Learning In Humans And Machines Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Learning In Humans And Machines book. This book definitely worth reading, it is an incredibly well-written.
Learning in Humans and Machines by European Science Foundation Pdf
Discusses the analysis, comparison and integration of computational approaches to learning and research on human learning. This book aims to provide the reader with an overview of the prolific research on learning throughout the disciplines. It also highlights the important research issues and methodologies.
Human-in-the-Loop Machine Learning by Robert Munro,Robert Monarch Pdf
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
Cesar A. Hidalgo,Diana Orghian,Jordi Albo Canals,Filipa De Almeida,Natalia Martin
Author : Cesar A. Hidalgo,Diana Orghian,Jordi Albo Canals,Filipa De Almeida,Natalia Martin Publisher : MIT Press Page : 257 pages File Size : 40,8 Mb Release : 2021-02-02 Category : Computers ISBN : 9780262362528
How Humans Judge Machines by Cesar A. Hidalgo,Diana Orghian,Jordi Albo Canals,Filipa De Almeida,Natalia Martin Pdf
How people judge humans and machines differently, in scenarios involving natural disasters, labor displacement, policing, privacy, algorithmic bias, and more. How would you feel about losing your job to a machine? How about a tsunami alert system that fails? Would you react differently to acts of discrimination depending on whether they were carried out by a machine or by a human? What about public surveillance? How Humans Judge Machines compares people's reactions to actions performed by humans and machines. Using data collected in dozens of experiments, this book reveals the biases that permeate human-machine interactions. Are there conditions in which we judge machines unfairly? Is our judgment of machines affected by the moral dimensions of a scenario? Is our judgment of machine correlated with demographic factors such as education or gender? César Hidalgo and colleagues use hard science to take on these pressing technological questions. Using randomized experiments, they create revealing counterfactuals and build statistical models to explain how people judge artificial intelligence and whether they do it fairly. Through original research, How Humans Judge Machines bring us one step closer tounderstanding the ethical consequences of AI.
Author : Paul R. Daugherty,H. James Wilson Publisher : Harvard Business Press Page : 264 pages File Size : 47,5 Mb Release : 2018-03-20 Category : Computers ISBN : 9781633693876
Human + Machine by Paul R. Daugherty,H. James Wilson Pdf
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.
Author : Brian Christian Publisher : W. W. Norton & Company Page : 459 pages File Size : 44,5 Mb Release : 2020-10-06 Category : Science ISBN : 9780393635836
The Alignment Problem: Machine Learning and Human Values by Brian Christian Pdf
A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.
Human-Machine Shared Contexts by William Lawless,Ranjeev Mittu,Donald Sofge Pdf
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
Gendered Configurations of Humans and Machines by Jan Büssers,Anja Faulhaber,Myriam Raboldt Pdf
In numerous fields of science, work, and everyday life, humans and machines have been increasingly entangled, developing an ever-growing toolbox of interactions. These entanglements affect our daily lives and pose possibilities as well as restrictions, chances as well as challenges. The contributions of this volume tackle related issues by adopting a highly interdisciplinary perspective. How do digitalization and artificial intelligence affect gender relations? How can intersectionality be newly understood in an increasingly internationally networked world? This volume is a collection of contributions deriving from the “Interdisciplinary Conference on the Relations of Humans, Machines and Gender” which took place in Braunschweig (October 16–19, 2019). It also includes the keynotes given by Cecile Crutzen, Galit Wellner and Helen Verran.
“There are words that are so familiar they obscure rather than illuminate the thing they mean, and ‘learning’ is such a word. It seems so ordinary, everyone does it. Actually it’s more of a black box, which Dehaene cracks open to reveal the awesome secrets within.”--The New York Times Book Review An illuminating dive into the latest science on our brain's remarkable learning abilities and the potential of the machines we program to imitate them The human brain is an extraordinary learning machine. Its ability to reprogram itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. But how do we learn? What innate biological foundations underlie our ability to acquire new information, and what principles modulate their efficiency? In How We Learn, Stanislas Dehaene finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain’s learning algorithms in our schools and universities, as well as in everyday life and at any age.
...This book explores AI ethics, surveys system thinking, and offers actionable tactics for aligning with engineering and product teams in the tech realm. Its engaging narrative provides a roadmap for iterative "designing in loops" product development in today’s AI-driven industry. — John Maeda, Author of How To Speak Machine Forget to design a solution once and for all - with Machine Learning, it simply doesn’t work! Since learning is inherently dynamic, designers must harness feedback loops to create solutions that adapt to changing environments and data. Discover how to work backward from humans, partner with ML field experts, build effective feedback loop mechanisms and design data-aware interactions. With Machine Learning, designers are crucial in keeping humans and society at the center. The book guides the reader in understanding the challenges and peculiarities of designing these systems. It provides methods and tools to apply a human-centered approach to problem-framing and solving. 'Human-Machine learning’ is a design paradigm that enables humans and machines to learn and adapt. Shifting our perspective from a growth to an adaptive mindset, the book presents the Human-Machine Learning paradigm as a way to tackle complex problems and drive positive change systemically. Six things you will find in this book: 1. The role of feedback in shaping human and machine learning 2. The role of designers in working backward from human needs in ML projects 3. How to design with and for data 4. How to design feedback loops at three levels of interactions: individual, organizational, and societal 5. A systemic perspective on designing with ML with a humanity-centered approach 6. How to design for Human-Machine Continual Learning
Lifelong Machine Learning, Second Edition by Zhiyuan Sun,Bing Leno da Silva Pdf
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
Human-Like Machine Intelligence by Stephen Muggleton,Nicholas Chater Pdf
This book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science.
Machine Learning and Human Intelligence by Rosemary Luckin Pdf
Intelligence is at the heart of what makes us human, but the methods we use for identifying, talking about and valuing human intelligence are impoverished. We invest artificial intelligence (AI) with qualities it does not have and, in so doing, risk losing the capacity for education to pass on the emotional, collaborative, sensory and self-effective aspects of human intelligence that define us. To address this, Rosemary Luckin--leading expert in the application of AI in education - proposes a framework for understanding the complexity of human intelligence. She identifies the comparative limitation of AI when analyzed using the same framework, and offers clear-sighted recommendations for how educators can draw on what AI does best to nurture and expand our human capabilities.
Human and Machine Problem Solving by K.J. Gilhooly Pdf
Problem solving is a central topic for both cognitive psychology and artificial intelligence (AI). Psychology seeks to analyze naturally occur ring problem solving into hypothetical processes, while AI seeks to synthesize problem-solving performance from well-defined processes. Psychology may suggest possible processes to AI and, in turn, AI may suggest plausible hypotheses to psychology. It should be useful for both sides to have some idea of the other's contribution-hence this book, which brings together overviews of psychological and AI re search in major areas of problem solving. At a more general level, this book is intended to be a contribution toward comparative cognitive science. Cognitive science is the study of intelligent systems, whether natural or artificial, and treats both organ isms and computers as types of information-processing systems. Clearly, humans and typical current computers have rather different functional or cognitive architectures. Thus, insights into the role of cognitive ar chitecture in performance may be gained by comparing typical human problem solving with efficient machine problem solving over a range of tasks. Readers may notice that there is little mention of connectionist ap proaches in this volume. This is because, at the time of writing, such approaches have had little or no impact on research at the problem solving level. Should a similar volume be produced in ten years or so, of course, a very different story may need to be told.
AI and Developing Human Intelligence by John Senior,Éva Gyarmathy Pdf
As the relationship between AI machines and humans develops, we ask what it will mean to be an intelligent learner in an emerging, socio-dynamic learningscape. The need for a new global view of intelligence and education is the core discussion of this future-focussed collection of ideas, questions, and activities for learners to explore. This fascinating guide offers activities to understand what needs to be changed in our educations systems and our view of intelligence. As well as exploring AI, HI, the future of learning and caring for all learners, this book addresses fundamental questions such as: How do we educate ourselves for an increasingly uncertain future? What is the purpose of intelligence? How can a curriculum focussing on human curiosity and creativity be created? Who are we and what are we becoming? What will we invent now that AI exists? AI and Developing Human Intelligence will interest you, inform you, and empower your understanding of "intelligence" and where we are going on the next part of our journey in understanding what it is to be human now and tomorrow.
Conscious Learning: Humans and Machines by Juyang Weng Pdf
This popular science compendium provides an approximate, holistic but first ever solution to the four Holy Grail questions: How does a brain work? How does the brain learn? How does its consciousness arise? How does learning require consciousness?The volume explains how human brains require and learn consciousness and why the new AI will overcome the current lack of conscious learning algorithms in AI. For human societies, it suggests how governments can make their taxpayers safer, more prosper and happier. For future AI, it calls for a thorough investigation in scientific infrastructures like government and private funding agencies, publication venues, professional societies and administrators that evaluate research. Why did a conscious learning algorithm not have a healthy environment to study? Why could rampant misconducts of data deletion in deep learning be allowed to grossly exaggerate AI performances for so long? The author's real-life accounts reveal deep reasons.The useful reference text benefits laymen in all walks of life, as well as professionals, researchers, academics and students in any areas.