Artificial Intelligence, Deep Learning, Can It Take Over? Bill Gates, Stephen Hawking and Elon Musk first warned us about Artificial Intelligence (AI). Elon Musk then turned around and with other technologists put 1B into starting a nonprofit research effort – OpenAI just to "keep an eye on it"! Facebook, Google, Amazon, Nvidia, Shopify and others are charging full steam at AI and even open sourcing it! So what is all the AI ruckus about? AI has been subject matter for science fiction for a long time now. Every SciFi show you can think of has a intelligent computer or robot as a sidekick or with some prominent role.

Sydell calls upon Latanya Sweeney's 2013 study of Google AdWords buys made by companies providing criminal-background-check services. Sweeney's findings showed that when somebody Googled a traditionally "black-sounding" name, such as DeShawn, Darnell or Jermaine, for example, the ad results returned were indicative of arrests at a significantly higher rate than if the name queried was a traditionally "white-sounding" name, such as Geoffrey, Jill or Emma. Important to note is that the algorithm doesn't actually look at arrest rates. Even if the ad indicates that somebody may have been arrested, it's entirely possible that nobody with that name exists in the background-check company's database at all. Professor Sweeney found this out firsthand when she Googled her own name.

This book covers the topic of recommender systems comprehensively, starting with the fundamentals and then exploring the advanced topics. Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: The context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.

Researchers have traced the paths of thousands of neurons in a tiny piece of mouse brain, creating the largest map of neuronal wiring to date. The atlas, published in March in Nature, shows not only how these neurons connect, but also how they function as the brain processes information1. The work is part of a massive effort, backed by more than 70 million in federal funding, to use the brain as a blueprint for intelligent machines. The findings could also offer clues about how the brain becomes wired during development and what happens when this wiring goes awry, says lead researcher R. Clay Reid, senior investigator of neural coding at the Allen Institute for Brain Science in Seattle, Washington. "In some way that no one has thought of yet, this will help [to advance our understanding of] autism," he says.

Soon we'll be chatting with virtual robots on Messenger, if Facebook gets its way. At the company's annual F8 developer conference on Tuesday, Facebook unveiled plans to get people to connect with bots via the messaging platform with over 900 million users. Bots to chat with customer service, news organizations, businesses, and apps are all on the table. "I've never met anyone who likes calling a business," CEO Mark Zuckerberg said. "No one wants to have to install a new app for every service or business they want to interact with. We believe there's got to be a better way to do this."

Editor's note, April 1, 2016: This project was formerly known as Project AIX and has now been renamed Project Malmo. In the airy, loft-like Microsoft Research lab in New York City, five computer scientists are spending their days trying to get a Minecraft character to climb a hill. That may seem like a pretty simple job for some of the brightest minds in the field, until you consider this: The team is trying to train an artificial intelligence agent to learn how to do things like climb to the highest point in the virtual world, using the same types of resources a human has when she learns a new task. That means that the agent starts out knowing nothing at all about its environment or even what it is supposed to accomplish. It needs to understand its surroundings and figure out what's important – going uphill – and what isn't, such as whether it's light or dark.

How would you go about teaching an artificial intelligence how to love? Much has been written about Spike Jonze's Her, the Oscar-nominated tale of love between man and operating system. Poetic license aside, is that really possible? What computers lack are bodies. The thoughts and feelings and emotions we call "love" are not abstract experiences; they're intertwined with senses and hormones.

This is the latest installment in a regular series of conversations with William McDonough (@billmcdonough), designer, architect, author and entrepreneur. Joel Makower: Tell me about the innovation future roundtable you recently convened. Bill McDonough: I have been working with companies that are looking at the future of mobility in India, and designing factories and other things for them. The chairman said he would like to connect to some of the advanced thinking across many sectors and integrate that with some conversations that he could participate in. The first person I thought of for that was Jack Hidary.

Rembrandt was arguably the first artist to really master the "selfie," and he did so all the way back in the 1600s. Now, a team of technologists working with Microsoft are bringing Rembrandt's technique into the modern age--they have produced a 3D printed painting in the style of the Dutch master. "Our goal was to make a machine that works like Rembrandt," Emmanuel Flores, director of technology for the project, told the BBC. "We will understand better what makes a masterpiece a masterpiece." To accomplish this feat, data on Rembrandt's works was gathered by computers, which discovered patterns in how he would paint certain features, like facial features, for example. Then, machine-learning algorithms were created that could output a new portrait in the familiar Rembrandt style.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the website, do not hesitate to contact any of the authors directly by e-mail: Ian ( lastname.firstname The book itself is now complete and we are not currently making revisions beyond correcting any minor errors that remain.