Slip on a pair of glasses connected to a computer or a smartphone, and feel like you're stepping into a virtual world. From entertainment and gaming to medical and industry uses, the question on everyone's mind is when will we stop asking have you tried it? Will virtual reality see widespread adoption, the way we've seen cellphones and computers lay claim to our collective mindshare? And are there more important questions we could be asking - questions we might not get even know to ask. The answer to both questions is yes, with an important caveat on the first: this may be the first time in history that the speed of technology will outpace the consumer drive to adopt, meaning that by the time people get really excited about VR, it will already have changed from the version early adopters are using today.

Car designs have continued to morph over the years. We have distinctive lines and shapes from each manufacturer, set basic shapes dependent on the desired type of vehicle (sports, compact, people carrier, etc.), and then we have the demands of the car industry for ever-increasing levels of safety and efficiency. With that in mind, you can see how designing a new car or modifying an existing car can be tough, especially with consumers demanding ever-more features be packed in. So Nissan has an idea: they want to let an artificial intelligence design its cars instead. The idea is not as crazy as it initially sounds.

The Semantics conference is one of the biggest events for all things semantics. Key research and industry players gathered this week in Leipzig to showcase and discuss, and we were there to get that vibe. Graphs are everywhere: we have social graphs and knowledge graphs and office graphs, and in the minds of most these have been associated with Facebook and Google and Microsoft. But the concept of Knowledge Graphs is broader and vendor-agnostic. All graphs can be considered as knowledge graphs, insofar as they represent information by means of nodes and (directional) edges.

Grim news for PC makers: they face a stark choice, either to overhaul their business model or just quit making PCs in the next few years. Analyst Gartner expects the number of PCs in use to decline from around 1.48 billion last year to around 1.33 by 2019, which is going to make life a lot harder for the companies that sell them. "The PC business model as we have traditionally known it is broken," said Tracy Tsia, research vice president at Gartner. While the top five mobile PC vendors have picked up 11 percent market share over the past five years (from 65 percent in 2011 to 76 percent in the first half of 2016), that has come at the expense of profit, Tsai said. Gartner predicts that the installed base of PCs will continue to decline over the next five years, which means continuing erosion of revenue and profit for PC vendors.

Data scientists are in short supply. But so too are managers that understand data science and machine learning enough to spot the opportunities for using these disciplines to optimize their businesses. McKinsey Global Institute has projected that by 2018, the U.S. alone will face a shortage of 1.5 million managers and analysts with the necessary analytics and data science expertise to fill demand. To combat this problem, Data science automation specialist DataRobot announced today that it has updated its DataRobot University curriculum with Data Science for Executives, a half-day offering that teaches executives interested in the benefits of advanced data science how to identify opportunities to optimize their business using machine learning. "If you look at the success of future companies, we think it's dependent on three things," says Jeremy Achin, co-founder and CEO of DataRobot.

In the Google TechTalk video presentation below, luminary Jeff Dean discusses the use of Deep Learning at Google – "Large-Scale Deep Learning for Intelligent Computer Systems." Jeff joined Google in mid-1999, and is currently a Google Senior Fellow in the Research Group, where he leads the Google Brain project. His areas of interest include large-scale distributed systems, performance monitoring, compression techniques, information retrieval, application of machine learning to search and other related problems, microprocessor architecture, compiler optimizations, and development of new products that organize existing information in new and interesting ways.

The head of driver-assistance system maker MobilEye has said that the company ended its relationship with Tesla because the firm is "pushing the envelope in terms of safety." That's the controversial quote that CEO Amnon Shashua gave to Reuters explaining why its years-long partnership was axed just when it began to bear fruit. Unfortunately, a fatal collision between a Model S and a box truck on a Florida highway this June made MobilEye reconsider its position. Given how instrumental MobilEye was in developing Autopilot, it's a surprise to see Sashua effectively talk down his company's product. He added that the technology is "not designed to cover all possible crash situations in a safe manner," and that Autopilot is a "driver assistance system and not a driverless system."

KDnuggets Contest for interesting blogs about Automated Data Science and Machine Learning is over - read the 3rd, 2nd, and 1st place winners.

This is the first article from a series of articles I will be writing about the math behind SVM. There is a lot to talk about and a lot of mathematical backgrounds is often necessary. However, I will try to keep a slow pace and to give in-depth explanations, so that everything is crystal clear, even for beginners. Part 1: What is the goal of the Support Vector Machine (SVM)? Part 2: How to compute the margin?