On October 19, 2017, Google DeepMind published a groundbreaking paper introducing AlphaGo Zero, an artificial intelligence that mastered the game of Go without relying on any human knowledge or pre-existing game data. Unlike its predecessors, which were trained on vast datasets of human games, AlphaGo Zero learned entirely from scratch by playing against itself. In just 10 hours, it discovered basic strategies; within 15 hours, it uncovered more complex ones; and by 55 hours, it had developed new techniques that even top human players had never used. After 72 hours of self-training, AlphaGo Zero defeated its previous versions, including AlphaGo Lee (which had beaten world champion Lee Sedol), with a perfect score of 100-0. It also outperformed AlphaGo Master with an impressive 89-1 record.
Once AlphaGo’s series of victories over human champions concluded, the game of Go was no longer about humans versus AI, but rather AI versus AI. Within just three days, AlphaGo Zero surpassed thousands of years of accumulated human knowledge in Go. It not only eclipsed AlphaGo Lee and AlphaGo Master, which were trained on human experience and big data, but also demonstrated the limitations of human expertise. This achievement was nothing short of astonishing.
Some argue that AlphaGo Zero proves that “algorithms are more important than big data.†This is indeed true. The power of Go lies not just in computation, but in the strength of the algorithm itself.
First, the victory of AI over top human players was a triumph of both algorithm and computational power. Due to limited processing capabilities, human players typically use local optimization algorithms, focusing on immediate and nearby consequences. In contrast, AI can theoretically apply global optimization algorithms, considering all possible future outcomes. While local and global optima often align, there are rare cases where they diverge. In those instances, AI can outperform even the best human players.
Second, AlphaGo Zero's success over AI models trained on human data highlights another key point: the superiority of self-learning algorithms. By not relying on human chess databases or biases, AlphaGo Zero effectively achieved a true global optimization. On the other hand, AI models trained on human data are constrained by the limitations of human decision-making, which may be influenced by local optimizations. This shows how human experience can limit AI progress—a sobering realization for humanity.
The idea of “the end of Go†is now closer than ever. Since Go is a game with full information and clear rules, there exists a theoretical “ultimate game†where every move is optimal from a global perspective. In such a scenario, neither player would make a single suboptimal move, leading to a perfect, balanced game.
The greatest value of AlphaGo Zero lies in its ability to reveal the limits of human experience. It reminds us that our personal experiences are often just local optima—solutions that work well in specific contexts but may not hold universally. What we consider "common sense" is frequently shaped by our own perspectives and constraints.
In many cases, humans rely on local optimization due to computational limits. However, in daily life, the main reason for this is often incomplete or inaccurate information. Human decisions are made under conditions of information asymmetry, and while they may be the best choices given the available data, they are not always the most optimal. If we could access complete and accurate information, our decisions would be much closer to the ideal, God-like perspective.
Moreover, people often make errors in judgment by focusing on short-term effects while ignoring long-term consequences. They see the visible and miss the invisible, focus on direct outcomes without considering secondary or tertiary impacts. These mistakes are not just due to limited resources, but also stem from algorithmic flaws and intellectual laziness. AlphaGo Zero avoids these pitfalls by considering the full impact of each move, ensuring a holistic approach.
In essence, AlphaGo Zero serves as a mirror, reflecting the limitations of human experience and challenging us to rethink the boundaries of our own knowledge. It pushes us to recognize that what we know is often just a fragment of the whole.
Sata 15P Connector,Sata Computer Connector Socket,Sata Disk Connector,Sata Hard Disk Connector
Dongguan ZhiChuangXing Electronics Co., LTD , https://www.zcxelectronics.com