Most AI computing happens in data centers packed with hundreds or thousands of servers. Get ready for AI to show up where you'd least expect it. In 2016, tech companies like Google, Facebook, Apple and Microsoft launched dozens of products and services powered by artificial intelligence. Next year will be all about the rest of the business world embracing AI. Artificial intelligence is a 60-year-old term, and its promise has long seemed like it was forever over the horizon.

The piano ditty below, which ascends jauntily, then finishes with a tuneful flourish, sounds a bit like a jingle composed for the latest toothpaste campaign. The tune was, in fact, dreamed up by a musical AI program developed at Google. And the program's latest compositions show how combining a powerful machine-learning approach with simple musical rules can produce creative works that sound remarkably human. Music composition is an enigmatic form of human creativity. Songwriting programs already exist, but they typically follow a specific set of rules, and they tend to produce tunes that feel rigid and mechanical.

A visitor uses his mobile phone as he walks past the Microsoft booth with a logo for cloud computing software application at the CeBit computer fair in Hanover, March, 6, 2012. A women holds her laptop as she walks in front of a cloud computing logo at the booth of IBM during preparations for the CeBIT trade fair in Hanover, March 9, 2014. NEW YORK President elect Donald Trump is pro-business and anti-red tape. But what if your business is red tape? Companies whose technology helps banks and investors cope with the welter of post financial crisis regulations and avoid increasingly hefty fines - a sector known as "regtech" - are sanguine about Trump's pledge to dismantle some of those reforms.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

It's been a year of change in the enterprise software market. SaaS providers are fighting to compete with one another, machine learning is becoming a reality for businesses at a larger scale, and containers are growing in popularity. Here are some of the top trends from 2016 that we'll likely still be talking about next year. As more and more companies adopt software-as-a-service products like Office 365, Slack, and Box, there is increasing pressure to collaborate for companies that compete with each another. After all, nobody wants to be stuck using a service that doesn't work with the other critical systems they have.

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."