A Turning Point in AI History
The triumph of AlphaGo over world Go champion Lee Sedol in March 2016 was not just a sporting feat. It was a technological milestone that marked the beginning of a new era in the development of artificial intelligence. The system, developed by Google DeepMind, achieved a 4-1 victory in Seoul, South Korea, surprising experts who estimated it would be at least ten years before an AI could defeat a professional Go player. What was most striking was that AlphaGo not only surpassed human logic, but also its strategic intuition, a capability until then considered exclusively human.
The Millennial Challenge of Go
Go, with over 2,500 years of history, is one of the most complex games ever created. Its 19x19 board generates more possible combinations than there are atoms in the observable universe. Unlike chess, which was conquered by computing with Deep Blue in 1997, Go requires a special sensitivity for patterns, territorial balance, and creativity. Before AlphaGo, AI programs barely reached an amateur level, and experts considered the intuition required to play Go to be beyond the reach of algorithms.
The Architecture Behind the Miracle
AlphaGo achieved the unthinkable thanks to a hybrid architecture that combined deep neural networks with the Monte Carlo Tree Search (MCTS). Two main networks guided its reasoning: the Policy Network, trained on millions of human moves to predict likely moves, and the Value Network, which estimated the probability of winning from a given position. This structure allowed AlphaGo to evaluate not only which move to make, but also why it was strategically sound.
Reinforcement Learning: The Evolutionary Leap
Beyond imitation, AlphaGo was trained through reinforcement learning, playing millions of games against itself. This process allowed it to discover unprecedented strategies, far from the human repertoire. The MCTS acted as an intelligent explorer, guided by the neural networks to focus on the most promising branches of the decision tree. Thus, AlphaGo not only replicated human knowledge but transcended it.
The Duel That Captured the World
The match between AlphaGo and Lee Sedol was watched by over 100 million viewers. Sedol, with 18 world titles, had underestimated the machine, predicting a 5-0 victory. However, after three consecutive losses, he acknowledged that AlphaGo was an exceptional and deeply unpredictable player. The games were decided by resignation, reflecting the psychological pressure the system exerted on its human opponent.
Move 37: Artificial Creativity?
In the second game, AlphaGo made the famous "Move 37," a move so unexpected that it was estimated only one in 10,000 humans would have considered it. Initially perceived as a mistake, it turned out to be a brilliant move that changed the course of the game. This moment sparked a philosophical debate about whether an AI can be creative. Although the system was simply maximizing its probability of winning, the effect of its move evoked a sense of genius that blurred the line between calculation and creativity.
Human Intuition Resists
The fourth game offered a respite for humanity. Lee Sedol achieved his only victory thanks to "Move 78," known as the "divine move." This intuitive move bewildered AlphaGo, which had dismissed that possibility as improbable. The ensuing ovation was a tribute to the human ability to surprise even the most advanced systems. Sedol received an additional prize for this victory, which became a symbol of intellectual resistance.
From AlphaGo to AlphaZero: Unprecedented Evolution
After the duel, DeepMind redirected its efforts to scientific applications. AlphaGo Zero was born, a version that surpassed its predecessor without human data, training solely against itself. In just three days, it defeated AlphaGo Lee 100 to 0. Then came AlphaZero, capable of mastering Go, chess, and shogi with the same self-learning approach. This advance demonstrated that systems could develop superior strategies without direct human intervention.
Technical and Scientific Legacy
AlphaGo accelerated research in deep learning and reinforcement learning, showing that AI could tackle highly complex problems. Its algorithms were applied in fields like biology, where DeepMind developed AlphaFold, a system capable of predicting protein folding with revolutionary accuracy. The legacy of AlphaGo is not limited to gaming but extends to science, medicine, and engineering.
Cultural and Geopolitical Impact
The event had global repercussions. China intensified its national AI strategy, recognizing the transformative potential of these systems. In the world of Go, AlphaGo raised the level of play and offered new strategic perspectives. However, it also caused demotivation among some professionals, who felt that the machine had surpassed the human. This phenomenon revived fears about the loss of relevance and the possible displacement of human capabilities.
Ethics and Control of Artificial Intelligence
The speed of technological progress raised urgent ethical questions. How to ensure that such powerful systems remain under human control? Researchers like Stuart Russell emphasized the need to develop AI aligned with human values. Although AlphaGo had no intentions, its strategic capability showed that the risk is not in the machine, but in its use. The governance of AI became a central theme on the scientific and political agenda.
Beyond the Game: A New Human-Machine Relationship
AlphaGo transformed the perception of AI, from a tool to a strategic interlocutor. The duel with Lee Sedol symbolizes the beginning of a symbiosis: the machine as a cognitive complement, capable of expanding the limits of human thought. Although Sedol retired acknowledging the superiority of AI, the future points not to replacement, but to collaboration. On this new board, the human brings intuition, ethics, and vision; the AI, analysis, exploration, and precision.