Pattern Retrieval on the Game of Go

Yen, Shi-Jim; Ho, Yu-Chie

doi:10.1007/978-981-15-4917-5_42

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In principle, the sheer amount of data at our disposal makes it possible for a machine to learn profound and complex processes like humans and be more productive. For example, in recent years, we have seen machines perform better in chess and Go tournaments (Yen & Ho, 2021) and make better decisions in selecting optimal routes (Timilsina, Aryal, & Kirkpatrick, 2020). Little by little, machines are proving to be capable of complex tasks and, in time, may succeed humans in such endeavors.…”

Section: Machine Learning and Artificial Intelligence: An Overviewmentioning

confidence: 99%

“Hasta la vista, baby” – will machine learning terminate human literature reviews in entrepreneurship?

Robledo

Aguirre

Hughes

et al. 2021

Journal of Small Business Management

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Can, and should, artificial intelligence (AI) and its machine learning (ML) variant be applied to study scholarly literature? With AI and ML rapidly disrupting industries, we investigate how scholars in entrepreneurship and small business management can capitalize on AI and ML to support their scholarship and comprehensively review, catalog, and analyze the literature. We examine various ML tools and deploy these tools against a published literature review to consider whether ML complements or substitutes scholars' agency. We show that ML can reinforce human findings to support replicability and robustness, adding additional layers of transparency and validity to conclusions from human-derived systematic reviews. Our contributions provide scholars with valuable guidance and a blueprint for adopting ML into their scholarship and not replacing their scholarship.

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Section: Machine Learning and Artificial Intelligence: An Overviewmentioning

confidence: 99%