Yet more proof that Minecraft is more than just a game comes our way today. Microsoft already has plans to use the platform, which it bought for 2.5 billion, to help kids learn and for virtual reality, and now we can add artificial intelligence development to that list, too. Today, Microsoft announced a project that enables artificial intelligence researchers to tap into the hit title to sculpt and develop their tech. AIX is a new software development platform that researchers can use to develop'agents' -- AI-powered characters -- which roam Minecraft worlds. The idea is to equip them with the smarts to behave like a regular player.

"Science often follows technology, because inventions give us new ways to think about the world and new phenomena in need of explanation." Or so Aram Harrow, an MIT physics professor, counter-intuitively argues in "Why now is the right time to study quantum computing". He suggests that the scientific idea of entropy could not really be conceived until steam engine technology necessitated understanding of thermodynamics. Quantum computing similarly arose from attempts to simulate quantum mechanics on ordinary computers. So what does all this have to do with machine learning?

This practical introduction is geared towards scientists who wish to employ Bayesian networks for applied research using the BayesiaLab software platform. Through numerous examples, this book illustrates how implementing Bayesian networks involves concepts from many disciplines, including computer science, probability theory, information theory, machine learning, and statistics. Each chapter explores a real-world problem domain, exploring aspects of Bayesian networks and simultaneously introducing functions of BayesiaLab. The book can serve as a self-study guide for learners and as a reference manual for advanced practitioners.

In the past, we have talked about how to build machine learning models on structured data-sets. However, life does not always give us data that is clean and structured. Much of the information generated by humans has little or no formal structure: emails, tweets, blogs, reviews, status updates, surveys, legal documents, and so much more. There is a wealth of knowledge stored in these kinds of documents which data scientists and analysts want access to. "Text analytics" is the process by which you extract the useful information out of text.

In a sparse lecture room at Stanford University, six students are rehearsing a presentation they'll later give to a roomful of VIPs from the university's artificial intelligence lab. Papers are strewn across the table. Hoodies hang over the cloth-covered cushion chairs. One student wears a pair of Pi earrings. Another wears a t-shirt that reads: "i: Be rational! A sheet of white poster board sits in the corner, with a few words scrawled in black marker. "Monitoring Hand Sanitation in Hospitals Using Computer Vision," it says. After a while, they give it a dry run. Scripts in hand, the students describe the images they captured from cameras mounted above hand dispensers at Stanford's Lucile Packard Children's Hospital, and they explain the machine learning techniques they've used--including something called "climbing the hill"--to analyze the footage and automatically determine whether doctors and visitors are practicing proper hand hygiene.

Editor's note: Kevin Gray is president of Cannon Gray LLC, a marketing science and analytics consultancy. He would like to thank Marco Vriens of Ipsos for his helpful comments on a draft of this article. Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering.

Google (NASDAQ:GOOGL) (NASDAQ:GOOG) is making big splashes in the cloud computing community this week. Under the leadership of Diane Greene, former VMWare co-founder, the company is striking deals left and right for its long-lagging cloud hosting business, thus attacking Amazon (NASDAQ:AMZN), IBM (NYSE:IBM) and Microsoft (NASDAQ:MSFT). If you follow these companies, you already know that. However, there is something else going on which is an absolutely fundamental shift in Google's strategy and it has to do with today's announced additions to cloud platform. If you are a regular reader of my articles, you might be aware of my under-the-hood series for the artificial intelligence market.

Microsoft has unleashed a social media AI onto the internet -- and she can be a bit of a jerk. On Wednesday, the company released Tay.ai, an artificial intelligence chat bot "with no chill" aimed at 18- to 24-year-olds living in the United States. Tay will talk on social networks popular with many youths, like Facebook, Kik Messenger, Snapchat and Twitter. A chat bot is a program designed to mimic human behaviour in conversation. Microsoft's Technology and Research team, along with the team behind its search engine Bing, built Tay to study "conversational understanding."

March 24--Hey, millennials: Microsoft wants to chat with you. The Redmond company introduced tay.ai Wednesday, an emoji-fluent, snarky chat bot aimed at 18- to 24-year-olds in the United States. The bot will reply to messages sent to it on any of those platforms in a style mimicking teenage Web speak. Tay, Microsoft says, was built by mining public data and using machine-learning technology and input from a staff that includes improvisational comedians.

BlinQ is a dating app that has developed a new feature recently. With the help of an AI developed by the University of Zürich, the app now can rank photos by attractiveness on a six-point scale. The worst one is simply "Hmm…" and the best is "Godlike". But let's test it with a bunch of video game (and other) examples and see how well they perform. Wario and Link from the CD-i Zelda games were too much for its face recognition feature (which certainly means they're both way above "Godlike"). But it works with almost everything else.