Natural And Artificial Reasoning

What are the limitations of computer models and why do we still not have working models of people that are recognizably human? This is the principle puzzle explored in this book where ideas behind systems that behave intelligently are described and different philosophical issues are touched upon. The key to human behavior is taken to be intelligence and the ability to reason about the world. A strong scientific approach is taken, but first it was required to understand what a scientific approach could mean in the context of both natural and artificial systems. A theory of intelligence is proposed that can be tested and developed in the light of experimental results. The book illustrates that intelligence is much more than just behavior confined to a unique person or a single computer program within a fixed time frame. Some answers are unraveled and some puzzles emerge from these investigations and experiments. Natural and Artificial Reasoning provides a few steps of an exciting journey that began many centuries ago with the word ‘why?’

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.

Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.

The Natural Language for Artificial Intelligence presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind. 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 that leads 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. Presents a comprehensive approach to natural language and its inherent and complex dynamics Develops language content as the next frontier, identifying the universal structure of language as a common structure that appears in both AI and cognitive computing Explains the standard structure present in cognition and AI, making them interchangeable Offers examples of the application of the universal language model in image analysis and conventional language

The book covers theoretical work, approaches, applications, and techniques for computational models of information, language, and reasoning. Computational and technological developments that incorporate natural language are proliferating. Adequate coverage of natural language processing in artificial intelligence encounters problems on developments of specialized computational approaches and algorithms. Many difficulties are due to ambiguities in natural language and dependency of interpretations on contexts and agents. Classical approaches proceed with relevant updates, and new developments emerge in theories of formal and natural languages, computational models of information and reasoning, and related computerized applications. Its focus is on computational processing of human language and relevant medium languages, which can be theoretically formal, or for programming and specification of computational systems. The goal is to promote intelligent natural language processing, along with models of computation, language, reasoning, and other cognitive processes.

Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) animal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psycholo gy through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial in telligence and to computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these prob lems and domains, empirical, experimental, and methodological studies will also appear from time to time. The perspective that prevails in artificial intelligence today suggests that the theory of computability defines the boundaries of the nature of thought, precisely because all thinking is computational. This paradigm draws its inspiration from the symbol-system hypothesis of Newell and Simon and finds its culmination in the computational conception of lan guage and mentality. The "standard conception" represented by these views is subjected to a thorough and sustained critique in the pages of this book. Employing a distinction between systems for which signs are signif icant for the users of a system and others for which signs are significant for use by a system, I have sought to define the boundaries of what AI, in principle, may be expected to achieve.