Deep Swarm And Evolution For Generative Artificial Intelligence
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Deep Swarm and Evolution for Generative Artificial Intelligence
"Generative AI or LLM (Large Language Model) is currently flourishing, but its core technology is mainly based upon correlation inference from big data, making it difficult to derive deep knowledge. For example, multi-fidelity is an important essence of human decision, but it cannot be realized by means of conventional deep learning. This book provides theoretical and practical knowledge about swarm and evolutionary approach, i.e., deep swarm and deep evolution, to generative AI. While the central theme of the book is generative AI, it also develops a discussion of AI in a broader sense. The development of such tools contributes for better optimizing methodologies with the integration of several machine learning and deep learning techniques. In particular, we will discuss how the "emergence" concept can contribute to the improvement of AI. Another goal of this book is to model human cognitive function in terms of "emergence" and to explain the feasibility of AI. In other words, to understand how intelligence emerges, to map it to the real world, and to provide causal explanations by means of evolutional and psychological mechanisms. To this end, this book focuses on human perceptions of utility and multi-fidelity. We describe the emergence of various cognitive errors, and irrational behaviors in the above-mentioned multi-objective situations. Furthermore, this book illustratively describes the intelligent behavior of living organisms. For instance, we explain how ants choose a preferred nest while solving a kind of optimal problem (i.e., Buffon's needle problem) and how bees choose a new nest while taking a majority vote. This is to clarify how to achieve AI in the direction of artificial life. We also describe 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. Sexual selection is extended as "novelty search" for the application of generative AI. Yet another emphasis is its real-world applicability. We provide empirical examples from real-world data to show that deep swarm and evolution is successfully applied when addressing tasks from such recent fields as robotics, time-series prediction, predictive control and image generation etc"--
Deep Swarm and Evolution for Generative Artificial Intelligence
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.
Swarm Intelligence and Deep Evolution
The book provides theoretical and practical knowledge about swarm intelligence and evolutionary computation. It describes the emerging trends in deep learning that involve the integration of swarm intelligence and evolutionary computation with deep learning, i.e., deep neuroevolution and deep swarms. The study reviews the research on network structures and hyperparameters in deep learning, and attracting attention as a new trend in AI. A part of the coverage of the book is based on the results of practical examples as well as various real-world applications. The future of AI, based on the ideas of swarm intelligence and evolution is also covered. The book is an introductory work for researchers. Approaches to the realization of AI and the emergence of intelligence are explained, with emphasis on evolution and learning. It is designed for beginners who do not have any knowledge of algorithms or biology, and explains the basics of neural networks and deep learning in an easy-to-understand manner. As a practical exercise in neuroevolution, the book shows how to learn to drive a racing car and a helicopter using MindRender. MindRender is an AI educational software that allows the readers to create and play with VR programs, and provides a variety of examples so that the readers will be able to create and understand AI.