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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.
What makes us human? In recent decades, researchers have focused on innate tendencies and inherited traits as explanations for human behavior, especially in light of groundbreaking human genome research. The author thinks this trend is misleading. As he shows in great detail in this engaging, thought-provoking, and highly informative book, what makes our species unique is our marvelous ability to learn, which is an ability that no other primate possesses. In his exploration of human progress, the author reveals that the immensity of human learning has not been fully understood or examined. Evolution has endowed us with extremely versatile bodies and a brain comprised of one hundred billion neurons, which makes us especially suited for a wide range of sophisticated learning. Already in childhood, human beings begin learning complex repertoires—language, sports, value systems, music, science, rules of behavior, and many other aspects of culture. These repertoires build on one another in special ways, and our brains develop in response to the learning experiences we receive from those around us and from what we read and hear and see. When humans gather in society, the cumulative effect of building learning upon learning is enormous. The author presents a new way of understanding humanness—in the behavioral nature of the human body, in the unique human way of learning, in child development, in personality, and in abnormal behavior. With all this, and his years of basic and applied research, he develops a new theory of human evolution and a new vision of the human being. This book offers up a unified concept that not only provides new ways of understanding human behavior and solving human problems but also lays the foundations for opening new areas of science.
Sometimes living in the shadows is the safest place to be... or so you would think, but you'd be wrong, because living in the shadows is what makes you a victim. As the youngest child living within a dysfunctional family, I thought that anger, rivalry and hatred were the norm, that every family possessed dirty little secrets they hid from the world... like alcohol and drug addiction, mental and physical abuse, depression, schizophrenia and suicide. Surviving those dirty little secrets while running from the school bully was hard, but it was nothing in comparison to coming up on the radar of the neighbourhood pedophile.
Categories of Human Learning covers the papers presented at the Symposium on the Psychology of Human Learning, held at the University of Michigan, Ann Arbor on January 31 and February 1, 1962. The book focuses on the different classifications of human learning. The selection first offers information on classical and operant conditioning and the categories of learning and the problem of definition. Discussions focus on classical and instrumental conditioning and the nature of reinforcement; comparability of the forms of human learning; conditioning experiments with human subjects; and subclasses of classical and instrumental conditioning. The text then takes a look at the representativeness of rote verbal learning and centrality of verbal learning. The publication ponders on probability learning, evaluation of stimulus sampling theory, and short-term memory and incidental learning. Topics include short-term retention, stimulus variation experiments, reinforcement schedules and mean response, systematic interpretations, and methodological approaches. The book then examines the behavioral effects of instruction to learning, verbalizations and concepts, and the generality of research on transfer functions. The selection is highly recommended for psychologists and educators wanting to conduct studies on the categories of human learning.
The educational writings of John Macmurray, one of the finest 20th century philosophers of his generation, have a special relevance for us today. In similar circumstances of international crisis he argued for the central importance of education addressing fundamental issues of human purpose - how we lead good lives together, the emphasis on wisdom rather than knowledge alone, the advancement of a truly democratic culture, and the overriding importance of community in human flourishing. This remarkable collection of articles from leading international scholars includes the hitherto unpublished John Macmurray lecture – Learning to be Human – and brings together invited contributions from a range of fields and disciplines (e.g. philosophy of education, moral philosophy, care ethics, history of education, theology, religious education, future studies and learning technologies) and a number of countries across the world (e.g. Australia, the UK and the USA). Countering overemphasis on technique and its typical separation from wider human purposes emblematic of much of our current malaise, this book asks what it might mean to take the education of persons seriously and how such a perspective helps us to form judgments about the nature and worth of contemporary education policy and practice. This book was originally published as a special issue of the Oxford Review of Education.
Today, more than ever, we are losing sight of our humanity. You were taught to feel like you don't belong and much of the teaching came from yourself. You wen't born that way, though. It's time you learned that you are more than enough just as you are right now. Finally, a guide that will help you uncover the human you truly are and the happy, content, relaxed, satisfied, and self-confident person you were meant to be. Do you remember who you were before everyone told you who you should be? Buried deep within our confused, stressed out, and depressed lives is a happy person looking to break free of the chains of what we've been told we should be, and live a life free of the pressures society can place on us. We're told that we need to look a certain way, live a life in accordance with the rules of a specific religion, or make an allotted amount of money. The World around us has become a hurried, chaotic, and technological war on our brains. Learning to Be Human Again will help you to uncover your potential as a human by changing your thinking habits using a variety of proven exercises. This guide will help you to simplify your life by changing your thinking and introducing tools to begin practicing better daily habits. Human nature is slipping away from us, and as a result, we're having a harder time coping with the world, society, and the people around us. Let's take a step back and learn just what it means to be a human first, so we can understand ourselves and everyone else a little better.
Compilation of Stern's columns from Chronogram in which he explores the intriguing concept of regional culture in its full meaning.
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.