1:56

can

2:00

watch with him. He clicks on the link.

2:03

The video starts. It

2:05

begins with the opening screen of the video game breakout

2:08

created by Atari in the 1970s. In

2:10

it, a player controls a rectangular paddle

2:13

at the bottom of the screen and tries to bounce a ball

2:15

into rows of rainbow-colored bricks at the

2:17

top. The

2:18

goal is to clear all of the bricks from the

2:20

screen. If the paddle misses

2:22

the ball too many times, the player loses.

2:24

The game starts to play.

2:27

At first, the player controlling the paddle is

2:29

unskilled, unable to even hit the

2:32

ball most of the time. But over

2:34

the course of the video, the player gets

2:36

good. Really good. By the

2:39

end, the player has figured out how to get the ball

2:42

behind the bricks where the ball bounces

2:44

between the upper wall and the bricks, clearing

2:47

them quickly without ever risking the ball passing

2:49

the paddle at the bottom. As

2:51

the video ends, Page looks up at Musk

2:53

in no-second. Let me be sure

2:56

I understand. The player is a computer

2:58

and it wasn't programmed to know how to play the game?

3:01

No seg nods. All they told

3:03

it was to maximize the number of points it achieved.

3:06

It figured out the rest on its own.

3:09

Page looks stunned. In

3:11

two hours, it came up with its own strategy

3:13

and by the end, it played better than any human

3:16

ever has. Just incredible.

3:19

Musk ships in his seat. Incredible

3:23

and terrifying.

3:25

Today, it's beating a video game. Tomorrow,

3:27

it's operating power plants and making military

3:30

decisions. But

3:32

at this moment, Page isn't thinking about

3:34

the downsides of AI. All

3:36

he's thinking about is that this technology should

3:39

belong to Google. What'd

3:41

you say the company that developed this was called?

3:44

DeepMind?

3:47

Soon, Google won't be

3:49

the only company interested in acquiring

3:51

DeepMind. And DeepMind

3:54

won't be the only leader in the field.

3:56

The major tech companies are about

3:58

to enter a race to develop

3:59

develop the most superior artificial intelligence

4:03

the world has ever seen and

4:05

potentially change society

4:09

forever. Business

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6:15

In 1958, a research psychologist

6:17

at Cornell University Laboratory named

6:20

Frank Rosenblatt programmed

6:22

a massive mainframe computer with

6:24

a mathematical algorithm that allowed the computer

6:26

to teach itself skills. He

6:29

demonstrated this ability by feeding a machine

6:31

two cards, one marked with a square

6:34

on its right, the other on its left. At

6:37

first, the computer couldn't tell one from

6:39

the other. But Rosenblatt

6:41

continued to feed it the cards, and

6:43

after just 50 trials, the computer

6:46

was able to distinguish left cards from

6:48

right ones with a high degree of accuracy.

6:52

At the time, the New Yorker declared it was the first

6:54

machine to rival the human brain. The

6:57

New York Times also predicted that in the future,

6:59

computers would walk and talk and

7:02

possess a superior intelligence to humans.

7:05

Soon, however, researchers in what was starting

7:07

to be called artificial intelligence ran

7:10

up against the limits of the technology at the time.

7:13

Computers just weren't powerful enough to do much

7:15

more than recognize some images. For

7:18

decades, scientists were in what they called

7:21

an AI winter, where few advancements

7:23

were made, and many researchers considered

7:25

AI nothing more than a pipe dream.

7:29

But by the 2010s, computers

7:32

had advanced dramatically. Plus,

7:34

the proliferation of the Internet meant that there were now

7:36

massive data sets, electronic

7:39

books, social media profiles, caches

7:41

of photos, maps that

7:43

could be used to train various AI

7:46

models. The dream of creating

7:48

artificial intelligence came

7:50

roaring back with a vengeance. The

7:53

big tech companies saw it as the key to the

7:55

future of their businesses, envisioning

7:57

a world where computers can diagnose

7:59

diseases, trade stocks,

8:02

right-wing briefs, and more.

8:04

In our new three-part series, we're

8:07

tracking the race between Google, Microsoft,

8:10

and Meta to develop the most powerful

8:12

AI possible. We'll dive

8:15

into the awe-inspiring breakthroughs and

8:17

the terrifying existential questions, the

8:19

corporate maneuvering, and the boardroom

8:22

backstabbing. This

8:25

is episode one. The

8:28

next big thing. It's

8:35

fall 2012. Chi

8:38

Lu knocks on the door of his boss's office

8:41

at the Microsoft Research Lab in Redmond, Washington,

8:44

roughly 15 miles east of Seattle. Lu

8:47

takes off his small oval glasses. As

8:50

he cleans them with his shirt, he notices

8:52

his hand is trembling. He's

8:54

nervous. Lu shakes

8:56

his head. He's not usually nervous

8:59

at work. He's one of the highest-ranking

9:01

executives at Microsoft. He helped develop

9:03

Bing, the company's search engine. And

9:05

now he's one of the lead researchers in artificial

9:08

intelligence. But he takes

9:10

this handshaking as

9:12

a sign of just how badly he wants

9:15

what he's about to ask for. Come

9:18

in. Lu replaces his

9:20

glasses and walks in. His boss

9:22

looks up from his computer and smiles. Chi,

9:25

what's going on? What's the urgent need for a meeting? I

9:28

just got a really exciting email regarding Jeff

9:30

Hinton. Lu pauses to

9:32

see how his boss reacts. Hinton

9:35

is a professor at the University of Toronto and one

9:37

of the leading academic researchers in artificial

9:39

intelligence. Lu's boss

9:41

nods. What's Jeff up to these

9:43

days? Still stubbornly clinging

9:46

to neural networks? Neural

9:48

networks are an algorithm that mimics the way neurons

9:50

fire in the brain. Most researchers

9:53

gave up on it decades ago. But

9:55

Jeff's just kept at it. one

10:00

that can identify common objects like

10:02

flowers, cars, and dogs with a high degree

10:04

of accuracy. Baidu in China offered

10:07

him 12 million dollars for it but he hasn't committed.

10:10

I think we should make an offer. Liu's boss

10:12

wrinkles his brow. I don't know.

10:16

We decided long ago that neural networks weren't

10:18

where we were gonna put our money or attention. There

10:20

are other ways to build artificial intelligences.

10:23

With respect, this is a major breakthrough. It's

10:25

gonna change AI research forever. Liu

10:29

bites his lip, deciding whether or not

10:31

to say the next part. After

10:33

a moment, he goes for it. We're

10:36

falling behind. Google beat us to better

10:38

speech recognition software even though we initially

10:41

led that research. We've lost several

10:43

of our best scientists to other companies in

10:45

part because they want to work with neural networks.

10:48

But if we buy Hinton's company, we can catch

10:51

up, even surpass the others. Liu's

10:54

boss, thanks for a moment. You

10:56

said Baidu's offering 12 million?

10:59

Liu nods. Okay,

11:02

you can offer up to 20 million. Liu

11:05

thanks his boss and leaves. As

11:08

he returns to his office, he hopes 20

11:11

million is enough.

11:12

Microsoft is trailing in the race

11:15

for AI and Liu fears that

11:17

if they lose this auction, they'll

11:19

be left in the dust.

11:24

It's close to midnight in

11:26

December 2012 in Lake Tahoe,

11:28

Nevada. Jeff

11:30

Hinton stands at a Jerry Riggs standing

11:33

desk inside a small hotel room. It's

11:35

an unsteady stack of an overturned

11:38

waste paper basket on top of a table, on

11:40

top of a bed. But an old

11:42

back injury means that Hinton risks a slipped

11:45

disc anytime he sits down, so he's willing

11:47

to go to extreme measures to never sit.

11:50

Two of Hinton's graduate students from the University

11:52

of Toronto hover over him. Together,

11:55

the three of them have founded the company DNN

11:57

Research, based on a neural network.

11:59

developed.

12:00

They're holding an auction to sell the company

12:03

while attending an artificial intelligence conference.

12:06

They've been receiving emails all day as companies

12:08

make bids for DNN research. At

12:11

the beginning there were four bidders Google,

12:13

Baidu, Microsoft and London based startup

12:15

DeepMind. But now as

12:18

midnight approaches only Baidu

12:20

and Google remain. Hinton

12:23

clicks on the email from a representative from Google. 44

12:26

million. I see if Baidu matched that.

12:30

He clicks on an email from Baidu's representative.

12:33

Yep 44 million. His

12:36

students smile. One of them with red

12:38

hair and glasses shakes his head. Oh 44

12:41

million this is crazy. The

12:43

other grad student who has dark hair rubs

12:46

his hand across his face. How

12:48

hard do you think they'll go? Hinton

12:51

crosses to the window looking out onto the mountains

12:54

barely visible in the dark. They'll

12:57

go high but I think we need

12:59

to take a step back. You know

13:01

we all agree that 44 million is enough

13:03

money right? We don't need more

13:06

than that. Both

13:08

grad students nod. So

13:10

maybe we don't pick the company that's going to offer

13:13

us the most money. It's not immodest

13:16

to say that whoever we sell this technology

13:18

to will achieve a big advantage in developing

13:21

artificial intelligence right? The

13:23

red-headed graduate student nods emphatically.

13:26

How could they not? We'll be handing them

13:28

the ability to train computers to learn

13:31

using more data than any human

13:33

could ever retain. Hinton nods.

13:35

Right so who we think will be

13:38

better guardians of this technology. I

13:41

guess that to me is as important a question

13:43

as who will pay us the most money.

13:45

The dark-haired student

13:47

paces for a moment. I

13:50

think Google. I mean their motto

13:52

is don't be evil. Hinton

13:54

turns to the red-headed student. What about

13:57

you? I agree. Google

13:59

seems like a But

16:00

with neural networks you just give it all the ingredients

16:02

and it figures out how to make the meal and it can

16:04

do it That way faster than it takes us to come

16:07

up with a recipe Hmm, then

16:10

I think we should buy deep-mind the

16:12

engineers eyebrows shoot up Really?

16:16

If neural networks are the next big thing then

16:18

we should be in on it I mean what

16:21

they're doing is really groundbreaking But

16:23

to be clear a lot of their biggest

16:26

claims are gonna take years if not decades

16:28

to achieve At Facebook

16:31

we don't really do long-term research

16:33

like that Zuckerberg trucks.

16:36

Well, the important thing is that Facebook stays in

16:38

the game We can't let the other companies

16:40

move into an area that we don't follow Zuckerberg

16:44

throws his cup in the trash. He's

16:46

determined to bring deep-mind into

16:48

the Facebook fold

16:51

But Mark Zuckerberg isn't the only big tech

16:54

CEO intent on buying

16:56

deep-mind By the time

16:58

Zuckerberg learns what deep mind claims

17:00

it's going to do Google CEO

17:03

Larry Page has already had

17:05

his sights on the company for months He

17:08

learned about deep mind on a private jet alongside

17:11

fellow tech billionaires and deep mind investors

17:13

Elon Musk and Luke Nocek and

17:16

the more page learns The

17:18

more he's certain he also wants

17:21

to buy the fledgling company to cement

17:23

Google as the industry leader in AI

17:27

Soon the founders of deep mind have a choice

17:29

to make one that will shape

17:31

the race for AI dominance It's

17:40

late 2013 in London The

17:43

founders of deep mind Shane leg

17:45

Demis has obvious and Mustafa Suleiman

17:48

sit at a conference table and deep minds headquarters

17:52

Asab is starts the conversation off a

17:54

former child chess prodigy as obvious

17:56

stopped playing competitively as a team

17:58

to pursue computer years.

20:00

Remember to join Wondery Plus in the Wondery app

20:03

or on Apple Podcasts to access

20:05

this live stream. Casey

20:07

Shane was murdered in the middle of an August night,

20:10

shot point blank while idling in his Dodge

20:12

pickup truck in North Indianapolis.

20:15

There was no physical evidence, no

20:17

known motive, and no one coming

20:19

forward with information. Except one woman

20:22

who swears to this day she saw Leon

20:24

Detroit Benson pull the trigger. Leon

20:27

Benson was sentenced to 60 years in prison.

20:29

All because one person swore they saw something.

20:32

But what if she was wrong? And what if we could

20:34

prove it? From Wondery and Campside

20:37

Media comes Season 3 of the

20:39

hit podcast Suspect, co-hosted

20:41

by me, Matt Cher, alongside

20:43

attorney Laura Bazalon. This

20:45

is a story of a botched police investigation,

20:48

a dangers of shaky eyewitness

20:50

testimony, and a community who feared

20:52

law enforcement.

20:55

Listen to Suspect, five shots in the

20:57

dark, wherever you get your podcasts,

21:00

or binge all eight episodes ad-free

21:02

on Wondery Plus. Find Wondery Plus

21:04

in the Wondery app or on

21:06

Apple Podcasts.

21:20

It's 2015 in Mello Park, California.

21:24

Greg Brockman cuts into a piece of chicken. He's

21:27

at a large table in the private dining room of a

21:29

large ranch-style hotel called The

21:31

Rosewood. A wall of windows

21:34

frames the Santa Cruz Mountains outside.

21:37

As the former CTO of Stripe, an online

21:40

payment company, Brockman has been to

21:42

The Rosewood many times. It's

21:44

a favorite spot for Silicon Valley bigwigs

21:46

to meet, but the view never

21:49

gets old. And this table

21:51

is full of bigwigs. Elon

21:53

Musk sits across from him, as well as

21:55

some of the most prominent AI researchers in

21:57

Silicon Valley. been

22:00

invited by Sam Altman, who's

22:02

sitting at the head of the table. 30 years

22:05

old, with big eyes and short curly

22:07

hair, he's the president of Y

22:09

Combinator, the startup accelerator.

22:12

Altman didn't say why he was inviting

22:14

them all to dinner, but Brockman's

22:17

pretty sure Altman's flirting with

22:19

starting his own AI company. Brockman

22:23

notices the man sitting next to him looking out

22:25

the window as well. He's an AI

22:27

researcher, and he turns to Brockman. I

22:30

spend so much time thinking about generating

22:32

images, sometimes I forget just how amazing

22:34

reality is. Before

22:37

Brockman can respond, he's interrupted

22:39

by Altman clinking his knife on his glass. You're

22:42

probably wondering why I asked you all here today,

22:45

although I'm sure some of you have started

22:47

to guess. It's no

22:50

secret that the big tech companies are going all

22:52

in on artificial intelligence. What

22:55

I gathered you all here to talk about

22:57

is if it would be possible to form a new

22:59

AI company, a startup, that

23:02

could act perhaps as a counterweight to

23:04

the big tech companies. Musk

23:07

jumps in almost immediately. Well,

23:09

I don't know how feasible it is, but I just want to say

23:11

that I think it's incredibly important. I

23:13

was an early investor in DeepMind, and the pace

23:16

that the technology is developing is mind boggling.

23:19

I genuinely think that there is a risk

23:21

of something truly devastating happening

23:23

to humanity as a result of AI in the next

23:25

five to 10 years. Altman

23:27

nods. Yes, I completely agree with

23:30

you, Elon. I was thinking that this new

23:32

lab should be a nonprofit, so it's not motivated

23:34

by the need to increase revenue. But

23:37

would it be possible for a new lab to

23:39

start now? I mean,

23:41

could a startup even compete with

23:43

the big money of Google and Facebook

23:46

and Microsoft? One of the

23:48

AI researchers cocks his eyebrows skeptically.

23:51

Well, the biggest hurdle is going to be recruiting talent.

23:54

The big tech companies are throwing ungodly

23:56

amounts of money at researchers. But

23:58

another scientist at the table. That

24:01

is true, but a lot of AI researchers

24:03

have concerns about the technology. You

24:06

could convince them to take pay cuts if

24:08

you had a mission that directly addressed

24:10

those concerns. Altman nods. Yes,

24:13

but to make any noteworthy progress, we'd

24:16

need a critical mass of researchers. Do

24:18

you think there's enough researchers willing to turn

24:21

down the money that a place like Google offers?

24:23

The researcher shrugs. That's

24:26

the $64,000 question, isn't it? $64 million.

24:31

Brockman stays quiet as the conversation continues.

24:35

It seems to go in circles, and the consensus

24:37

is that it's hard. But

24:40

Brockman notices that no one actually says

24:42

it's impossible.

24:46

After dinner, Altman gives Brockman

24:48

a ride home. Brockman

24:51

looks out the window as they drive past the offices

24:53

of one tech company after another. You

24:57

know, I think we should

24:59

do it. Altman looks

25:01

stunned. Really? You're

25:03

in? People seem pretty pessimistic.

25:06

It's worth a shot. Like one of the guys said,

25:09

we need to fine-tune our mission to be clear we're talking

25:11

about using AI to benefit humanity,

25:13

and that we're aware of the risks. And

25:16

so in that vein, I guess I

25:18

think we should make the technology open source.

25:22

You mean release it to everyone? Yeah.

25:25

I mean, we know the tech companies will keep their developments

25:27

behind lock and key. We can take

25:29

more of the approach of academia, you know, put it

25:32

all out in the open. Hmm.

25:35

But as everyone keeps saying, this technology

25:37

could be dangerous, do you

25:39

really think it's a good idea to put it out there for everyone

25:41

to use? I think it'll make us more

25:43

mindful of how we develop the technology. You

25:46

know, mutually assured destruction. Yeah,

25:49

that makes sense to me. So

25:52

you really want to do this? Crockman

25:54

nods,

25:55

and Altman breaks out into a big grin.

25:59

Over the next several months, Altman

26:02

and Brockman secure promises for over

26:04

a billion dollars in financing for

26:06

their new endeavor, which they call

26:08

OpenAI, including donations

26:11

from Elon Musk and PayPal co-founder

26:13

Peter Thiel. Brockman sets

26:15

about recruiting 10 prominent researchers

26:18

from companies like Google, DeepMind,

26:20

and Facebook.

26:21

He can't offer them as much money as those companies,

26:24

but he sells them on his and Altman's vision.

26:27

Ultimately, nine of them agree

26:29

to come on board. OpenAI

26:33

is officially a new player in

26:35

the race for AI, but

26:37

Google has a trick up its sleeve

26:39

to keep its advances. It's 2015

26:47

in Madison, Wisconsin. Jeff

26:50

Dean runs his hand over his square jaw,

26:52

a smile quivering at the edge of his lips

26:54

as he stares down at what looks like an ordinary

26:57

computer chip. Dean is

26:59

one of the co-founders of Google Brain, the

27:02

AI research wing of Google. And

27:04

his chip is about to make

27:06

his life a lot easier. Dean's

27:10

sitting in an office in Google's hardware lab. Far

27:13

from the prying eyes of Silicon Valley, this

27:15

is where Google designs all its data

27:18

center hardware. And now, they've

27:20

invented a new kind of computer chip.

27:24

Dean looks up at the engineer who runs the

27:26

lab. So this is it? Yeah,

27:29

that's it. It looks so...

27:32

ordinary. Two

27:34

years ago, in 2013, Google

27:37

released its new speech recognition software

27:39

on its Android. The software

27:41

relied on neural networks, and

27:44

Dean soon realized Google had a major

27:46

problem on its hands.

27:48

He calculated that if everyone who owned

27:50

an Android used the voice search function

27:52

for even just three minutes per day, Google's

27:56

data centers would crash under the usage.

27:59

He figured out that... they would need to double their

28:01

data centers to keep up with demand.

28:04

That wasn't sustainable, so instead, Dean

28:06

tapped the lab in Madison to build a new,

28:09

more efficient ship. The

28:11

engineer sits back down on his side of the desk.

28:14

It looks ordinary, but it can run trillions more

28:16

calculations per second. That's

28:19

amazing. I still think

28:21

it's genius that you realize that for our

28:23

purposes with the neural networks, the calculations

28:25

could be less precise. Hey,

28:28

when you're doing gazillions of calculations like

28:30

a neural network is, who needs decimal point?

28:33

Integers will get you close enough. Dean

28:36

stands up. Thank you

28:38

for this. I know you and your team

28:40

worked really hard to make this happen, and it's

28:42

going to make a big difference. For

28:45

years,

28:46

Google has been acquiring companies and scooping

28:49

up the best researchers. But

28:51

now, it has the best hardware,

28:54

too. At this

28:56

rate, no one will be able to catch

28:58

up to them.

28:59

But Facebook hasn't been sitting idly

29:02

by. And in the fall of 2015, they

29:06

make an announcement that causes the AI world

29:08

to sit up and take notice of the social

29:11

media site. It's

29:20

October 2015 in Menlo Park, California.

29:24

Chief Technology Officer Mike Schrepper

29:26

stands at the end of a conference table at

29:28

the company's headquarters. A gaggle

29:30

of reporters fill the room. Behind

29:33

Schrepper is a large screen displaying a PowerPoint

29:36

presentation of Facebook's latest research.

29:39

The slide behind him shows a drawing

29:41

of a player wearing a large headset.

29:44

We're very excited about the future of

29:46

virtual reality. We believe

29:48

that it will change the way humans work, socialize,

29:51

and more. Schrepper

29:53

catches one of the reporters covering up a yawn.

29:56

Schrepper can't blame her.

29:58

Most of this presentation is all about the future. been made

30:00

public before. So far there's

30:02

been nothing new or exciting. Fortunately,

30:05

Shrepper is confident his next announcement

30:08

will wake her up, as well

30:10

as everyone else in the room. He

30:13

hits enter on his laptop, advancing the

30:15

slide. There's a photo of a Go

30:17

board. As many of you know, here

30:19

at Facebook we use artificial intelligence

30:21

to recognize people in photos users

30:23

post. Well, we've been teaching

30:25

that same artificial intelligence to play

30:27

Go. It's already beaten traditionally

30:30

coded Go computer programs, and we're

30:32

confident that not too far in the future,

30:34

it will be able to beat a top human player.

30:38

Just as Shrepper predicted, the

30:40

reporters in the room are suddenly interested.

30:44

Although computers had long beaten top

30:46

chess players, Go was a far

30:48

more complicated game. In

30:50

Go, players take turns, placing

30:52

either black or white tiles on a 19 by 19

30:55

board, trying to surround the most territory.

30:58

For every move in Go, there were 200 possible

31:02

options, as opposed to chess, where

31:04

each move generates roughly 35 options.

31:07

And no computer had the processing power

31:09

to be able to calculate every outcome in

31:11

Go. Creating an artificial

31:14

intelligence that could beat a top human player

31:17

would be a major breakthrough. It would

31:19

show a level of sophistication of thought

31:21

that computers had never achieved before,

31:24

and established Facebook as one of the leaders

31:26

of AI. A slew of

31:28

reporters raised their hands, Shrepper

31:30

points to one up front. You,

31:32

and the Blue. How are you training

31:35

the neural network to play Go? We've

31:37

been feeding it vast numbers of images of Go

31:40

boards, teaching it to see what a successful

31:42

move looks like. We're pretty sure that

31:44

human players unconsciously use visual

31:46

pattern recognition to know if a move is good or

31:48

bad. And what kind

31:50

of timetable are you looking at for it taking on

31:53

a human player? Well, it's still

31:55

early days, and I don't want to make promises we

31:57

can't deliver on, but let's just say...

32:00

soon. Schrepper

32:02

fights back a smile as he watches the reporters

32:05

rush to write down what he's just said. This

32:08

is a major story. Facebook

32:10

has invested a lot of money into AI

32:13

research, and now it's

32:15

starting to pay off. But

32:18

just days later, Google's DeepMind

32:21

makes a cryptic announcement of

32:23

its own. It's

32:29

November 2015. Head

32:37

of Facebook,

32:40

AI Jan Lekun, sits in his office

32:42

watching a video on YouTube. It's

32:45

an interview with Demis Hassabis, one

32:47

of the founders of DeepMind, now a

32:49

Google company. Hassabis

32:52

is looking directly into the camera, the

32:54

top of his head frequently cut off by the frame.

32:57

There's a large white board behind him with

32:59

unreadable math equations and other charts.

33:02

AI is about making

33:04

machines smart.

33:06

A few ways of doing that. Hassabis

33:08

talks generally about how AI is different

33:10

from traditionally programmed computers.

33:13

But then the man interviewing him gets a sly

33:15

smile on his face. Hassabis

33:18

smiles back conspiratorially as he answers.

33:26

First, he describes how their AI

33:28

has learned how to play a variety of video

33:30

games from the 1980s. But then, he hints

33:33

at something more. And yeah, as you say,

33:35

things are going well, and now we're applying that

33:37

to other domains, and in a few months

33:40

time I think we'll have some other big announcements. Okay,

33:43

I'm waiting for that. Lekun

33:46

rewinds it and listens to it again. Could

33:49

he be talking about Go? But

33:56

Lekun pushes the thought from his mind. AI

34:00

firm is months away from beating a top

34:02

human player at Go. The

34:04

AI research community is small. The

34:07

Kun would know. But

34:09

the Kun can't ignore the uneasy

34:11

feeling in his stomach. Fisabes

34:14

doesn't make a lot of public appearances and

34:16

the timing of this so soon

34:18

after Facebook's announcement feels pointed.

34:22

The Kun shuts off the video. But

34:24

Facebook wants to beat DeepMind and

34:27

Google and he needs to get back

34:29

to work. The race

34:31

for AI is now

34:33

the race to beat Go.

34:54

It's March 2016 in Seoul, South Korea.

34:57

Demis Hasabes stands in a crowded room

35:00

inside the Four Seasons Hotel staring intently

35:02

at a TV monitor, watching his two men

35:04

hunch over a Go board. A sign

35:06

identifies one of the men as Lee Seadall.

35:09

He's one of the top ranked Go players in the world.

35:12

The other is identified as AlphaGo,

35:15

but that's the name of the AI

35:17

playing, not the man in the seat across from Seadall.

35:20

That man is a DeepMind employee tasked

35:22

with physically making AlphaGo's moves

35:24

for it. This is the first game

35:26

of a five-game tournament between AlphaGo

35:29

and Seadall. Hasabes

35:32

takes his eyes off the screen and sneaks a glance

35:34

at Google chairman Eric Schmidt and the leader of Google

35:37

Brain, Jeff Dean. They're

35:39

watching the monitor with unreadable expressions.

35:42

The fact that both men flew all the way to Seoul

35:45

for this proves just how important

35:47

Google is taking this match. Creating

35:50

an AI that can beat Go is

35:53

one of the holy grails of AI research, and

35:55

Facebook is nipping at Google's

35:58

heels.

35:59

It's clearly ahead with an AI already

36:02

competitive with a top human player, but

36:04

no one knows better than Hasabas how

36:06

fast AIs work. If

36:09

AlphaGo fails against CEDOL, there'll

36:11

be plenty of opportunities for Facebook to catch

36:15

on. Hasabas runs his hand through his dark, thinning

36:17

hair. His head is slicked with sweat,

36:20

in part because of how many people are in the room, but

36:23

also nerves. For

36:25

most of the games, CEDOL seemed like he was in the

36:27

lead, but recently AlphaGo

36:29

has mounted a comeback, but Hasabas

36:32

can't be sure exactly. He's

36:34

not a go-grandmaster, and the commentators

36:37

are in disagreement with each other about who

36:40

really has the upper hand. There's

36:43

a rumble in the crowd. Hasabas

36:45

looks up to see CEDOL place a tile.

36:49

Then, within a second, AlphaGo

36:51

flashes its next move on a computer monitor

36:53

on a table perpendicular to the Go board.

36:56

The DeepMind employee makes the move on AlphaGo's

36:59

behalf. CEDOL

37:01

hunches forward and gets

37:03

up and paces the room. Then

37:06

after a moment, he walks back to the table and

37:09

offers his hand to the DeepMind

37:11

employee. The

37:14

viewing room erupts in shears. Hasabas

37:17

breaks out into a grin. CEDOL

37:19

has resigned. AlphaGo has

37:22

won. It's just one

37:25

game. The real test will be how

37:27

AlphaGo performs over the next four, but

37:29

still, artificial intelligence

37:32

just beat one of the best Go players

37:35

in the world. And Google

37:38

seems impossible to beat.

37:43

It's

37:45

spring 2016 in a bar in San Francisco. One

37:48

good fellow takes a glass of beer that's been thrust

37:51

into his hand. Oh, another one?

37:53

Thank you. Of course, we're just happy to have

37:55

you. Good fellow is one of the

37:57

leading AI researchers in the world.

37:59

and he just recently left Google to join OpenAI,

38:03

and his new colleagues have taken him out for welcome

38:05

drinks. Goodfellow raises

38:07

his glass and thanks. I'm

38:10

happy to be here. I really believe

38:12

in your mission. A few

38:14

years ago, Goodfellow was the first person

38:16

to figure out how to use neural networks

38:18

to generate photo-realistic images,

38:21

rather than just analyze them. But

38:23

recently, Goodfellow has started to grow concerned

38:26

about how people might use this

38:28

technology to spread misinformation.

38:31

Right now, the AI-generated

38:33

images still have obvious flaws, but

38:35

the technology is advancing quickly.

38:38

Soon, AI will be able to create

38:40

photo-realistic images of celebrities

38:42

and politicians, and Goodfellow

38:44

is confident that convincing fake

38:46

videos aren't too far behind. The

38:49

potential for abuse is enormous. With

38:52

those concerns in mind, Goodfellow

38:54

decided to leave Google and move to open

38:57

AI. Although Google had

38:59

some ethical guardrails in place, Goodfellow

39:01

felt they were primarily focused on racing

39:04

ahead. He was drawn to OpenAI's

39:06

strong sense of ethics and non-profit

39:09

status. Goodfellow's colleague

39:11

holds up his glass. I propose a

39:13

toast to AGI in

39:16

three years. Works to generate

39:18

photo-realistic images, rather

39:20

than just analyze them. But

39:23

recently, Goodfellow has started to grow concerned

39:25

about how people might use this

39:27

technology to spread misinformation.

39:31

Right now, the AI-generated images still

39:33

have obvious flaws, but the technology

39:35

is advancing quickly. Soon,

39:38

AI will be able to create photo-realistic

39:40

images of celebrities and politicians, and

39:43

Goodfellow is confident that convincing

39:45

fake videos aren't too far behind.

39:48

The potential for abuse is enormous. With

39:52

those concerns in mind, Goodfellow

39:54

decided to leave Google and move to open

39:56

AI. Although Google had

39:58

some ethical guardrails in place, In place, Goodfellow

40:01

felt they were primarily focused on racing

40:03

ahead. He was drawn to open

40:05

AI's strong sense of ethics and

40:07

non-profit status. Goodfellow's

40:10

colleague holds up his glass. I

40:12

propose a toast to AGI

40:15

in three years.

40:16

As

40:18

his colleagues clink their glasses and cheer,

40:20

Goodfellow gets a sinking feeling

40:22

in his stomach. AGI

40:24

stands for Artificial General Intelligence.

40:27

It's the shorthand used for creating an AI

40:29

that can do anything a human could do. But

40:33

the current AI is limited in nature, only

40:36

able to play games or translate text.

40:39

People developing AGI have much

40:41

bigger ambitions. It's

40:44

exactly the kind of advancement that Goodfellow

40:46

is having second thoughts about. He

40:49

thought open AI shared those reservations,

40:51

but now he's

40:53

not sure.

40:55

He's starting to wonder if any of the major

40:57

AI research companies are seriously

40:59

reckoning with the potential consequences of

41:02

what they're building.

41:04

But over at Facebook,

41:06

they aren't concerned with the consequences

41:08

of winning, but the consequences

41:10

of losing.

41:18

It's summer 2016 in Menlo Park,

41:20

California. Facebook head of AI

41:22

research, Jan LeCun, stands in front of

41:24

a conference table in Building 20, the

41:26

marquee building of the Facebook campus. Top

41:29

Facebook executives ring the conference table.

41:32

They're performing a mid-year review with each department.

41:35

Right in front sits Mark Zuckerberg,

41:38

his mouth a straight line. Next

41:41

to him is CTO Mike Schreupfer, who's

41:43

sitting with his arms crossed.

41:45

LeCun powers through the rest of his

41:47

presentation on what the AI team is up

41:50

to.

41:50

It's not a presentation

41:53

LeCun is enjoying giving. Earlier

41:55

in the year, DeepMind's AlphaGo beat

41:58

Lee Cidal in four out of five games

42:00

of Go. Although it was

42:02

undoubtedly an exciting moment in the development

42:05

of AI, AlphaGo's victory

42:07

took the wind out of the sales of Facebook's

42:09

AI team. They desperately

42:11

wanted to be the first company to develop an artificial

42:14

intelligence that had mastered Go. And

42:17

in the aftermath of Google's victory, Facebook's

42:20

AI research seems

42:21

uninspired. So

42:24

as you can see, we're pursuing further

42:26

advances in image recognition and

42:28

translation. These are both tools

42:30

which will immediately impact user experience,

42:33

whether that be from instantaneously

42:35

translating posts or quickly

42:37

removing inappropriate pictures.

42:40

As Lacun wraps up, Zuckerberg

42:42

stands and leaves without saying

42:44

a word. Most of the other

42:47

executives leave as well, but Shrepper

42:49

stays behind. He crosses

42:51

to Lacun, his eyes sparking behind

42:54

his dark-rimmed glasses.

42:55

That presentation was one big nothing

42:58

burger. You didn't say anything meaningful. Lacun

43:01

can't argue with that. I

43:04

was just giving an update. Here's the deal.

43:06

Mark wants Facebook to be seen as a company

43:09

that innovates, so we need something we

43:11

can point to and say Facebook is doing this

43:13

better than the other AI companies. What

43:16

can that be? Lacun

43:19

hesitates, thinking. One

43:21

of his colleagues is hovering nearby. Video.

43:25

Lacun thinks about it. Video

43:28

recognition is an area where there's been less

43:30

work. He's right. We

43:33

can focus on video. Good.

43:36

Do that. Lacun

43:39

watches him go and nods.

43:42

Facebook is putting its stakes in

43:44

video recognition to try to claw

43:46

its way back into the race.

43:49

But Lacun wonders if it will be enough

43:51

to catch up to Google before

43:54

the search engine giant gets so far

43:56

ahead. There's no

43:58

catching up.

44:01

On our next episodes, Google, Facebook,

44:04

and OpenAI all come face

44:07

to face with the downsides of artificial

44:09

intelligence as researchers'

44:11

ethical concerns are

44:14

put to the test.

44:37

From Wondery, this is episode one

44:39

of the Rise of AI

44:40

for Business Wars.

44:42

A quick note about recreations you've been hearing. In

44:45

most cases, we can't know exactly what was said.

44:47

Those scenes are dramatizations, but they're based

44:49

on historical research. To read

44:51

more about artificial intelligence, we recommend

44:54

Genius Makers by Cade Met. I'm

44:57

your host, David Brown. Austin Ratless

44:59

wrote this story. Karen Lois, our senior

45:01

producer and editor, edited and produced

45:03

by Emily Frost, sound designed by

45:06

Kyle Randall, voice acting by

45:08

Bobby Foley, fact checking by Gabrielle

45:10

Jolie. Our senior managing producer

45:13

is Ryan Lorde. Our managing producer

45:15

is Matt Gantt. Our coordinating

45:17

producer is Desi Blala. Our producer

45:19

is Dave Schelling. Our executive producers are

45:22

Jenny Lower Beckman and Marshall Loomey.

45:25

For Wondery.