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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 book introduces readers to artificial intelligence (AI) through the lens of playable media and explores the impact of such software on everyday life. From video games to robotic companions to digital twins, artificial intelligence drives large sectors of the culture industry where play, media and machine learning coexist. This book illustrates how playable media contribute to our sense of self, while also harnessing our data, tightening our bonds with computation and realigning play with the demands of network logic. Author Eric Freedman examines a number of popular media forms - from the Sony AIBO robotic dog, video game developer Naughty Dog’s Uncharted and The Last of Us franchises, to Peloton’s connected fitness equipment - to lay bare the computational processes that undergird playable media, and addresses the social, cultural, technological and economic forces that continue to shape user-centered experience and design. The case studies are drawn from a number of related research fields, including science and technology studies, media studies and software studies. This book is ideal for media studies students, scholars and practitioners interested in understanding how applied artificial intelligence works in popular, public and visual culture.
Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day.
This book introduces the reader to evidence-based non-formal and informal science learning considerations (including technological and pedagogical innovations) that have emerged in and empowered the information and communications technology (ICT) era. The contributions come from diverse countries and contexts (such as hackerspaces, museums, makerspaces, after-school activities) to support a wide range of educators, practitioners, and researchers (such as K-12 teachers, learning scientists, museum curators, librarians, parents, hobbyists). The documented considerations, lessons learned, and concepts have been extracted using diverse methods, ranging from experience reports and conceptual methods to quantitative studies and field observation using qualitative methods. This volume attempts to support the preparation, set-up, implementation, but also evaluation of informal learning activities to enhance science education.
Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Get the tools, resources and insights you need to explore artificial intelligence in the classroom and explore what students need to know about living in a world with AI. For many, artificial intelligence, or AI, may seem like science fiction, or inherently overwhelming. The reality is that AI is already being applied in industry and, for many of us, in our daily lives as well. A better understanding of AI can help you make informed decisions in the classroom that will impact the future of your students. Drawing from a broad variety of expert voices from countries including Australia, Japan, and South Africa, as well as educators from around the world and underrepresented student voices, this book explores some of the ways AI can improve education. These include educating learners about AI, teaching them about living in a world where they will be surrounded by AI and helping educators understand how they can use AI to augment human ability. Each chapter offers activities and questions to help you deepen your understanding, try out new concepts and reflect on the information presented. Links to media artifacts from trusted sources will help make your learning experience more dynamic while also providing additional resources to use in your classroom. This book: • Offers a unique approach to the topic, with chapter opening scenes, case studies, and featured student voices. • Discusses a variety of ways to teach students about AI, through design thinking, project-based learning and STEM connections. • Includes lesson ideas, activities and tools for exploring AI with your students. • Includes references to films and other media you can use in class to start discussions on AI or inspire design thinking and STEM projects. In Teaching AI, you’ll learn what AI is, how it works and how to use it to better prepare students in a world with increased human-computer interaction.
“A beautiful book… an instant classic of the genre.” —Dwight Garner, New York Times • A New York Times Critics’ Top Book of 2021 • A New York Times Book Review Editors' Choice • Named a Best Nonfiction Book of 2021 by Kirkus • Winner of the 2021 National Jewish Book Award in Autobiography & Memoir • Winner of the New England Society Book Award in Nonfiction MIT psychologist and bestselling author of Reclaiming Conversation and Alone Together, Sherry Turkle's intimate memoir of love and work For decades, Sherry Turkle has shown how we remake ourselves in the mirror of our machines. Here, she illuminates our present search for authentic connection in a time of uncharted challenges. Turkle has spent a career composing an intimate ethnography of our digital world; now, marked by insight, humility, and compassion, we have her own. In this vivid and poignant narrative, Turkle ties together her coming-of-age and her pathbreaking research on technology, empathy, and ethics. Growing up in postwar Brooklyn,Turkle searched for clues to her identity in a house filled with mysteries. She mastered the codes that governed her mother's secretive life. She learned never to ask about her absent scientist father--and never to use his name, her name. Before empathy became a way to find connection, it was her strategy for survival. Turkle's intellect and curiosity brought her to worlds on the threshold of change. She learned friendship at a Harvard-Radcliffe on the cusp of coeducation during the antiwar movement, she mourned the loss of her mother in Paris as students returned from the 1968 barricades, and she followed her ambition while fighting for her place as a woman and a humanist at MIT. There, Turkle found turbulent love and chronicled the wonders of the new computer culture, even as she warned of its threat to our most essential human connections. The Empathy Diaries captures all this in rich detail--and offers a master class in finding meaning through a life's work.
Making is a dynamic and hands-on learning experience that directly connects with long-established theories of how learning occurs. Although it hasn't been a focus of traditional education or had a prominent place in the classroom, teachers find it an accessible, exciting option for their students. The maker movement brings together diverse communities dedicated to creating things through hands-on projects. Makers represent a growing community of builders and creators—engineers, scientists, artists, DIYers, and hobbyists of all ages, interests, and skill levels—who engage in experimentation and cooperation. Transferring this innovative, collaborative, and creative mindset to the classroom is the goal of maker education. A makerspace isn't about the latest tools and equipment. Rather, it's about the learning experiences and opportunities provided to students. Maker education spaces can be as large as a school workshop with high-tech tools (e.g., 3D printers and laser cutters) or as small and low-tech as the corner of a classroom with bins of craft supplies. Ultimately, it's about the mindset—not the "stuff." In Learning in the Making, Jackie Gerstein helps you plan, execute, facilitate, and reflect on maker experiences so both you and your students understand how the knowledge, skills, and attitudes of maker education transfer to real-world settings. She also shows how to seamlessly integrate these activities into your curriculum with intention and a clearly defined purpose.