"Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology." After using AWS Machine Learning for a few hours I can definitely agree with this definition, although I still feel that too many developers have no idea what they could use machine learning for, as they lack the mathematical background to really grasp its concepts. Here I would like to share my personal experience with this amazing technology, introduce some of the most important – and sometimes misleading – concepts of machine learning, and give this new AWS service a try with an open dataset in order to train and use a real-world AWS Machine Learning model. Luckily, AWS has done a great job in creating this documentation, so that everybody can understand what machine learning is, when it can be used, and what you need in order to build a useful model. You should check out the official AWS tutorial and its ready-to-use dataset.

It's almost a given that you'll ride in an autonomous car at some point in your life, and when you do, the AI controlling it just might have honed its skills playing Minecraft. It sounds crazy, but open-world games like Minecraft are a fantastic tool for teaching learning algorithms--which power the next generation of advanced artificial intelligence--how to understand and navigate three-dimensional spaces. Achieving that is a major stepping stone toward creating AI that can interact with the real world in complex ways. It's easy to consider videogames mindless escapism, but because they generate such vast amounts of information--think of the expansive world players create in Minecraft--they are exceptionally well suited to teaching an AI how to perceive the world and interact with it. "It's hard for a human to teach AI," says Xerox researcher Adrian Gaidon, because they are "worse than the worst toddlers in the world--you have to explain everything."

A pair of super-rich American technology gurus are planning to undergo surgery to install experimental implants directly into their brains. The two men are currently trying to find a doctor willing to perform this untested and highly risky procedure, The Mirror has learned. If they survive the operation, the men hope to be able to directly communicate with the primitive forms of artificial intelligence currently being developed in labs across the world. But critics and conspiracy theorists fear these pioneering implants are the first step towards creating a society where every human is plugged into "the matrix". Zoltan Istvan, a U.S. Presidential candidate, personally knows both of these would-be bionic men.

It has already been cheerfully offering advice to customers hoping to buy a phone in Tokyo, but Pepper the'emotional' robot is now about to enrol in school. The expressive humanoid, which has been developed by Japanese corporation SoftBank Robotics, is designed to identify and react to human emotions. It is now due to attend classes at Shoshi High School in Waseda, in the Fukushima Prefecture of Japan – making it the first time a robot will'study' alongside human students. Pepper the robot has become the world's first humanoid to enroll into a high school. Pepper is intended to be used for customer service in banks, shops and for greeting people. However, SoftBank has said it – or he as they company seems to prefer – could become a companion in people's homes in the future too.

ROC curves can also be constructed from clinical prediction rules. The graphs at right come from a study of how clinical findings predict strep throat (Wigton RS, Connor JL, Centor RM. In that study, the presence of tonsillar exudate, fever, adenopathy and the absence of cough all predicted strep. The curves were constructed by computing the sensitivity and specificity of increasing numbers of clinical findings (from 0 to 4) in predicting strep. The study compared patients in Virginia and Nebraska and found that the rule performed more accurately in Virginia (area under the curve .78)

Peter Drucker famously said "what gets measured gets managed." This mantra has been embraced by companies worldwide and particularly in Silicon Valley. Firms are trying to optimize their their sales calls, their supply chain, their HR department and anything else they can put a number on. But with the rush to measure everything, many companies don't think about what the path to optimization looks like and whether their metrics match their goals. One recent example is Twitter's new feed algorithm which was supposed to surface the most relevant tweets rather than use the standard timeline.

The standard way to measure the impact of drought is by its economic effect. Last year, for example, the severity California's four-year drought was broadly characterized by an estimate that it would cost the state's economy 2.7 billion and 21,000 jobs. However, there are many experts who feel economic measures alone are inadequate to fully assess the impact of this complex phenomenon, which affected more than one billion people worldwide in the last decade. They argue that there is an urgent need to come up with better methods for measuring the overall effects of drought because the duration and severity of droughts are widely expected to increase in the future due to global warming. To provide such a comprehensive view, a pair of Vanderbilt doctoral students has assembled a multi-disciplinary team of graduate students from around the country to conduct a multi-faceted study of how people are affected by and responding to drought conditions in the United States.

A new public data set for self-driving cars shows that even a straightforward city commute may push automated driving to the limit. It also highlights how sharing data could help self-driving cars hit the roads far sooner. Researchers from Oxford University released the detailed data set, which highlights some of the most challenging issues that self-driving cars will face. The data consists of thousands of hours of data from the same 10-kilometer stretch of road over the course of a year. And it shows how weather, lighting, and even the features of roads themselves can vary wildly in a relatively short period of time.

The robotics industry has been rapidly advancing and, with the creation of robots that are becoming more and more like humans, it doesn't show any signs of stopping. Now, researchers from China created a very realistic-looking robot called Jia Jia, the "robot goddess," which took three years to make. Jia Jia bears the appearance of a female adult and possesses many human facial features, such as blinking, as well as realistic facial expressions and hair that closely resembles human hair. Researchers created Jia Jia in an attempt to mimic a human in as many ways as possible. "Her" mouth is designed to correspond with the statements that she makes and her eyes casually scan her surroundings much like a real human.

There's a line I often say to my colleagues…heck, I say it to my friends and family too: The one thing that's constant about being around me is change! Well, that and the fact that I'll always be thinking about (and talking about) food. Change and innovation is what Facebook is all about, too. F8, Facebook's annual conference for developers and service-building entrepreneurs, took place in San Francisco this past week and there's much buzz about Chatbots, the latest addition to Facebook's Messenger platform. Chatbots--(and the AI that powers them)--are important because they are in many ways the future of communication and, for sure, the future of customer service.