Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Walk into a conference room on the 16th floor of an Uber Engineering building on Market Street in San Francisco. You enter an intense discussion around a table with software and data engineers, data scientists, modeling experts, and even a product manager. How to determine a fraudulent user. Fraud prevention is one of the fastest growing areas of research and development at Uber. As our platform has grown, so has the international underworld that tries to undermine it.

When I was in high school, one of the required classes I had to take was a shop class. I had been taking mostly what the school called "enriched" courses, or what were mostly academic classes that featured primarily reading, writing, and arithmetic. A shop class had more of a trade focus. I was surprised when the first lesson on the first day of my shop class was a richer academic experience than any of the enriched classes I had taken. The instructor started talking about systems, and how many manufacturing processes involved breaking products down into different systems.

With nary a CFO or CIO among the attendees, this is a gathering aimed squarely at those of you working down in the trenches of cloud application development, testing, and operations. Walk into almost any educational session and you'd hear about IoT, Industrial IoT, storage and how to deal with huge amounts of it, testing, microservices, and containers. In this expert handbook, we explore the issues and trends in cloud development and provide tips on how developers can pick the right platform. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

Since the application of robotics is largely confined to manufacturing and AI is still in its infancy, the answer now is obviously no. One LinkedIn author recently argued that business schools should be proactive and develop a "Robot MBA" for a future of intelligent non-human workers. Silliness aside, having transformed unskilled jobs, automation is set to disrupt professional life, where MBAs can be a point of differentiation for humans. "The automation we've seen in the last 100 years mostly replaced muscle," says Urs Peyer of Insead. A study by Deloitte and Oxford University found that 35 per cent of current jobs are at high risk of computerisation, with white collar professions among those most under threat.

Students mostly couldn't tell'Jill Watson' wasn't human; 'Yep!' One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

Robots are like dogs because, according to some experts, a badly-trained robot could end up misbehaving just like a badly-trained dog. This warning came at a meeting discussing the future of robot and human interactions, held in London this week. But the panel, who emphasised the importance of regulations controlling AI, agreed a doomsday situation in which robots take over is unlikely to happen soon. Robots are like dogs because, according to some experts, a badly-trained robot could end up misbehaving just like a badly-trained dog. Organised by the EPSRC UK Robotics and Autonomous Systems Network (UK-RAS Network), UK Robotics Week included a series of events across the country, aiming to get the public engaged with the developments and debate in and around robotics.

Lux Research recently attended the EmTech Digital Conference in San Francisco. At the event, Deep Genomics CEO Brendan Frey described how the company wants to use a form of artificial intelligence (AI) called neural networks to connect genotype to phenotype – in other words, to see how the genetic code of an individual determines the traits (hair color, disease, persona) the individual presents. To illustrate the gap, he surveyed the audience by show of hands, how many believed that understanding genomics would change our lives (some 80%); how many had actually had a genetic test done (perhaps 25%), and how many of those had found the results to be valuable (about 5%). "That's the gap," he explained, since so many conditions lack genetic correlation, and so many genetic traits are not actually expressed. The goal of Deep Genomics is to complement genomic data with other biologically-relevant data, including personal history, behavior, environment, and nutrition; and use machine learning to connect genes with traits.

BMW wants to make a completely autonomous car called iNEXT, suited for both city streets and highways, by 2021. For that, the German car maker is teaming up with Intel and Mobileye, which will provide the underlying technology for the iNEXT to roam streets without human assistance. The iNEXT model is the first in a range of fully autonomous vehicles planned by the automaker. BMW is betting self-driving cars will be widely used as automated taxis and in ride-sharing services. Uber already has the ride-sharing possibilities in mind and has deployed a self-driving car in Pittsburgh.