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.

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.

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling. This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient. We show experimentally that this method reduces gradient variance even in a context where the cost of synchronization across machines cannot be ignored, and where the factors for importance sampling are not updated instantly across the training set.

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.

Cleveland Clinic to use IBM Watson for Genomic Research: Researchers at Cleveland Clinic will use IBM Watson technology in the area of genomic research to help oncologists deliver personalized medicine by uncovering new cancer treatment options for patients. The Lerner Research Institute's Genomic Medicine Institute at Cleveland Clinic plans to evaluate Watson's ability to help oncologists develop more personalized care to patients for a variety of cancers. Clinicians lack the tools and time required to bring DNA-based treatment options to their patients and to do so, they must correlate data from genome sequencing to reams of medical journals, new studies and clinical records. At a time when medical information is doubling every five years, a faster option is needed. This use of Watson aims to find the "needle in the haystack" through identifying patterns in genome sequencing and medical data to unlock insights that will help clinicians bring the promise of genomic medicine to their patients.