This is the latest version of a popular drone brand. Right now, there are around 2.5 million drones that regularly fly over American skies, according to the Federal Aviation Administration. In 2020, that number could almost triple, with 7 million drones projected to be active in the skies over our heads, according to a new report) released by the agency today. Of the drones currently buzzing around, they're split between roughly 1.5 million hobbyist drones and 500,000 commercial (the later being flown by companies for moneymaking purposes). But as that number increases, it will shape U.S. aerospace for decades to come.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

When a RNN is trained on sequences of words, it learns to represent each word as a high dimensional vector which encodes the model's understanding of that word. If you take a step back and view the image as a whole, the large scale structure of the image is determined by words' part of speech. Nouns tend to lie in the center of the image, verbs tend to lie on the upper right side, and first names form a large orange cluster in the bottom left part of the image. The RNN learned all of this semantic understanding without a human ever having to code a definition of concepts like nouns, verbs, universities, cities, meetings, or social media.

The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model. Extrapolating the first four days to our 60-day contest, you might expect the winning accuracy to get close to 100%. But in fact, this is what happened: The winning entry -- submitted by Chenglong Chen -- was just 6% more accurate than the best model submitted a week into the contest. As the Kaggle competition went on, more and more teams entered and existing teams refined and resubmitted their entries: Given that over 1,000 smart data scientists worked on this task, it's fair to say that 71% accuracy on this task is very close to the best possible accuracy with today's technology.

Earlier this week, Amazon held a secret, invitation-only event for the machine-learning and robotics community. The three-day event, at the Parker Palm Springs resort in California, had hundreds of guests from the business, entertainment, and robotics sectors. Business Insider got photos of the event from one of the attendees. Here's what it was like:

When Microsoft unleashed Tay, an artificially intelligent chatbot with the personality of a flippant 19-year-old, the company hoped that people would interact with her on social platforms like Twitter, Kik, and GroupMe. The idea was that by chatting with her you'd help her learn, while having some fun and aiding her creators in their AI research. The good news: people did talk to Tay. She quickly racked up over 50,000 Twitter followers who could send her direct messages or tweet at her, and she's sent out over 96,000 tweets so far. The bad news: in the short time since she was released on Wednesday, some of Tay's new friends figured out how to get her to say some really awful, racist things.

In August 2015, Chinese ecommerce giant Alibaba announced that its cloud computing business, Aliyun, would offer a machine learning service to help enterprise customers streamline analytics software development. The service is based on Aliyun's Open Data Processing Service (ODPS) technology, which is capable of processing 100 petabytes of data in six hours. The DT PAI platform offers a drag and drop interface to simplify the process for developers. "What used to take days can be completed in minutes," said Xiao Wei, senior product expert with Alibaba's cloud business, as the service was announced.

OMG! Did you hear about the artificial intelligence program that Microsoft designed to chat like a teenage girl? It was totally yanked offline in less than a day, after it began spouting racist, sexist and otherwise offensive remarks. Microsoft said it was all the fault of some really mean people, who launched a "coordinated effort" to make the chatbot known as Tay "respond in inappropriate ways." To which one artificial intelligence expert responded: Duh! Well, he didn't really say that.

Reducing data in the data center has been a mentality in the past, but the Internet of Things (IoT) demands more, more, and more still. Withholding information from analytics systems is in essence selling IoT systems short; actively seeking it, on the other hand, invites challenges perhaps never-before-seen by even the most seasoned data scientists. In this interview with Chris O'Connor, General Manager of Watson Internet of Things Offerings at IBM, he discusses how the power of cognitive computing is being harnessed through the company's Watson platform – now exposed to developers through a set of application programming interfaces (APIs) – to turn the IoT data deluge into increasingly valuable insights. For those unfamiliar, can you briefly describe Watson, and then fill us in on what it's been up to since its Jeopardy! O'CONNOR: Watson is a true learning platform.

Today's security teams are tasked with protecting critical embedded, IT, and business systems from a growing number of cyber threats, some of which can mutate to expose vulnerabilities and evade traditional defense mechanisms. In this interview with Amir Husain, Founder and CEO of SparkCognition, he addresses the shortcomings of traditional security technologies against advanced attacks, such as Stuxnet, and reveals how artificial intelligence (AI) can augment the expertise of security professionals equipped with limited resources. With all the attack vectors in the Internet of Things (IoT), what is the biggest challenge security teams face? HUSAIN: The challenge is enormous and actually has two dimensions. First, attacks are becoming more sophisticated and the likelihood is increasing that an attack that has never been seen before will target physical infrastructure.