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Twitter buys a machine learning company to improve your videos
Facebook isn't the only social network to see some value in artificial intelligence. Twitter has bought Magic Pony, a small London outfit that specializes in using machine learning to process visuals, at an unofficial worth of around $150 million. It's hoping to use the newly acquired talent to "enhance [its] strength" in both live and pre-recorded videos. What exactly it will do isn't clear, but Twitter chief Jack Dorsey points to Magic Pony's work on sharpening blurry video elements as an example -- it's easy to see machine learning improving the quality of clips (especially on Periscope) without consuming more of your all-important bandwidth.
Apple iOS 10 uses AI to help you find photos and type faster
Apple is making artificial intelligence a big, big cornerstone of iOS 10. To start, the software uses on-device computer vision to detect both faces and objects in photos. It'll recognize a familiar friend, for instance, and can tell that there's a mountain in the background. While this is handy for tagging your shots, the feature really comes into its own when you let the AI do the hard work. There's a new Memories section in the Photos app that automatically organizes pictures based on events, people and places, complete with related memories (such as similar trips) and smart presentations. Think of it as Google Photos without having to go online.
Computer vision is key to Amazon Prime Air drone deliveries
For all of Amazon's grand plans regarding delivery drones, it still needs to figure out concepts we take for granted with traditional courier methods. Namely, figuring out how to drop off your latest order without destroying anything (including the UAV itself) during transit and landing. That's where advanced computer vision comes in from Jeff Bezos' new team of Austria-based engineers, according to The Verge. The group invented methods for reconstructing geometry from images and contextually recognizing environmental objects, giving the drones the ability to differentiate between, say, a swimming pool and your back patio. Both are flat surfaces, but one won't leave your PlayStation VR headset waterlogged after drop-off.
AI-powered cameras make thermal imaging more accessible
As cool as thermal cameras may be, they're not usually very bright -- they may show you something hiding in the dark, but they won't do much with it. FLIR wants to change that with its new Boson thermal camera module. The hardware combines a long wave infrared camera with a Movidius vision processing unit, giving the camera a dash of programmable artificial intelligence. Device makers can not only use those smarts for visual processing (like reducing noise), but some computer vision tasks as well -- think object detection, depth calculations and other tasks that normally rely on external computing power.
Smart 3D modeling lets you mess with faces in videos
Have you ever wanted to mess with a video by making its cast say things they never would on camera? You might get that chance. Researchers have built a face detection system that lets you impose your facial expressions on people in videos. The software uses an off-the-shelf webcam to create a 3D model of your face in real time, and distorts it to fit the facial details in the target footage. The result, as you'll see below, is eerily authentic-looking: you can have a dead-serious Vladimir Putin make funny faces, or Donald Trump blab when he'd otherwise stay silent.
Computers learn how to spot hidden facial expressions
Machines are good at spotting obvious emotions like smiles, but they're not so hot at detecting the extremely brief microexpressions that reveal when people are covering up their true feelings. They may have a keener eye in the future, though: researchers have developed a computer vision algorithm that magnifies facial expressions, making it possible to catch the tiniest bit of displeasure or surprise. While some humans have a knack for spotting these subtle cues, the algorithm is far more effective in early tests -- you likely wouldn't fool the computer into thinking everything was hunky dory.
Smart camera warns you when guns enter your home
Anxious that you might face an armed home invasion, or that your kids might find the key to the gun cabinet? NanoWatt Design thinks it has a way to give you that crucial early warning. Its crowdfunded GunDetect camera uses computer vision to detect firearms and send a text alert to your phone. If it works as promised, you'll know there's an intruder at the door, or that you need to rush into the den before there's a terrible accident. If you're intrigued, it'll take a minimum $349 pledge to reserve a Cloud GunDetect (which requires a service subscription to process images) and $549 for a Premium model which does all the image recognition work itself. Provided that NanoWatt reaches its funding goal, both cams should ship in February.
Military AI interface helps you make sense of thousands of photos
It's easy to find computer vision technology that detect objects in photos, but it's still tough to sift through photos... and that's a big challenge for the military, where finding the right picture could mean taking out a target or spotting a terrorist threat. Thankfully, the US' armed forces may soon have a way to not only spot items in large image libraries, but help human observers find them. DARPA's upcoming, artificial intelligence-backed Visual Media Reasoning system both detects what's in a shot and presents it in a simple interface that bunches photos and videos together based on patterns. If you want to know where a distinctive-looking car has been, for example, you might only need to look in a single group.
Computer algorithm picks the world's most creative art
Who would you trust to determine history's most creative art? A room full of seasoned critics? Rutgers University researchers think a machine can do the job. They've developed a computer vision algorithm that ranks the creativity of art based on how similar it is to earlier works in terms of everything from color and texture to the presence of familiar objects. The code treats art history as a network -- groundbreaking pieces are connected to later derivatives, and seemingly unique content may have a link to something produced in the distant past.
Computers can tell how much pain you're in by looking at your face
Remember Baymax's pain scale in Big Hero 6? In the real world, machines might not even need to ask whether or not you're hurting -- they'll already know. UC San Diego researchers have developed a computer vision algorithm that can gauge your pain levels by looking at your facial expressions. If you're wincing, for example, you're probably in more agony than you are if you're just furrowing your brow. The code isn't as good at detecting your pain as your parents (who've had years of experience), but it's up to the level of an astute nurse.
Qualcomm's next chips will help smartphones think for themselves
Qualcomm teased the prospect of smartphones that learn a couple of years ago, and it's now much closer to making them a practical reality. The chip designer has revealed its next big mobile processor, the Snapdragon 820, will be one of the first that can handle its Zeroth cognitive computing platform. In short, it'll let your phone learn about you (and the world around you) to take action on its own. You should see photo apps that detect whole scenes, security tools that protect against unknown viruses and interfaces that depend more on expressions and head movement than button taps. It gets more ambitious than that, though. Zeroth allows for always-on sensors that detect your surroundings (such as through motion or sound) and help your phone anticipate what you want.
Microsoft's imaging tech is (sometimes) better than you at spotting objects
Many computer vision projects struggle to mimic what people can achieve, but Microsoft Research thinks that its technology might have already trumped humanity... to a degree, that is. The company has published results showing that its neural network technology made fewer mistakes recognizing objects than humans in an ImageNet challenge, slipping up on 4.94 percent of pictures versus 5.1 percent for humans. One of the keys was a "parametric rectified linear unit" function (try saying that three times fast) that improves accuracy without any real hit to processing performance.
Computers can now describe images using language you'd understand
Software can now easily spot objects in images, but it can't always describe those objects well; "short man with horse" not only sounds awkward, it doesn't reveal what's really going on. That's where a computer vision breakthrough from Google and Stanford University might come into play. Their system combines two neural networks, one for image recognition and another for natural language processing, to describe a whole scene using phrases. The program needs to be trained with captioned images, but it produces much more intelligible output than you'd get by picking out individual items. Instead of simply noting that there's a motorcycle and a person in a photo, the software can tell that this person is riding a motorcycle down a dirt road. The software is also roughly twice as accurate at labeling previously unseen objects when compared to earlier algorithms, since it's better at recognizing patterns.
Google's latest object recognition tech can spot everything in your living room
Automatic object recognition in images is currently tricky. Even if a computer has the help of smart algorithms and human assistants, it may not catch everything in a given scene. Google might change that soon, though; it just detailed a new detection system that can easily spot lots of objects in a scene, even if they're partly obscured. The key is a neural network that can rapidly refine the criteria it's looking for without requiring a lot of extra computing power. The result is a far deeper scanning system that can both identify more objects and make better guesses -- it can spot tons of items in a living room, including (according to Google's odd example) a flying cat. The technology is still young, but the internet giant sees its recognition breakthrough helping everything from image searches through to self-driving cars. Don't be surprised if it gets much easier to look for things online using only vaguest of terms.
Google uses computer vision and machine learning to index your photos
Tags are so 2008. Google doesn't want you to waste time tagging your photos, except for the people in them. The web giant wants to be able to recognize more abstract concepts like "sunset" or "beach" automatically and attach that metadata without further input. In yet another post-I/O update, Google+ photos now uses computer vision and machine learning to identify objects and settings in your uploaded snapshots. You can simply search for "my photos of trees" or "Tim's photos of bikes" and get surprisingly accurate results, with nary a manually added tag in sight. You can perform the searches in Google+, obviously, but you can also execute your query from the standard Google search page. It's pretty neat, but sadly Mountain View seems to have forgotten what cats look like.
Qualcomm's Snapdragon S4 flexes its imaging muscle (video)
While we already know that Qualcomm's Snapdragon S4 will offer a quad-core variant, incorporate LTE and use a 28nm manufacturing process, the company posted an article to its media blog ahead of Mobile World Congress showcasing the new features provided by the chipset's Image Signal Processor. You're likely familiar with some of the imaging functionality available in Qualcomm's existing Snapdragon processors -- technology like Scalado's Rewind (pictured above) which we've covered before. The new SoC cranks things up a notch with support for up to three cameras (two in the back for 3D plus one front-facing), 20-megapixel sensors and 1080p HD video recording at 30fps. In addition to zero shutter lag, the Snapdragon S4 includes proprietary 3A processing (autofocus, auto exposure and auto white balance) along with improved blink / smile detection, gaze estimation, range finding and image stabilization. Rounding things off are gesture detection / control, augmented reality and computer vision (via Quacomm's FastCV). Want to know more? Check out the source link below, then hit the break for video demos of the S4's image stabilization and gesture-based imaging chops.
Microsoft acquires VideoSurf, promises to bring better video search and discovery to Xbox Live
Terms of the deal aren't being disclosed, but Microsoft announced today that it has acquired the content analytics firm VideoSurf, and it's being crystal clear about where it will be putting it to use: Xbox Live. VideoSurf, for those not familiar, is a company that specializes in computer vision technology, which lets it "tag" individual frames of videos in real time and deliver better search and discovery services. Earlier this year, it released an Android application that basically amounts to Shazam for video. As for its plans for Xbox Live, Microsoft says that it will integrate the technology across the platform "over time," and that it will "augment the Xbox 360 ecosystem and evolve search and discovery of entertainment content on Xbox Live." The official press release is after the break.
Occipital announces investment, new hires
Occipital, the company behind the popular iPhone and iPad app 360 Panorama, announced on Wednesday that it received US$7 million in venture funding. 360 Panorama lets you take panorama pictures by shooting a series of pictures at one and stitching them on the phone before saving them to your camera roll. Besides a healthy infusion of cash, Occipital is also expanding beyond its iOS application to create a computer vision platform that other developers can use. Occipital will create the backbone and developers will use their creative skills to produce innovative apps with advanced imaging and camera features like eye tracking. Occipital has also added four new members to its board of directors including Jason Mendelson and Brad Feld of a venture capital firm Foundry Group, Manu Kumar of venture capital firm K9 Ventures and Gary Bradski, the creator of OpenCV, an open source computer vision library.
Kinect hacks let you control a web browser and Windows 7 using only The Force (updated)
Hacking the Xbox 360 Kinect is all about baby steps on the way to what could ultimately amount to some pretty useful homebrew. Here's a good example cooked up by some kids at the MIT Media Lab Fluid Interfaces Group attempting to redefine the human-machine interactive experience. DepthJS is a system that makes Javascript talk to Microsoft's Kinect in order to navigate web pages, among other things. Remember, it's not that making wild, arm-waving gestures is the best way to navigate a web site, it's just a demonstration that you can. Let's hope that the hacking community picks up the work and evolves it into a multitouch remote control plugin for our home theater PCs. Boxee, maybe you can lend a hand? Update: If you're willing to step outside of the developer-friendly borders of open-source software then you'll want to check out Evoluce's gesture solution based on the company's Multitouch Input Management (MIM) driver for Kinect. The most impressive part is its support for simultaneous multitouch and multiuser control of applications (including those using Flash and Java) running on a Windows 7 PC. Evoluce promises to release software "soon" to bridge Kinect and Windows 7. Until then be sure to check both of the impressive videos after the break. [Thanks, Leakcim13]
The Blaze Blink Now saves you from Computer Vision Syndrome, is always watching
Big Brother is watching you, and you'd better remember. Forget for just a moment and you could find yourself committing thoughtcrime doupleplusquick. Enter the £49.99 ($78) Blink Now. It's ostensibly a device designed to save you from Computer Vision Syndrome, a modern affliction that causes headaches and blurry vision among those who spend a little too much time basking in the glow of a CRT or LCD. But, we're thinking its purposes are rather more nefarious. It's basically just a little LCD that you hang in your monitor and connect via USB. It presents an eye, always looking at you, lazily and nonchalantly blinking. In theory, if you see an eye blinking you'll blink more often yourself and cut down on eye strain. In reality, the thinkpol is always watching and this is just another reminder.
