- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
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.
Contents
Preface. Introduction. Evolutionary Approach to AI. Swarm Intelligence Approach to AI. Emergence in Multiple Objectives. Emergence Applied to Deep Learning and Generative AI. Emergence of Intelligence for AI. Conclusion. Appendix A Software Packages. Appendix B Source Codes Generated by LLMs. Appendix C Example of Interaction with LLMs. References. Index.
