Automation has become an increasingly disruptive force in the labor market. Self-driving cars threaten the job security of millions of American truck drivers. At banks, automated tellers are increasingly common. And at wealth management firms, robo -advisers are replacing humans. "Any job that is routine or monotonous runs the risk of being automated away," Yisong Yue, an assistant professor at the California Institute of Technology, told Business Insider.

Fan Hui is speaking to a chatty audience at the 2016 European Go Congress in St. Petersburg, Russia, gushing over a game of Go played by one of his mentors. Hui's enthusiasm is infectious--the room's chatter subsides as he pulls up slides of the complex Chinese game, whose players battle to dominate a 19 19 board with black and white tiles called stones. Hui's mentor, AlphaGo, has studied strategies built over the game's 4,000-year history, and has played thousands of practice matches. But a training regiment that took Hui years to perfect, AlphaGo did in about four weeks. The game Hui showed to the Saint Petersburg audience is one that the machine played against itself, and to Hui, is an example of the beauty of AlphaGo's strategic mind Even the smartest humans miss patterns that computers see instantly. Problems like file-compression, translation, and custom drug fabrication have millions of variables and data points whose limits exceed human understanding dozens of times over.

Bono and The Edge are excited by Nuritas' potential for impact and profitability. Try to imagine walking into your local grocery store ten years from now. You head towards the back, as you've done countless times before, to snatch a handful of your favorite cereal bars. Only this time, the packaging looks a bit different. You notice that there are two new versions, each with a label below the brand name.

NEW YORK--Are you ready to turn your car, washing machine or running shoes into a point of sale? Visa is teaming up with IBM Watson to bring secure payment experiences to all sorts of connected products and services, embracing the ecosystem techies inelegantly refer to as the Internet of Things. IBM and Visa announced the collaboration Thursday at an event in Munich, Germany, where IBM is opening up a $200 million Watson Internet of Things headquarters. Via the partnership, the companies say they can support payments and commerce on virtually any of the 20 billion connected devices that Gartner estimates to be part of the global economy by 2020. "What we've seen over the last 12 months is serious companies committing serious business to IoT," says Brett Greenstein, vice president for IBM Watson IoT. Watson is IBM's cognitive computing platform, which leverages natural language processing and machine learning to learn from and extract patterns and meaning from mounds of unstructured data, from health care to sports.

IBM Machine Learning leverages parts of the Watson supercomputer to be used with the IBM z System Mainframe. The tech giant's initiative aims to train and deploy analytics models in the private cloud. According to Tech Republic, IBM plans to bring soon some of the core machine learning technology from IBM Watson to the private cloud and mainframes, as the company announced on Wednesday, Feb. 15. The tech giant has called its new cognitive platform IBM Machine Learning. The upcoming machine learning platform will be launched first on the z System mainframe.

A town in the Netherlands is trialling special pavement lights designed to help smartphone users cross the road safely. The LED strips have been embedded into the ground at a pedestrian crossing in Bodegraven, close to three schools. The hope is that they'll catch the eye of pedestrians who are too distracted by their smartphones to bother looking at the road, telling them when to cross and when not to cross by either glowing green or red, depending on the traffic light signals. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

What supercomputers will look like in the future, post-Moore's Law, is still a bit hazy. As exascale computing comes into focus over the next several years, system vendors, universities and government agencies are all trying to get a gauge on what will come after that. Moore's Law, which has driven the development of computing systems for more than five decades, is coming to an end as the challenge of making smaller chips loaded with more and more features is becoming increasingly difficult to do. While the rise of accelerators, like GPUs, FPGAs and customized ASICs, silicon photonics and faster interconnects will help drive performance to meet many of the demands of such emerging applications as artificial intelligence and machine learning, data analytics, autonomous vehicles and the Internet of Things, down the road new computing paradigms will have to be developed to address future workload challenges. Quantum computing is among the possibilities being developed as a possible solution as vendors look to map out their pathways into the future.

Chatbots look likely to be a cornerstone of many brands' marketing campaigns this year with the programs capable of generating automated engagement with consumers. Able to sit within messaging apps and communicate when prompted, chatbots can serve a wide variety of roles; the Guardian is distributing news; Activision engaged fans with the Call of Duty Series, Domino's Pizza is accepting food orders, and KLM's flight assistant houses boarding pass and flight times. For creative brands with on tap digital talent, the sky's the limit. The Drum has touched down with Paul Walsh, the founder of bCRM, a chatbot campaign management tool, which is now integrated with BotKit, one of the most widely used bot creation tools, to outline the basics for prospective brands. The Drum: What benefits can chatbots bring to consumers?

IBM has announced the launch of IBM Machine Learning, a cognitive platform for the z System mainframe. The new platform uses the core technology from IBM Watson to perform big data analysis for clients using z System for cloud data storage. The new Machine Learning platform automates the creation and deployment of analytic models, allowing data scientists to perform analysis in-house. Models can be written in any language (Scala, Java, Python), using any framework and data type required for best results. IBM Machine Learning also includes a function called Cognitive Automation for Data Scientists, which helps data scientists to choose the correct algorithm for their needs.

First you do not always need to normalize (standardize) the input vectors (feature vectors), sometimes is good, sometimes is bad. In general you scale your feature vector when the magnitude of a feature dominates the others, so the model cannot pick up the contribution of the smaller magnitude features. Read here for a detailed explanation. Second there are two general classes of machine learning problems: classification and regression. In a classification type problem the output (dependent variable) is discrete, so you do not need to normalize it.