If you walk down the street and see two people meeting in front of a café, you know they'll shake hands or even hug a few seconds later, depending on how close their relationship is. Left: still frame given to the algorithm which had to predict what happened next. MIT used deep-learning algorithms -- neural networks that teach computers to find patterns by themselves from an ocean of data -- to build an artificial intelligence which can predict what action will occur next, starting from nothing but a still frame. Each of these networks was programmed to classify an action as either a hug, handshake, high-five, or kiss. These networks are then merged to predict what happens next.

Based on the agreement, both parties will work together on new technology innovation, enhanced user experience and developing a new e-commerce platform for enterprise-level products. Together, the companies are dedicated to driving the next generation of e-commerce artificial intelligence solutions, and conducting associated research and development for high-speed interconnect products. A key technology that JD.com has developed is JD Camera, an application for image recognition and similar image search in mobile terminals. JD Camera facilitates ease-of-shopping for users by allowing customers to quickly and easily search for their favorites products with just a photo rather than detailed language descriptions. "In the future, with the help of the Joint Lab, Camera will be enhanced from general photo-based searches to more advanced imaged-based searches that will allow users to view, select and purchase from suggested recommendations with an advanced image match algorithm for such items as clothing, make-up, furniture, etc.," said Weng Zhi, vice president of technology, JD.com.

In its first version, Radar scans your Yahoo, Gmail, Outlook, Hotmail or AOL account to find your travel plans. From there, the app is a little less like a search engine and a little more like a chat bot. Once it finds your next trip to a major US city, it will make some basic suggestions, which you can refine based on some conversational, pre-populated answers like "Adventurous" or "Family friendly." Even without travel plans, you can peruse options in any city in the country. Radar currently draws from sources like TripAdvisor and Yelp to recommend destinations, restaurants and even must-order dishes, but as Yahoo VP of product management Conrad Wai explained to VentureBeat Radar will also surface additional sources from its search engine.

If you're lucky enough to get a ticket to this year's Wimbledon tennis championships, be prepared to be scanned by a supercomputer. Cameras linked to IBM's Watson "machine-learning" platform may be monitoring your facial expressions and trying to work out what emotions you are displaying. If Watson learns quickly enough over the fortnight, it will apparently be able to work out which player you are supporting just by reading your face. The All England Lawn Tennis Club (AELTC) and its tech partner IBM are remaining tight-lipped on the details of the new technology - not least because it needs legal approval and raises privacy concerns. But it is another example of how sport is becoming increasingly digital, for fans, players and venues alike. Even if Watson isn't tracking your every cheer and grimace at the championships - which begin on Monday 27 June - it will be digesting millions of conversations on social media platforms, such as Twitter, Facebook and Instagram, and using natural language processing to identify common topics - not necessarily just about tennis.

As sensors are attached to more and more industrial machines, these connected devices create a massive glut of data. But to unleash the true value of Internet of Things (IoT) data analysis, businesses need to have a clear understanding of its strengths and weaknesses. In a panel discussion at the 2016 IoT Data Analytics & Visualization conference in Palo Alto, Calif., several speakers said the most important thing to keep in mind is that the industrial Internet should be about improving business processes, not just about implementing cool new technology. Nauman Sheikh, founder and CEO of analytics consulting firm Asrym Inc., said he recently worked with a large public utility to put sensors on maintenance trucks to do predictive maintenance. Sheikh built the predictive models that analyzed the data coming from the sensors to identify vibration patterns and other signs that a truck might be at risk of breaking down.

Download This asset is brought to you by: The information submitted is collected by both UBM and our sponsor. Click here to view our sponsor's privacy policy. Enterprise security personnel are defending a castle riddled with holes, secret passageways, and protected by ineffective barriers. These weak points are a consequence of poor quality security software, inferior hardware, and backdoors planted by malicious insiders. The end result is a begrudging acceptance that the attackers are winning the cybersecuirty war.

An African adage that says "if you don't know where you are coming from, you will not know where you are going to" got me studying the history of technology, so I can interpret the technology crystal ball. The advancements in technology is very rapid and it is important to peek into the future, because it belongs to those that shape it today. Throughout my quest to understand how technologies we use today have metamorphosed into what they are, the prophecies about'Singularity' keeps resounding in my head because of its imminence in our society. Singularity literally means the quality or condition of being singular. However, the term can be defined from different perspectives; mathematicians define it as a point at which a function takes an infinite value, while physicists describe it as a time when matter is intensely dense.

Watching TV can be a very educational experience for a computer. In a paper that will be presented this week at the International Conference on Computer Vision and Pattern Recognition, researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) created an algorithm that can predict how humans will behave in certain situations. The algorithm'watched' 600 hours of TV shows culled from clips posted on YouTube, including The Office, Big Bang Theory, and Desperate Housewives. The purpose was to see if it could accurately predict what humans would do during an interaction--would they shake hands? After feeding it the background material, the researchers had the algorithm watch new clips, and froze the clip just before an action was about to happen, and asked the algorithm to predict what happened next. That's worse than humans, who were able to correctly predict what would happen 71 percent of the time.

MIT researchers have trained a set of connected computer systems to understand body language patterns so that it can guess how two people will interact. MIT researchers have trained a set of connected computer systems to understand body language patterns so that it can guess how two people will interact. Sometimes a lean is just a lean and sometimes a lean leads to a kiss. A new deep-learning algorithm can predict the difference. MIT researchers have trained a set of connected computer systems to understand body language patterns so that it can guess how two people will interact.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.