Burrowed in the alleys of Hongik-dong, a hushed residential neighborhood in eastern Seoul, is a faded stone-tiled building stamped \u201cKorea Baduk Association,\u201d the governing body for professional Go. The game is an ancient one, with sacred stature in South Korea.\u00a0<\/p>\n
But inside the building, rooms once filled with the soft clatter of hands dipping into wooden bowls of stones now echo with mouse clicks. Players hunch over their monitors and replay their matches in an AI program. Others huddle around a Go board and debate the best next move, while coaches report how their choices stack up against the AI\u2019s. Some sit in silence, watching AI programs play against each other.\u00a0<\/p>\n
Ten years ago AlphaGo, Google DeepMind\u2019s AI program, stunned the world by defeating the South Korean Go player Lee Sedol. And in the years since, AI has upended the game. It\u2019s overturned centuries-old principles about the best moves and introduced entirely new ones. Players now train to replicate AI\u2019s moves as closely as they can rather than inventing their own, even when the machine\u2019s thinking remains mysterious to them. Today, it is essentially impossible to compete professionally without using AI. Some say the technology has drained the game of its creativity, while others think there is still room for human invention. Meanwhile, AI is democratizing access to training, and more female players are climbing the ranks as a result.\u00a0<\/p>\n
For Shin Jin-seo, the top-ranked Go player in the world, AI is an invaluable training partner. Every morning, he sits at his computer and opens a program called KataGo. Nicknamed \u201cShintelligence\u201d for how closely his moves mimic AI\u2019s, he traces the glowing \u201cblue spot\u201d that represents the program\u2019s suggestion for the best next move, rearranging the stones on the digital grid to try to understand the machine\u2019s thinking. \u201cI constantly think about why AI chose a move,\u201d he says.<\/p>\n
When training for a match, Shin spends most of his waking hours poring over KataGo. \u201cIt\u2019s almost like an ascetic practice,\u201d he says. According to a study in 2022 by the Korean Baduk League, Shin\u2019s moves match AI\u2019s 37.5% of the time, well above the 28.5% average the study found among all players.<\/p>\n
\u201cMy game has changed a lot,\u201d says Shin, \u201cbecause I have to follow the directions suggested by AI to some extent.\u201d The Korea Baduk Association says it has reached out to Google DeepMind in the hopes of arranging a match between Shin and AlphaGo, to commemorate the 10th anniversary of its victory over Lee. A spokesperson for Google DeepMind said the company could not provide information at this time. But if a new match does happen, Shin, who has trained on more advanced AI programs, is optimistic that he\u2019d win. \u201cAlphaGo still had some flaws then, so I think I could beat it if I target those weaknesses,\u201d he says.<\/p>\n
Go is an abstract strategy board game invented in China more than 2,500 years ago. Two players take turns placing black and white stones on a 19\u00d719 grid, aiming to conquer territory by surrounding their opponent\u2019s stones. It\u2019s a game of striking mathematical complexity. The number of possible board configurations\u2014roughly 10170<\/sup>\u2014dwarfs the number of atoms in the universe. If chess is a battle, Go is a war. You suffocate your enemy in one corner while fending off an invasion in another.<\/p>\n To train AI to play Go, a vast trove of human Go moves are fed into a neural network, a computing system that mimics the web of neurons in the human brain. AlphaGo, which was later christened AlphaGo Lee after its victory over Lee Sedol, was trained on 30 million Go moves and refined by playing millions of games against itself. In 2017, its successor, AlphaGo Zero, picked up Go from scratch. Without studying any human games, it learned by playing against itself, with moves based only on the rules of the game. The blank-slate approach proved more powerful, unconstrained by the limits of human knowledge. After three days of training, it beat AlphaGo Lee 100 games to zero.\u00a0<\/p>\n Google DeepMind retired<\/a> AlphaGo that same year. But then a wave of open-source models inspired by AlphaGo Zero emerged. Today, KataGo is the program most widely used by professional Go players in South Korea. It\u2019s faster and sharper than AlphaGo. It\u2019s learned to predict not just who will win, but also who owns each point on the board at any given moment. While AlphaGo Zero pieced together its understanding of the board by looking at small sections, KataGo learned to read the whole board, developing better judgment for long-term strategies. Instead of just learning how to win, it learned to maximize its score.<\/p>\n The software has reshaped how people play. For hundreds of years, professional Go players have navigated the game\u2019s astronomical complexity by developing heuristics that replaced brute calculation. Elegant opening strategies imposed abstract order on the empty grid. Invading corners early was a bad bargain. Each generation of Go players added new principles to the canon.\u00a0<\/p>\n But \u201cAI has changed everything,\u201d says Park Jeong-sang, a South Korean Go commentator. \u201cFundamental moves that were once considered common sense aren\u2019t played at all today, and techniques that didn\u2019t exist before have become popular.\u201d\u00a0<\/p>\n The starkest shift has been in opening moves. Go starts on a blank grid, and the first 50 moves were canvases for abstract thinking and creativity, where players etched their personalities and philosophies. Lee Sedol fashioned provocative moves that invited chaos. Ke Jie, a Chinese player who was defeated by AlphaGo Master in 2017, dazzled with agile, imaginative moves. Now, players memorize the same strain of efficient, calculated opening moves suggested by AI. The crux of the game has shifted to the middle moves, where raw calculation matters more than creativity.<\/p>\n Training with AI has led to a homogenization of playing styles. Ke Jie has lamented the strain of watching the same opening moves recycled endlessly. \u201cI feel the exact same way as the fans watching. It\u2019s very tiring and painful to watch,\u201d he told<\/a> a Chinese news outlet in 2021. Fans revel when a player breaks from the script with offbeat moves, but those moments have become rarer. Over a third of moves by the top Go players replicate AI\u2019s recommendations, according to a study<\/a> in 2023. The first 50 moves of each game are often identical to what AI suggests, many players say.\u00a0<\/p>\n \u201cGo has become a mind sport,\u201d says Lee Sedol, who retired three years after his 2016 defeat to AlphaGo. \u201cBefore AI, we sought something greater. I learned Go as an art,\u201d he says. \u201cBut if you copy your moves from an answer key, that\u2019s no longer art.\u201d\u00a0<\/p>\n Playing Go is no longer about charting new frontiers, some players say, but about following the dictates of a superhuman oracle. \u201cI used to inspire fans by advancing the techniques of Go and presenting a new paradigm,\u201d says Lee. \u201cMy reason for playing Go has vanished.\u201d<\/p>\n The players who have stayed in the game are trying to reinvent their craft. But it can be hard to discern what the new principles are.<\/p>\n Disarmingly slight and formidably calm, Kim Chae-young, one of the top female Go players in the world, grew up learning the game from her father, who was also a professional Go player. But when AI began to reshape the game, she found herself starting over. \u201cI needed time to abandon everything I had learned before,\u201d says Kim who shared her screen with me as she pointed her cursor to the blue spots suggested by KataGo. \u201cThe intuition I had built up over the years turned out to be wrong.\u201d\u00a0<\/p>\n As she leaned close to her monitor, her blinking screen showed the winning probabilities of each move, with no explanations. Even top players like Kim and Shin don\u2019t understand all of AI\u2019s moves. \u201cIt seems like it\u2019s thinking in a higher dimension,\u201d she says. When she tries to learn from AI, she adds, \u201cit\u2019s less about rationally thinking through each move, but more about developing a gut feeling\u2014an intuition.\u201d<\/p>\n Researchers are trying to discover the superhuman knowledge encoded in game-playing AI programs so that humans can learn it too. In 2024, researchers at Google DeepMind extracted new chess concepts<\/a> from AlphaZero, a generalized version of AlphaGo Zero that can also play chess, and taught them to chess grandmasters using chess puzzles. The Go concepts that players have picked up from AI systems so far are \u201cprobably only a small portion of what you could potentially learn,\u201d says Nicholas Tomlin, a computer scientist at Toyota Technological Institute at Chicago, who coauthored a study<\/a> probing Go concepts encoded in AlphaGo Zero.<\/p>\n But extracting those lessons remains a struggle. \u201cTop-tier players haven\u2019t yet been able to deduce the general principles behind AI moves,\u201d says Nam Chi-hyung, a Go professor at Myongji University. Although they can emulate AI\u2019s moves, they have yet to glean a new paradigm for the game because its reasoning is a black box, she says. Go may be in an epistemic limbo.\u00a0<\/p>\n Even if AI is an opaque teacher, it\u2019s a democratic one. It has supercharged training for female Go players, who have long been underdogs of the game. For decades, training meant studying under top male players, and the most competitive matches took place in male circles that were difficult for women to break into, says Nam. \u201cFemale players never had access to that experience,\u201d she says. \u201cBut now they can study with AI, which has made their training environment much more favorable.\u201d More broadly, AI has narrowed the gap between players by helping everyone perfect their opening moves.<\/p>\n Female players have climbed the ranks over the last few years as a result. In 2022, Choi Jeong, then the top female player in the world, became the first woman to reach the finals<\/a> of a major international Go tournament. Dubbed \u201cGirl Wrestler\u201d for her fierce, combative style of play, she took on Shin. She lost, but the match broke new ground for women in Go. In 2024, Kim made headlines for winning<\/a> the Korean Go League\u2019s postseason playoffs. She was the only female player in the tournament.\u00a0<\/p>\n Training with AI has given Kim newfound confidence. Analyzing male players\u2019 moves with AI has shattered their veneer of infallibility. \u201cBefore, I couldn\u2019t gauge just how strong top male players were\u2014they felt invincible. Now, I know that they make mistakes, and their moves aren\u2019t always brilliant,\u201d she says. \u201cAI broke the psychological barrier.\u201d<\/p>\n Although AI has mastered Go far better than any player, fans continue to prefer watching people play. \u201cA Go game between AI programs is not very fun for fans to watch,\u201d says Park, the Go commentator. Such matches are too complex for fans to follow, too flawless to be thrilling, he says.\u00a0<\/p>\n Players can mimic AI\u2019s opening moves, but in the middle game\u2014where the board branches into too many possibilities to memorize\u2014their own judgment takes over. Fans revel in watching players make mistakes and mount comebacks, exuding personality in every stone on the board. Shin\u2019s playing style is combative but marked by machinelike poise. Kim deftly navigates\u00a0 the most chaotic positions on the board.\u00a0<\/p>\n \u201cIn Go, every move is a choice you make, and your opponent responds with a choice of their own,\u201d says Kim Dae-hui, 27, a Go fan and amateur player. \u201cWatching that process unfold is fun.\u201d<\/p>\n With fans like Kim still watching, Shin finds meaning in his game. \u201cI can play a kind of Go that tells a story that only a human can,\u201d he says.\u00a0<\/p>\n After his retirement, Lee searched for a new job where he could have an edge as a human. He started making board games, giving speeches, and teaching students at a university. \u201cI\u2019m looking for a new domain that I can enjoy and excel at,\u201d he says.<\/p>\n But lately, he feels more hopeful for the game he left behind. \u201cIt\u2019s every Go player\u2019s dream to play a masterpiece game,\u201d he says\u2014a game of technical brilliance, with no mistakes, fought to a razor\u2019s edge between evenly matched players. \u201cIt\u2019s like a mirage,\u201d Lee says, chuckling. \u201cMaybe AI can help us play a masterpiece.\u201d\u00a0<\/p>\n Shin hopes he can do that. To Shin, AI is a teacher, a companion, and a North Star. \u201cI may be one of the strongest human players, but with AI around, I can\u2019t be so arrogant,\u201d he says. \u201cAI gives me a reason to keep improving.\u201d<\/p>","protected":false},"excerpt":{"rendered":" Burrowed in the alleys of Hongik-dong, a hushed residential neighborhood in eastern Seoul, is a faded stone-tiled building stamped \u201cKorea Baduk Association,\u201d the governing body for professional Go. The game is an ancient one, with sacred stature in South Korea.\u00a0 But inside the building, rooms once filled with the soft clatter of hands dipping into wooden bowls of stones now echo with mouse clicks. Players hunch over their monitors and replay their matches in an AI program. Others huddle around a Go board and debate the best next move, while coaches report how their choices stack up against the AI\u2019s. Some sit in silence, watching AI programs play against each other.\u00a0 Ten years ago AlphaGo, Google DeepMind\u2019s AI program, stunned the world by defeating the South Korean Go player Lee Sedol. And in the years since, AI has upended the game. It\u2019s overturned centuries-old principles about the best moves and introduced entirely new ones. Players now train to replicate AI\u2019s moves as closely as they can rather than inventing their own, even when the machine\u2019s thinking remains mysterious to them. Today, it is essentially impossible to compete professionally without using AI. Some say the technology has drained the game of its creativity, while others think there is still room for human invention. Meanwhile, AI is democratizing access to training, and more female players are climbing the ranks as a result.\u00a0 For Shin Jin-seo, the top-ranked Go player in the world, AI is an invaluable training partner. Every morning, he sits at his computer and opens a program called KataGo. Nicknamed \u201cShintelligence\u201d for how closely his moves mimic AI\u2019s, he traces the glowing \u201cblue spot\u201d that represents the program\u2019s suggestion for the best next move, rearranging the stones on the digital grid to try to understand the machine\u2019s thinking. \u201cI constantly think about why AI chose a move,\u201d he says. When training for a match, Shin spends most of his waking hours poring over KataGo. \u201cIt\u2019s almost like an ascetic practice,\u201d he says. According to a study in 2022 by the Korean Baduk League, Shin\u2019s moves match AI\u2019s 37.5% of the time, well above the 28.5% average the study found among all players. \u201cMy game has changed a lot,\u201d says Shin, \u201cbecause I have to follow the directions suggested by AI to some extent.\u201d The Korea Baduk Association says it has reached out to Google DeepMind in the hopes of arranging a match between Shin and AlphaGo, to commemorate the 10th anniversary of its victory over Lee. A spokesperson for Google DeepMind said the company could not provide information at this time. But if a new match does happen, Shin, who has trained on more advanced AI programs, is optimistic that he\u2019d win. \u201cAlphaGo still had some flaws then, so I think I could beat it if I target those weaknesses,\u201d he says. AI rewrites the Go playbook Go is an abstract strategy board game invented in China more than 2,500 years ago. Two players take turns placing black and white stones on a 19\u00d719 grid, aiming to conquer territory by surrounding their opponent\u2019s stones. It\u2019s a game of striking mathematical complexity. The number of possible board configurations\u2014roughly 10170\u2014dwarfs the number of atoms in the universe. If chess is a battle, Go is a war. You suffocate your enemy in one corner while fending off an invasion in another. To train AI to play Go, a vast trove of human Go moves are fed into a neural network, a computing system that mimics the web of neurons in the human brain. AlphaGo, which was later christened AlphaGo Lee after its victory over Lee Sedol, was trained on 30 million Go moves and refined by playing millions of games against itself. In 2017, its successor, AlphaGo Zero, picked up Go from scratch. Without studying any human games, it learned by playing against itself, with moves based only on the rules of the game. The blank-slate approach proved more powerful, unconstrained by the limits of human knowledge. After three days of training, it beat AlphaGo Lee 100 games to zero.\u00a0 Google DeepMind retired AlphaGo that same year. But then a wave of open-source models inspired by AlphaGo Zero emerged. Today, KataGo is the program most widely used by professional Go players in South Korea. It\u2019s faster and sharper than AlphaGo. It\u2019s learned to predict not just who will win, but also who owns each point on the board at any given moment. While AlphaGo Zero pieced together its understanding of the board by looking at small sections, KataGo learned to read the whole board, developing better judgment for long-term strategies. Instead of just learning how to win, it learned to maximize its score. The software has reshaped how people play. For hundreds of years, professional Go players have navigated the game\u2019s astronomical complexity by developing heuristics that replaced brute calculation. Elegant opening strategies imposed abstract order on the empty grid. Invading corners early was a bad bargain. Each generation of Go players added new principles to the canon.\u00a0 But \u201cAI has changed everything,\u201d says Park Jeong-sang, a South Korean Go commentator. \u201cFundamental moves that were once considered common sense aren\u2019t played at all today, and techniques that didn\u2019t exist before have become popular.\u201d\u00a0 The starkest shift has been in opening moves. Go starts on a blank grid, and the first 50 moves were canvases for abstract thinking and creativity, where players etched their personalities and philosophies. Lee Sedol fashioned provocative moves that invited chaos. Ke Jie, a Chinese player who was defeated by AlphaGo Master in 2017, dazzled with agile, imaginative moves. Now, players memorize the same strain of efficient, calculated opening moves suggested by AI. The crux of the game has shifted to the middle moves, where raw calculation matters more than creativity. Training with AI has led to a homogenization of playing styles. Ke Jie has lamented the strain of watching the same opening moves recycled endlessly. \u201cI feel the exact same way as the fans watching. It\u2019s very tiring<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-74107","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nA mysterious mind<\/strong><\/h3>\n
Go players find a new identity<\/strong><\/h3>\n