When MIT Computer Science and Artificial Intelligence Lab researchers showed videos of a drumstick hitting and brushing through various objects, subjects were fooled into believing that the sounds they heard actually came from the objects and materials on screen. Instead, a computer programmed to analyze the video and apply the correct sounds from its own library of samples chose the audio clips for all the videos. And the subjects were none the wiser. The team's work is described in a new paper released Monday and being presented next week at the Computer Vision and Pattern Recognition conference in Las Vegas. To be clear, there really isn't any such thing as an Auditory Turing test.

Researchers recently used an artificial intelligence to run a complex experiment, which it learnt to perform from scratch in under an hour. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out," said co-lead researcher Paul Wigley from the Australian National University Research School of Physics and Engineering. This suggests that even physicists are on track to having their jobs augmented if not outright captured by artificial intelligence. The experiment involved the creation of a Bose-Einstein condensate, an extremely cold gas trapped in a laser beam. At a billionth of a degree Kelvin, it is even colder than outer space.

New technology could lead humans to relinquish control over decisions to use lethal force. As artificial intelligence advances, the possibility that machines could independently select and fire on targets is fast approaching. Fully autonomous weapons, also known as "killer robots," are quickly moving from the realm of science fiction toward reality. The unmanned Sea Hunter gets underway. At present it sails without weapons, but it exemplifies the move toward greater autonomy.

Good examples and easy to follow.

So Watson might then suggest, "'If you save your dry cleaning and do it tomorrow, you can avoid traffic," says Greenstein. Or along similar lines, "The weather is worse tomorrow, so I'll pick you up 10 minutes early." But for all of the natural language possibilities in this Local Motors project, a car's AI might soon be able to do a lot more than converse with you and check traffic. In fact, Greenstein tells us that IBM is currently working with major automobile manufacturers in North America, Europe, and Asia, developing technologies that are anywhere from one to four years out from market. "Some [work] is voice interface, hands-free driving, simpler user experience," Greenstein says. "There are also some car companies who are talking to us about how to make maintenance and diagnostics better, so if something happens, you don't just get a red light."

Even though the buzz around neural networks, artificial intelligence, and machine learning has been relatively recent, as many know, there is nothing new about any of these methods. If so many of the core algorithms and approaches have been around for decades, why is it just now that they are getting their day in the sun? To answer that question, we can take a look at what has happened over the last five years or so with the attention and tooling around data. And we can also point to the dramatic increase in scalable compute power, or to be more specific about it, performance per watt and bit. These two factors combined have fed the development fury, growing data analysis well beyond the standard database and calculation approaches that have themselves been around for decades. The point is, we are at peak "data hype"--there was a rush to develop a host of new tools and frameworks (Hadoop, as but one example) to support larger, more complex datasets, then a secondary effort to push the performance of the data analysis on new or enhanced frameworks.

Google announced in a blog post on Thursday that it has set up a new AI research group in Europe to focus on machine learning (ML). Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Google Research, Europe -- as the group is known -- is based out of Google's office in Zurich, Switzerland, which is home to Google's largest engineering office outside the US. Google said the group, which is expected to grow to over 100 people in the coming years, will focus on three key areas: machine intelligence, natural language processing and understanding, and machine perception. Companies like Amazon, Facebook, and Microsoft are all investing heavily in these areas as they look to make their platforms and services more intelligent.

This archive file was compiled from an interview conducted at the Googleplex in Mountain View, California, 2013. As late as the 1980s and the 1990s, the common person seeking stored knowledge would likely be faced with using an 18th century technology - the library index card catalogue - in order to find something on the topic he or she was looking for. Fifteen years later, most people would be able to search, at any time and any place, a collection of information that dwarfed that of any library. And unlike the experience with a library card catalogue, this new technology rarely left the user empty-handed. Information retrieval had been a core technology of humanity since written language -- but as an actual area of research it was so niche that before the 1950s, nobody had bothered to give the field a name.

Google has made no secret of its AI ambitions, and on Thursday it announced the next step in its bold plans to realize them: a brand-new research group in Europe focused squarely on machine learning. Based in Google Research offices in Zurich, Switzerland, the new group will focus on three key areas of artificial intelligence: machine intelligence, machine perception, and natural language processing and understanding, according to a blog post by Emmanuel Mogenet, head of Google Research for Europe. It will research ways to improve machine-learning infrastructure and enable the technology for practical use, for instance. Researchers will also work closely with linguists to advance natural language understanding, Mogenet said. Zurich, meanwhile, is home to Google's largest engineering office outside the U.S. Researchers there developed the engine that powers Knowledge Graph as well as the conversation engine that powers the Google Assistant in its Allo messaging app. Google's presence in Europe hasn't been entirely smooth, however: It's facing ongoing scrutiny over antitrust concerns and tax issues.

You might offer your employees gym membership. You may provide them with fresh fruit and free coffee. No doubt you give them opportunities to learn, chances to feel challenged, work that enthuses and excites them. But sometimes, a seemingly perfect package still can't satisfy. Today, we face a global crisis; one rarely discussed, but widely felt. Approximately 450 million people worldwide have a mental health problem.