商品描述
This book provides theoretical and practical knowledge about swarm and evolutionary approach of generative AI and Large Language Models (LLMs). The development of such tools contributes to better optimizing methodologies with the integration of several machinelearning and deep learning techniques. In particular, it discusses how the "emergence" concept can contribute to the improvement of AI.The book aims to model human cognitive f unction in terms of "emergence" and to explain the feasibility of AI. To this end, it focuses on human perceptions of "utility." It describes the emergence of various cognitive errors, and irrational behaviours in the multiobjective situations. It also reviews the cognitive differences and similarities between humans and LLMs. Such studies are important when applying LLMs to real-world tasks that involve human cognition, e.g., financial engineering and market issues.
The book describes the intelligent behaviour of living organisms. This is to clarify how to achieve AI in the direction of artificial life. It describes sexual selection, which is a well-known natural phenomenon that troubled Darwin, i.e., why evolutionarily useless items evolved such as peacock feathers and moose antlers etc. The book shows how sexual selection is extended as "novelty search" for the application of generative AI, i.e., the image generation with diffusion model. Real-world applications are emphasised. Empirical examples from real-world data show how the concept of deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, e-commerce Web Shop and image generation etc.
商品描述(中文翻譯)
這本書提供有關群體和進化方法的生成式人工智慧（Generative AI）及大型語言模型（Large Language Models, LLMs）的理論和實踐知識。這些工具的發展有助於更好地優化方法論，並整合多種機器學習和深度學習技術。特別是，它討論了「湧現」（emergence）概念如何促進人工智慧的改進。這本書旨在以「湧現」的角度來建模人類的認知功能，並解釋人工智慧的可行性。為此，它專注於人類對「效用」（utility）的感知。書中描述了在多目標情境中各種認知錯誤和非理性行為的湧現。它還回顧了人類與LLMs之間的認知差異和相似性。這些研究在將LLMs應用於涉及人類認知的現實任務時非常重要，例如金融工程和市場問題。
這本書描述了生物體的智能行為。這是為了澄清如何在人工生命的方向上實現人工智慧。它描述了性選擇，這是一個困擾達爾文的著名自然現象，即為什麼進化上無用的項目會進化，例如孔雀的羽毛和麋鹿的角等。這本書展示了性選擇如何擴展為生成式人工智慧的「新穎性搜尋」（novelty search），即使用擴散模型進行圖像生成。強調了現實世界的應用。來自現實數據的實證例子顯示，當處理來自機器人技術、電子商務網店和圖像生成等最近領域的任務時，深度群體和進化的概念是如何成功應用的。
作者簡介
Hitoshi Iba received a PhD from the University of Tokyo, Japan, in 1990. From 1990 to 1998, he was with the Electro Technical Laboratory (ETL) in Ibaraki, Japan. He is currently a Professor at the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, the University of Tokyo. He has (co-)authored more than 200 papers and authored more than 40 books in English, Japanese, and Chinese. He is also an underwater naturalist and experienced PADI divemaster having completed more than a thousand dives.
作者簡介(中文翻譯)
伊場仁志於1990年獲得日本東京大學的博士學位。從1990年到1998年，他在日本茨城的電氣技術研究所（ETL）工作。目前，他是東京大學資訊科學與技術研究所資訊與通訊工程系的教授。他已（共同）撰寫超過200篇論文，並著作超過40本英語、日語和中文的書籍。他也是一位水下自然學家，並且是一位經驗豐富的PADI潛水長，已完成超過一千次潛水。
