Handbook of Human-AI Collaboration

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.