Handbook of Human-AI Collaboration
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Beschreibung
This Open Access book presents the historical evolution of artificial neural networks and the principles that underpin deep learning. It introduces the main concepts of Foundation Models employed in Large Language Models (LLMs) and more generally in Large Whatever Models (LWMs). It addresses the crucial need for explainability in both language and hybrid models, projecting future directions in the field.
The work extends beyond technical dimensions to explore the intricate dynamics of Human-AI Collaboration, from the foundations of human-centered AI methodologies to generalized AI-human intelligence. The book explores challenges of multimodal foundation models in particular when it comes to multimodal perception, generation and embodiment.
Contributors delve into topics such as complex reasoning, planning, argumentation, and applications in education and personal growth. Human-Large Whatever Models Interaction is examined in the context of co-adaptation, co-evolution, and the reciprocal influence between AI and human cognition, emotions, and behaviours. Benchmarking criteria and datasets for evaluation are discussed, providing insights into the evolving landscape of human-AI interaction. The societal impact of foundation models is explored in-depth, considering the dynamics of AI-driven techno-social systems, role distribution in AI-human collaborations, and the long-term implications on society. Ethical and legal aspects encompass conceptual backgrounds, metrics, and regulatory frameworks. The critical roadmap on foundation models addresses diverse stakeholders, including policy and decision-makers, the public sector, researchers, and developers.
As the book unfolds, it illuminates the intricate interplay between society and foundation models, providing a comprehensive overview of the past, present, and potential future trajectories of foundation models in the ever-evolving landscape of artificial intelligence.
This book is open access, which means that you have free and unlimited access Discussing the possibility of AI systems collaborating with people instead of developing systems that replace them Discusses conceptual backgrounds and the development of ethical metrics for AI development and application Demonstrates how ethical considerations can inform sustainable practices in the design and deployment of AI systems
Autor*in
Mohamed Chetouani
Themen in »Handbook of Human-AI Collaboration«
Open Access Human AI Deep Neural Network (DNN) Foundational Models to Assis Humans Supporting Learning and Reasoning with Foundation Models Hybrid AI Large Generative Models (LGMs) Large Language Models (LLMs) Human and AI agents LGM nodes Ethical Guidelines and Legal Frameworks The EU Legal Choice Architecture (CA) AI Ethics and Sustainability