There is a strange dichotomy at the moment surrounding the future of work. In public, political movements throughout the western world have seen populist campaigners railing against the threat to jobs from low-wage migrants entering a country, and outsourcing to low-cost regions by multinationals. What hasn't really been touched on is the impact automation might have on jobs in the future. What began with the famous study from Oxford University academics Carl Benedikt Frey and Michael Osborne back in 2013, which highlighted the huge number of white and blue collar jobs that could be disrupted by automation, has progressed to growing interest from governments around the world.. For instance, a report by the British government's Science & Technology Select Committee into AI examined the issue from a range of aspects, from ethics to employment.

We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Here we discuss clustering and density estimation on the grid. The grid can be seen as an 2-dimensional or 3-dimensional array.

Whenever the Kingdom of Hyrule has been in danger, a young boy named Link has risen to the challenge of saving the land from all manner of pixelated evil. The latest chapter of Link's ongoing quest in the video game series The Legend of Zelda is about to be released. And while the graphics have improved since the 1980s, Link is still an empty vessel for players to inhabit, only facing danger with a push of the joystick. Videogame heroes take up a larger amount of people's imaginations today than they ever have before. In the cultural economy they are as big a force as the heroes in books and movies.

Diversity (or lack of it) is still a major challenge for tech companies. Poised to revolutionize the world of work in general, some organizations are leveraging technology to root out bias, better identify and screen candidates and help close the diversity gap. That starts with understanding the nature of bias, and acknowledging that unconscious bias is a major problem, says Kevin Mulcahy, an analyst with Future Workplace and co-author of The Future Workplace Experience: 10 Rules for Managing Disruption in Recruiting and Engaging Employees. AI and machine learning can be an objective observer to screen for bias patterns, Mulcahy says. "The challenge with unconscious bias is that, by definition, it is unconscious, so it takes a third-party, such as AI, to recognize those occurrences and point out any perceived patterns of bias. AI-enabled analysis of communication patterns about the senders or receivers -- like gender or age -- can be used to screen for bias patterns and present the pattern analysis back to the originators," Mulcahy says.

The latest artificial-intelligence techniques are being adopted by companies at a blistering pace. Before long, hackers might start taking a closer look, too, and they could cause all sorts of trouble by tricking these systems with illusory data. Speaking at a recent AI conference in Barcelona, Spain, Ian Goodfellow, a research scientist at OpenAI who has done pioneering work on deceiving machine-learning systems, said attacking the systems is easy. "Almost anything bad you can think of doing to a machine-learning model can be done right now," he said. "And defending it is really, really hard."

It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or, AI as it's commonly referred to), is an area of computer science where computers are "developed" to behave much the way humans do.

With the adoption of Apple's Siri and Amazon's Alexa, the Internet of Things (IoT) is already part of our home lives. Many of us also use a smart thermostat to monitor the temperature of our living rooms, and maybe even a smart doorbell to keep an eye on the front porch. What technologies do you think are likely to change the way people do business? I definitely believe embedding communications via WebRTC (Web Real-Time Communication) will be the next big thing for businesses, especially since it directly affects our rate of communication and in turn our rate of efficiency. Anything that will make life easier will affect our productivity and is going to be here to stay.

Not to be a Grinch or a Scrooge, but you'd better watch out when it comes to some of this season's hottest Christmas toys. Last year, I wrote about Mattel's Hello Barbie, which responds realistically to your child by using natural language processing, machine learning and advanced analytics to parse what a child says and respond accordingly. In order to do this, the toy has to record what the child says, send it to a cloud-based server, and receive instructions back. On Barbie, there's a "listen" button the child has to press to start recording what she says in order to get a response. Mattel claims that only they and their tech partner (currently) have access to these recordings, but with the newest generation of talking toys, the audience is a bit larger.

Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

From mid-2017, expertise currently distributed across several different locations will be pooled in Unterschleissheim. 'In order to succeed, we are establishing new forms of collaboration under'project i 2.0', with small teams of specialists for rapid response and collaboration across the company, as well as a high level of individual decision-making authority,' explains Frohlich. The new work structures will be characterised by agile teams, short distances - and, above all, short decision-making processes. At the new campus, software developers will be able to take the code they have just written across the way for testing in an actual vehicle. 'We are combining the advantages of a start-up, such as flexibility and speed, with those of an established company, like process security and industrialisation expertise,' adds the Development head.