All language subtitles for The.Age.Of.A.I.S01E02.WEBRip.x264-ION10

You know how they say

there are two certainties
in life, right?

Death and taxes.

Can't we get rid of
one of those?

See, 100 years ago,
life expectancy was only 45,

can you believe that?

Then by the 1950s,
it was up to 65,

and today, it's almost 80.

Tomorrow, who knows? Right?

Healthcare has made
huge progress.

We've eradicated epidemics
that used to kill millions,

but life is fragile.

People still get sick,
or pass away

for reasons that maybe

should be, someday curable.

What if we could
improve diagnosis?

Innovate to predict illness
instead of just react to it?

In this episode,

we'll see how machine learning
is combating

one of the leading
causes of blindness,

and enabling a son
with a neurological disease

to communicate with his family.

AI is changing
the way we think

about mind and body,

life and death,

and what we value most,

our human experience.

...and our other co-captain, Number 8!
Tim Shaw!

We've lived
with his football dream,

All the way back
to sixth grade

when his coach said,

"This kid is gonna go
a long way."

From that point on,

Tim was doing pushups
in his bedroom at night,

Tim was the first one
at practice.

Tim took it seriously.

Number 8, Tim Shaw!

I don't know what
they're doing out there,

and I don't know
who they comin' to!

For as long as he can remember,

Tim Shaw dreamed
of three letters...

N-F-L.

He was a natural
from the beginning.

As a kid,
he was fast and athletic.

He grew into 235 pounds
of pure muscle,

and at 23,
he was drafted to the pros.

His dream was real.

He was playing
professional football.

Hello,
I'm with Tim Shaw.

You get to start
this season right.
What's it feel like?

It's that amazing
pre-game electricity,

the butterflies are there,
and I'm ready to hit somebody.
You might wanna look out.

Hey, Titans fans,
it's Tim Shaw here,

linebacker
and special teams animal.

He loves what he does.

He says,
"They pay me to hit people!"

I'm here to bring you
some truth,

a little bit of truth,

and so we'll call it
T-Shaw's truth,

'cause it's not
all the way true,
but it's my truth.

In 2012,

my body started to do things
it hadn't done before.

My muscles were twitching,
I was stumbling,

or I was not making a play
I would have always made.

I just wasn't
the same athlete,

I wasn't the same
football player
that I'd always been.

The three letters
that had defined Tim's life
up to that point

were not the three letters
that the doctor told him
that day.

A-L-S.

Okay...

A-L-S, which
stands for "amyotrophic
lateral sclerosis,"

is also known as
Lou Gehrig's Disease.

It causes the death of neurons
controlling voluntary muscles.

He can't even
scratch his head...

Better yet?

...none of those
physical things

that were
so easy for him before.

He has to think about
every step he takes.

So Tim's food comes
in this little container.

We're gonna
mix it with water.

As the disease progresses,

muscles weaken.

Simple everyday actions,
like walking, talking,
and eating,

take tremendous effort.

Tim used to call me
on the phone in the night,

and he had voice recognition,

and he would speak to the phone,

and say, "Call Dad."

His phone didn't recognize
the word "Dad."

So, he had said to me...

"Dad,
I've changed your name.

I'm calling...
I now call you "Yo-yo."

So he would say into his phone,
"Call Yo-yo."

Tim has stopped
a lot of his communication.

He just doesn't talk
as much as he used to,

and I, I miss that.

I miss it.

- What do you think
about my red beard?
- No opinion.

That means
he likes it,

just doesn't wanna
say on camera.

Now, my favorite was when
you had the handlebar moustache.

Language,
the ability to communicate
with one another.

It's something that makes us
uniquely human,

making communication
an impactful application
for AI.

Yeah, that'll be fun.

My name is Julie.

I'm a product manager
here at Google.

For the past year or so,
I've been working on
Project Euphonia.

Project Euphonia
has two different goals.

One is to improve
speech recognition

for people who have a variety
of medical conditions.

The second goal
is to give people
their voice back,

which means actually recreating
the way they used to sound

before they were diagnosed.

If you think about
communication,

it starts with
understanding someone,

and then being understood,

and for a lot of people,

their voice is like
their identity.

In the US alone,
roughly one in ten people

suffer acquired
speech impairments,

which can be caused by
anything from ALS,

to strokes, to Parkinson's,
to brain injuries.

Solving it is a big challenge,

which is why Julie partnered
with a big thinker to help.

Dimitri
is a world-class research
scientist and inventor.

He's worked at IBM,
Princeton, and now Google,

and holds over 150 patents.

Accomplishments aside,

communication
is very personal to him.

Dimitri has a pretty
strong Russian accent,

and also he learned English
when he was already deaf,

so he never heard himself
speak English.

Oh, you do? Oh, okay.

Technology can't yet
help him hear his own voice.

He uses AI-powered
Live Transcribe

to help him communicate.

Okay,
that's awesome.

So we partnered up with Dimitri
to train a recognizer

that did a much better job
at recognizing his voice.

The model that you're using
right now for recognition,

what data did you train it on?

So, how does
speech recognition work?

First, the sound of our voice
is converted into a waveform,

which is really just
a picture of the sound.

Waveforms are then matched
to transcriptions,

or "labels" for each word.

These maps exist for most words
in the English language.

This is where
machine learning takes over.

Using millions
of voice samples,

a deep learning model
is trained

to map input sounds
to output words.

Then the algorithm uses rules,
such as grammar and syntax,

to predict each word
in a sentence.

This is how AI
can tell the difference

between "there," "their,"
and "they're."

The speech
recognition model
that Google uses

works very well for people

who have a voice
that sounds similar

to the examples that were used
to train this model.

In 90% of cases,
it will recognize
what you want to say.

Dimitri's not in that 90%.

For someone like him,
it doesn't work at all.

So he created a model
based on a sample of one.

But making
a new unique model

with unique data
for every new and unique person

is slow and inefficient.

Tim calls his dad "Yo-yo."

Others with ALS may call
their dads something else.

Can we build one machine

that recognizes
many different people,

and how can we do it fast?

So this data
doesn't really exist.

We have to actually collect it.

So we started this partnership
with ALS TDI in Boston.

They helped us collect
voice samples

from people who have ALS.

This is for you, T. Shaw.

One, two, three!

I hereby accept your ALS
ice bucket challenge.

When the
ice bucket challenge
went viral,

millions joined the fight,
and raised over $220 million
for ALS research.

There really is a straight line
from the ice bucket challenge

to the Euphonia Project.

ALS Therapy
Development Institute
is an organization

that's dedicated to finding
treatments and cures for ALS.

We are life-focused.

How can we use
technologies we have

to help these people right away?

Yeah, they're
actually noisier.

That's a good point.

I met Tim a few years ago

shortly after
he had been diagnosed.

Very difficult to go public,

but it was made
very clear to me

that the time was right.

He was trying to understand
what to expect in his life,

but he was also trying
to figure out,

"All right,
what part can I play?"

All the ice bucket challenges
and the awareness

have really inspired me also.

If we can just step back,

and say, "Where can I
shine a light?"

or "Where can I give a hand?"

When the ice bucket
challenge happened,

we had this huge influx
of resources of cash,

and that gave us the ability

to reach out to people with ALS
who are in our programs

to share their data with us.

That's what got us
the big enough data sets

to really attract Google.

Fernando
didn't initially set out

to make speech recognition
work better,

but in the process of better
understanding the disease,

he built a huge database
of ALS voices,

which may help Tim
and many others.

It automatically
uploaded it.

Oh.

How many
have you done, Tim?

2066?

Tim,
he wants to find every way
that he can help.

It's inspiring to see
his level of enthusiasm,

and his willingness to record
lots and lots of voice samples.

To turn
all this data into real help,

Fernando partnered
with one of the people

who started
the Euphonia Project,
Michael Brenner...

- Hey, Fernando.
- Hey, how are you doing?

...a Google
research scientist

and Harvard-trained
mathematician

who's using machine learning

to solve scientific
Hail Marys, like this one.

Tim Shaw has recorded
almost 2,000 utterances,

and so we decided
to apply our technology

to see if we could build
a recognizer that
understood him.

The goal, right, for Tim,
is to get it so that it works

outside of the things
that he recorded.

The problem is
that we have no idea

how big of a set that
this will work on.

Dimitri had recorded
upwards of 15,000 sentences,

which is just an incredible
amount of data.

We couldn't possibly
expect anyone else

to record so many sentences,

so we know that
we have to be able to do this

with much less recordings
from a person.

So it's not clear it will work.

- That didn't
work at all.
- Not at all.

He said, "I go
the opposite way,"

and it says,
"I know that was."

When it doesn't recognize,

we jiggle around
the parameters of
the speech recognizer,

then we give it
another sentence,

and the idea is that
you'll get it to understand.

Can we go to the beach?

- Yes! Got it.
- Got it.

That's so cool.
Okay, let's try another.

If Tim Shaw
gets his voice back,

he may no longer feel
that he is defined,

or constrained,
by three letters,

but that's a big "if."

While Michael
and team Euphonia work away,

let's take a moment and imagine
what else is possible

in the realm of the senses.

Speech.

Hearing.

Sight.

Can AI predict blindness?

Or even prevent it?

Santhi does not
have an easy life.

It's made more difficult
because she has diabetes,

which is affecting her vision.

If Santhi doesn't
get medical help soon,
she may go blind.

Complications of diabetes

include heart disease,

kidney disease,

but one of the really
important complications

is diabetic retinopathy.

The reason it's
so important is that

it's one of the lead causes
of blindness worldwide.

This is particularly true
in India.

In the early stages,
it's symptomless,

but that's when it's treatable,

so you want to screen them
early on,

before they actually
lose vision.

In the early stages,

if a doctor is
examining the eye,

or you take a picture
of the back of the eye,

you will see lots of those
bleeding spots in the retina.

Today, the doctors are
not enough to do the screening.

We are very limited
ophthalmologists,

so there should be other ways

where you can screen
the diabetic patients

for diabetic complications.

In the US,

there are about 74 eye doctors
for every million people.

In India, there are only 11.

So just keeping up with
the sheer number of patients,

let alone giving them
the attention and care
they need,

is overwhelming,
if not impossible.

We probably see
about 2,000 to 2,500 patients

every single day.

The interesting thing
with diabetic retinopathy

is there are ways
to screen and get
ahead of the problem.

The challenge is that
not enough patients
undergo screening.

Like Tim Shaw's
ALS speech recognizer,

this problem is also
about data,

or lack of it.

To prevent more people
from experiencing vision loss,

Dr. Kim wanted to
get ahead of the problem.

So there's a hemorrhage.

All these are exudates.

Dr. Kim
called up a team at Google.

Made up of doctors
and engineers,

they're exploring ways
to use machine learning

to solve some of the world's
leading healthcare problems.

So we started with
could we train an AI model

that can somehow help
read these images,

that can decrease the number
of doctors required

to do this task.

So this is
the normal view.

When you start
looking more deeply,

then this can be
a microaneurysm, right?

- This one here?
- Could be.

The team
uses the same kind
of machine learning

that allows us
to organize our photos

or tag friends
on social media,

image recognition.

First, models are trained

using tagged images of things
like cats or dogs.

After looking at
thousands of examples,

the algorithm learns
to identify new images

without any human help.

For the retinopathy project,

over 100,000 eye scans

were graded
by eye doctors

who rated each eye scan
on a scale from one to five,

from healthy to diseased.

These images were then used

to train a machine
learning algorithm.

Over time,
the AI learned to predict

which eyes showed signs
of disease.

This is the assistant's view

where the model's predictions

are actually projected
on the original image,

and it's picking up
the pathologies very nicely.

To get help
implementing the technology,

Lily's team reached out
to Verily,

the life sciences unit
at Alphabet.

So, how was India?

Oh, amazing!

Verily
came out of Google X,

and we sit at the intersection
of technology, life science,
and healthcare.

What we try to do is
think about big problems

that are affecting
many patients,

and how can we bring
the best tools

and best technologies

to get ahead of the problems.

The technical pieces
are so important,
and so is the methodology.

How do you capture
the right image,

and how does the algorithm work,

and how do you deploy
these tools not only here,

but in rural conditions?

If we can speed up
this diagnosis process

and augment the clinical care,

then we can prevent blindness.

There aren't many
other bigger problems that
affect more patients.

Diabetes affects
400 million worldwide,

70 million in India alone,

which is why Jessica
and Lily's teams

began testing AI-enabled
eye scanners there,

in its most rural areas,

like Dr. Kim's
Aravind Eye Clinics.

- Is the camera on?
- Now it's on.

Yeah.

So once the camera is up,

we need to check
network connectivity.

The patient comes in.

They get pictures
of the back of the eye.

One for the left eye,
and right eye.

The images are uploaded
to this algorithm,

and once the algorithm
performs its analysis,

it sends the results back
to the system,

along with
a referral recommendation.

It's good.
It's up and running.

Because the algorithm
works in real time,

you can get
a real-time answer to a doctor,

and that real-time answer
comes back to the patient.

Once you have
the algorithm,

it's like taking
your weight measurement.

Within a few seconds,

the system tells you whether
you have retinopathy or not.

In the past,

Santhi's condition could've
taken months to diagnose,

if diagnosed at all.

By the time
an eye doctor would've
been able to see her,

Santhi's diabetes might have
caused her to go blind.

Now, with the help
of new technology,

it's immediate,

and she can take
the hour-long bus ride

to Dr. Kim's clinic in Madurai

for same-day treatment.

Now thousands of patients

who may have waited weeks
or months to be seen

can get the help they need
before it's too late.

Thank you, sir.

Retinopathy

is when high blood sugar
damages the retina.

Blood leaks,
and the laser treatment

basically "welds"
the blood vessels

to stop the leakage.

Routine eye exams
can spot the problem early.

In rural or remote areas,
like here,

AI can step in and be
that early detection system.

I think
one of the most important
applications of AI.

is in places
where doctors are scarce.

In a way, what AI does
is make intelligence cheap,

and now imagine what
you can do when you make
intelligence cheap.

People can go
to doctors they
couldn't before.

It may not be
the impact that catches
the most headlines,

but in many ways it'll be
the most important impact.

AI now is
this next generation of tools

that we can apply to clinically
meaningful problems,

so AI really starts
to democratize healthcare.

The work
with diabetic retinopathy

is opening our eyes
to so much potential.

Even within these images,

we're starting to see
some interesting signals

that might tell us about
someone's risk factors
for heart disease.

And from there,
you start to think about

all of the images that
we collect in medicine.

Can you use AI or an algorithm

to help patients and doctors

get ahead of a given diagnosis?

Take cancer as an example
of how AI can help save lives.

We could take a sample
of somebody's blood

and look for the minuscule
amounts of cancer DNA

or tumor DNA in that blood.
This is a great application
for machine learning.

And why stop there?

Could AI accomplish

what human researchers
have not yet been able to?

Figuring out how cells
work well enough

that you can understand
why a tumor grows
and how to stop it

without hurting
the surrounding cells.

And if cancer
could be cured,

maybe mental health disorders,

like depression, or anxiety.

There are facial
and vocal biomarkers

of these
mental health disorders.

People check their phones
15 times an hour.

So that's an opportunity

to almost do, like,
a well-being checkpoint.

You can flag that
to the individual,

to a loved one,

or in some cases
even to a doctor.

If you look at the overall
field of medicine,

how do you do a great job
of diagnosing illness?

Having artificial intelligence,

the world's greatest
diagnostician, helps.

At Google,

Julie and the Euphonia team
have been working for months

trying to find a way
for former NFL star Tim Shaw

to get his voice back.

Yes! So Zach's team,
the DeepMind team,

has built a model
that can imitate your voice.

For Tim, we were lucky,

because, you know, Tim has
a career of NFL player,

so he did multiple
radio interviews
and TV interviews,

so he sent us
this footage.

Hey, this is Tim Shaw,
special teams animal.

Christmas is coming,
so we need to find out

what the Titans players
are doing.

If you gotta hesitate,
that's probably a "no."

Tim will be able
to type what he wants,

and the prototype will say it
in Tim's voice.

I've always loved attention.

Don't know if you
know that about me.

She's gonna
shave it for you.

Interpreting speech
is one thing,

but re-creating the way
a real person sounds

is an order of magnitude harder.

Playing Tecmo Bowl,
eating Christmas cookies,
and turkey.

Voice imitation
is also known as
voice synthesis,

which is basically
speech recognition in reverse.

First, machine learning
converts text back
into waveforms.

These waveforms are then
used to create sound.

This is how Alexa
and Google Home
are able to talk to us.

Now the teams from DeepMind
and Google AI

are working to create a model

to imitate the unique sound
of Tim's voice.

Looks like it's computing.

But it worked this morning?

We have to set expectations
quite low.

I don't know how
our model is going to perform.

I hope that Tim will understand

and actually see the technology
for what it is,

which is a work in progress
and a research project.

After six months
of waiting, Tim Shaw is
about to find out.

The team working on
his speech recognition model

is coming to his house
for a practice run.

Good girl,
come say hello.

- Hi!
- Oh, hi!

Welcome.

- Hi!
- Come in.

Thanks for having us.

He's met
some of you before,
right?

How are you
doing, Tim?

- Hi, Tim.
- Good to see you.

- Hello.
- Hello.

Hi.

Hi, I'm Julie.
We saw each other
on the camera.

It's warmer here
than it is in Boston.

As it should be.

Okay.

Lead the way, Tim.

I'm excited
to share with Tim
and his parents

what we've been working on.

I'm a little bit nervous.
I don't know if the app

is going to behave
the way we hope it will behave,

but I'm also very excited,
to learn new things

and to hear Tim's feedback.

So I brought
two versions with me.

I was supposed to pick,
but I decided
to just bring both

just in case one is better
than the other,

and, just so you know,

this one here was trained

only using recordings
of your voice,

and this one here was trained
using recordings of your voice,

and also from other participants
from ALS TDI

who went through
the same exercise of...

So, okay.

I was hoping we could
give them a try.

Are we ready?

Who are you talking about?

It got it.

It got it.

Is he coming?

Yes.

Are you working today?

It's wonderful.

Cool.

Thank you
for trying this.

- Wow!
- It's fabulous.

What I love,
it made mistakes,

- and then it corrected itself.
- Yeah.

I was watching it like,
"That's not it,"

- and then it went...
- Then it does it right.

These were phrases,

part of the 70%
that we actually used

to train the model,

but we also set aside
30% of the phrases,

so this might not do as well,

but I was hoping that
we could try some of these too.

So what
we've already done

is him using phrases
that were used
to train the app.

That's right.

Now we're trying to see
if it can recognize phrases

- that weren't part of that.
- Yes, that's right.

So let's give it a try?

Do you want me to?

Do you have the time to play?

What happens afterwards?

Huh. So, on the last one,

this one got it,
and this one didn't.

- We'll pause it. So...
- I love the first one,
where it says,

- "Can you help me
take a shower?"

- That's not at all what he said.
- -I know,

you've gotta be really careful
what you ask for.

So if, when it's
interpreting his voice,

and it makes some errors,

is there a way we can
correct it?

Yeah. We want to add
the option

for you guys to fix
the recordings,

but as of today,
because this is
the very first time

we actually tried this,

we don't have it yet.

This is still
a work in progress.

We have
a speech recognition model

that works for Tim Shaw,

which is, you know, one person,

and we're really hoping
that, you know,

this technology can work
for many people.

There's something else
I want you to try,

if that's okay?

We're working with
another team at Google
called DeepMind.

They're specialized in
voice imitation and synthesis.

In 2019,

Tim wrote a letter
to his younger self.

They are words written by
a 34-year-old man with ALS

who has trouble communicating

sent back in time

to a 22-year-old
on the cusp of NFL greatness.

So let me
give this a try.

I just like using this letter
because it's just so beautiful,

so let me see
if this is gonna work.

So, I've decided to
write you this letter

'cause I have so much
to tell you.

I want to explain to you

why it's so difficult
for me to speak,

the diagnosis, all of it,

and what my life is like now,

'cause one day,
you will be in my shoes,

living with the same struggles.

It's his voice,
that I'd forgotten.

We do.

We're so happy
to be working with you.

It's really an honor.

The thought
that one day,

that can be linked with this,

and when you speak
as you are now,

it will sound
like that, is...

It's okay. We'll wait.

There is
a lot of unknown

and still a lot of research
to be conducted.

We're really trying to have
a proof of concept first,

and then expand to not only
people who have ALS,

but people
who had a stroke,

or a traumatic
brain injury,
multiple sclerosis,

any types of
neurologic conditions.

Maybe other languages,
too, you know?

I would really like this
to work for French, for example.

Wouldn't it be
a wonderful opportunity

to bring technology
to problems that we're solving

in life science and healthcare,

and in fact,
it's a missed opportunity

if we don't try to bring
the best technologies

to help people.

This is really
just the beginning.

Just the beginning indeed.

Imagine the possibilities.

I think in the imaginable
future for AI and healthcare

is that there is
no healthcare anymore,

because nobody needs it.

You could have an AI
that is directly talking
to your immune system,

and is actually preemptively
creating the antibodies

for the epidemics
that are coming your way,

and will not be stopped.

This will not happen
tomorrow, but it's
the long-term goal

that we can point towards.

Tim had never
heard his own words

read out loud before today.

Neither had his parents.

Every single day
is a struggle for me.

I can barely move my arms.

Have fun.

I can't walk on my own,

so I recently started
using a wheelchair.

I have trouble
chewing and swallowing.

I'd kill for a good pork chop.

Yes, my body is failing,
but my mind is not giving up.

Find what's most important
in your life,

and live for that.

Don't let three letters, NFL,

define you...

...the same way I refuse
to let three letters define me.

One of the things
Tim has taught us,

and I think it's a lesson
for everyone...

Medically speaking,
Tim's life has an end to it.

In fact, five years ago
we were told he only had
two to five years left.

We're already past that.

He has learned very quickly

that today
is the day that we have,

and we can ruin today

by thinking about yesterday

and how wonderful it used to be,

and, "Oh, woe is me,"
and "I wish it was like that."

We can also ruin today

by looking into the future,

and in Tim's case,

how horrible
this is going to be.

"This is going to happen,"

"I won't be able
to do this anymore."

So if we go either
of those directions,

it spoils us
from being present today.

That's a lesson for all of us.

Whether we have
an ALS diagnosis or not,

try to see the good
and the blessing of every day.

You're here with us today.

It's going to be a good day.