
Why Artificial Intelligence had more difficulties cracking Go than Chess
It almost took two Decades
In 1997, an artificial intelligence (AI) named Deep Blue, developed by IBM, won a six-game match against the then-world chess champion Garry Kasparov, one of the best chess players in history. Grandmasters suffering from Kasparov’s dominance over decades were baffled. This victory marked a significant milestone in the development of AI, illustrating its potential to tackle complex tasks and scenarios. Stockfish and AlphaZero, the most advanced chess programs today, detached machine play further from human play. The structured, predictable nature of chess, with its finite number of possible moves, made it a suitable test bed for AI's calculating prowess.
Fast forward nearly two decades and AI found a new Everest to climb: the ancient and intricate game of Go, a strategic board game played between two players who take turns placing black and white stones on a grid, with the objective of gaining territorial control and capturing the opponent's stones. With its vast search space and emphasis on intuition and strategy, Go was perceived as a far greater challenge for artificial intelligence. Go "survived" another 19 years after Kasparov got dethroned by Deep Blue. In 2016, Google DeepMind's AI, AlphaGo, defied expectations by defeating Go champion Lee Sedol in a five-game match (documented in an award-winning documentary).
In machine learning and AI, 20 years is more than a lifespan. Why did it take so long?
Tackling differences in Chess and Go
This second victory demonstrated that AI had moved beyond the rigid logic and calculations that won it chess, evolving to navigate the vast complexities and nuanced strategies inherent to Go. Unlike chess, Go presents an incredibly high number of possible moves and board positions, surpassing the number of atoms in the observable universe. This intricate web of possibilities makes it challenging for AI systems to effectively explore and evaluate all potential moves, leading to a need for innovative approaches.