Go, Biology and AI’s Role in Strategic Thinking: A Brief Insight into How Go and Biological Concepts Shape AI Development

Reconsidering the Interpretability Problem of GAI This chapter explores the explainability challenges of generative artificial intelligence (AI) systems, emphasizing their rapid advancements and societal impact. The authors argue that while these systems exhibit exceptional performance through complex architectures, their ＂＂black-box＂＂ nature raises concerns about decision transparency, particularly in sensitive fields like healthcare. The paper distinguishes explainability from interpretability and discusses biological learning mechanisms as a potential model for enhancing AI transparency. Ethical and societal implications, such as privacy and trust, are also addressed, proposing that achieving a balance between AI performance and explainability is critical. This research advocates for a gradual development approach to improve transparency while ensuring ethical and fair AI integration. Go and Mental Referencing: An Analysis of Cognitive Symbol Systems in the Chessboard This chapter examines the game of Go through the lens of cognitive and symbolic systems. The authors position Go as a ＂＂physical symbol system,＂＂ where the black-and-white stones and the board serve as a medium for conveying strategic intent and logical reasoning. Using Herbert Simon’s hypothesis on physical symbol systems, the paper delves into how players interpret and manipulate these symbols for decision-making. It explores the interplay between mental representation, intentionality, and multi-modal cognition, likening the game’s dynamic symbol interactions to human cognitive processes. The study also connects Go’s symbolic and strategic nature to advancements in artificial intelligence, highlighting its potential as a model for understanding human-machine cognition and decision-making.