Author: Audrey Watters
Publisher: MIT Press
ISBN: 026254606X
Category : Education
Languages : en
Pages : 325
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Book Description
How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.
Author: Bill Ferster
Publisher: JHU Press
ISBN: 1421415402
Category : Business & Economics
Languages : en
Pages : 216
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Book Description
Technology promises to make learning better, cheaper, faster—but rarely has it kept that promise. The allure of educational technology is easy to understand. Classroom instruction is an expensive and time-consuming process fraught with contradictory theories and frustratingly uneven results. Educators, inspired by machines’ contributions to modern life, have been using technology to facilitate teaching for centuries. In Teaching Machines, Bill Ferster examines past attempts to automate instruction from the earliest use of the postal service for distance education to the current maelstrom surrounding Massive Open Online Courses. He tells the stories of the entrepreneurs and visionaries who, beginning in the colonial era, developed and promoted various instructional technologies. Ferster touches on a wide range of attempts to enhance the classroom experience with machines, from hornbooks, the Chautauqua movement, and correspondence courses to B. F. Skinner’s teaching machine, intelligent tutoring systems, and eLearning. The famed progressive teachers, researchers, and administrators that the book highlights often overcame substantial hurdles to implement their ideas, but not all of them succeeded in improving the quality of education. Teaching Machines provides invaluable new insight into our current debate over the efficacy of educational technology.
Author: Dale Lane
Publisher: No Starch Press
ISBN: 1718500572
Category : Computers
Languages : en
Pages : 290
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Book Description
A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+
Author: Wendell Wallach
Publisher: Oxford University Press
ISBN: 0199737975
Category : Computers
Languages : en
Pages : 288
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Book Description
"Moral Machines is a fine introduction to the emerging field of robot ethics. There is much here that will interest ethicists, philosophers, cognitive scientists, and roboticists." ---Peter Danielson, Notre Dame Philosophical Reviews --
Author: Anne Foerst
Publisher: Dutton Books
ISBN:
Category : Computers
Languages : en
Pages : 232
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Book Description
A thought-provoking analysis of the theological implications of artificial intelligence addresses important--and controversial--questions raised by robotics about the definition of humanity, what it means to have a soul, and what robots can teach us about our relationship with God.
Author: Kence Anderson
Publisher: "O'Reilly Media, Inc."
ISBN: 1098110706
Category : Computers
Languages : en
Pages : 253
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Book Description
Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs
Author: Robert (Munro) Monarch
Publisher: Simon and Schuster
ISBN: 1617296740
Category : Computers
Languages : en
Pages : 422
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Book Description
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology 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. About the book 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 create 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's inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past. Table of Contents PART 1 - FIRST STEPS 1 Introduction to human-in-the-loop machine learning 2 Getting started with human-in-the-loop machine learning PART 2 - ACTIVE LEARNING 3 Uncertainty sampling 4 Diversity sampling 5 Advanced active learning 6 Applying active learning to different machine learning tasks PART 3 - ANNOTATION 7 Working with the people annotating your data 8 Quality control for data annotation 9 Advanced data annotation and augmentation 10 Annotation quality for different machine learning tasks PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING 11 Interfaces for data annotation 12 Human-in-the-loop machine learning products
Author: Lars Meyer
Publisher: eBookIt.com
ISBN: 1456643916
Category : Computers
Languages : en
Pages : 149
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Book Description
Discover the Dawn of a New Era: The Ties Between Artificial Intelligence and Our Future Imagine a world where every aspect of life is enhanced by artificial intelligence--our jobs, our ethical decisions, and even the way we interact with one another. "When Machines Learn: AI and the Transformation of Society" is a gripping exploration of this not-so-distant future, where AI is not only a tool but a transformative force reshaping every facet of human existence. Dive into the heart of innovation with the pioneering journey through the evolution of artificial intelligence. Unravel the mysteries of its fundamentals and witness history unfold, as AI advances from primitive concepts to sophisticated systems that seamlessly integrate into our daily lives. Take a front-seat tour of the most groundbreaking applications that are setting the stage for a revolution. With humanity at a critical crossroads, explore the intricate dance between AI and its impact on the workforce. Jobs will evolve, industries will be reborn, and a new skilled labor market will rise from the ashes of automation. This book peels back layers of fear and uncertainty, revealing the silver lining of opportunities that lie ahead. Yet, this brave new world comes with its moral dilemmas. Delve into the heart of AI's ethical considerations, addressing the challenging questions of bias, equality, and the importance of transparent and accountable systems. What are the implications for privacy in a data-driven world? How do we navigate the tightrope between technological advancement and the protection of individual rights? Discover how AI will shape the future of humanity, not just through technological prowess but by redefining the core of societal values. Human-AI collaboration paints a vivid picture of enhanced human capabilities, while case studies of AI-driven innovation showcase the explosive potential at our fingertips. The book casts a gaze upon the global stage, examining international policies and the geopolitical race for AI supremacy. It shows us how artificial intelligence can redefine the way we live our daily lives, from personalized experiences to groundbreaking strides in health, education, and transportation. Prepare yourself with knowledge to face the imminent AI future. If you're curious about how AI will disrupt, reconstruct, and innovate the world as we know it, this book is your essential guide to understanding and embracing the AI revolution. Step into the pages of "When Machines Learn" and navigate the boundless possibilities of tomorrow.
Author: David Foster
Publisher: "O'Reilly Media, Inc."
ISBN: 1492041890
Category : Computers
Languages : en
Pages : 301
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Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
