Learning Technology

When Temple Grandin was born, her parents knew that she was different. It wasn’t until years later that she was diagnosed with autism, a brain disorder that makes communication difficult. Today, Dr. Temple Grandin is a scientist and professor of animal science at Colorado State University. Her career has revolutionized the livestock industry - each year, half the cattle in the United States are handled in cruelty-free facilities she designed. She is also a passionate advocate for autism, using her experience to prove that people with this disorder can have “normal” lives.

Whether we like it or not, the AI revolution is coming to education. In Brave New Words, Salman Khan, the visionary behind Khan Academy, explores how artificial intelligence and GPT technology will transform learning, and offers a road map for teachers, parents, and students to navigate this exciting (and sometimes intimidating) new world. A pioneer in the field of education technology, Khan examines the ins and outs of these cutting-edge tools and how they will revolutionize the way we learn and teach.

Drs. Barbara Oakley and Terrence Sejnowski's popular Online course Learning How to Learn, has enrolled more than 1.8 million students. In this much needed follow-up to A Mind for Numbers, the authors teach kids and teens how to learn effectively at a time when they most need these skills. Learning How to Learn teaches them about the importance of both focused concentration and letting their minds wander, how the brain makes connections between different pieces of information, why procrastination is the enemy of problem solving, and much more.

Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the listener through the essential principles we need in order to derive knowledge from data.

The deep-learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep-learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole - and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill.

Artificial intelligence is smarter than humans. It can process information at lightning speed and remain focused on specific tasks without distraction. AI can see into the future, predicting outcomes and even use sensors to see around physical and virtual corners. So why does Intelligence frequently get it so wrong? The answer is us. Humans design the algorithms that define the way that AI works and the processed information reflects an imperfect world.

More than half a century ago, the naturalist Farley Mowat accepted an assignment to investigate why wolves were killing Arctic caribou. Mowat’s account of the summer he lived in the frozen tundra alone – studying the wolf population and developing a deep affection for these wild creatures (who were no threat to caribou or man) – is today celebrated as a classic of nature writing, at once a tale of remarkable adventure and an indelible record of the myths and magic of wolves.

(ISC)2 Certified Information Systems Security Professional (CISSP) Official Study Guide, 9th Edition has been completely updated based on the latest 2021 CISSP Exam Outline. This bestselling Sybex study guide covers 100% of the exam objectives. You'll prepare for the exam smarter and faster with Sybex thanks to expert content, knowledge from our real-world experience, advice on mastering this adaptive exam, access to the Sybex online interactive learning environment, and much more. Reinforce what you've learned with key topic exam essentials and chapter review questions.

This book walks you through the "need-to-know" aspects of artificial intelligence, including natural language processing, speech recognition, and the power of machine learning to show you how to make the most of this technology in a practical, tactical way. Case studies show how real-world companies are taking the next leap forward. Artificial intelligence and machine learning provide a data-driven basis for more robust and intensely-targeted marketing strategies - and companies that effectively utilize these latest tools will reap the benefit in the marketplace.

In this audiobook, you'll get a clear overview of artificial intelligence technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent - really - are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements.

Developed by experienced professors and professionally narrated for easy listening, this course is a valuable tool when preparing for your ITIL 4 foundation exam. The audio is focused and high-yield, covering the most important topics you need to know to succeed on The Information Technology Infrastructure Library (ITIL) Level 4 foundation exam. The material is accurate, up-to-date, and broken down into bite-sized chapters. There are key takeaways following each chapter to drive home key points and quizzes to review commonly tested questions.

What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.

In Wiley's Certified Information Security Manager (CISM) Study Guide, you'll get the information you need to succeed on the demanding CISM exam. You'll also develop the IT security skills and confidence you need to prove yourself where it really counts: on the job. Chapters are organized intuitively and by exam objective so you can easily keep track of what you've covered and what you still need to study. You'll also get access to a pre-assessment, so you can find out where you stand before you take your studies further.

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.

People are now exposed to more information than ever before, provided both by technology and by increasing access to every level of education. These societal gains, however, have also helped fuel a surge in narcissistic and misguided intellectual egalitarianism that has crippled informed debates on any number of issues. Today, everyone knows everything and all voices demand to be taken with equal seriousness, and any claim to the contrary is dismissed as undemocratic elitism.

If you read (or listen to) nothing else on artificial intelligence and machine learning, listen to these ten articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.