"I wish I could touch you," Theodore says, laying in bed. Until she speaks up, tentatively. "How would you touch me?" It's a famously poignant scene from the movie Her, as the character Theodore is about to make vocal love to an artificial intelligence living in his ear. But according to half a dozen experts I interviewed, ranging from industrial designer Gadi Amit to the usability guru Don Norman, in-ear assistants aren't science fiction. In fact, a notable pile of discreet, wireless earbuds enabling just this idea are coming to market now. Sony recently released its first in-ear assistant, the Xperia Ear. Intel showed off a similar proof-of-concept last year. The talking, bio-monitoring Bragi Dash will be reaching early Kickstarters soon, while fellow startup Here has raised 17 million to compete in the smart earbud space.

I am trying to visualize decision tree structure in pyspark. But all the tools are for data. I could not find any for visualizing tree structure. Or is there a way I can visualize using the rules from toDebugString?

The Department of Defense announced in early February, in an address to the Economic Club of Washington by Defense Scretary Ashton Carter, that its Strategic Capabilities Office was innovating "new roles and game-changing capabilities to confound potential enemies." The Washington Post's Dan Lamothe wrote an exclusive piece on the SCO, a hitherto unknown agency within the DoD, on March 8. In that piece, Lamothe explained that the future of war is now- and the future is the SCO's artificial intelligence. The SCO's drone program, Perdix, originated at MIT in 2010- 2011. They fit easily in the hand and are surprisingly light-weight.

Robots learn socially accepted behavior by reading and understanding children's books, particularly stories about chivalry. Researchers developed a technology called "Quixote" that can teach robots how to align their goals with proper human behavior in social settings. The increasing growth of artificial intelligence has come with fear that these robots could be a threat to humanity. To lessen this anxiety, a team of researchers developed a method that will train AI how to behave in social settings. The new technology is called "Quixote" and it teaches robots to read children's stories, understand acceptable social behavior in societies and learn standard event sequences.

In the artificial intelligence market, IPsoft and IBM lead the field when it comes to the breadth of solutions they can offer, according to a comparison of providers conducted by PA Consulting Group.

When a person's intelligence is tested, there are exams. When artificial intelligence is tested, there are games. But what happens when computer programs beat humans at all of those games? This is the question AI experts must ask after a Google-developed program called AlphaGo defeated a world champion Go player in four out of five matches in a series that concluded Tuesday. Long a yardstick for advances in AI, the era of board-game testing has come to an end, said Murray Campbell, an IBM research scientist who was part of the team that developed Deep Blue, the first computer program to beat a world chess champion.

Google's AlphaGo computer program has won a third and decisive encounter with a top-ranked player of the Chinese board game Go in a victory marking significant developments in artificial intelligence. Lee Sedol, who is the world's second best player of the strategy game, lost three games in a row in Seoul this week, with the latest AlphaGo victory on Saturday handing Google the best-of-five match. "I've never played a game where I felt this amount of pressure, and I wasn't able to overcome this pressure," Lee said at a post-game press conference. Go has simple rules, but is highly intuitive and complex in practice. Mastering it has been an exceptionally difficult task for even the world's best IT designers.

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic. So what exactly is "machine learning" anyway?

Cloud computing is famously scalable. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

And then, at the very top layer you have detectors that can tell you whether you're looking at a person or a dog or a sailplane or whatever it is."