In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art.

When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls - information about the caller, the recipient, the time, the duration, the location - and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems?

A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview.

The history of computing could be told as the story of hardware and software or the story of the Internet or the story of "smart" handheld devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development.

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Amazon.com started off delivering books through the mail. But its visionary founder, Jeff Bezos, wasn't content with being a bookseller. He wanted Amazon to become the everything store, offering limitless selection and seductive convenience at disruptively low prices. To do so, he developed a corporate culture of relentless ambition and secrecy that's never been cracked. Until now.

Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence - and even more so if we hope to participate in the future, rather than being simple bystanders.

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.

Expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data-centric issues. Discover two comprehensive manuscripts in one audiobook.

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja.

"Correlation does not imply causation". This mantra has been invoked by scientists for decades and has led to a virtual prohibition on causal talk. But today, that taboo is dead. The causal revolution, sparked by Judea Pearl and his colleagues, has cut through a century of confusion and placed causality - the study of cause and effect - on a firm scientific basis.

With the world going remote, and data science following along, you can start and pursue a career in data right here, right now. How do I start? What do I need to know? What is data analytics? I'm sure one, if not all, of these questions have run through your mind. Well, the answer is data literacy: the ability to read, understand, and communicate data into information. Building a solid foundation of practical knowledge is the first step to being successful in data. From there, you can finally decide what direction you should go in and start gaining the required knowledge.

Chemistry and Our Universe: How It All Works is your in-depth introduction to this vital field, taught through 60 engaging half-hour lectures that are suitable for any background or none at all. Covering a year’s worth of introductory general chemistry at the college level, plus intriguing topics that are rarely discussed in the classroom, this amazingly comprehensive course requires nothing more advanced than high-school math. Your guide is Professor Ron B. Davis, Jr., a research chemist and award-winning teacher at Georgetown University.

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.

Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now - in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?

In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it.

The confluence of four technologies - elastic cloud computing, big data, artificial intelligence, and the internet of things - writes Siebel, is fundamentally changing how business and government will operate in the 21st century. Siebel masterfully guides listeners through a fascinating discussion of the game-changing technologies driving digital transformation and provides a roadmap to seize them as a strategic opportunity.

Artificial intelligence is changing our world faster than we can imagine, and it will impact every area of our lives. And this is happening whether we like it or not. You might have heard that many jobs will be replaced by automation and robots, but did you also know that at the same time a huge number of new jobs will be created by AI? This book covers many fascinating and timely topics related to artificial intelligence, including: self-driving cars, robots, chatbots, and how AI will impact the job market, business processes, and entire industries, just to name a few.

Many people today are so dazzled by the long-term potential for artificial intelligence that they overlook the much clearer and more immediate potential for a new form of "collective intelligence": the intelligence of groups of people and computers working together. In Superminds, Thomas Malone explains what we need to do to take advantage of this potential. Groundbreaking and utterly fascinating, Superminds will change the way you work - both with others and with computers - for the better.

In The Second Machine Age, Andrew McAfee and Erik Brynjolfsson predicted some of the far-reaching effects of digital technologies on our lives and businesses. Now they’ve written a guide to help listeners make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.

Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture.

In T-Minus AI: Humanity's Countdown to Artificial Intelligence and the New Pursuit of Global Power, author Michael Kanaan explains the realities of AI from a human-oriented perspective that's easy to comprehend. A recognized national expert and the U.S. Air Force's first Chairperson for Artificial Intelligence, Kanaan weaves a compelling new view on our history of innovation and technology to masterfully explain what each of us should know about modern computing, AI, and machine learning.

The Amazon Way on IoT is for the leader who wants to understand how the Internet of things is transforming business and society. Listeners will discover business cases, key concepts, technologies and tools to help develop, explain and execute their own IoT approach through understanding Amazon's and other leading companies sophisticated IoT technologies and strategies.

World-renowned economist Klaus Schwab, founder and executive chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work.

In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art.

In this audiobook, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general listener, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context.

A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview.

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Data Science for Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today's social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks. He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning.

How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times best-selling author Martin Ford uncovers the truth behind these questions from some of the brightest minds in the artificial intelligence community.

The history of computing could be told as the story of hardware and software or the story of the Internet or the story of "smart" handheld devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development.

In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it.

Expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data-centric issues. Discover two comprehensive manuscripts in one audiobook.

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.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.

Critical thinking is regularly cited as an essential 21st century skill, the key to success in school and work. Given our propensity to believe fake news, draw incorrect conclusions, and make decisions based on emotion rather than reason, it might even be said that critical thinking is vital to the survival of a democratic society. But what, exactly, is critical thinking? Haber describes the term's origins in such disciplines as philosophy, psychology, and science.

Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20x in the last 3 years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However…To stand out in one of these positions, having foundational knowledge of interpreting data is essential.

As organizations shift from monolithic applications to smaller, self-contained microservices, distributed systems have become more fine-grained. But developing these new systems brings its own host of problems. This expanded second edition takes a holistic view of topics that you need to consider when building, managing, and scaling microservices architectures. Through clear examples and practical advice, author Sam Newman gives everyone from architects and developers to testers and IT operators a firm grounding in the concepts.