The Game Is Not over Yet—Go in the Post-AlphaGo Era

Abstract

The game of Go was the last great challenge for artificial intelligence in abstract board games. AlphaGo was the first system to reach supremacy, and subsequent implementations further improved the state of the art. As in chess, the fall of the human world champion did not lead to the end of the game. Now, we have renewed interest in the game due to new questions that emerged in this development. How far are we from perfect play? Can humans catch up? How compressible is Go knowledge? What is the computational complexity of a perfect player? How much energy is really needed to play the game optimally? Here, we investigate these and related questions with respect to the special properties of Go (meaningful draws and extreme combinatorial complexity). Since traditional board games have an important role in human culture, our analysis is relevant in a broader context. What happens in the game world could forecast our relationship with AI entities, their explainability, and usefulness.

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…How can one come up with better alternatives? This challenge is solved by the field of artificial intelligence analysis tools such as AlphaGO [287][288][289], which provide probability distribution of smart moves and their assessment.…”

Technology as (Dis-)Enchantment. AlphaGo and the Meaning-Making of Artificial Intelligence

“…This sentiment may not be shared by the whole community, since most players are used to the existence of better players. (Egri-Nagy and Törmänen, 2020b: 7)…”

“…The availability of open-source Go programs may have deprived many Go professionals of a significant part of their income, but it also made the Go world more egalitarian. Still, even when it comes to high-level play, Go professionals retain an advantage over machines: ‘they can explain why particular moves are good or bad’ (Egri-Nagy and Törmänen, 2020b: 7). Given the absence of explainable AI, Go professionals may serve as priests of the sacred Go machine, interpreting and communicating AI-generated Go knowledge to lay audiences.…”

Advancing Human Understanding with Deep Learning Go AI Engines

“…Deep learning neural networks opened up a novel source of knowledge. We think that the game is not over yet for humans [10], and consider this development as an opportunity. Pairing human skills (e.g., high-level reasoning, thought experiments, counterfactuals, explanations, narratives) with an unlimited supply of high-quality statistical experiments is indeed a new situation.…”

“…All scientific theorems are conjectural-says the Popperian approach. However, in the case of Go, we know that the ultimate truth exists in the form of the complete traversal of the game tree, though it may not be realizable [10].…”