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.

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.

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.

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?

Long gone are the days of swapping tapes with friends after school, reading about the latest bands in your weekly magazine or tuning into Top of the Pops at the end of the week for the chart countdown. The excitement of discovering new music has definitely declined as the digitalisation of music has taken over the industry. Spotify is the most popular music streaming service out there and as such, harnesses the most valuable asset a company like this can have – data. Used by over 100 million people, with 30million of those paid subscribers and 55% of those linking their accounts to social media. Around 5million playlists are created or edited daily and in 2015 Spotify users streamed over 20bn hours of music.

OpenML stands for Open Machine Learning and is an online platform, which aims at supporting collaborative machine learning online. It is an Open Science project that allows its users to share data, code and machine learning experiments. At the time of writing this blog I am in Eindoven at an OpenML workshop, where developers and scientists meet to work on improving the project. Some of these people are R users and they (we) are developing an R package that communicates with the OpenML platform. The OpenML R package can list and download data sets and machine learning tasks (prediction challenges).

Tesla announced the latest software update for its self-driving cars on Sunday, revealing that the vehicles will be relying more heavily on radar to make decisions. The announcement comes several months after it was revealed that a driver using autopilot was killed when his Tesla failed to notice a truck stretching across the road in front of him. All Tesla cars manufactured after October 2014 had already been using radar for autonomous driving, but relied more heavily on the car's optical camera. The update to Version 8.0 significantly increases the role radar will play. Tesla's announcement came with some interesting insight into how its vehicles will avoid accidents in the future--and apparently make the driving experience a smoother one.