In previous articles, we've defined some of the terms being thrown around lately like "machine learning" and "artificial intelligence". These disruptive technologies will soon change the world as we know it. While some pundits predicted that we were years away from a computer that could beat a human expert at "Go", this achievement was recently announced. If a "deep learning" program can now beat a game that has more possible moves than atoms in the known universe, then what's stopping us from unleashing it upon the stock market and making millions? The idea of using computers to trade stocks is hardly new.

As the co-founder of App Developer Magazine, Richard has several industry recognitions and endorsements from tech companies such as Microsoft, Apple and Google for accomplishments in the mobile market. He was part of the early Google AFMA program, and also involved in the foundation of Google TV. He has been developing for mobile since 2003 and serves as CEO of Moonbeam Development, a mobile app company with 200 published titles in various markets throughout the world. He has been a featured presenter at trade-shows and conferences, and stays active with new projects relating to mobile development.

Mark Zuckerberg at F8. "I've never met anyone who likes calling businesses." ANALYSIS: Love it or hate it, Messenger is Facebook's most important product. Already, Facebook the social network feels like the clunky backend of the real product, an app where you actually talk to your friends, who barely post anything personal on the site itself any more. It's not as splashy as Snapchat, but there's a reason it consistently beats those apps in engagement and app downloads. Sixty billion messages are sent a day on Messenger.

While handwriting recognition might be considered deep-learning 101, Japanese is a whole other ballgame. That's because the language includes symbolic characters such as kanji, which is composed of elements that can be read independently, making it difficult to know where one ends and another begins. There are also more than 2,000 common pictograms made up of dozens of strokes. The trick is to tackle one squiggle at a time. Reactive's algorithm queries the neural network for a match, adds another stroke and repeats, all the while refining the probability of an accurate hit.

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.