An investigation of intelligence as an emergent phenomenon, integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence. Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI. One of Downing's central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI.

What is intelligence? Are truly intelligent machines a practical reality? If so, can they work in harmony with human beings and improve the quality of our lives? How are they designed, built, and controlled? The fact is that machines with brains are no longer the stuff of science fiction. Research focused on developing smarter, more flexible

Comparing the human brain with so-called artificial intelligence, the author probes past, present, and future attempts to create machine intelligence

The Intelligence Quotient Approximator (IQA [/ika/]) is a conceptual formula use to predict correct IQ of a person after taking a series of IQ tests using the derivative of ratio of ma and ca by 100. It is a simple and efficient way of predicting the intelligence quotient of a person. It is based on the assumption that if there is a simple and efficient way of doing things in life we don’t look outside the box when the solution is right inside the box. No complex formula is needed by teachers, psychometrists and laymen to measure and assess people’s intelligence. The IQA is meant to predict the Approximation of IQ of students (people) based on the Standardized Tests they have taken using the IQA formula. The IQA can be used by anybody because it is simple, provided you can read, count and write. In using the IQA to predict the IQ of a person whether she is ‘subnormal’ (like 80% or 90), ‘normal’ (100%) or ‘supernormal’ (genius: 120% or maxima genius: 200+).

This book introduces readers to artificial intelligence (AI) through the lens of playable media and explores the impact of such software on everyday life. From video games to robotic companions to digital twins, artificial intelligence drives large sectors of the culture industry where play, media and machine learning coexist. This book illustrates how playable media contribute to our sense of self, while also harnessing our data, tightening our bonds with computation and realigning play with the demands of network logic. Author Eric Freedman examines a number of popular media forms - from the Sony AIBO robotic dog, video game developer Naughty Dog’s Uncharted and The Last of Us franchises, to Peloton’s connected fitness equipment - to lay bare the computational processes that undergird playable media, and addresses the social, cultural, technological and economic forces that continue to shape user-centered experience and design. The case studies are drawn from a number of related research fields, including science and technology studies, media studies and software studies. This book is ideal for media studies students, scholars and practitioners interested in understanding how applied artificial intelligence works in popular, public and visual culture.

Since the time of Turing, computer scientists have dreamed of building artificial general intelligence (AGI) - a system that can think, learn and act as humans do. Over recent years, the remarkable pace of progress in machine learning research has reawakened discussions about AGI. But what would a generally intelligent agent be able to do? What algorithms, architectures, or cognitive functions would it need? To answer these questions, we turn to the study of natural intelligence. Humans (and many other animals) have evolved precisely the sorts of generality of function that AI researchers see as the defining hallmark of intelligence. The fields of cognitive science and neuroscience have provided us with a language for describing the ingredients of natural intelligence in terms of computational mechanisms and cognitive functions and studied their implementation in neural circuits. Natural General Intelligence describes the algorithms and architectures that are driving progress in AI research in this language, by comparing current AI systems and biological brains side by side. In doing so, it addresses deep conceptual issues concerning how perceptual, memory and control systems work, and discusses the language in which we think and the structure of our knowledge. It also grapples with longstanding controversies about the nature of intelligence, and whether AI researchers should look to biology for inspiration. Ultimately, Summerfield aims to provide a bridge between the theories of those who study biological brains and the practice of those who are seeking to build artificial brains.

Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. - Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning - Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists - Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations - Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes - Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology