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.

The work outlines a novel conceptual and theoretical framework for understanding Artificial General Intelligence and based on this framework outlines a practical roadmap for the development of AGI with capability at the human level and ultimately beyond.

“Only a small community has concentratedon general intelligence. No one has tried to make a thinking machine . . . The bottom line is that we really haven’t progressed too far toward a truly intelligent machine. We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack. . . . We have got to get back to the deepest questions of AI and general intelligence. . . ” –MarvinMinsky as interviewed in Hal’s Legacy, edited by David Stork, 2000. Our goal in creating this edited volume has been to ?ll an apparent gap in the scienti?c literature, by providing a coherent presentation of a body of contemporary research that, in spite of its integral importance, has hitherto kept a very low pro?le within the scienti?c and intellectual community. This body of work has not been given a name before; in this book we christen it “Arti?cial General Intelligence” (AGI). What distinguishes AGI work from run-of-the-mill “arti?cial intelligence” research is that it is explicitly focused on engineering general intelligence in the short term. We have been active researchers in the AGI ?eld for many years, and it has been a pleasure to gather together papers from our colleagues working on related ideas from their own perspectives. In the Introduction we give a conceptual overview of the AGI ?eld, and also summarize and interrelate the key ideas of the papers in the subsequent chapters.

This book uncovers mathematical structures underlying natural intelligence and applies category theory as a modeling language for understanding human cognition, giving readers new insights into the nature of human thought. In this context, the book explores various topics and questions, such as the human representation of the number system, why our counting ability is different from that which is evident among non-human organisms, and why the idea of zero is so difficult to grasp. The book is organized into three parts: the first introduces the general reason for studying general structures underlying the human mind; the second part introduces category theory as a modeling language and use it for exposing the deep and fascinating structures underlying human cognition; and the third applies the general principles and ideas of the first two parts to reaching a better understanding of challenging aspects of the human mind such as our understanding of the number system, the metaphorical nature of our thinking and the logic of our unconscious dynamics.

This text weaves together the history, theory and research of body-mind psychotherapy. The author lays a foundation for an understanding of the connection between the body and the mind and how the therapist/student can work to integrate the two into a healthy, functioning whole. The book is centred around two main themes: integration of the body and mind to access an organic source of intelligence and recognition that our bodies are motivated at every level (even down to cellular and molecular level). It also recognizes that emotion is an inherent part of our bodily process.

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.

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective