From games to biology and beyond: 10 years of AlphaGo’s impact

Ten years ago, our AI system AlphaGo became the first program to defeat a world champion at the complex game of Go – reaching a milestone in the field a decade before many experts thought possible.

The achievement heralded the beginning of what is now recognized as the modern era in artificial intelligence (AI). With a single creative play, the famous ‘Move 37,’ AlphaGo demonstrated the potential of AI and signaled that we now had the techniques to begin tackling real-world scientific problems.

Today, this breakthrough continues to inform our work building systems on the path to artificial general intelligence (AGI). We believe AGI will be the most profound technology ever invented and potentially the ultimate tool to advance science, medicine, and productivity.

To make the game tractable, AlphaGo used deep neural networks combined with advanced search and reinforcement learning – an AI approach DeepMind pioneered .

Mathematical reasoning: The most direct descendant of AlphaGo’s architecture, AlphaProof learned to prove formal mathematical statements using a combination of language models and AlphaZero’s reinforcement learning and search algorithms. Alongside AlphaGeometry 2, it became the first system to achieve a medal-standard (silver) at the International Mathematical Olympiad (IMO), proving AlphaGo's methods could unlock advanced mathematical reasoning and laying the foundation for our most capable general models.

In 2016, over 200 million people watched AlphaGo face world-champion Go player Lee Sae Dol in Seoul. The match was defined by ‘Move 37’ in Game 2, a play so unconventional that professional commentators initially thought it was a mistake. But it proved to be decisive. One hundred or so moves later, the stone was in exactly the right position for AlphaGo to win the game. It was a display of incredible foresight and the AI system’s ability to go beyond mimicking human experts and find entirely new strategies.

Go has long been a proving ground for AI research because of the game’s sheer complexity. There are 10 170 possible positions on the board—far more than the number of atoms in the observable universe.

AlphaGo learned a model of plausible Go moves by first learning from games played by human experts, and then playing hundreds of thousands of games against itself, improving as the strongest winning strategies were reinforced. The system then considered only the most potentially fruitful paths and from that smaller subset of moves, found the one most likely to lead it to win.