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ALPHAGO TO ZERO

The Complete Games

Vol. 1: AlphaGo vs Lee Sedol 9P

by Michael Redmond 9P and Chris Garlock

Buy the full book at gobooks.com

Lee Sedol takes a smoking break during Game 1

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 AlphaGo upended thousands of years of human history. Since then they’ve released a comprehensive series of videos and game commentaries analyzing all phases of the AlphaGo phenomenon. In Volume 1 of a 4-volume series on AlphaGo, Redmond and Garlock take an in-depth look at the March 2016 showdown between AlphaGo and Lee Sedol 9P, with new insights into the match and each game, links to videos and commentaries, and photos, including some never previously published. As the AlphaGo Team says in their introduction, “Chris and Michael experienced first-hand the anticipation and excitement felt by the entire AlphaGo team, and understand better than anyone the significance of these games in showcasing what can be achieved when human and machine come together to solve complex problems.”

Copyright Page

© 2020 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 and Myron Souris

Contents

ALPHAGO TO ZERO: The Complete Games, Vol. 1

Copyright Page

Contents

How to Use This Book

About the Authors

Foreword by the AlphaGo team

Introduction by Michael Redmond and Chris Garlock

Lee Sedol – Game 1: Uncharted territory

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

Lee Sedol – Game 2: The “Hand of God”

Lee Sedol – Game 3: Play your own game

Lee Sedol – Game 4: Revenge of the humans

Lee Sedol – Game 5: Thinking outside the box

AlphaGo vs Lee Sedol 9P: The Short Version

Undefeated: The Games of AlphaGo Master

Buy This Book

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.

EPUB

This book is available for the Go Books app on iOS and Mac (see gobooks.com/devices.html) as well as EPUB for any platform. For reading as EPUB, please check gobooks.com/epub.html for our up-to-date recommendations on EPUB readers that work best with this book.

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 15-minute video overviews of each match, click on the screengrab at the beginning of each commentary; we also have links to the full video game commentaries at the end of each commentary and you can also check out our 90-second summaries here.

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.

Garlock and Redmond enjoy a pre-match coffee

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 player, Garlock lives in Washington, DC with his wife, Lisa, an art therapist.

Foreword by the AlphaGo team

The AlphaGo team in Seoul

Go has to be one of the most beautiful and beguiling games ever devised. For decades mastering the kind of intuitive thinking needed to deal with its complexity and rival the astonishing skills of professional go players was thought to be beyond the scope of even the most advanced computer systems. Which was precisely why we, the DeepMind AlphaGo team, were drawn to it.

DeepMind was founded in 2010 with the aim of creating intelligent systems that can learn how to solve any complex problem without being taught how, and then use these systems to help find solutions to some of society’s biggest challenges and unanswered questions. Put another way, we want to solve intelligence and then use it to solve everything else.

Perhaps surprisingly, we use games as a way to evaluate the performance of our systems, and as both players and enthusiasts, we knew the most complex and subtle board game of all is go. It was with this in mind that we approached AlphaGo, a project that would prove the challenge of a lifetime, leading us on a journey that was at times both tense and exhilarating and one that would have an impact on the world of AI and the game of go that none of us could have foreseen.

It began in 2014, when lead researcher David Silver, lead programmer and computer go expert Aja Huang and Chris Maddison, then an intern, began to design what would become AlphaGo. Until then, most go-playing systems – including Zen, Deep Zen and Crazy Stone – used preprogrammed, hand-crafted rules that tried to account for every eventuality in a game. While there had been a lot of progress using these techniques, we decided to try something new. We chose to create a system using algorithms loosely modelled on the brain, known as neural networks, coupled with a simple but powerful technique known as reinforcement learning that can be used to incrementally train a system through a process of trial and error.

Initially, the team’s sole aim was to prove that a neural network could learn to hold its own against a strong amateur go player. But by late 2014, the prototype of AlphaGo was already able to predict the moves of a professional player with around 55% accuracy. This early promise led to the creation of a larger team with a broader range of skills, including engineering ace Julian Schrittwieser, who worked together to refine the system and improve its level of play.

For all of us, this proved an immensely creative and collaborative time as we pulled together to share and explore a range of different ideas and approaches. Led by Dave, we were able to unite a diverse range of skill sets and navigate the tricky road towards success. But because AlphaGo represented uncharted territory, and there were no similar systems we could test its strength against, we had to benchmark it against human play.

This proved immensely difficult to plan for however, forcing us to challenge a professional go player before we were even sure that AlphaGo was strong enough to meet the demands of the match. The first game took place in October 2015 when AlphaGo challenged European go champion Fan Hui. Before the match, we were all nervous and unsure whether AlphaGo could compete against such a skilled player. Throughout, Fan Hui played with tenacity and great skill, but in the end AlphaGo won in five straight games, becoming the first computer program to defeat a human professional at the game of go. But all of us knew that we would not know the true potential of our system until it took on a player at the very top of the game.

The tournament match against Lee Sedol in March 2016 is now written in the annals of AI history. But when we arrived in Seoul, we were still filled with uncertainty. Following the Fan Hui games five months earlier, AlphaGo had only been calibrated against older versions of itself and so we had no way of knowing if it could rival the skill of a legendary player like Lee Sedol. Thankfully, the hospitality and warmth of the South Korean people did a lot to alleviate our fears. For many of us, it was our first time in Seoul, and nothing could prepare us for the level of interest in both the games, and our team. One of the most memorable moments from our trip was watching with awe as the matches were broadcast live on electronic billboards around the city. We later found out that the match had been watched by more than 200 million people worldwide.

Witnessing the poise and charisma with which Lee Sedol played his games must surely be the greatest privilege of all, however. In spite of AlphaGo’s eventual success, Lee Sedol played with inspiration and creativity, winning the fourth game deservedly after a series of masterful strokes. In spite of AlphaGo’s overall win, human ingenuity in this challenging domain was on full display. On account of AlphaGo’s unique style of play – demonstrated most vividly in the now celebrated Move 37, which showcased the full extent of AlphaGo’s creativity – many new approaches emerged that both challenge, and add to, centuries of received wisdom.

In January 2017, we revealed that a new prototype of AlphaGo had played a series of unofficial online games against some of the strongest professional go players on the Tygem and FoxGo servers. This was an improved version of the AlphaGo that had played Lee Sedol in 2016. Over one week, “Master” or “Magister”, played 60 games at fast-time controls, winning all of them. Again, we and the community took delight in the innovative and successful moves AlphaGo played during these matches.

A later version of AlphaGo took us one step further. Unlike its predecessor, which was trained on human games, AlphaGo Zero learnt entirely from scratch and was free to develop its own strategies – wholly unique and unconstrained by human play.

To explain these strategies, their origins and their impact on the game of go, Chris and Michael have written this invaluable book. No two people are better placed to take you on the journey from AlphaGo’s earliest games to the recent discoveries of its latest version, AlphaZero. With ring-side seats throughout the complete Lee Sedol games, Chris and Michael experienced first-hand the anticipation and excitement felt by the entire AlphaGo team, and understand better than anyone the significance of these games in showcasing what can be achieved when human and machine come together to solve complex problems.

– The AlphaGo team

Introduction by Michael Redmond and Chris Garlock

Garlock and Redmond

Why a book on the AlphaGo games? Isn’t that format as obsolete, as, well, human go after AI’s decisive triumph? After all, the AlphaGo AI has not only beaten the world’s top go players, but successive versions have become even stronger, culminating in AlphaGo Zero, which learned the game on its own and surpassed the Master version in just three months.

From the historic AlphaGo-Lee Sedol showdown in Seoul in March 2016 to the release of AlphaGo Zero in November 2017, we have had a front-row seat, commenting, analyzing and reporting as AlphaGo upended thousands of years of human history. Like millions around the world, we have been amazed and enthralled as this dramatic story has unfolded before our eyes. This book is our attempt to capture that story, to tell you what we saw at the time, what we see now, and to try to place this moment in the history of the game, to get a sense of where we may be headed.

In this series of books, we’ll look at games from four distinct “chapters” in the AlphaGo story: the AlphaGo-Lee Sedol showdown in March 2016 (Volume 1), the Master-pro series in January 2017 (Volume 2), the Master self-play games released in mid-2017 (Volume 3, which will include the Fan Hui and Ke Jie games), and the AlphaGo Zero games released in December 2017 (Volume 4). At the beginning of each chapter, we’ll provide an overview of the games and their place in the overall AlphaGo story, as well as some of our own personal recollections and perspectives. The game commentaries are based on Michael’s original commentaries but include new comments based on Michael’s accumulated analysis, which we should note includes reviewing every AlphaGo game ever released.

Note that neither of us are programmers, so this book won’t explain how AlphaGo works technically. Instead, we’ll tell the story of this match as we saw it, and its impact on the players and the game.

Looking back now, Michael can see a continuity in the way AlphaGo changed its bias for opening moves and ended up with the big shimaris, shoulder hits, and 3–3 invasions. There are also some games that show new moves that had a big impact on conventional opening theory and we’ll take a look at those as well. Watch too for Michael’s new comments about AlphaGo in general, about how it changed as it became stronger, how it changed the way we play go, and how his opinions of its moves have changed in some cases.

The invention of the printing press gave us the book, capturing oral traditions and bringing literacy to the masses. But books have never really been static objects; when William Shakespeare died in 1616, only about half of his plays had ever been printed, yet multiple editions have proliferated since then, in every language and every media. In our own lifetime we have progressed from printed records of go games to increasingly sophisticated computer go game recording programs to video commentaries. Each medium has its strengths and weaknesses. In this book – which is really an e-book – we’ll attempt to take advantage of the former and minimize the latter, providing pages that can be printed – or viewed as if printed – but also including variations that are playable on your computer, as well as links to sgf and video commentaries.

When AlphaGo defeated Lee Sedol, some said it was the end of go. As we have seen since then, it is rather more likely that in many ways the game has just begun.

(l-r): DeepMind CEO Demis Hassabis, Lee Sedol, former Alphabet executive chairman Eric Schmidt

We’d also like to express our admiration and appreciation to the entire DeepMind AlphaGo team. They were the heart and soul behind the machine that brought us this match. The global drama, the historic aspect, the quality of the games, the brilliance of both AlphaGo and Lee Sedol, as well as the staggering amount of media coverage was an incredibly generous gift to go. In addition to bringing go to new audiences, Google DeepMind donated the $1 million in prize money to UNICEF, STEM (science, technology, engineering and math) charities, and go organizations.

Very special thanks to our entire AlphaGo to Zero book team – Anders Kierulf, Myron Souris, Phil Straus, John Pinkerton and Mike Samuel – who each contributed mightily yet with great patience and good humor over the last three years as we wrestled with a series of technical challenges as well as the usual endless drafts and revisions.

The AlphaGo-Lee Sedol match delivered beautiful games, but more than that, it did what go always does: it brought people together in friendship and cooperation, and that, like the game itself, is beautiful.

– Michael Redmond and Chris Garlock
March 2020
Tokyo and Washington, DC

AlphaGo vs. Lee Sedol, Game 1

Uncharted territory

by Chris Garlock

The crush of reporters at the swanky Seoul Four Seasons Hotel was intense. Michael Redmond and I had been brought down to the pre-match press conference through a back stairway and were startled to emerge into such a huge throng of reporters, photographers and camera crews. But we commentators were just the appetizer for the real star, Lee Sedol, who passed just inches away before being swallowed up into the crowd. He seemed so small, so quiet, to be carrying such high expectations, not just for his fellow go players around the world, but for an entire species.

As we looked out at the ballroom jammed with hundreds of reporters, Michael and I looked at each other and laughed. Go had finally, amazingly and unexpectedly, arrived on the world stage. We didn’t know what the outcome would be but it was clear that our little world would never be the same. What we didn’t fully realize then was that everyone’s world was about to change.

Rumors of a strong go-playing computer program had circulated in the go community in the Fall of 2015, but they were vague and unsubstantiated. As the Managing Editor of the American Go E-Journal, one of the few English-language go publications in the world, it was my job to track any and all go-related news and these rumors were tantalizing. But go-playing programs had been in the mix for years and, like so much software – and there were plenty of go players who hailed from the computer and software worlds – they promised much more than they delivered, never playing much better than a reasonably skilled amateur. In fact, in one of the first tournament games of my own amateur career, I’d played a computer program in a New York City hotel back in the late 1980’s and beat it handily.

Then in late January 2016, news broke that a go-playing AI called AlphaGo had beat professional go player, Fan Hui 2P. Not just beat him, but swept the match 5–0. The shocking news rocketed around the world, receiving international coverage including write-ups in the New York Times, Bloomberg News, the BBC, Wall Street Journal, MIT Technology Review, Wired, NPR and blowing up on the AGA’s Facebook and Twitter feeds.

And there was more shocking news. AlphaGo had challenged Lee Sedol 9P to a million-dollar best-of-5 match. Beating Fan Hui was absolutely impressive; he was a top European player with multiple European championships, but Lee Sedol had been the #1 player in the world for much of the previous decade, with a staggering collection of top professional titles. While Myungwan Kim 9P admitted that “I was shocked at how AlphaGo played,” and told the E-Journal “It played like a human professional,” he added that “I am sad that this computer program might beat me, but I don’t think it can beat Lee Sedol.”

Barely two months later, here Michael and I were in Seoul at the Four Seasons, under the glare of television news crew lights, with the whole world watching … a game of go. It was surreal.

Snapshot from one of our walks

Michael and I had arrived the day before the match, giving us a chance to settle in. Michael of course has extensive experience doing television go commentary for NHK TV and we’d worked together many times previously, but as we met the crew Google DeepMind had hired to produce the AlphaGo show, we realized we were at a whole new level. These were world-class professionals, many of whom had worked on producing videos for events like the Olympics or the NFL. Michael and I had talked beforehand about the challenges we’d have to deal with. We would have a global audience that fell into two groups. The biggest group were those who’d never heard of the game before, while the other was our community of go players. So we had to be able to simultaneously explain basics about the game while diving deep into the tactics and strategies that even some of our core audience might not fully understand. While this played to our strengths as a professional-amateur team, it also created a dynamic tension around balancing our commentary that continued throughout the match.

One of the techniques that Michael and I have developed over the years of working together is to take long walks, partly for health and relaxation, but also as an opportunity to talk through ideas about our commentaries. What worked and why, what didn’t, and what changes might work better. Michael might go off on a long tangent about go history while we’re strolling through a Beijing housing development, and then we’d talk through how much of that to use in the commentary.

Michael Redmond talking with Christopher Maddison and Thore Graepel from the AlphaGo team

But for the commentary on this high-profile match between man and machine, we were entering a lot of uncharted territory. Neither of us knew much about AI in general or about AlphaGo specifically. We got to know most of the DeepMind crew – we’d often breakfast with them at the Four Seasons over custom-made omelettes – pumping them for insights and inside info, but while they were friendly and happy to explain basics, they understandably kept their cards fairly close to the vest, especially during the early matches when the outcome was still in question. Several of the team came on-air to discuss AlphaGo but we never did get into the control room during the match.

So as the timer clicked down the last few seconds to Game 1 on March 9, 2016, like the millions around the world watching, we waited to see what would unfold before us.

CLICK HERE to watch the Game 1 highlight video

White: Google DeepMind AlphaGo NR
Black: Lee Sedol 9P
Komi: 7½ points
Date: 2016–03–09
Event: Google DeepMind Challenge Match #1

Commentary by Michael Redmond 9P. All commentary from 2016 unless otherwise noted.

Moves 1–5

Move 6

I came into this match with expectations that Lee Sedol would be playing a fuseki that he was comfortable with, and just push it into a fight as he normally does. In general, foreseeing his actual moves is a bit difficult, as he can play a great variety of openings, such as komoku and shimari, komoku and star point, etc. At this point in the game I was expecting a move on the upper side any one of which would be accepted to be good by most pros.

Move 7

This is unorthodox, and probably just a bad move, in that Black has a lopsided position with too many stones low on the right side. Because this move has never been played in the past by a pro, I immediately got the idea that Sedol was trying to find a weakness in the program, assuming that playing a move that was not in its database would cause the program to play bad moves. I got into trouble with the programmers for voicing this idea in the live YouTube program, for it is completely wrong. I soon learned that AlphaGo has a policy network that is built from, but not limited by data from human games. The policy network can find move options that look promising even in a brand new position such as this.

From the start I questioned Sedol’s insistence in trying to find a special weakness in the program, as I think he would have played better if he had just played his best moves, instead of trying to trick the program into making a mistake. In hindsight, I feel confident in saying that his strong attachment to this idea was self-defeating.

2020: Black’s move looked bad in 2016, and more recently, the engine analysis has agreed that it is bad indeed. Now I would say that the ‘AI’ style is a big shimari (corner enclosure) in the upper right.

A common mini-Chinese opening.

The relatively new mini-Chinese.

This is slightly unusual, but not necessarily bad. Black is inviting a fight on the upper side. I have the idea that Lee Sedol likes to play a shimari when he has two 3–4 points, while he sometimes plays mini-Chinese when he has a hoshi in the lower right corner.

Move 8

Move 9

2020: While Black’s pincer here looked perfectly natural to me in 2016, I now know that most AIs, and most young pros, will almost always play an attachment underneath at A.

Move 10

White’s attachment in the corner is unusual (the jump at A would be a more common move), but is sometimes played when White wants to settle this group quickly, and avoid a fight. Because Black has stones on the upper and right sides that will participate in any fight that starts here, this move seems to fit in well with the overall position.

Moves 11–14

Move 15

2020: In 2016 I had the idea that Black should have tried to gain a tempo with the peep.

2020: My idea from 2016 was for Black to peep once at 1, preventing White from playing at B. Then Black would have had sente to protect the upper side. For me, this still seems a logical way for Black to play.

Moves 16–21

Move 22

Black has forced a fight on the upper side by being greedy on the right side. The Black stone at A is badly positioned, as it would be better at B.

2020: I think Sedol had the idea to create an unbalanced position and complicate the game, in the hope of finding a weakness in AlphaGo. When he was lacking in knowledge of AlphaGo, maybe it was worth trying, while now I would say it was probably counter-productive.

Moves 23–25

Move 26

Up to this point I was wondering if AlphaGo would be avoiding complicated fights, but now AlphaGo plays strongly to punish Black’s weakness on the upper side.