![\"Lee](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/03\/SEI_288143490.jpg\")
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Lee Sedol faced AlphaGo in 2016<\/p>\n
AP Photo\/Ahn Young-joon\/Alamy<\/p>\n<\/p>\n
The first time that AlphaGo revealed its full power, it prompted a visceral reaction<\/a>. Lee Sedol, the world\u2019s greatest player of the ancient Chinese board game Go, had grown visibly agitated at the artificial intelligence\u2019s prowess. The hushed crowd in downtown Seoul, South Korea, could barely contain its gasps. It was quickly dawning on Lee, and the tens of millions watching at home, that this AI was different<\/a> to those that had come before.<\/p>\n It wasn\u2019t just beating Lee, but it was doing so with an almost human-like aptitude. \u201cAlphaGo actually does have an intuition,\u201d Google co-founder Sergey Brin told New Scientist<\/a> in 2016, shortly after AlphaGo went 3-0 up. \u201cIt makes beautiful moves. It even creates more beautiful moves than most of us could think of.\u201d<\/p>\n The series ended with Google DeepMind\u2019s AlphaGo system winning 4-1. Lee said he was \u201cin shock\u201d.<\/p>\n It is now a decade since this defining moment for AlphaGo and AI at large. Marvelling at AI is a commonplace experience with the success of large language models like ChatGPT. AlphaGo was, in many ways, our first glimpse at what was to come. Ten years on, what is the legacy of AlphaGo and has the technology lived up to its potential?<\/p>\n \u201cLarge language models are now quite different in some ways from AlphaGo, but there\u2019s actually an underlying technological thread that really hasn\u2019t changed,\u201d says Chris Maddison<\/a> at the University of Toronto, who was part of the original AlphaGo team.<\/p>\n That underlying technology is neural networks \u2013 mathematical structures inspired by the brain and written into code. Historically, creating a game-playing machine would involve a human writing down the rules it should follow in different situations. With a neural network, the machine learns by itself.<\/p>\n But even with a neural network, cracking Go was a tall order. The ancient Chinese game, which sees two players moving black and white counters to gain territory on a 19-by-19 board, allows for 10171 possible positions. By comparison, there are only 1080 atoms in the entire observable universe.<\/p>\n The breakthrough came from Maddison and his colleagues trying to recreate the intuition of a human player by training a neural network to predict the next strongest move based on millions of moves from real games. Human players, of course, wouldn\u2019t need to play so many games to build up their intuition, but they also never could \u2013 a distinct advantage for AI.<\/p>\n AlphaGo also wasn\u2019t restricted to learning from human players; it could play millions of games against itself to hone its skills. \u201cBy learning through these games, it could discover new knowledge and could go beyond human-level players,\u201d says Pushmeet Kohli<\/a> at Google DeepMind.<\/p>\n The final system that beat Lee was more complex than Maddison\u2019s early models but the overarching message was simple: neural networks worked. \u201cAlphaGo definitively showed that neural nets can do pattern recognition better than humans. They can essentially have intuition that surpasses humans,\u201d says Noam Brown<\/a> at OpenAI.<\/p>\n Other alphas<\/p>\n So what happened next? After AlphaGo, Google DeepMind and AI researchers set to applying that fundamental lesson to real-world applications, like in mathematics and biology. One of the most striking examples of this was AlphaFold, an AI that could predict how proteins would look in three-dimensional space from their chemical make-up far better than any human-designed program, and which won<\/a> the team behind it the Nobel prize in chemistry.<\/p>\n