A public policy leader addresses how artificial intelligence is transforming the future of labor—and what we can do to protect the role of workers. As computer technology advances with dizzying speed, human workers face an ever-increasing threat of obsolescence. In Human Work In the Age of Smart Machines, Jamie Merisotis argues that we can—and must—rise to this challenge by preparing to work alongside smart machines doing that which only humans can: thinking critically, reasoning ethically, interacting interpersonally, and serving others with empathy. The president and CEO of Lumina Foundation, Merisotis offers a roadmap for the large-scale, radical changes we must make in order to find abundant and meaningful work for ourselves in the 21st century. His vision centers on developing our unique capabilities as humans through learning opportunities that deliver fair results and offer a broad range of credentials. By challenging long-held assumptions and expanding our concept of work, Merisotis argues that we can harness the population’s potential, encourage a deeper sense of community, and erase a centuries-long system of inequality.
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.
A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human.
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+
This open access volume provides insight into how organizations change through the adoption of digital technologies. Opportunities and challenges for individuals as well as the organization are addressed. It features four major themes: 1. Current research exploring the theoretical underpinnings of digital transformation of organizations. 2. Insights into available digital technologies as well as organizational requirements for technology adoption. 3. Issues and challenges for designing and implementing digital transformation in learning organizations. 4. Case studies, empirical research findings, and examples from organizations which successfully adopted digital workplace learning.
The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? Smart machines are no longer science-fiction. They are being used by businesses right here and now. And, every day, their capabilities are growing. So exactly what is a smart machine? Everything you've always wanted to know about smart machines other cognitive computing systems that are able to make decisions and solve problems without human intervention. By the end of reading this book, you will master the discussion about the following topics in Smart Machines: 01 - The Rise of Smart Machines: Smart machines are built on top of the breakthroughs in computer science. the advent of computers in the 20th century laid the modern groundwork for Smart Machines. Researchers, analysts and technology leaders agree that Smart Machines will change our lives 02 - Smart machines definitions: Why did Smart Machines emerge now and not later? General-purpose technology (GPT) is an economics term for innovation. Smart machines are devices that can teach themselves how to do things. Smart machines are built on top of existing technologies. The surge of progress in machine learning thanks to big data. 03 - Artificial intelligence of Smart Machines: Defense Advanced Research Projects Agency (DARPA) Projects. 04- Cognitive computing of Smart Machines: This categorization of Cognitive Computing and the discussion of the types of applications. Cognitive technologies are very good at analyzing large amounts of data and identifying anomalies. 05 - The real-world examples of Smart Machines: From DeepBlue to Watson. 06 - Smart Machine technologies: Smart Dust, Virtual Personal Assistants, Smart Data Discovery, Smart Workspace, Conversational User Interfaces, Smart Robots, Commercial UAVs (Drones), Autonomous Vehicles: Autonomous Vehicles. 07 - Smart machines go to school to learn:Central to machine learning is the process of feeding training data into a mathematical prediction model. The outlier cases are the best chances for learning 08 - Smart machines are wiser and smarter every day: Future machine learning, and the large increase in available data and the rise of crowdsourcing 09 - Smart machines speak multi-languages: One particularly complex problem facing machine translation today is dealing with rare languages 10 - Artificial Intelligence Levels: Support for Humans, Repetitive Task Automation, Context Knowledge and Learning, Self-awareness. 11 - Cognitive Task Types: Analyzing Numbers, Words and Images Analysis, Performing Digital Tasks, Performing Physical tasks 12 - The Great Convergence: The approach of key vendors to achieve the great convergence. The attributes of cognitive architecture. People will continue to have advantages over even the smartest machines. 13 - Smart robotics from vision to reality: Simultaneous Localization and Mapping. Funding the robotics projects. Robots navigate on planets. Developing robots for government and consumer-oriented applications. Robots in Japan. Robots Deployment. Social acceptance of robots. 14 - The business benefits of Smart Machines: Smart machines will change the way work. The use of Smart Machines in information technology. 15 - Smart machines are efficient: what do we really mean by machines replacing jobs? 16 - Smart machines raise challenging questions: The impact of Smart Machines on labor market. machines get smarter and more independent, and may not to our advantage. 17 - The future of Smart Machines: The growing role of industry labs and federal agencies. The advance of Smart Machines cannot be stopped so we must be ready for it.
A comprehensive look at the promise and potential of online learning In our digital age, students have dramatically new learning needs and must be prepared for the idea economy of the future. In Getting Smart, well-known global education expert Tom Vander Ark examines the facets of educational innovation in the United States and abroad. Vander Ark makes a convincing case for a blend of online and onsite learning, shares inspiring stories of schools and programs that effectively offer "personal digital learning" opportunities, and discusses what we need to do to remake our schools into "smart schools." Examines the innovation-driven world, discusses how to combine online and onsite learning, and reviews "smart tools" for learning Investigates the lives of learning professionals, outlines the new employment bargain, examines online universities and "smart schools" Makes the case for smart capital, advocates for policies that create better learning, studies smart cultures
The concept of the 'learning organization' is one of the most popular management ideas of the last few decades. Since it was conceived as an idea in its own right, it has been given various definitions and meanings, such that we are still faced with the question as to whether any unified understanding of what the learning organization really is can be established. This Handbook offers extensive reviews of both new and traditional perspectives on the concept and provides suggestions for how the learning organization can best be defined, practiced, studied, and developed in future research. With contributions from long-standing scholars in the field as well as those new to the area, this book aims to bridge the gap between traditional and more critical perspectives, and in doing so find alternative features and angles to take the idea forward. In addition to elaborating on and developing older definitions of the learning organization and suggesting updated and even new definitions, the chapters also provide focused explorations on pertinent aspects of the learning organization such as ambidexterity, gender inclusivity, and systems thinking. They also survey organizations that have made efforts towards becoming learning organizations, how the learning organization can best be measured and studied, and the universality of the idea itself. Some of the questions raised in this book are answered, or at least given tentative answers, while other questions are left open. In this way, the book has the ambition to take the learning organization an important step further, whilst having no intentions to take any final step; instead, the intention is that others will endeavour to continue where this book stops.
How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of educational organisations and systems.
