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.

[fanfare music playing]

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

[crowd cheering]

[John 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.

[crowd screaming and cheering]

[whistle blows]

[announcer] Number 8, Tim Shaw!

[crowd cheering]

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

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

[Robert Downey Jr.] For as long as he can remember,

Tim Shaw dreamed of three letters...

N-F-L.

[whistle blows]

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.

[reporter] 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!"

[crowd cheering]

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.

[Tim Shaw speaking]

[Tim from 2015 interview] 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.

[Tim speaking]

[Downey] 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.

[Tim speaking]

Okay...

[Downey] 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.

[Sharon Shaw] 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.

[Tim speaking]

[Downey] 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...

[voice breaking] "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."

[Sharon] 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.

[snorts] That means he likes it,

just doesn't wanna say on camera.

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

[Downey] Language, the ability to communicate with one another.

It's something that makes us uniquely human,

making communication an impactful application for AI.

[Sharon] Yeah, that'll be fun.

[Julie Cattiau] 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.

[Downey] 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.

[Downey] 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.

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

He uses AI-powered Live Transcribe

to help him communicate.

[Cattiau] 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?

[Downey] 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."

[Cattiau] 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.

[Downey] 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.

[Downey] 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?

[Cattiau] 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.

[all] One, two, three!

[all cheering]

I hereby accept your ALS ice bucket challenge.

[yelping softly]

[Downey] 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.

[Downey] 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.

[John] It automatically uploaded it.

[Tim] Oh.

How many have you done, Tim?

2066?

[Fernando Vieira] 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.

[Downey] 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?

[Downey] ...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.

[Tim speaking]

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.

[Brenner] 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