Feeling overwhelmed by AI? It's not you-it's the breakneck speed of technological progress. To quickly get into the AI conversation, you need a clear and simple foundation of knowledge to build on. This book is a friendly primer on the basic concepts of AI, how it's already snuck into our daily lives, and what we need to know to prepare for the future. Ben Jones, an expert at breaking down technical concepts from teaching thousands of people the basics of data literacy, lays out everything you need to know to join the AI conversation, from the history of AI to the deep learning revolution happening today. This technology is here to stay. Time for you to pull a seat up to the table. Praise for AI Literacy Fundamentals: "I can't think of a better written and more thoroughly researched introduction to the fundamental concepts of AI Literacy than Ben's wonderful book. I cannot recommend it enough. Read. Be inspired. Be ready for our changing world." -James Wilson, author of Artificial Negligence

The vast majority of people in the world today do not receive a formal education that adequately prepares them for the level of data literacy required of them in their careers and by their communities. As a result, many are being left behind by the transition to data-driven dialogues and decisions all around them, and they're seeking ways to break down the barriers that are preventing them from participating. Data Literacy Fundamentals covers foundational topics such as the overall goal of data, various ways of measuring and categorizing the world, five different forms of data analysis and when they apply, pros and cons related to how we display data in tabular or graphic form, and the way teams work together to convert data into insight.This book has been written for anyone who is just getting started with data and who wants to feel more confident in their understanding of what it is, what it isn't, and what it's used for. This invaluable resource will cure you of your "dataphobia", teach you the basic concepts of data, and set you on a path of learning that will ultimately result in fluency in the language of data.

Understanding Artificial Intelligence Provides students across majors with a clear and accessible overview of new artificial intelligence technologies and applications Artificial intelligence (AI) is broadly defined as computers programmed to simulate the cognitive functions of the human mind. In combination with the Neural Network (NN), Big Data (BD), and the Internet of Things (IoT), artificial intelligence has transformed everyday life: self-driving cars, delivery drones, digital assistants, facial recognition devices, autonomous vacuum cleaners, and mobile navigation apps all rely on AI to perform tasks. With the rise of artificial intelligence, the job market of the near future will be radically different???many jobs will disappear, yet new jobs and opportunities will emerge. Understanding Artificial Intelligence: Fundamentals and Applications covers the fundamental concepts and key technologies of AI while exploring its impact on the future of work. Requiring no previous background in artificial intelligence, this easy-to-understand textbook addresses AI challenges in healthcare, finance, retail, manufacturing, agriculture, government, and smart city development. Each chapter includes simple computer laboratories to teach students how to develop artificial intelligence applications and integrate software and hardware for robotic development. In addition, this text: Focuses on artificial intelligence applications in different industries and sectors Traces the history of neural networks and explains popular neural network architectures Covers AI technologies, such as Machine Vision (MV), Natural Language Processing (NLP), and Unmanned Aerial Vehicles (UAV) Describes various artificial intelligence computational platforms, including Google Tensor Processing Unit (TPU) and Kneron Neural Processing Unit (NPU) Highlights the development of new artificial intelligence hardware and architectures Understanding Artificial Intelligence: Fundamentals and Applications is an excellent textbook for undergraduates in business, humanities, the arts, science, healthcare, engineering, and many other disciplines. It is also an invaluable guide for working professionals wanting to learn about the ways AI is changing their particular field.

Artificial Intelligence is at the top of the agenda for education leaders, scientists, technologists and policy makers in educating the next generation across the globe. Beyond applying AI in daily life applications and educational tools, understanding how to learn and teach AI is increasingly important. Despite these emerging technology breakthroughs, AI learning is still new to educators especially to K-16 teachers. There is a lack of evidence-based studies that inform them about AI learning, including design principles for building a set of curriculum content, and pedagogical approaches as well as technological tools. Teaching AI concepts and techniques from programming languages and developmentally appropriate learning tools (e.g., robotics, serious games, software, intelligent agents) across different education levels emerged in recent years. The primary purpose of this book is to respond to the need to conceptualize the emerging term “AI literacy” and investigate how to teach and learn AI in K-16 education settings. This book examines different aspects of learning artefacts, pedagogies, content knowledge and assessment methods of AI literacy education, from theoretical discussions to practical recommendations for curriculum and instructional design. An exhaustive summary of current evidence with examples is illustrated in this book, as well as cutting-edge research that serves as an AI literacy model for different countries’ contexts. Part I, “Conceptualizing AI literacy”, provides a detailed discussion on the development of the concepts and frameworks on AI literacy education, discusses the differences and similarities between AI in education (AIED) and AI literacy education, and illustrates the reasons why K-16 students need to learn AI. These concepts are brought together in Part II, “K-16 AI literacy education” to further summarize the pedagogies, learning content, learning tools and assessment methods to inform K-16 educators how to design their AI instruction at each education level. After that, part III “AI literacy for instructional designers” explores how instructional designers (i.e., AI developers and teachers) prepare themselves to become ready to design developmentally appropriate tools, platforms, services and curricula to empower students with AI literacy skills.

This book is associated with the 'Data Literacy Level 1' on-demand online course: https://dataliteracy.com/courses/data-literacy-level-1 For most of us, it's rare to go a full day without coming across data in the form of a chart, map or dashboard. Graphical displays of data are all around us, from performance indicators at work to election trackers on the news to traffic maps on the road. But few of us have received training or instruction in how to actually read and interpret them. How many times have we been misled simply because we aren't aware of the pitfalls to avoid when interpreting data visualizations. Learning to See Data will teach you the different ways that data can be encoded in graphical form, and it will give you a deeper understanding of the way our human visual system interprets these encodings. You will also learn about the most common chart types, and the situations in which they are most appropriate. From basic bar charts to overused pie charts to helpful maps and many more, a wide array of chart types are covered in detail, and conventions, pitfalls, strengths and weaknesses of each of them are revealed. This book will help you develop fluency in the interpretation of charts, an ability that we all need to hone and perfect if we are to make meaningful contributions in the professional, public and personal arenas of life. The principles covered in it also serve as a critical background for anyone looking to create charts that others will be able to understand. "This book is clear and evocative, thorough and thoughtful, and remarkably readable: a marvelous launchpad into the world of data." –Tamara Munzner, Professor, University of British Columbia Computer Science "Everyone of us needs good data literacy skills to survive in the modern world. Without them, it's hard to succeed at work, or survive the onslaught of information (and misinformation) across all our media. Ben's book provides the necessary building blocks for a strong foundation. From that foundation, Ben's approach will inspire you to own the process of developing your skills further." –Andy Cotgreave, Technical Evangelism Director, Tableau

This book is for K12 students who want to learn AI, for teachers who want to teach AI and bring AI into the classroom, and for any individual who wants to understand AI in a simple and effective way.Artificial Intelligence is all around us. This book demystifies AI for K12 students and teachers using a unique combination of concept learning, hands-on plugged and unplugged exercises, context of how AI is used in industries from finance to marketing, and project ideas for students to apply their own creativity and build their own AIs. The ten fully illustrated color chapters cover both Machine Learning and Deep Learning, a comprehensive overview of AI Ethics, and popular AI algorithms from Linear Regression to Convolutional Neural Networks. Teacher's corners provide teachers with additional resources for bringing AI into the classroom. The book is paired with extensive online resources in curriculum, datasets, exercises, and code.The two authors (Nisha Talagala and Sindhu Ghanta) have extensive experience building industry AI solutions and have applied their knowledge to teach AI to K12 students. This book comes from their experiences of teaching AI to thousands of students around the world.