For enterprises that were first to adopt the cloud, it is now becoming commonplace. They've moved in and have already been seeing the benefits of making the shift. These organizations have implemented the cloud, partnered with the right vendors, begun storing data,running apps and they are ready for the next big step. Enterprises realize that the massive loads of data they are storing can provide real insights into their consumer base, which can be used to better serve them. The problem is it would take a lot humans and time to quickly and efficiently understand all this information.

There's pleasure to be had in the hunt for gratification for sure. That's why we love Tinder, flash sale sites, and fishing (like the saying goes, "they call it fishing, not catching.") But when you don't want to hunt--but just to find–search technology is outmoded and stuck in the '90s, giving us clunky, cluttered, and impersonal lists we have to wade through. What most of us are doing when we "search" is not enjoying the process of "searching" but doing what we have to do to get what we want. Search is a means to an end, not an end in and of itself.

Ryan Anderson, a solutions architect for IBM Watson, took his work home with him when he decided to make a functional'Harry Potter' sorting hat for his two daughters: Lucy, 8, and Julia, 6. "I was thinking of fun projects and, coincidentally, I have a couple daughters and they are mad keen on'Harry Potter' - they've read the books like 5 times," he told Tech Insider. The hat works simply enough. You place it on your head (that part is actually for fun, you could just talk to it) and tell the sorting hat a few things about yourself so it can sort you appropriately. The sorting hat runs on Watson's Natural Language Classifier, which interprets the intent behind a set of text. So since Anderson coded that'honesty' is a characteristic of Hufflepuff, the hat will dub you a badger if you describe yourself as honest or use similar words to do so.

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.