Lee Sedol vs. AlphaGo: What Really Happened in the Match

In this video, we're finally going to

talk about Alph Go from a Go players

perspective. So, I will show you some of

the key moments in all Alph Go matches.

And even if you've watched the Alph Go

documentary, then we will talk about

some of the things that weren't covered

there. So, here we go. As you probably

know, Alph Go was developed by Google's

subsidiary, Deep Mind. And because I'm

not really tech-savvy, I can only

attempt to explain in the simplest of

words how that wonderful AI worked. So

they started by taking a database of

human go games and they used it to train

Alph Go and they had several neural

networks working together on this. The

first neural network looked through all

the games and it learned to generalize

the patterns and predict what the next

move was going to be. The prediction

wasn't perfect, but it was good enough

for now. Another neural network was

shown many positions and then it was

told who won the game from there. And so

it learned to predict who was going to

win the game from any position that it

saw. Again, in both cases, the

predictions weren't really accurate. It

wasn't 100%. Maybe it was only 60%. Now,

armed with this knowledge, the AI would

play many, many games against itself,

only to gain new data for reinforcement

learning. Now that more games were

played, the AI would use those games to

generalize and find patterns, and then

the process would repeat. This is

probably a crazy oversimplification on

my part. So, if you're an AI developer

or you just know more about this, you

can leave your criticism in the

comments. Now, everyone talks about the

big match between Alph Go and Lisa Doll,

and we'll get to it as well, but let's

not forget that there was another match

prior to that. So, let's travel back to

October 2015. There's a professional go

player from China. His name is Funway

and he's living in France. And at that

time, he became the European Go champion

for the third time consecutively. So,

Deep Mind contacted him and offered to

play a match against their new AI.

Naturally, he was skeptical, but he

agreed to play. His skepticism quickly

gave way to surprise, then shock, then

despair. He lost five games in a row in

that match, 5 to zero. That was

astounding. This is the first match lost

by a professional goal player to a

machine. However, I remember that back

then it didn't cause any sensation yet.

Instead, this 50-0 result started some

whispering and muttering that Funway was

not maybe a real professional, that he'

lived in Europe long enough to forget

the skills of an Asian pro. I imagine

there must have been a lot of pressure

on his shoulders. Go players often say

that the AI brought a lot of innovation

into go theory in general and opening

and joking specifically. However, that

Alph Go version was still not quite the

ruthless, unbeatable machine that we

would know later. This Alph Go played

very much like a human here. Let me show

you a couple of moments from those first

games. This is the very first game in

the match against Funway. Alph Go is

playing white here. This entire opening

setup until here looks very human. These

jokies on the left were played long

before AlphaGo. At this point, it's

white's turn and AlphaGo approaches the

corner. White responds with a ka and

white slides. The most classic of all

variations and it's the one that was

criticized by AI and it got replaced by

this in modern go. However, at that

time, black maybe exchange

here

on then protects the corner and Alph

Go just plays a normal two space

extension. This simple shape that was so

incredibly common 10 years ago, you will

hardly ever see it now. Now, let's see

one more example from game four of their

match. In this game, AlphaGo is playing

black and look at the joseeki that it

chose on the left. Alph Go

approaches

[Music]

attachment. White pulls

back. White connects like

this. White protects the

corner. Black extends. White extends.

And Alpha Go jumps. And once again, this

is something you'll be likely to see in

a human game because this jump as

protection on the left is not really

necessary. There is some invasion here,

but it's not really that dangerous. So,

in a modern game, you'd be much likely

to see a tener key somewhere on the

right, for example. And while we're on

the topic of this joseki, the modern AI

would actually not even connect this way

anymore. In the modern game, you're much

more likely to see a simple solid

connection. And after white jumps, black

jumps here or here. That is the modern

approach. But okay, forget about opening

and joking. Alph Go actually made some

visible mistakes that Katago today picks

up on. Let me show you a couple. Once

again, this is game one of their match.

Alpha goes white. Fun just invaded at

the bottom here. And next, he played

here to save these stones. But actually,

as we now know, before playing here,

black could have made a very useful

exchange. I could include this example

into the useful moves in the second line

video. So instead of this, black could

push here first. This move doesn't look

very powerful, but in fact, it makes the

whole white formation weaker and exposes

this cutting point. Now, if white blocks

like this, which seems like the obvious

response, black can actually cut later.

And now white wouldn't be able to atari

because now black can escape with atari

and all of these white stones are in

danger. Alphago's mistake was that

after fun responded here it also failed

to see the importance of this move.

White should have turned here. This

makes the all the white group so much

stronger. But instead of this alpha go

played

here. Black responds. White pushes.

White

blocks. White saves the stones and black

gets a chance to play here. White can't

block. So in the game, white had to

respond like this. A great exchange for

black. Now we're still in the same game,

but let's skip a few moves forward.

Right now, Alph Go plays here to get

control of the center. But according to

Kato, this is a three-point mistake. And

the reason for that is that once again

there's an urgent move on the board that

both black and white can take. Right now

it's black's turn. So punlay could play

here which protects the corner and

threatens to save the two stones. White

would be forced to capture them and

black and now tenarchy protecting the

corner incent. This is amazing. So if we

know this instead of this move, white

could also play something like this. I

know this is a move on the first line

and I personally would never be able to

find this, but it's capturing these

stones and it's threatening the corner.

If black doesn't respond right now, this

next jump could be quite devastating.

And even if black

responds, white has moves in the future

like this one to enter black's corner

and reduce it. So, however big this move

in the center may seem, that was an

urgent point. And by the way, Funway

didn't just lose the match peacefully.

When he saw he was losing, he tried

different strategies. He even used some

of the tricks that he learned from Alph

Go itself in this very match. But

experience teaches us that changing your

own style for a particular opponent or

event is usually not the best thing to

do. And if anything, it makes you play

worse. So when we put all five games of

this match under the microscope of

modern AI, we see that the game Punway

handled best was the very first one.

This game

categ's middle game accuracy in this

game was higher than that of AlphaGo. He

played a great game and his loss was

never bigger than 10 points, usually

around five or six. For Deep Mind, it

was time for new challenges. And next,

they decided to challenge the player who

had a semi-leendary status at the time,

Lisa Doll Non Pro from South Korea, the

winner of many world championships.

Remember all those inaccuracies and

humanlike play in this match? Lisa Doll

saw the match, too. So, it's no surprise

that he couldn't help feeling a little

skeptical at that point. He said back

then that the level of AlphaGo's first

opponent wasn't really on par with his

own, and that was the main reason the

machine won so easily. In his turn, he

was confident about winning the match.

The only doubt in his mind was about the

final score. Would it be 4 to 1 or 5 to

zero in his favor? Oh well, DeepMind had

some surprises up their sleeve. They had

four or five months before the next

match to train, train, train Alph Go

more. Millions of practice games for the

machine. How strong did it become?

Nobody outside of Deep Mind knew. And

certainly not Lisa Doll. He would only

get a chance to discover the current

level of Alph Go on March 9th, 2016 on

the day of their first game. the first

game that he started to play in a

relaxed manner, not expecting that Alph

Go would start a fight in the early

opening. Let me remind you how that

happened. Alph Go is white here and I

remember that the commentators were

saying that this looks like a

comfortable game for Lisa Doll. Black

has a lot of points on the right, these

two stones, everything. And the white

stones don't seem to be making any

points. But AlphaGo had a plan here.

White caps. Black tries to get into the

center by attaching fully expecting

something like

this where white now has a cutting point

and the black stones escape into the

center. But Alph Go had a different

plan. It peeps here. White has to

connect.

Next white

pushes and cuts.

AlphaGo turns this game into a

disconnected running fight right from

the beginning. Now Lisa Doll needs to

live with those three stones at the top,

manage these two stones in the center

and also think of something for this

stone here on the upper left. At the

time, most of the commentators seem to

be saying that this looks like a

difficult fight, but this is a

comfortable game for Lisia Doll. And

they were saying that right until late

in the middle game when it became

obvious that Lisa Doll was losing. But

if we ask the modern AI about this game,

it will show you that actually right

from the opening the game was

comfortable for white for AlphaGo and

that winning gap would only increase

after this first fight. However, at the

end when it was already clear that Alph

Go was going to win the first game,

something interesting happened. Alph Go

seemed to have made a mistake. Let me

show you how that happened. At this

point in the endame, when everyone was

counting that white was a few points

ahead, here's what happened.

AlphaGo blocked here living with this

group and Lucid Doll played the

Atari. Now this seemingly starting a

co black takes and after this move

instead of playing this co Alphago just

makes second eye. The commentator said

that this was probably the first clear

mistake that AlphaGo made in this game

and it is true. White doesn't have to

protect like this. White can keep

playing this co considering how many

local co- threats white has in this

area. But there was something that they

didn't know and that was revealed a

little later about the style of AlphaGo.

AlphaGo's style was a little similar to

that of Lie Ho. That is not playing the

most aggressive moves killing the

opponent but rather trying to aim for

the most secure path to victory even if

it means playing some slack moves and

not trying to maximize the win or kill

something. So, at this point, these

slightly passive moves in the lower left

corner only meant that AlphaGo knew it

was winning this game, and it didn't

really matter if it was winning by 10 or

by three points. A win is a win. Lisa

Doll was shocked after the first game,

and he said at the press conference

after it that he believed now the

chances were 50/50. Probably at that

time, he was still underestimating

AlphaGo a little bit. Let's take a look

at game two. In this game, Alph Go is

playing black and the opening looks

fairly peaceful until now. Now Lisa doll

approaches the

corner. Black picks. White

stands. White jumps and white wants to

make this position a little higher.

So white plays here. Having played this

move, Lisa Doll left for a little smoke

break. And to his surprise, when he came

back, he saw this move played by

AlphaGo. All the commentators, everyone

watching this game live and Lisa Doll

himself were shocked when they saw this

shoulder hit on the fifth line because

we all know that a shoulder hit is good

when it's leaning on the opponent's

position and making it low. So, for

example, if white has a stone here, this

shoulder hit would be perfectly

reasonable, making white low on the

third line. But now that white is high,

this move seems to be giving white

territory on the fourth line. And yet,

just like Shuaku's famous ear redne,

this move seems to be tying the entire

black position together. It's helping

and developing the lower stones, helping

the upper stones, and also preparing to

save these stones here and start a fight

because at any moment in the future,

AlphaGo can connect here and try to

attack these white stones separately.

This move is preparing for that. Lisa

Doll said after the match that it was

this move that showed him that AlphaGo

could think and play creatively instead

of just looking for moves in a big

database. After losing the second game,

Lisa Doll was put on the verge of losing

the entire match. So, game three was

supposed to be his last stand and no

surprise there, he started a very big

fight in the opening. AlphaGo is white

in this game and it just played

here. Lisa Doll responds like this and

white responds with a very light move to

jump out of here. And would you just

look at that? Lisa doll attaches trying

to separate the white stones and start a

fight. AlphaGo parries this

way. Black

separates. White

pushes. Black saves the corner. The

black and white stones are all

disconnected and they will keep on

fighting like this for the next 50 moves

or so. And this is the result of that

fight that AlphaGo handled beautifully.

Nobody died in the process, but AlphaGo

got a big moyo at the bottom and a

comfortable lead in this game. Now we

skip a few moves forward and this is the

same game closer to the endame. Alpha

goes white and white has too many points

at the bottom. At this point, Lisa Doll

feels that if he doesn't invade this

area, if he doesn't live here, it

wouldn't be enough territory for him.

So, he makes this exchange

first. AlphaGo answers.

Then a few more exchanges in other

places. Now this atari

first white ignores and saves this

stone. And now the final

fight, an invasion. If Lisa doll lives

here, all of white's points will be gone

and AlphaGo will lose this game. So,

white needs to kill these stones. Let's

see how that's going to

work. White limits the

space. Black

attaches. White separates these

stones. Black attaches here. There's

some weaknesses.

So white

connects. Now, a tricky move to the

second line. Preparing to make shape to

the top and to play

here. Alph Go

separates. Leafy doll makes a double

tiger's mouth. White needs to block.

Black

pushes. This is white's

sente. And white

connects. Now

[Music]

push and

cut. And these white stones are also

separated from the rest of the

groups. White blocks. Black plays this

honey.

White responds

and black makes an eye and prepares for

a co. Many people believed at the time

that machines were weak at handling

co-fights because you know a co involves

a lot of factors. You need to calculate

all of the possible co- threats and

evaluate which one to respond to and

which one not to respond to. So we can

almost say that this co-ight was the

final test for AlphaGo and it did just

fine. After this co-fight, Lisa Doll

resigned the game. The score in the

match became three to zero and AlphaGo

won the match. But believe it or not,

and I think many of you know what I'm

talking about, the most spectacular move

in the match was yet to be played in

game four. In this game, AlphaGo is

playing black. And black has a scary

territory/mo at the top of the board.

Lacy Doll did a very good job at taking

a lot of points everywhere and leaving

those four stones up there lightly,

hoping to save them later in the game.

And the moment to show some miracles of

Sabaki is now AlphaGo just played here

trying to just swallow those stones in

there. White plays here threatening to

rescue

them and AlphaGo makes a very solid

shape. Connecting here would be

impossible for white. Black is too

strong around.

So, white cuts here as a

probe and black responds this way. Just

separating all the white

stones. Another useful

exchange. Black

blocks

this. Black captures that stone. And

next comes the move that only led Doll

could see.

white wedges. This move is like a bomb

explosion. It just activates all the AGI

in this shape at the same time. Of

course, now that we have superhuman AI

to tell us how things could have been

played, we know that actually black is

leading by about 10 points here and

nothing is changed by this amazing tuji.

Black should respond like this and after

this white shouldn't be able to get any

advantage out of this. But in that

particular game, at that moment, that

version of Alph Go couldn't find the

right move. It played

here. And this allows white

to play

this and then

cut. Now, there's a possible throw in

and snap back here, a cut here, this

atarian cut here, this atari. All of

those possibilities allow white to

escape with these stones through here.

Save everything. Alph Go started making

unreasonable moves, not being able to

understand how to win the game anymore.

And Lisa Doll won this game in the end.

Lisa Doll said after this game that he

had won many world championships, but he

was never congratulated and greeted this

much after winning just one game.

[Music]

[Applause]

[Music]

[Applause]

In the end, Alph Go won this match 4 to1

and Deep Mind showed the whole world

that the AI was now stronger in Go than

human players. But Deep Mind was not

going to stop there. The alpha go that

played against Lisa Doll was stronger

than the one that played against Fon

Huay. So what if we give it more time

and let it train more? In December that

same year 2016, we got an answer to this

question. A mysterious player registered

on Taien Go server and played 60 games

against all the strongest players in the

world and won all 60. Of course, this

was another version of Alph Go that

became known as Alph Go Master. Another

match between that version of Alph Go

and China's strongest player Ko was held

the following year and Ko never had a

single chance. That match was 3 to zero.

Alph Go got to superhuman level. Did

Deep Mind stop there? No, they didn't.

They released yet another version of

Alph Go in October the same year, 2017.

It was called Alph Go Zero and it was

entirely different from all previous

versions because no human games were

used for its training. It didn't start

from any human games at all. It

developed all of its skills and

strategies from scratch. So they it

started playing with itself just making

random moves at first and then little by

little it surpassed all previous

versions and then got above them. It

played a match against the previous

strongest version Alph Go Master. out of

100 games and it won 89 of them and lost

only 11. This shows that maybe we're

somewhat lucky that that Alph Go version

never played against any human player.

The results that it showed indicate that

maybe it was about three stones stronger

than Alph Go Lee, for example, which

means that if Deep Mind had waited

another year and allowed Alph Go Zero to

play that match against Lisa Doll, that

match wouldn't have been that pretty and

it wouldn't have been very close either.

Once DeepMind managed this gargantuan

task of developing a superhuman Go AI,

they published their findings and that

allowed other developers to replicate

their process and make equally strong

AI. Thanks to that, we now have several

of them. The strongest ones currently

being fine art and galaxy from China.

But at that level, it doesn't really

matter which one is the strongest. Even

Kate that is easily available for free

plays at the level that exceeds that of

any human professional player. Lisa Doll

said something interesting in one of his

interviews after the big match.

Something that struck a sad chord in my

Go player heart. People always believed,

he said, that they were creative and Go

was the perfect playground to show their

creativity. But Alph Go demonstrated

that people were actually very

conservative and conventional in their

playing and in their judgment of go

positions. From teacher to student,

knowledge would be passed on. Most of us

would just remember that this is how you

need to play here. The funny thing is

that now with AI being the new benchmark

of perfection, we've become just as

conventional except with a different set

of moves. Alph Go made a revolution in

the Go world. It changed opening theory

forever. A lot of new variations and

ideas were introduced that were

surprising in 2017, but now less than 10

years later, all of them are accepted as

the new golden standard. The changes

that the AI brought into the Go world

are so monumental and so numerous that

we'll have to take a very good look at

them in some of our future videos. Until

then, keep making creative magic on the

Goboard and don't overdo it with those

sunsign invasions. Okay. By the way, you

can also watch these lessons on our

platform

gomagic.org except there you'll watch

them with interactive quizzes right

within the lessons and practical

exercises right after them. And if you

enjoy watching these Go videos and you

don't want to miss others like this one,

go smash that like button, subscribe to

our YouTube channel, and this is Go

Magic.

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