Advancing Human Understanding with Deep Learning Go AI Engines

Egri-Nagy, Attila; Törmänen, Antti

doi:10.3390/proceedings2022081022

Cited by 3 publications

(1 citation statement)

References 8 publications

(7 reference statements)

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“…However, there are now new possible applications. Superhuman Go engines can be used to deepen human understanding of the game [8,9]. There is no evidence that the current AIs are close to perfect play.…”

Section: Motivationmentioning

confidence: 99%

The cost of passing - using deep learning AIs to expand our understanding of the ancient game of Go

Egri-Nagy

Törmänen²

2023

IJNC

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In addition to their playing skills, AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games if we define new measures based on their raw output. For the ancient game of Go, we develop a numerical tool for context-sensitive move-by-move performance evaluation and for automating the recognition of game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. In this paper, we investigate the properties of this measure and describe some applications for developing learning tools and analyzing a large number of games. As we use AI tools to gain new insights into the ancient game of Go and develop a more precise, quantified understanding, this work fits into the more significant and general project of utilizing superhuman AI engines for deepening human understanding and growing human knowledge.

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Section: Motivationmentioning

confidence: 99%

The cost of passing - using deep learning AIs to expand our understanding of the ancient game of Go

Egri-Nagy

Törmänen²

2023

IJNC

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The cost of passing – using deep learning AIs to expand our understanding of the ancient game of Go

Egri-Nagy

Törmänen

2022

2022 Tenth International Symposium on Computing and Networking (CANDAR)

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Problems and Opportunities of Artificial Intelligence

Gürsakal¹,

Çelik

Batmaz

2022

Akademik Yaklaşımlar Dergisi

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This article reviews Artificial Intelligence (AI)’s challenges and opportunities and discusses where AI might be headed. In the first part of the article, it was tried to reveal the differences between Symbolic AI and Deep Learning approaches, then long promises but short deliveries of AI were mentioned. When we review the problems of AI in general terms, it is a problem that the media has high expectations about AI and keeps the problems and restrictions it creates low. Today, while AI is stuck with issues such as deepfake applications and carbon footprints that create moral and climatologic problems; on the other hand, it is struggling with problems such as deep learning models requiring huge amounts of data. Another problem with deep learning is that deep learning models are a black-box and not open to improvements because it is not known where mistakes were made. Among the new paths ahead of AI are Hierarchical Temporal Memory (HTM) models and hybrid models that generally try to bridge the gap between Symbolic AI and Connectionist AI. If we consider that the most important leaps in AI have been made with the features of the brain that AI can imitate, then the developed HTM models may also be a new opportunity for AI.

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Advancing Human Understanding with Deep Learning Go AI Engines

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 3 publications

References 8 publications

The cost of passing - using deep learning AIs to expand our understanding of the ancient game of Go

The cost of passing - using deep learning AIs to expand our understanding of the ancient game of Go

The cost of passing – using deep learning AIs to expand our understanding of the ancient game of Go

Problems and Opportunities of Artificial Intelligence

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