When you look at cloud skills, it's more important to think about what's coming rather than what's already here. Why? Thousands of IT people will complete cloud certification programs this year. If you delay, the job market may be flooded by the time you're ready. That's not to say you won't find a market for your new skills, but the money and demand may be much different -- and not in a good way. The best career tactic is to think ahead.

Support vector machines is one of the most popular methods of classification in machine learning although they can be used as a black box, understanding what's happening behind scenes can be very useful not to mention interesting. In an internal learning course, I decided to implement SVMs and my objective with this article to mention some of the difficulties encountered. If you're planning to explore on how to implement support vector machines, have in mind this issues and the problem will be a little bit more easy to affront.

In his newly published scan of the literature, expert Thomas B. Sheridan concludes that the time is ripe for human factors researchers to contribute scientific insights that can tackle the many challenges of human-robot interaction. Massachusetts Institute of Technology Professor Emeritus Sheridan, who for decades has studied humans and automation, looked at self-driving cars and highly automated transit systems; routine tasks such as the delivery of packages in Amazon warehouses; devices that handle tasks in hazardous or inaccessible environments, such as the Fukushima nuclear plant; and robots that engage in social interaction (Barbies). In each case, he noted significant human factors challenges, particularly concerning safety. No human driver, he claims, will stay alert to take over control of a Google car quickly enough should the automation fail. Nor does self-driving car technology consider the value of social interaction between drivers such as eye contact and hand signals.

It's interesting to watch a new CEO's strategy begin to transition from discussion to execution. IT leaders are witnessing such a shift at Microsoft. Former CEO Steve Ballmer made Windows Everywhere the cornerstone of his strategy, leveraging the entrenched position of Windows and Microsoft Office on the desktop to gain traction in mobile, an increasingly important platform that Microsoft essentially missed. Going by market share of Windows Phones, this strategy essentially failed, and current CEO Satya Nadella has not only abandoned Windows Everywhere but is seemingly focusing the organization away from staples Windows and Office. Windows, and the operating system itself, are quickly becoming little more than gateways to internet-based applications.

The event will start by looking at the misconceptions of automation relating to the development of fully automated, or'robotised', container terminals where the entry level of investment can go into billions of dollars and ROI may not be visible for a decade. After this, automation experts will outline the case for existing and new terminals to implement process automation and automated decision making. Interactive Q&A panel sessions will draw attention to these – as well as other – areas of automation and review how real time data can deliver an ROI within 3-6 months. Automation will be presented as a decision for C-Level executives to make in order to reach world-class efficiencies so their terminal can increase in volume and throughput, all while saving on the bottom line and making a safer environment for today and tomorrow. Moreover, this event will reinforce the case for the port and terminal industry to step-up on automation investment and take on projects to win bigger business in a highly competitive market.

When most people think about artificial intelligence and cognitive computing, they think of futuristic technological landscapes overrun with evil robots. But a growing number of business and IT leaders are beginning to grasp how cognitive computing is radically reshaping the present IT landscape. This next wave of information technology is giving businesses the ability to outthink their competition and giving society the ability to solve some of our most pressing problems. In 2011, when a first-generation cognitive system outplayed two human Jeopardy champions, it did only one thing: answer natural-language questions. Today, that family of cognitive solutions has more than 30 capabilities, accessed via application programming interfaces (APIs) and delivered from the cloud.

Global law firm Baker & McKenzie has paired with Euromoney Thought Leadership to reveal what is driving the global financial services industry towards a rapid embrace of Artificial Intelligence and Machine Learning, and the implications both for the industry and financial markets as a whole. Ghosts in the machine: Artificial intelligence, risks and regulation in financial markets shows an industry rapidly seeing the potential benefits of AI, while being increasingly concerned about risk and the ability of regulators to keep pace. The survey asked 424 senior executives from financial institutions and fintech companies around the world as well as leading experts in the field for their view on how AI will affect the financial sector, what risks and benefits AI will bring to the sector, what the associated regulatory and legal challenges will be and many more questions. Surprisingly similar views emerged from across the globe, with only a few notable variations, such as AI investment seen as lagging slightly in Asia Pacific compared to other regions and Latin American participants seeing AI as a regulatory tool to combat money laundering first rather than market misconduct, the top choice elsewhere. The results provide some insights into the likely future of the financial sector.

AUSTIN, Texas (Reuters) - The Alamo Drafthouse Cinema Plaza here is a strip mall with a pet-accessories store, a Thai restaurant and a yogurt shop, an unlikely venue to display the high-tech future. But one Saturday morning in March, Google did just that. A small convoy of its driverless cars cruised into the fading asphalt parking lot to give test drives – test rides, actually – to American mayors visiting Austin's annual South by Southwest tech-and-culture festival. Mayor Richard J. Berry of Albuquerque, New Mexico, was impressed with how the cars dodged pedestrians and fallen tree limbs. Sam Liccardo, mayor of San Jose, California, right in Google's backyard, was impressed that he got to see the cars at all. "These things are crawling all over my city" in tests, "but I had to come to Austin to ride in one," said Liccardo. "This is going to change cities."

If you live in a connected home, you've likely spent a great deal of time customizing it for your needs and preferences. That's because the smart home is actually rather dumb, until you've invested some time teaching it: While some smart-home components--such as the Nest thermostat--are capable of learning on their own, there are no connected-home hubs that do. The Klug Home promises to change that. The Klug Home, from Singapore-based Intraix, is a 79 smart hub that plugs into one of your router's USB ports. It's available for preorder on Indiegogo, with delivery expected in October 2016.

Researchers at Regenstrief Institute and Indiana University School of Informatics and Computing say they now can detect cancer cases using data from free-text pathology reports at least as well--and faster--than clinicians reviewing reports manually. The researchers used existing data algorithms and open source machine learning tools to create a breakthrough electronic approach that could significantly speed patient diagnoses and public health reporting. "We think that it's no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not," says Shaun Grannis, MD, senior study author and interim director of the Regenstrief Center of Biomedical Informatics. Machine learning, which in healthcare is most frequently associated with initiatives using IBM's Watson Health technology, uses established algorithms to find meaningful patterns in data automatically, and then uses these known patterns to uncover fundamental relationships, Grannis explains. At Regenstrief/IU, machine learning identified patterns of language in pathology reports, enabling algorithms to create a rule that if certain factors or findings are found in the automated pathology review, then a patient is likely to have cancer. Researchers sampled 7,000 free-text pathology reports from more than 30 hospital participants in the Indiana Health Information Exchange to predict if a report was positive or negative for cancer.