A British Airways aircraft was possibly hit by a drone Sunday near Heathrow airport as it was coming to land, which is likely to increase demands for greater checks on the flights of the devices. The Airbus A320 flight from Geneva, carrying 132 passengers and five crew members, appears to have not been significantly impacted and was cleared for its next flight, according to news reports. The U.K. Civil Aviation Authority said it was aware of "a possible incident" with a drone at Heathrow on Sunday, which is subject to investigation by the Metropolitan Police. It reminded drone users of the country's "dronecode," which prohibits drones from flying above 400 feet (about 122 meters) and requires them to stay away from aircraft, helicopters, airports and airfields. "It is totally unacceptable to fly drones close to airports and anyone flouting the rules can face severe penalties including imprisonment," the CAA said in a statement Sunday.

For the past few minutes I've been chatting with George Washington, and honestly he seems rather drunk. He also appears to have been hanging around with 20-somethings, because he keeps saying things like "cool, haha," and "u wanna join my army or wut?" This, of course, is not actually America's first president. It is automated, conversational artificial intelligence, known as a chatbot, created by Drunk History, a comedy TV show, and made available through the messaging program Kik. You can now chat with all sorts of bots through a number of messaging services including Kik, WeChat, Telegram, and now, Facebook Messenger. Some are simply meant to entertain, but a growing number are designed to do something useful.

Brad has worked in the network and computer security field in both the public and private sectors. He has done everything from conducting penetration tests to reverse engineering... In 2010, Steffen Rendle, currently a senior research scientist at Google, introduced a seminal paper in the world of machine learning. In this work, Rendle described a concept known as a factorization machine. Many years have passed since such an impactful algorithm has been introduced in the world of machine learning.

Big innovations come in unexpected bursts. We grow accustomed to life and work as we know it, until something apparently simple brings about bold change. For example, we used phones for 100 years, but making them mobile transformed the world.

Thoughts about character-based word embedding and vocabularies in NLP:character:word:embedding:vocabulary: Goal In this summary we compare the two standard methods of single character embedding and full word embedding. Motivation In order to teach a computer to understand words in order to perform natural language tasks, we have to map characters or words to a vector space the computer naturally acts on. Steps The mapping of character, words or even complete sentences into a vector space is usually called embedding. Given some text, there are two distinct methods to compute word embeddings manageable by a computer. Children learning to read to start by recognizing individual characters before they are able to understand the meaning of words.

If the Facebook (FB) of the future suggest some similarities to Skynet, the self-aware artificial intelligence villain in "The Terminator" movies, it's not hard to see why. Facebook CEO Mark Zuckerberg this week provided a glimpse of where he sees the social-media giant in a decade, and it involves a novel combination of drones, lasers, artificial intelligence and virtual reality. In other words, it's not the Facebook of Zuckerberg's formative years at Harvard, which he dropped out of 11 years ago to focus on building a social network for college students. To some, it may seem like a vision from a science-fiction movie. Facebook is getting into the business of chat bots, computer programs designed to simulate a conversation.

Mike Finley is a co-founder of AnswerRocket in charge of natural language processing and machine learning. Big innovations come in unexpected bursts. We grow accustomed to life and work as we know it, until something apparently simple brings about bold change. For example, we used phones for 100 years, but making them mobile transformed the world; we had the Internet for decades before the Web browser put digital education, entertainment and shopping in the hands of billions; and we documented our lives with physical pictures, paper records, CD-ROMs and thumb drives until Jeff Bezos brought us "the cloud." When individual creativity is enhanced by technical ingenuity, new behaviors and capabilities emerge.

"In machine learning, is more data always better than better algorithms?" No. There are times when more data helps, there are times when it doesn't. Probably one of the most famous quotes defending the power of data is that of Google's Research Director Peter Norvig claiming that "We don't have better algorithms. We just have more data.". This quote is usually linked to the article on "The Unreasonable Effectiveness of Data", co-authored by Norvig himself (you should probably be able to find the pdf on the web although the original is behind the IEEE paywall).

The problem of building an autonomous robot has traditionally been viewed as one of integration: connecting together modular components, each one designed to handle some portion of the perception and decision making process. For example, a vision system might be connected to a planner that might in turn provide commands to a low-level controller that drives the robot's motors. In this talk, I will discuss how ideas from deep learning can allow us to build robotic control mechanisms that combine both perception and control into a single system. This system can then be trained end-to-end on the task at hand. I will show how this end-to-end approach actually simplifies the perception and control problems, by allowing the perception and control mechanisms to adapt to one another and to the task.

The US Department of Defense (DoD) wants contractors to mine your social media posts to develop new ways for the US government to infer what you're really thinking and feeling -- and to predict what you'll do next. Pentagon documents released over the last few months identify ongoing classified research in this area that the federal government plans to expand, by investing millions more dollars. The unclassified documents, which call on external scientists, institutions and companies to submit proposals for research projects, not only catalogue how far US military capabilities have come, but also reveal the Pentagon's goals: building the US intelligence community's capacity to forecast population behavior at home and abroad, especially groups involved in political activism. They throw light on the extent to which the Pentagon's classified pre-crime R&D has advanced, and how the US military intends to deploy it in operations around the world. A new Funding Opportunity Announcement document issued by the DoD's Office of Naval Research (ONR) calls for research proposals on how mining social media can provide insight on people's real thoughts, emotions and beliefs, and thereby facilitate predictions of behavior.