American Go player Kelin Pelrine has beaten a top-ranked AI system in the board game Go. Perline played 15 games against the AI, winning 14 of them. This victory was possible because of a previously unknown weakness which was found by another computer program that investigated the AI system for weaknesses. That said, Pelrine played the games without direct computer support.
FAR AI, a California-based research firm, has designed this program. The program played over one million games against KataGo to find weaknesses a human player can exploit. The winning strategy isn’t very difficult for human players to learn either, according to Pelrine, who beat another AI system named Leela Zero using the same method.Â
The exact cause behind the AI’s loss is yet to be confirmed. That said, the researchers believe that one likely reason behind Pelrine’s victory is that his strategy is rare — the system hasn’t been trained enough to go against it in a game. The fact that it managed to pull one game back does indicate that it might be able to overcome this weakness if trained specifically.Â
The more important thing to note here is the problem that this victory highlights with AI systems — understanding specific situations and specific situations only. These systems can only understand situations that they’ve already been exposed to and cannot take generalised actions like humans do and often find easy.Â
In 2016, Go world champion four games beat Lee Sedol to one by Google’s AlphaGo. While AlphaGo itself isn’t publicly available, the systems Pelrine beat are considered on par with Google’s research project. The victory marks a turning point in AI-based programs taking over board games.
DeepNash, another AI system from Deepmind taught itself how to play Stretego, claiming a human-expert level in the game. CSAIL and Meta have also released Cicero, which plays Diplomacy using a multi-step process by combining language models with strategic reasoning to come up with the best move possible and leave the rest to luck.
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