Are psychometric tests valid for a new reality of artificial intelligence systems, technology-enhanced humans, and hybrids yet to come? Are the Turing Test, the ubiquitous CAPTCHAs, and the various animal cognition tests the best alternatives? In this fascinating and provocative book, José Hernández-Orallo formulates major scientific questions, integrates the most significant research developments, and offers a vision of the universal evaluation of cognition. By replacing the dominant anthropocentric stance with a universal perspective where living organisms are considered as a special case, long-standing questions in the evaluation of behavior can be addressed in a wider landscape. Can we derive task difficulty intrinsically? Is a universal g factor - a common general component for all abilities - theoretically possible? Using algorithmic information theory as a foundation, the book elaborates on the evaluation of perceptual, developmental, social, verbal and collective features and critically analyzes what the future of intelligence might look like.

Stan Franklin is the perfect tour guide through the contemporary interdisciplinary matrix of artificial intelligence, cognitive science, cognitive neuroscience, artificial neural networks, artificial life, and robotics that is producing a new paradigm of mind. Along the way, Franklin makes the case for a perspective that rejects a rigid distinction between mind and non-mind in favor of a continuum from less to more mind.

This book describes and explores six current approaches to the study of mind: the neuroscientific, the behavioral, the competence approach, the ecological, the phenomenological, and the computational. No other book in cognitive science covers such a broad range of research programs and topics in such a balanced fashion. The first chapter is a mini-history and philosophy of psychology which reviews some of the scientific developments and philosophical arguments behind these six different approaches. Each subsequent chapter presents work that is on the frontiers of research in its field.

Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration. Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.

The mind is what the brain does. This book tries to map a mind model to the corresponding brain so as to not only deepen our understanding of both the brain and the mind, but also unveil computational underpinnings. That is why the words “Brain-Mind” are hyphenated in the title. This volume strives to unify natural intelligence with artificial intelligence. It approaches intelligence through not only what intelligence is but also how intelligence arises. Examples of disciplinary questions related to the material in this book: Biology: How does each autonomous cell interact with the environment to give rise to animal behaviors, and what cellular roles is the genome likely to play? Neuroscience: From an overarching perspective, how does a brain self-wire, perform top-down attention, and develop its functions? Psychology: How does an integrated brain architecture accomplish multiple psychological learning models and develop brain’s external behaviors? Computer Science: How does a brain-like network compute, adapt, reason, and generalize, and how is the automaton theory related to the brain-like network? Electrical Engineering: How does a brain-like network perform general-purpose, nonlinear, feedback sensing-and-control, beyond traditional nonlinear control? Mathematics: How does a brain-like network perform general-purpose, nonlinear optimization, and how does a brain realize emergent functionals? Physics: How do meanings arise from physics, and how does a brain-like network treat space and time in a unified way, reminiscent of relativity? Social sciences: How do computational principles of human brains provide insight into possible solutions to a variety of social and political problems? Juyang Weng received his BS degree from Fudan University, and MS and PhD degrees from University of Illinois, Urbana-Champaign, all in Computer Science. He is a professor at the Dept. of Computer Science and Engineering, a faculty member of the Cognitive Science Program and the Neuroscience Program, Michigan State University, East Lansing, Michigan, USA. He is a fellow of IEEE.

How can human-level artificial intelligence be achieved? What are the potential consequences? This book describes a research approach toward achieving human-level AI, combining a doctoral thesis and research papers by the author. The research approach, called TalaMind, involves developing an AI system that uses a 'natural language of thought' based on the unconstrained syntax of a language such as English; designing the system as a collection of concepts that can create and modify concepts to behave intelligently in an environment; and using methods from cognitive linguistics for multiple levels of mental representation. Proposing a design-inspection alternative to the Turing Test, these pages discuss 'higher-level mentalities' of human intelligence, which include natural language understanding, higher-level forms of learning and reasoning, imagination, and consciousness. Dr. Jackson gives a comprehensive review of other research, addresses theoretical objections to the proposed approach and to achieving human-level AI in principle, and describes a prototype system that illustrates the potential of the approach. This book discusses economic risks and benefits of AI, considers how to ensure that human-level AI and superintelligence will be beneficial for humanity, and gives reasons why human-level AI may be necessary for humanity's survival and prosperity.

The Natural Language for Artificial Intelligence presents natural language as the next frontier because it identifies something that is most sought after by scholars: The universal structure of language that gives rise to the respective universal algorithm. In short, this book presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind that, at the same time, interprets the context of reality. It is a non-static approach to natural language, which is defined as a complex system whose parts interact with the ability to generate a new quality of behavior and whose dynamic elements are mapped in order to be understood and executed by intelligent systems, guiding the paradigms of cognitive computing. The book explains linguistic functioning in the dynamic process of human cognition when forming meaning. After that, an approach to artificial intelligence (AI) is outlined, which works with a more restricted concept of natural language, leading to flaws and ambiguities. Subsequently, the characteristics of natural language and patterns of how it behaves in different branches of science are revealed, to indicate ways to improve the development of AI in specific fields of science. A brief description of the universal structure of language is also presented as an algorithmic model to be followed in the development of AI. Since AI aims to imitate the process of the human mind, the book shows how the cross-fertilization between natural language and AI should be done using the logical-axiomatic structure of natural language adjusted to the logical-mathematical processes of the machine.