number
of organizations and countries.

What are we

in Mexico
and the United States around the world?

All of the rest of us are going to decide

is how this
AI is going to affect our lives.

And I'm not saying this again,
as someone that is reflexively against AI,

it's amazing what it can do.

We have a
right to its benefits in our lives,

but we also have a right

to decide what those benefits

should be for ourselves
and the trust to know

that that is happening
in the way we want it to happen.

So the simple truth is,

people are going to abuse this stuff.

What we need to decide

is what are the rules
we want to put in place

that bring the most benefits while
minimizing the harms as much as possible.

And unfortunately, right now

we live in a world where people like
Elon Musk and Sam Altman,

on one day are warning about

the evils of AI as they
themselves are building them,

which should make us very suspicious.

when Sam Altman

actually just before my op ed

came out, Sam Altman

in an interview said,
you know, will judges

be able to subpoena your AI,
your personal assistant,

so that they would be able to use
that information against you in court?

And that is a legitimate issue
also addressed in my op ed.

What I note he's not pointing out
is he already has all that information

and is happily using it against us.

It's a little bit like the snake
from The Jungle Book warning

the little kid about the tiger.

I I'm worried about both of them.

we're the ones that need the protections,
not Sam Altman.

and certainly not the US government.

So your concerns are well-founded,

but there are people
trying to build these things in the world

on principles
other than how rich can we get

and how much of
the world we can take over?

but in the end,

it's our decisions that matter.

it's our ability to,

to collectively decide

what we want these borders to be.

It shouldn't be me.

Just because I build them
first doesn't mean I get to decide.

I want this to not be treated
like it's science fiction.

I think we feel that way
maybe today about AI.

But I'll also say about neuro
technologies.

Elon Musk
also has a neurotechnology company.

And trust me,
if you didn't like having Facebook

knowing everything about you,
just wait until they get inside

your head to literally,

let's take these issues
genuinely seriously now.

And ultimately, I think the best way to do
it is to empower individual people

to be able to truly make decisions
about their own data

and their own interactions
with artificial intelligence,

rather than broad guidelines.

Or on the other side, just

trust me, I'm here to save the world.

so I wish I had a more concrete

answer for you, but all I can say
is what I personally am doing,

and I'm hoping that we'll be able
to prove something with our new trust.

So there were a number of hands, up.

Okay.

And get first.

thank you for an interest in that.

well, I have a question for AI since,

I've been doing these,

tools or

make better some of them.

And really, changed my life.

I guess some need for,
to build up my state of the brain.

and, I and some other to do

and make my life easier.

And in number three.

But those two are only useful for me for
a moment.

And I think there

might be many

people doing the same
as I'm doing at this moment.

But we have the resources
that we must have.

And so I need

would like to help other people

and to have this, these
to make our lives better.

And but my question for you is.

What or where

should I start if I want to?

You have any and the air.

what should be my first?

Yes. Where can I go?

yeah.

So there are different ways
to answer that question.

One is whether you specifically
want to have an impact in the AI world.

And even then, we could break it up.

Do you mean the bleeding edge
mathematics call researchers

that are looking at convergence proofs
and complexity

theorems, around
what's possible with given structures?

or, you know, the build

community that's really going out
and trying to make something.

And even then I would say that
there's a whole other space

which is just going and building things,

that, that they happen to use.

AI is secondary, that they solve
a problem of interest is primary.

so that's really what
that latter group was,

what it was the experience for me.

I didn't plan on studying,
machine learning.

I couldn't have I,
I mean, I was a big science

fiction nerd as a kid,
so of course, I'd read.

I don't know what the first thing

there is an AI in an episode of Star Trek.

Maybe that was the very first
AI I'd ever seen.

There's the AI in 2001,

but as a very little kid, I
probably wouldn't have watched that movie.

but that was my and most people's
introduction to artificial intelligence.

For me, it's interesting

and noteworthy
that I was a secondary thing.

I was studying brains and people,
and then I got introduced

to a specific subfield,
theoretical neuroscience.

where kind of like theoretical physics,

we would use mathematical principles
to study the brain.

And it turns out
if that's where you're starting from,

then you very quickly become a machine
learning researchers

because it's mathematically principled
and it looks like the brain

does something
at least roughly similar to that

usually, especially for early vision

and early audition.

so then for me, I was always a tool.

It was always a way to understand
something else.

So that was that is and how I have always
engaged with it in this way.

It's kind of funny because then
I get introduced as being an AI expert.

Well, let's be fair.

There are people that spend
every minute of their life

working on the next limb
or the next generative model.

Actually, the guy who came up

with the original diffusion
model is a former lab mate of mine,

eyes, the original first author
on the very first paper, a brilliant guy

who then wrote a whole essay
apologizing for it later,

because of

how he didn't like how it's being used.

and, you know,

he put a lot of work, for example,
into using AI modeling in education.

That was something he thought
was worthwhile, whereas putting artists

out of business
didn't feel like a great job to him.

so there are all these different ways
you could go about doing it.

And I'm not saying my way
is the right way.

The right way for you
is kind of where you want to take it.

There are communities out there
kind of around the world,

that are in these build spaces,

I'm sure, for example,
that there are Reddit communities

working on stuff like this,
although you couldn't get me

to spend 30s on Reddit for anything ever.

So, yeah,

you know, there's a lot, at UC Berkeley,

there is a really vibrant community,

hackers that just get together.

So I'm the chair of the Neurotic Collider
Lab at UC Berkeley,

the chair of the external board, and,

this what we do is just students
from across the university,

the neuroscience Institute,
the School of Engineering,

even the business school,
they all come together,

they form up a team
and they try and build a product.

And then, you know, we take it from there
and it's really cool.

Most of what they come up with
doesn't go anywhere.

Some of it's genuinely interesting
and a whole lot of it uses.

I mean, you can't do modern day
neuroscience

without machine learning.

and but they're in the computer
science department at Cal.

We have a whole new school of data
and society,

where people doing stuff like you.

I'm not pitching you on, Cal,
but I bet there's a hacker

culture around here that's doing that
same sort of thing of building

and trying out ideas
and seeing what connects.

You don't happen to be down the street
from,

you know, a $1 billion venture fund
that could look at it.

But, I know there's some money
in this town and there's probably some VCs

that are playing around here
looking for interesting ideas.

I will

simply say, whatever approach you take,

put the problem first.

I super cool, but again, still a tool.

Overwhelmingly,

the hardest thing is finding
the right question.

When Amazon tried to hire me
to be their chief scientist,

one of the projects I would have
run is a deep neural network

that would help speed up
the hiring process,

that would filter out
who they ought to bring in for interviews.

And I saw what they were doing with it,

and I told them at the time,
this isn't going to work.

And then it actually came out publicly

that it just it would not hire women,

which is exactly what I expected.

but they're not necessarily bad people.

So they did
everything you're supposed to do.

They took out all the women's colleges
and all the names of everyone off the.

They took out everything
that could trigger a human being.

So you could see that
this app job applicant was a woman.

And I did something amazing,
something no human could do.

It figured out
who was a woman and wouldn't hire them.

So why did the whole system fail?

Because Amazon hired

a bunch of brilliant young, largely men.

That doesn't make them bad,
but it just was the case.

And for the seven years
prior to that, working on their PhDs,

their advisor had said,
all right, we're going to build an AI,

and it's going to identify
all of the dog breeds in photographs,

which is an actual competition
that I claim every year.

and here is all the training data.

Here's all of the right answers.

And the question,
is there a dog in this picture?

And if there is, what breed is it?

Never.

That's a perfect example of a
well posed question.

Amazon was confronting
an ill posed question

how could an AI improve our hiring?

And the first thing you have to do there
is figure out what the right question is.

What Amazon chose was,

let's build an AI

and train it on who got a promotion
in their first year at Amazon.

Well, guess what?

The single biggest correlate of

getting a promotion
your first year at Amazon is being male.

So that's what the the
I learned no matter what they did,

no matter how they tried to retrain it,
that's all it would learn

because it doesn't know
what makes a great employee.

It just knows the correlations.

Same as GPT.

It doesn't know language.

It knows the correlations it produces.

The next best word.

Both of them did amazing things, one

traumatically wrong,
but still kind of amazing.

And the other,

it produces errors.

Again, if we mis understand
a large language model

as actually a reasoning machine, that's

really, truly thinking things out,

then then you're mystified as to why

it just makes things up every now and then
and completely confabulation.

So when you're working on a problem,

start question first.

What is the problem that I want to solve
so much

that I'm willing to put time into it,
even if no one ever knows that I did.

And if this is a problem,
why is it a problem?

Why haven't we figured it out?

Because if it's still a problem today,
that almost certainly means

no one is thinking about it correctly.

So now you have a place

where you can build some really cool
AI to do something interesting.

Only once you've reached that point.

So I wish I had a recommendation
for a bunch of really

cool communities
for you to connect with here in Mexico.

Around the world I have the social skills
of an autistic badger.

I forget incredibly well on a stage,
but I enjoy talking

to random people about as much as I enjoy
poking out my eyes.

So I love going into my lab

all by myself and building something new.

and then I listen to the world

and see if it actually works
and learn from that.

And then people come to me

and talk to me
about that thing that I built

and how it worked,
and maybe a way to do it better.

So maybe I am suggesting that also is

don't just build a thing,

release it into the world.

There's
nothing like the learning experience

of releasing something
and seeing it getting used or not,

and then studying why it worked or didn't

and trying again and again.

Because it takes a long time
to get good at this stuff.

The solving of the problem stuff,
the AI stuff also takes a long time,

but it will inevitably connect you

into community, which I can say as someone

genuinely on the spectrum.

And yet I get to travel around the world.

World leaders ask my opinions about things

that should scare all of us.

and yet it happens.

Despite my total lack of social skills,
despite the fact that I don't network

with anyone because I build things
and I put them out into the world,

people come to me and

you should give that a try.

I have, I have for sure.

One is did you see the as

can the way of making decisions.

This is about you stand by money
on that one thing that weighs

you building up confidence.

the second question scared me.

So I just want to know,

the first time I ever wonder,
what would you recommend me?

you know, paper

towels, those things that you love, you so

you actually think. Go.

Okay.

So to the first one,

I think that the problem
of automated navigation

systems, paradoxically,
sometimes making traffic worse

is exactly some of the issue with AI

that as it gets built in a way,

I'm actually going to return it
and say it's a it's a problem with us.

We thought, oh God, if only I knew

that secret fastest route

that only the locals knew
then I could get around.

But then if you make that available
to everybody,

things go awry.

and often people

building these systems
don't really think through

the chain of causation
that runs out from there.

So my comments earlier, for example,
about inexperienced early career workers

being harmed by the AI, the very AI that's
helping them do their job

because it's robbing them of the chance
to learn

how to do their job,
I think is a very similar phenomenon.

All of these unintended
negative consequences.

it's one

of the
reasons why, while I am not reflexively

supportive of the idea
that we should sandbox all new AI.

So this is idea much like drug discovery,

where we should test it extensively
before it gets released into the wild.

One of the reasons I don't believe in
it is just, how are you going to stop

everyone at this university,
much less everyone in the world?

From building AI?

There's no realistic
constraint on it. But,

but it has implications.

And I alluded to one earlier, Google Maps

causing,

cognitive decline.

So one of the things we know
is good for long term

cognitive
health is navigating through space.

Well, if you're using an AI to tell you

when to make your turns,
then you're not doing that anymore.

So here's how I use Google Maps.

So I'm not saying Google Maps is bad.

I get to be a native everywhere

I go, in every city in the world.

I can open up my phone and it can tell me
how to get to where I want to go.

Not always the best route,
but a good enough route.

And the way I use Google Maps
is I open it up.

I look at its route, the way
I use it at home when I'm in a new city.

I don't do terrible things to myself.

When I'm at home, I open it up.

I check out to make sure that there isn't
a traffic jam or something,

and then I try and beat Google Maps.

It's given me its route
and how long it thinks I will take.

And then I think,
what do I know about Berkeley, California?

What do I know about San Francisco?

What do I know about LA?

That I can beat Google Maps
without cheating, without speeding

and driving like a crazy person?

Then I have to think about it.

I got the benefits of the
AI telling me how traffic is

and what the best route would be,
but I'm still thinking

deeply about the problem
and reaping those benefits.

So that's how I.

I wish the people building these systems

thought through them
as much as I'm trying to.

The overwhelming ethic
in AI development right now and technology

development writ
large is make life easier.

I hope I convinced someone here today

that we should actually build technology
to make life

more challenging in a productive way.

and that's what I'd love
to see out of Waze in Google Maps.

and then the other one is,

I mean, the dirty truth is, yes,

because most of the AI working in

hiring today is bad.

It's lazy,
it's unscientific, it's unproven,

and it's largely
just looking for keywords on your resume.

Absolutely lutely.

You could have GPT

all by itself, read to your resume
and help you optimize it.

Now, the very things
that it would optimize

are the things we shouldn't
be using and hiring.

What does the recruiter do
when they look at a resume?

They give you five seconds.

Now look at your name,
your school, and your last job.

and it turns out
once I know almost anything about you,

none of those are meaningfully
predictive anymore.

So I can be very realistic

about it and say, absolutely,
Gemini and Gpt3 could help you

revise your resume,
make it more likely to get hired.

But what it's going to do is implant,

aspects that recruiters respond to

that are not, in fact, predictive

of whether
you're actually good at the job.

so feel free,

play the game on the system.

But in reality, here's
what I tell all of my students

don't get a job with a resume.

Get them to come to you,

but build something.

in fact, this may seem a little off topic,
but when we look at women and wage

gap penalties between men and women,
when you turn that around

and you look at employees
that are actively recruited,

wage gap quickly disappears.

When I'm not hiring a generic person
to fill a job,

I'm hiring you.

Amazon wanted me to be

their chief scientist for people.

that is the only way
I tell my students to get a job.

now, I also want to be realistic.

Most people don't have the privilege
of doing it that way, but,

I strongly recommend

that you don't march to the front door
with a resume.

You come in the back door

because people there wanted you to be
there.

It's just it doesn't compare in terms of
the career outcomes that emerged from it.

But genuinely,
you are right in speculating, GPT

actually does a pretty spectacular job
in polishing resumes.

Hey. Dave.

Dave. Hey.

It's the opening night.

Welcome.

For the significant full

inclusion for us, candidates
are going to see a more.

At that point.

It's time once again, both keynote.

Okay.

And this includes nuclear waste
and security removal

from us. And.

Nuclear security and social networking.

liquid metal enthusiast.

lot of us in the way meaningful

community.

No city.

This site could easily put.

Thousands of years in behind.

Put a.

Uniform on a good way to say,

Same. No.

I think

we missed the moment, but maybe we can.

I got about 15% of that begin with.

What is,

a kind of of.

Okay.

I can hear you now. Yes.

What changes? Yes.

You can go from moving

over to.

The to city and.

Guys,

may in 2013, at some point.

Okay.

Us one those that oppose.

And you know

me said can be the,

That these policy issues with us

take us to you for the final.

Year.

The California

entertainment.

Groups again to for.

You can us for to see some.

Unity in this game so.

Please have

for this is. A.

What that what we have to.

For missing

individuals that you cannot forget.

Who? Moses

said about me.

And I assume.

Yeah.

that's an excellent question.

And it's interesting because you frame it

well in terms of,

how many communities,

as you say, such as? Well, Hakka.

I do a lot of work in South Africa.

or, an India
where there are just parts of the country

that are plugged into the global data
stream

and parts that aren't,

and so

I think you're right.

It's not just an issue of, of inequality,
but also that there's

this privileged position of being able
to hold back sets of information.

It's interesting that the flip side
also happens with, information

that might otherwise be available
being expunged from the record.

I've visited China many times.

I think it's an amazing place,
but they've made the decision

that certain kinds of information
absolutely will never enter

their data sphere, information
that ought to be there.

And so, in a sense, there's this complex

really of the data
that's easy to get, the surface

level data, the data
that people see as economically valuable,

you know, open AI was supposed to be
a nonprofit helping the world

very quickly and demonstrably changed
into something wildly different.

As soon as billions of dollars
started flooding in from Microsoft.

So I,

as I was saying earlier,
when I was talking about data trust,

I'm a firm believer in the idea

that these systems
should be working for us.

there's plenty of wonderful

business opportunities
for people building things.

They also they don't get to own me.

They don't get to turn my
my books into knowledge that is available

not for me, but from, there I necessarily.

And there's this interesting question.

Also, I think one of the concerns is that,

you know, as people access these systems

and different language
versions of large models

will be somewhat different, particularly

in, you know, say,

Mandarin Chinese versus English,

but there's also a lot of underlying
model structure.

So there's a huge potential homogenization

that I will facilitate
throughout the world.

And that's deeply problematic,
cause us all to we have this amazing tool

that could help us
to all think differently.

And it seems much more likely
that in the near term at least,

it will cause us to all think
more homogeneously.

even if we're speaking different

languages, I suspect it will induce people

to use similar patterns of thought
and similar pieces of information.

Some of which may not even be true.

in that sense,

if we weren't talking about
the private property of OpenAI or Google,

then we might feel like there's
a real value in being able

to have a broader
range of knowledge available in the world.

I would love to know

if someone living,

you know, much as their ancestors

did in the mountains of Borneo,

something that could make my life better,
but I just don't have access to it.

It would be wonderful
if somehow I could find that.

but I think you're on to a pretty profound

challenge.

in these spaces, most of the AI I built,

has been about solving a specific problem.

A specific model solves
a specific problem.

Even in the domains
in which I have a lens.

large language models in my work,

they're more like an interface.

You can talk to them so that our suicide,

risk prevention
model could act, or you could talk to them

to help us identify
and diagnose postpartum depression. But

our actual

diagnoses
aren't through the large language model.

They are through a specialized model.

And what we find is we have to develop, in

many cases, a different model
for every group of people

that we're working with,
even within one notional community.

So even if we're just talking
about Mexico, absolutely,

we would have different models
in the North in the South,

different models
for the cities versus rural areas.

different model,

clearly different models for Spanish
speaking and non Spanish speaking.

Mexicans,

all of which have to be there.

when we build models
for our education system,

in the States, all originally in English,

we quickly found that the way it spoke to
people had to be distinctly different

for different populations,
or they just wouldn't engage with it.

so again and again, in

most of my work, we're looking at

trying
to, well, describe a specific problem

and building a specific mean machine
learning or artificial intelligence tool

that could be used in that space,
often use

by a person,

say being used by a doctor or nurse
or teacher,

sometimes working completely on its own.

and in that sense,
I think you're identifying

some of the challenges with existing

large language models is and again,

it doesn't have to be this way,

but it is the way that it currently is

that they tend to just suck everything in

and make very little distinction
within them.

That does not exist
within the corpus of language

they've been trained on,
or the corpus of images.

A diffusion model has been trained on.

And it would actually be amazing

if these systems
actually understood these differences.

So one of the emerging areas of, research,

is developing causal modeling in AI.

And this takes a different forms.

Probably the most common right now

is less exciting to me,
but is very exciting in places like OpenAI

and Google is combining reinforcement
learning models with lens.

You stick them together
and you create an independent agent

that can go out and collect its own data
about the world.

but that runs into the
some of the very problems

you're talking about in terms of, well,
maybe people didn't want to share that

data with an agent, or they didn't know
they were interacting with an agent.

And also fair to what you were saying,
in some places in the world,

how would such an agent actually
get access to that information physically?

How could it be present to get information

in a place
that isn't connected, into the net?

And again, AI is not

AI as much as anyone have a say in this,
but I know more than anyone

should
I have the say of how these things happen.

So I love questions like that
because it brings in a very different

maybe there are parts of our selves
in our lives

that we want to preserve.

and preserve in a way

that isn't simply just more data points

in some giant companies database.

But I also, in my heart, feel like,

how are the ways that we can share
that my sister and brother in law

go to Wallach, every year and hike around

and spend time in villages,
and they absolutely love it.

but, not everyone

just does.

Not everyone wants to share their data
with the world.

Not everyone can travel to other parts
of the world to experience it.

And finding that

not even a dividing line, maybe something
messy and fuzzy

between what we keep for ourselves
and what we share with the world,

and how we want to make certain
that that's respected.

And I think that's one of the big things.

Are those distinctions being respected?

And I don't have to think

the Sam Altman is a bad guy.

I don't happen to think he's a great guy,
but I don't happen to think

he's a bad guy, that his opinion alone
should decide these questions.

These are things

we collectively should decide,
and that just isn't happening right now.

So that is unfortunately the best

flailing answer I can offer to you.

In the back.

Okay, great.

Thank.

Hey. Thank you again for

the insightful,
for sharing your thoughts on this.

we. I some,

work for me. for.

And to see you, they start to encourage

us to use this part of our space.

So, I was just,

so that I, you know,
what be I sharing that,

we actually may spend.

it's also like, in,

this work.

So right now.

I, I may be today, reading it,

and then we might.

but I think that I still have some.

kind of a favorite,
at least from what I do.

These three days.

So if you have any surprises

on the species
or the price of the weekend,

if you go to

get out,

for the days we think that the price,

just for us can be developments or,

some, some things as basic as, like

operations or.

Yeah. And, you know,

yeah.

so I don't have

specific courses to recommend, but,

I will say, though
admittedly not what you're asking for,

but which are already doing,
just experimenting with it.

again, I'm really encouraging people
to engage and explore.

having said that,
I think some of the best sources

for interesting insights
about these sorts of technologies.

I highly recommend MIT Technology Review.

is a good source,
and they have published some articles

about specifically
about using large language models and,

and thoughts
and recommendations around that.

I have a newsletter.

You're welcome to sign up to it.

I don't specialize in talking about LMS,
but I certainly have included a couple

of specific, episodes of that newsletter
in which I went through people using it in

for education purposes, and for,

even for financial modeling purposes

and recommendations around
how to get the most out of it.

and it turns out
there are actually a fair number of,

how to write with GPT videos on YouTube
right now.

I wouldn't recommend them
because I think they're very much

of the vein of it's going to speed it
all up and make it so fast.

Let's write a book in a day.

And that's taking the exact
wrong lesson out of this.

So steer away from those sorts of things.

Feel free to look at it's
all free on my site.

Feel free to look

and see some of the recommendations
I've had in my past newsletters,

and check out Technology Review.

I'm not an MIT grad.

I decided to go to a place
where people are,

collaborated with each other instead of
stabbing each other in the back.

But it's still an amazing place
with brilliant people, and

it's a good source of info.

Thank you.

Thank you very much.

so some of those things to me today,
so we'll say thank you

to people. So

in the code society,

Something about.

Small business.

Thank you.

Thank you very much for your time.

Thanks.