Human Machine

AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.

A strikingly original exploration of what it might mean to be authentically human in the age of artificial intelligence, from the author of the critically-acclaimed Interior States. • "At times personal, at times philosophical, with a bracing mixture of openness and skepticism, it speaks thoughtfully and articulately to the most crucial issues awaiting our future." —Phillip Lopate “[A] truly fantastic book.”—Ezra Klein For most of human history the world was a magical and enchanted place ruled by forces beyond our understanding. The rise of science and Descartes's division of mind from world made materialism our ruling paradigm, in the process asking whether our own consciousness—i.e., souls—might be illusions. Now the inexorable rise of technology, with artificial intelligences that surpass our comprehension and control, and the spread of digital metaphors for self-understanding, the core questions of existence—identity, knowledge, the very nature and purpose of life itself—urgently require rethinking. Meghan O'Gieblyn tackles this challenge with philosophical rigor, intellectual reach, essayistic verve, refreshing originality, and an ironic sense of contradiction. She draws deeply and sometimes humorously from her own personal experience as a formerly religious believer still haunted by questions of faith, and she serves as the best possible guide to navigating the territory we are all entering.

Consciousness is widely perceived as one of the most fundamental, interesting and difficult problems of our time. However, we still know next to nothing about the relationship between consciousness and the brain and we can only speculate about the consciousness of animals and machines. Human and Machine Consciousness presents a new foundation for the scientific study of consciousness. It sets out a bold interpretation of consciousness that neutralizes the philosophical problems and explains how we can make scientific predictions about the consciousness of animals, brain-damaged patients and machines. Gamez interprets the scientific study of consciousness as a search for mathematical theories that map between measurements of consciousness and measurements of the physical world. We can use artificial intelligence to discover these theories and they could make accurate predictions about the consciousness of humans, animals and artificial systems. Human and Machine Consciousness also provides original insights into unusual conscious experiences, such as hallucinations, religious experiences and out-of-body states, and demonstrates how ‘designer’ states of consciousness could be created in the future. Gamez explains difficult concepts in a clear way that closely engages with scientific research. His punchy, concise prose is packed with vivid examples, making it suitable for the educated general reader as well as philosophers and scientists. Problems are brought to life in colourful illustrations and a helpful summary is given at the end of each chapter. The endnotes provide detailed discussions of individual points and full references to the scientific and philosophical literature.

Will the workplace of the future be overrun by machines and robots? Are the new frontiers of artificial intelligence (AI) on the cusp of dethroning us in efficiency, intelligence and innovative potential? Automation and AI will augment our human world and potential. The winners of the future of work are those that harness the power of machines to their advantage. Human/Machine is the only guide you need to understand the fourth industrial revolution. It sets out a road map to the challenges ahead, but also unlocks the wondrous opportunities that it offers. Human/Machine explores how we will work symbiotically with machines, detailing how institutions, companies, individuals and education providers will evolve to integrate seamlessly with new technologies. With exclusive case studies, this book offers a glimpse into the future and details how top companies are already thriving on this very special relationship. From gamification in job training to project management teams integrated with bots and predictive technologies that fix problems in the supply chain before they happen, the authors deliver a powerful manifesto for the adoption and celebration of automation and AI. In a much more fluid, skills-based economy, we will all need to prove our worth and future-proof our skills base. This book offers a blueprint to avoid being left behind and unearth the opportunities unique to human-machine partnership ecosystems.

Reproduction of the original: The Human Machine by Arnold Bennett

Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers. Discusses the foundations, metrics, and applications of human-machine systems Considers advances and challenges in the performance of autonomous machines and teams of humans Debates theoretical human-machine ecosystem models and what happens when machines malfunction

There is now a serious discussion taking place about the moment at which human beings will be surpassed and replaced by the machine. On the one hand we are designing machines which embed more and more human intelligence, but at the same time we are in danger of becoming more and more like machines. In these circumstances, we all need to consider: • What can we do? • What should we do? • What are the alternatives of doing it? This book is about the human-centred alternative of designing systems and technologies. This alternative is rooted in the European tradition of human-centredness which emphasises the symbiosis of human capabilities and machine capacity. The human-centred tra dition celebrates the diversity of human skill and ingenuity and provides an alternative to the 'mechanistic' paradigm of 'one best way', the 'sameness of science' and the 'dream of the exact language'. This alternative vision has its origin in the founding European human-centred movements of the 1970s. These include the British movement of Socially Useful Technology, the Scandinavian move ment of Democratic Participation, and the German movement of Humanisation of Work and Technology. The present volume brings together various strands of human-centred systems philosophy which span the conceptual richness and cultural diversity of the human-centred movements. The core ideas of human-centredness include human-machine symbiosis, the tacit dimension of knowl edge, the system as a tool rather than a machine, dialogue, partici pation, social shaping and usability.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.