This book discusses an integrative approach to the currently high profile topics of artificial intelligence, quantum information, and quantum biology, with applications in the biosciences and the emerging fields of computational phenomenology and basal cognition. Specifically, the book addresses theoretical constructs of artificial intelligence and quantum information within scale-free work space architectures as pertaining to neuroscience and biology as well as to self-organizing systems generally. The past few years have seen a rapid convergence of interests between researchers working in evo-devo biology and neuroscience and those working in AI, machine learning, and the physics of information, with much of this convergence driven by recognition that the Free Energy Principle applies not just to nervous systems, but to physical systems in general. The authors develop a scale-free, minimal architecture that associates a generic semantics with any well-defined physical interaction. The presentation is accessible to a broad audience, including advanced undergraduates. The book is appropriate for students and researchers in AI, the physics of information, and the life sciences, particularly those working in the growing interdisciplinary field of active inference.

In addition, this book:

• Presents tools that characterize physical interactions in terms of actionability by problem-solving agents
• Includes logically regulated distributed information flow incorporating context and causality
• Addresses quantum information, holographic screens, and Markov blankets as applied to active inference and learning

This book discusses an integrative approach to the currently high profile topics of artificial intelligence, quantum information, and quantum biology, with applications in the biosciences and the emerging fields of computational phenomenology and basal cognition. Specifically, the book addresses theoretical constructs of artificial intelligence and quantum information within scale-free work space architectures as pertaining to neuroscience and biology as well as to self-organizing systems generally. The past few years have seen a rapid convergence of interests between researchers working in evo-devo biology and neuroscience and those working in AI, machine learning, and the physics of information, with much of this convergence driven by recognition that the Free Energy Principle applies not just to nervous systems, but to physical systems in general. The authors develop a scale-free, minimal architecture that associates a generic semantics with any well-defined physical interaction. The presentation is accessible to a broad audience, including advanced undergraduates. The book is appropriate for students and researchers in AI, the physics of information, and the life sciences, particularly those working in the growing interdisciplinary field of active inference.