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  • 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.

  • [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.