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ALPHAGO TO ZERO

The Complete Games

Vol. 2: Undefeated: The Games of AlphaGo Master

by Michael Redmond 9P and Chris Garlock

Buy the full book at gobooks.com

From the historic AlphaGo-Lee Sedol showdown in Seoul in March 2016 to the release of AlphaGo Zero in November 2017, Michael Redmond 9P and Chris Garlock have had a front-row seat, commenting, analyzing and reporting as the AlphaGo AI upended thousands of years of human history. In this unique 4-volume series, they’re providing a comprehensive record of this astounding development, with extensive game commentaries, personal insights, photos, and videos.

Volume 1 covered the AlphaGo-Lee Sedol match in detail; Volume 2 explores the thrilling series of games that occurred in early 2017 when the mysterious player Master appeared and easily defeated dozens of top players, in a classic man vs. machine challenge. Delayed by the pandemic, this latest volume provided an opportunity to more closely review these remarkable games as AI continues to embed itself ever more deeply in our everyday lives.

Copyright Page

© 2023 Michael Redmond

All rights reserved. Reproduction in whole or in part is prohibited.

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Cover design by Mike Samuel

Conversion to Go Books by Anders Kierulf

Contents

ALPHAGO TO ZERO: The Complete Games, Vol. 2

Copyright Page

Contents

How to Use This Book

About the Authors

Introduction by Michael Redmond and Chris Garlock

Game 1: Pan Tingyu 1P (B)

The chapters in gray are included only in the full book.

Game 2: Zhang Ziliang 1P (B)

Game 3: Ding Shixiong 3P (W)

Game 4: Xie Erhao 4P (B)

Game 5: Yu Zhiying 5P (W)

Game 6: Li Xiangyu 3P (W)

Game 7: Qiao Zhijian 4P (W)

Game 8: Han Yizhou 5P (B)

Game 9: Meng Tailing 6P (W)

Game 10: Meng Tailing 6P (W)

Game 11: Chen Hao 5P (B)

Game 12: Wang Haoyang 6P (B)

Game 13: Liu Yuhang 1P (B)

Game 14: Yan Zaiming 3P (W)

Game 15: Park Junghwan 9P (B)

Game 16: Lian Xiao 7P (B)

Game 17: Lian Xiao 7P (B)

Game 18: Ke Jie 9P (W)

Game 19: Ke Jie 9P (B)

Game 20: Park Junghwan 9P (W)

Game 21: Chen Yaoye 9P (B)

Game 22: Chen Yaoye 9P (B)

Game 23: Kim Junghyun 6P (W)

Game 24: Park Junghwan 9P (W)

Game 25: Park Junghwan 9P (B)

Game 26: Yun Chanhee 6P (W)

Game 27: Fan Tingyu 9P (W)

Game 28: Meng Tailing 6P (W)

Game 29: Mi Yuting 9P (B)

Game 30: Tang Weixing 9P (B)

Game 31: Li Qincheng 9P (W)

Game 32: Gu Li 9P (B)

Game 33: Gu Li 9P (W)

Game 34: Dang Yifei 9P (W)

Game 35: Jiang Weijie 9P (B)

Game 36: Gu Zihao 5P (W)

Game 37: Park Yeonghun 9P (W)

Game 38: Tuo Jiaxi 9P (W)

Game 39: Iyama Yuta 9P (W)

Game 40: Meng Tailing 6P (B)

Game 41: Kim Jiseok 9P (B)

Game 42: Yang Dingxin 4P (W)

Game 43: Kang Dongyun 9P (W)

Game 44: An Sungjoon 7P (B)

Game 45: Shi Yue 9P (W)

Game 46: Lian Xiao 7P (B)

Game 47: Tan Xiao 7P (W)

Game 48: Park Junghwan 9P (B)

Game 49: Weon Seongjin 9P (B)

Game 50: Ke Jie 9P (B)

Game 51: Chou Chun-hsun 9P (B)

Game 52: Fan Tingyu 9P (B)

Game 53: Huang Yunsong 5P (W)

Game 54: Nie Weiping 9P (W)

Game 55: Chen Yaoye 9P & Meng Tailing 6P (B)

Game 56: Cho Hanseung 9P (W)

Game 57: Shin Jinseo 6P (W)

Game 58: Chang Hao 9P (B)

Game 59: Zhou Ruiyang 9P (W)

Game 60: Gu Li 9P (B)

Masterpieces: AlphaGo vs. AlphaGo

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About Go Books

How to Use This Book

This e-book takes full advantage of the multi-platform power of electronic books, providing playable game diagrams and links to video game commentaries as well as hyperlinked text and photos.

To get the most out of the game commentaries, we recommend:

Reading the introduction and

viewing the brief game overview video before

diving into the detailed commentary.

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If your tablet is set to dark mode, then you can’t see the diagrams as all the black lines and stones disappear. Changing it to regular mode makes the diagrams normal.

Diagrams

Review each diagram and the comments, then use the controls (see below) to play and replay the sequences.
TAP on the board to bring up the move controls:

▶ To play through the moves automatically

＜ To peel back (reverse) the moves, one at a time

＞ To go forward in the move sequence, one at a time

Videos and SGFs

To see video commentaries on each game, click on the screengrab (or link) at the beginning of each commentary.

Comments, corrections and suggestions welcome: please email them to us at books@gobooks.com.

About the Authors

Michael Redmond 9P

MICHAEL REDMOND is a top professional go player as well as a popular commentator. Born in California, he moved to Japan in 1977 at the age of 14 to begin studying to be a professional go player. Redmond became a pro in 1981, made the quarterfinal in the fourth Fujitsu Cup World Go Championship in 1991 and the quarterfinal in the fifth Dongyang Securities World Cup in 1993. He has participated in the final sections of 16 international pro tournaments and in 2000 was promoted to 9P, the highest professional rank. In 2018, Redmond achieved 600 wins in official pro tournaments. A longtime popular TV commentator in Japan – in 2000 he won the TV Program Producer Society Prize – Redmond teamed up with Chris Garlock to provide the English-language live video commentary for the historic 2016 match between Lee Sedol 9P and the AlphaGo AI, as well as the subsequent series of AlphaGo commentaries on the AGA’s YouTube channel. Redmond lives near Tokyo, Japan.

Chris Garlock

CHRIS GARLOCK is the Managing Editor of the American Go E-Journal and Vice President of Communications for the American Go Association. The E-Journal is the most widely-read English language go publication in the world and Garlock coordinates its online and broadcast coverage of go tournaments and events. He’s helped pioneer electronic and broadcast go coverage, including live broadcasts of top-board games with real-time commentary from venues around the world. An amateur 3-dan go player and avid tennis (and now pickleball) player, Garlock lives in Washington, DC with his wife, Lisa, an art therapist.

Introduction

by Michael Redmond
and Chris Garlock

Garlock and Redmond

In the first days of 2017, rumors started to ricochet around the online go community. A mysterious online player had been making huge waves by defeating dozens of top professionals on go sites in Asia in recent days.

“Master” first appeared on December 30, 2016 (Beijing time), registering from Korea. Achieving 30 consecutive wins against many former and current world go champions, Master defeated Park Junghwan four times and Ke Jie twice. After that, Master appeared on a different go site and logged another 30 consecutive wins. That made it 60 games in a row with no losses. Was AlphaGo the Master?

Michael Redmond 9P had been on holiday when the games were being played but word had of course spread swiftly through the professional community about the mysterious online player racking up win after win. “That kind of record was simply mindblowing,” he says, “and even before I got the game files off the internet it was clear that something new was happening. Anyone – or anything – that could win 60 straight games could probably give a 2-stone handicap, and these were top professionals who were losing to a player no-one had ever heard of.” The 60 Master games were not only evidence that AlphaGo had attained a whole new level of play, but an incredibly rich “treasure trove for professionals,” says Redmond.

In March 2017 Redmond and Garlock began a series of commentaries on the Master games, completing 16 by early May. In June 2020, they launched their AlphaGo vs. The World series, providing brief commentaries on all 60 Master games, directed and produced by Stephen Hu.

In this book, Redmond and Garlock return to these games with brand-new commentaries by Redmond, along with links to their previous video commentaries. In May 2023, more than six years after Master first stunned the go world, Garlock chatted with Redmond about how their understanding of these games has changed, and about the impact AI is having on the game of go today.

Chris Garlock
You’ve spent a great deal of time reviewing this particular set of commentaries, first and foremost, in 2017 when the Master games first appeared, then for our AlphaGo versus The World series and then again for this book; I want to get just sort of general thoughts about the games looking back and then another thing I want to ask about is that a big difference between when we did the initial commentaries in 2017 and subsequently is that you have had access to AI analysis tools, so I want to talk about how that has changed how you look at these games. I’d also like to get your suggestions about the best way for readers to use the book and then just a general final question; it’s been six years now since the original Master games; Where will the game go in the future?

Michael Redmond
Well, to begin with, Master was not nearly as weird as Zero was. Master actually looked a lot like a human player. I guess because it started with the seed of human games, that made it play shapes that we felt comfortable with. It didn’t even play the three-three point invasions very much; I think I counted just three times it played the three-three invasions earlier than I’d expected and when I look at it now it was like the earliest was move 19 and that’s really late by modern standards. It was also the fact that when Master did play something that looked unusual to us it was generally so successful that it was easy to agree that this was working, and I think that was at least partly because it was playing online against human players in a limited amount of time.

Because Master was playing fairly human moves, I think that’s what made me like it so much. Also, those games had an immediate effect on the way professionals play go.

Chris Garlock
Can you be more specific?

Michael Redmond
The shoulder hits that it played against corner enclosures, the way it never slides into the second line and punished players who did: immediately people stopped playing that slide to the second line after playing a kakari to the star point. Now you see people playing an attachment and the double-hane all the time even though that was thought to be wrong before Master.

Chris Garlock
We reviewed some of the Master games in early 2017 and then reviewed the entire series in 2020 when we did the AlphaGo vs. The World series, and now you’ve gone back and redone those commentaries for this book. You didn’t have access to AI for the first reviews, had it for the AG vs The World, but obviously you’ve become much more expert at using AI to analyze positions since then; why did you decide to go back and take a much closer look at these games?

Michael Redmond
Well, Master looked like a much stronger player than the Lee Sedol version; it was seeing the Master games that convinced me that we were probably not going to be catching up, and I think the reason that I revisited these games so much was because my understanding improved a lot with the tools I was using. Because at the time of the Master games, we did not have access to AI and then we had very limited access after that. In fact, the first AI I used wasn’t very strong; it gave me some insights but it wasn’t all that useful and so when I was looking at these games there were still huge gaps in my understanding of what was going on. So in my original Master commentaries I was struggling with that and when I look at them now the answers I found didn’t seem to be very convincing. But I’ve become more proficient with the latest AI tools, it’s amazing to see how steady Master is. Many of the games that professionals play now are three hours each, and still the win/lose graph goes all over the place, so Master’s ability to play good moves in any position is still sort of amazing.

Chris Garlock
I want to hear more about how you’ve learned to use AI tools and how that’s changed over the last few years. I think of things like cooking or tennis, where I’ve gotten better knives or better racquets, but they’re not that helpful without improved technique and experience.

Michael Redmond
The big difference when I started to use KataGo was that it was giving a point score, and the point score was fairly accurate. So adding that to the winning percentage, it gives me a much better idea of exactly where the game is. So because the winning percentage was very difficult to use, it was misleading because first of all, anything up to about 70% is, it’s not a big deal for humans. It’s close enough to an even game that professional players don’t really have to worry about. And then later in the game you get something like 80%, 90%, 95%, 99%, and it can still be a half-point difference. So having the point score on one side is really helpful in completing my understanding of what the computer, as I might say, what the computer is trying to tell me. And it gives much more flow and information. So that was a huge leap forward in my understanding of the numbers, and I really depend on that point score very much.

Chris Garlock
Are you still using KataGo? Did you use it for the new Master commentaries?

Michael Redmond
I’m using an old version of KataGo; it’s sort of at the limit of what I feel comfortable with. I think there’s some new versions that I haven’t gone to yet. But I’m okay with it.

Chris Garlock
How do you use it?

Michael Redmond
I look through the game for a point where I think that it went one way or the other and I focus on that point. My most important comments revolve around trying to find and understand the losing move. The AI win/lose graph makes it a lot easier to find key points in the game, so I can start with that. And then as I look through the game, there’s going to be points where I wonder if that’s really true, or if I have some questions of my own, and of course, the interface allows me to put moves in and see what the AI says about that, so I can test variations.

Human understanding of the middle game was pretty limited. Before AI, I could put a lot of time in it and finally get my own answer, but working out the rest of the game was always tough. So using the AI has helped me with my understanding of turning points in games. It’s also helped me with my understanding of the opening, and I think it actually is getting better, and the younger players are a lot stronger because they’re starting with a good opening.

Chris Garlock
In looking through the new commentaries on the Master games, they seem much more simplified, they’re very elegant. Was that intentional or just sort of the way that it happened as you went through these games again?

Michael Redmond
So I think it’s partly because Master did not make any simple mistakes. So the win/lose graph, if you look at Master’s games, it goes up for Master and then it stays there. And so that does simplify the game and means I could focus on where the game was going where the human still had a chance to win the game, to have an even game. And so there’s this key point in the game every time, which created a pattern for my commentaries. I was looking at a position where I was making suggestions as to how the player could have tried to win the game. And then after that, all I have to do is explain the meaning of the moves, which is maybe things that some of the viewers will not know without my saying it, but basically, they’re just relatively easy comments and they’re simpler because they’re basic knowledge.

Chris Garlock
Well, that sets up the next question, which is What’s the best way for readers to use this book? There’s a lot of games, even with the comments being simplified, you’ve got a lot of comments, a lot of diagrams. I think there are probably a couple of different readers. There are folks who are sort of interested from a historical standpoint, and there are folks who are active players, who are looking to improve. And so maybe they’re looking for some keys that they that they can take away. Thoughts or suggestions on how to use the book most effectively?

Michael Redmond
These Master games feature very human openings because one player is always a human. So I think that it’s going to be relatively easy for people to find an opening that they like, and I think you want to learn from the openings mainly at first.

Readers can also learn from comments in the middle game; just basic knowledge about shapes and things like that.

Chris Garlock
Because these go AI tools are now so readily available – I have one on my iPhone – why do people need a professional when they’ve got an AI in their pocket that is so much stronger than any professional? What’s the value that somebody like you or a go book brings to our understanding of the game?

Michael Redmond
The problem with AI is that they don’t speak in a human language, so they’re just giving you winning percentages and stuff like that. It’s not really communication. I think that we human players need to attach a story to something in order to understand it. We’re just storytellers. So I think my job as a commentator is to start with what the AI has given me or test what I want to say and get a result there, but then I have to explain why it’s working. So that’s what I’m doing here is explaining at least my take on why the computer is showing such a move or why this is getting a better score.

Chris Garlock
Right. We can’t play the perfect move every time, so even before AIs, even if you could find one really good move, if you didn’t have not just one follow up, but probably five, six, maybe 10 follow ups to that move, you could just crash and burn. I think this is sort of the same thing on steroids, right?

Michael Redmond
Right. And the funny thing is that when I look at top players today who have come up using the AIs, they actually have what seems to be a fairly direct understanding of what the computer is saying to them, so I think it’s changing the way that professional human players are understanding the game. The way they think seems to be changing a little bit to make a closer match with the AI; that’s something really interesting.

Chris Garlock
Can you talk a little bit more about how you think the game is changing?

Michael Redmond
Well, I think it’s mainly the language they use when they talk about the game. It does have more percentages in it and stuff like that. And they’re sort of getting acclimatized to understanding what the percentages actually mean. One of the problems is that when we have a percentage and we’re looking at a position, it really depends on a number of factors. I don’t understand some of the ways people interpret the numbers. It’s very hard for me to articulate it but the younger players do seem to have kind of a more direct connection. When I talk about computer analysis of a game, what I do is I completely translate it into a human story, but younger players don’t seem to get that far; they seem to be drifting away from the human explanation that I think is necessary.

Chris Garlock
Before we had GPS, we had maps, and you had to know how to read and understand a map. Then we had MapQuest, which provided clear, turn-by-turn directions, but if you missed a turn, you’d be in big trouble and could easily get lost. I’m wondering if we’re seeing a similar thing in go AIs – and perhaps this applies to AI in general – in that it’s very powerful and often right, but not always, so we still need to be able to understand their strengths and weaknesses?

Michael Redmond
Sure; if I’m using GPS I know that it has a tendency to direct me to big main streets and so I can just ignore it sometimes and it’s going to recalculate, so that’s sort of similar to these young players where they’ve used the go AI to the extent that they just they don’t have to reason it out and they have a kind of a gut feeling about what the numbers mean in this particular type of position, and it sort of links to an intuitive understanding of the position that they already have and the numbers and the link there is more direct, some more natural link where they can understand that in this position the numbers might mean more or less depending on various things.

But I was talking about signs and also the human understanding of the position does work with the computers because with experience we just even know to a certain extent that this is a position which will affect the numbers in a certain way. It’s very important to know that it’s actually just an average of various positions.

So right now all professionals are very dependent on AIs, they’re using it as a kind of a starting point. Before AIs, we did start new openings, we did have new moves, but it took a long time for us to understand it, so the AIs are actually sort of jump-starting that. These days, players start with an evaluation of the move, and they can move on from there and make variations and investigate complicated variations of the joseki. It’s the same process.

And sometimes you can’t always fully trust what computer tells you. So in the end, the humans have to have their own kind of set of variations for the local fights; there’s still moves that are missing sometimes if you just simply trust the computer, so you have to really investigate these joseki variants.

And so yeah, so it’s going to continue to improve the game. And at the top levels, interesting things are happening because some players have their favorite openings, which they research and they have secret weapons; it’s really exciting.

– Michael Redmond and Chris Garlock
June 2023 Tokyo and Washington, DC

Game 1: Pan Tingyu 1P (B)

CLICK HERE to watch the Game 1 video

Born in Taipei, Taiwan in 2001. Became 1P in 2015, promoted to 4P in 2021. He was 15 at the time of this game.

Pan, taking Black, plays a modern version of the mini-Chinese, and Master shows a new move in the upper left corner, which has since become the standard move for White in the Chinese opening pattern.

In this series of games, we see a very gradual procession from an even position to a slightly advantageous position and then a definitely advantageous position for Master. In the process, Master is not doing anything unusual, it’s playing very normal looking moves, very steady moves, and allowing the human player to, you might say, make the mistakes or make the challenges.

This has always been a characteristic of a strong player. The strong player doesn’t need to do anything really fancy to take an advantage, just play naturally good moves, and the weaker player sort of falls by himself. We were seeing that in this game; against a top professional, it was obvious that Magist (as AlphaGo Master was going by then) was not just a normal player.

White: AlphaGo Master
Black: Pan Tingyu 1P
Komi: 6½ points
Date: 2016–12–29

Moves 1–19

An example of how White might play if Black extends at 1. The hanging connection at 10 is the way to create aji for a ko later with potential for eyes on the side.

Moves 20–21

7: connects

While allowing White to capture the stone at A might seem to be bad for Black, this is actually getting a better score than the game sequence. White’s group does not have eyes, and Black is ready to attack at B next.

Moves 22–26

Move 27

This is where the game started to go downhill for Black, and in a way that I could understand. Most young pros now would just invade at A, and that would have been ok for Black. Black could not attack on the top, so just living on the side (as happened in the game) was not good enough.

Moves 28–31

When White answers A at 4, Black can escape with this sequence and White’s group on the left is in trouble.

I still very much like this variation that I discussed in the video and consider it to be better than the game. Since Black could not attack on the upper side he should have tried for some kind of a trade. Probably Black should not have started this fight by playing at A, but this variation seems the best locally.

Moves 32–48

Move 49

I never understood this move by Black and would call it the losing move, but Black was behind already.

Moves 50–58

Moves 59–71

Black has decided to take as much territory as possible and hope for a successful invasion on the lower side.

Moves 72–100

Moves 101–110

Move 111

Black managed to live, but is still behind in territory! White does not need to kill Black’s group in the center.

Moves 112–128

Move 129

Black makes a final try, which turns out to be a suicide mission.

Moves 130–146

146 moves. AlphaGo Master wins by resignation.

AlphaGo to Zero – The Complete Games

Volume 3

Masterpieces: AlphaGo vs. AlphaGo

To mark the end of the Future of Go Summit in Wuzhen, China in May 2017, DeepMind released a set of 50 AlphaGo vs AlphaGo games, played at full length time controls, in which AlphaGo became its own teacher, playing millions of high level training games against itself. The games, DeepMind suggested, “we believe contain many new and interesting ideas and strategies.”

World champion Shi Yue 9P said the games were “Like nothing I’ve ever seen before – they’re how I imagine games from far in the future.” Said fellow world champion Gu Li 9P, “AlphaGo’s self play games are incredible – we can learn many things from them.”

In Volume 3, Michael Redmond 9P and E-Journal Managing Editor Chris Garlock – whose 2017–18 series of video commentaries were even more popular than the previous AlphaGo videos – bring their expertise to bear on this mind-bending set of AI vs AI games. Unlike the Master-human games, which generally ended fairly quickly, the AG-AG games are dramatic roller-coaster rides as groups die and then come back to life, and as the lead shifts from white-knuckle move to white-knuckle move.

You won’t want to miss this exciting next chapter in the AlphaGo story!