The rail industry has come up with a plan that may as well be out of a science-fiction movie to cope with growing demand and overcrowding: charging rail passengers for journeys by fingerprint or iris scan. The Rail Delivery Group (RDG), the organisation representing train operators and Network Rail, claims biometric technology would enable fares to be automatically charged marking the start of an era that could radically accelerate commute times. The technology represents the next step from travellers being able to us smartphones' Bluetooth signals to open station barriers. That will be trialled on Chiltern Railways' route between London Marylebone and Oxford Parkway over the coming months. The use of digital signalling technology will also allow trains to operate closer together, cutting delay, according to the RDG.

Machine learning is gaining momentum and whether we want to admit it or not, it has become an essential part of our lives. As Adam Geitgey, Director of Software Engineering at Groupon, told JAXenter a few months ago, "anyone who knows how to program can use machine learning tools to solve problems." I think that in five years, machine learning won't be thought of as "magic" anymore. It will be a very common tool that nearly all programmers use to solve problems – just like how most programmers today know about databases and networking. Geitgey explained that even if you don't need a deep mathematical background to be able to apply machine learning, learning Python --"by far the most popular programming language today for machine learning"-- is a must.

In conclusion, employing a Machine Learning method can be extremely tedious, but can also serve as a revenue charger for a company. However, this is only possible by implementing Machine Learning in newer and more innovative ways. Machine Learning is only beneficial if there are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which Machine Learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you're achieving the required outcomes, you shouldn't stop experimenting and trying to find better and more innovative algorithms.

Conventionally, skin cancer's primarily diagnosed visually. It starts with a clinical screenings, then, if needed, followed by dermoscopic analyses, a biopsies and histopathological examinations. A team mainly from Stanford University, California, has reported in Nature that mHealth can provide an alternative. Classifying skin lesions using images is challenging, owing to fine-grained variabilities in their appearance. Convolutional Neural Networks (CNN) offer potential for dealing with fine-grained object categories. The team demonstrates skin lesion classifications using a single CNN, trained end-to-end directly from images using only pixels and disease labels as inputs.

Episode Summary: Accenture is a leading global professional services company in the tech space, providing services to many of the Fortune 500 and their global equivalents. The company recently conducted a study, combined with expertise from economists and AI researchers, about the longer-term economic impact of artificial intelligence around the world. In this episode, I spoke with Chief Technology Officer Paul Daugherty, who has been with Accenture since 1986, and who was joined by Global Technology R&D Lead Marc Carrel-Billiard. We met up at a coffee shop after an AI Summit in San Francisco, and I asked Paul and Marc about what they had learned from this newly-published study and what they consider to be the significant impacts of *AI and automation on the future job market. Brief Recognition: Paul Daugherty is Accenture's CTO and leads the company's Technology Innovation & Ecosystem group.

Can artificial intelligence make you a better skier? The ski manufacturer teamed up with sports tech company PIQ to put an AI-powered computer -- complete with an LED display -- right on a pair of skis. Called the Rossingol Hero Master, the idea is similar to the PIQ Robot accessory, which attaches to ski boots and analyzes your turns, speed and other data while you ride. This takes that idea to the next level with the technology built right into the skis. Rather than just sending the data to an app on your phone, the Hero Master comes with its own LED display so skiers can see real-time stats and other information (it also tracks stats in its own app, as well). The prototype uses onboard sensors and PIQ's algorithms to analyze speed, turning angles, and other data.

I first heard of Spark in late 2013 when I became interested in Scala, the language in which Spark is written. Some time later, I did a fun data science project trying to predict survival on the Titanic. This turned out to be a great way to get further introduced to Spark concepts and programming. I highly recommend it for any aspiring Spark developers looking for a place to get started. Today, Spark is being adopted by major players like Amazon, eBay, and Yahoo!

This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameter-free, and usable and easy-to-interpret by non-statisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. Readers are invited to further formalize the technology outlined here, and challenge my proposed methodology.

Bayesian inference is a way to get sharper predictions from your data. It's particularly useful when you don't have as much data as you would like and want to juice every last bit of predictive strength from it. Although it is sometimes described with reverence, Bayesian inference isn't magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Bayesian inference is based on the ideas of Thomas Bayes, a nonconformist Presbyterian minister in London about 300 years ago. He wrote two books, one on theology, and one on probability. His work included his now famous Bayes Theorem in raw form, which has since been applied to the problem of inference, the technical term for educated guessing. The popularity of Bayes' ideas was aided immeasurably by another minister, Richard Price. He saw their significance, refined them and published them. It would be more accurate and historically just to call Bayes' Theorem the Bayes-Price Rule.

Companion robots are playing an ever-increasing role in helping busy people look after the elderly and children. With an aging population and an increasing labor shortage, the demand for companion robots for elder care and children's companions is on the rise. Xiaoyuzaijia is an intelligent companion robot produced by Zaijia.com, a Beijing-based startup engaged in internet hardware and intelligent home appliances businesses. It specializes in security monitoring, human-computer emotional communication and entertainment functions. If the elderly people are ill at home, the robot can bring medicine and water. The robot will open the door when the guest comes.