We’ve all heard that we need to improve our security. Remember your password? Are you absolutely certain you remember every single password you need to remember? How about your fingerprint? Sure someone can’t use playdough to trick your fingerprint sensor? What about headphones that know who you are?
While this may sound stupid the science behind the idea is sound. Every ear is unique, very much the same way we have unique fingerprints or eyes. By using the way sound resonated within your ear canal, the headphones create a unique sound, something that can be measured and compared.
Reported to have 99% accuracy and only taking a second to do the measurements needed, listening to all that music may actually help you unlock your phone. The system is praised by the general manager of NEC saying that as it doesn’t “require particular actions such as scanning a part of the body over an authentication device” it would enable “a natural way of conducting continuous authentication”.
This solution may be a few years out though with the company behind it, NEC, saying that they are looking to commercialize the system for the 2018 fiscal year. So looking forward to listening to music while you bank? Take your headphones out to talk to someone and your phone will start to doubt if you truly are who you say you are.
Let’s picture the scene, you upload a snap of a special occasion to your Facebook account to share with your friends, family and followers, as there are many people in the scene you decide to tag everyone individually, you think this will be easier for everyone, but there’s a problem, Facebook doesn’t recognize you as the moment the flash went off, you were pictured with your head to one side attempting to avoid a wasp which came hurtling towards you at warp speed.
Help may soon be at hand with an experimental algorithm which has been devised by Facebook’s artificial intelligence lab. This new technique is software which can recognise people in photographs even when it is unable to see their faces; instead it relies on points of reference which are unique to each individual. This can include hair style, specific clothing and even the pose which you often strike.
In order to test the final algorithm, researchers downloaded 40,000 public photos from Flickr which contained a mixture of clearly and obscure images of individuals. The algorithm was able to recognize the identities of individuals with an 83% success rate. As this is Facebook I am sure privacy of these photos were high on their agenda, cough cough.
This experiment does have the potential for real world applications, but there needs to be caution exercised if a machine can identify you without you wanting it to. If someone takes a photo before uploading the image to Facebook and you happen to be in the background, this algorithm will identify you for all to see.
A new website has crept up recently, created in a collaboration between Cornell University and Visipedia research project.
The website isn’t fully automated just yet; it current works off an image that you upload and then asks you to pinpoint certain features such as the beak, tail and eye(s). From here it will search through the millions of archived images from eBird.org and find matching images with known species. Along with other images and the possible species, it will give you sounds and songs that the bird is known to make
Along with other images and the possible species, it will give you sounds and songs that the bird is known to make. The only drawback is that the service only currently works on birds found in North America and Canada.
The system uses machine-learning technology, which betters itself the more it is used; Science professor Serge Belongie said:
“Computers can process images much more efficiently than humans—they can organize, index, and match vast constellations of visual information such as the colors of the feathers and shapes of the bill. The state-of-the-art in computer vision is rapidly approaching that of human perception, and with a little help from the user, we can close the remaining gap and deliver a surprisingly accurate solution.”
I tested out the service, but because I have zero knowledge on North American or Canadian birds; it didn’t work. However, the service did present me with similar images. The service is free to use but is currently unavailable on smartphones and tablets. It will be added to the Merlin app once all of the kinks have been sorted.
Are you an avid bird watcher? or do you have an image of a bird that you want to know the species of? Then head over to Merlin Bird Photo ID and take a look at what it has to offer.
Thank you to engadget for providing us with this information.
The Kinect has been used in a lot of different ways, but its main purpose is and always was Gaming. Although, the Kinect sensor has been used in other non-game related experiments as well, which include turning a bath tub into a giant touch-screen gadget as well as aiding the well-known VR headset gear, the Oculus Rift.
But South Korea apparently found a more interesting and practical method of using the Kinect sensor. Programmer Jae Kwan Ko explains to The Verge that he found a way to ‘harness’ its features and turn it into an ultimate border patrol gadget. Since South Korea and North Korea don’t see eye to eye and tensions at the border run high most of the time, Ko apparently developed an easier way to keep an eye on what’s going on near the border perimeter.
He reportedly developed a hardware as well as software system which uses the Kinect to detect moving objects. It was delivered to the US Army in August and since the, instilled in the Demilitarized Zones on the border perimeter, watching and analysing each and every moving object. It is said that the system can even detect whether a moving object is human or an animal, as well as having the capacity to trigger alerts at the army base if it detects human movement.
One can ask why Kinect and not something more high-tech. Well, there might be two reasons here. First and most important is that Ko has been creating and even specializes in windows application for Windows Phone, Windows 8 and Kinect. This is the most important key factor. Another good reason, in my opinion, might be the price. High-tech non mass-produced gadgets will always cost a whole lot more than something which is already available on the market. Combining brains with creativity results in great things being achieved, and this one could be one of them.
Computers that can identify objects seem a thing from the future. Apparently, it is more close to reality than any of us think. Birmingham Young University from Provo, US – has found a way to make computers identify objects without the need of a human helping hand.
According to Dah-Jye Lee, BUY engineer, algorithms have become so advanced that they can make a piece of software identify objects by themselves from images and even videos. Lee is the founder of this algorithm and from what he describes, it is based on the computer making decisions on its own based on the shapes identified on the images or videos analysed.
“In most cases, people are in charge of deciding what features to focus on and they then write the algorithm based off that,” said Lee, a professor of electrical and computer engineering. “With our algorithm, we give it a set of images and let the computer decide which features are important.”
Lee’s algorithm is said to learn on its own, just as a child learns to distinguish a cat from a dog. He explains that instead of teaching a child the difference between the latter, we are better off showing the two images and let the child distinguish them on his or her own. Just like a child, the algorithm has been shown four image datasets from CalTech, namely motorbikes, faces, airplanes and cars, having the algorithm output 100% accurate results on each of the datasets. However, the algorithm had a lower rate of success with human faces, being able to accurately distinguish 99.4%, but still gave a better result than other object recognition systems.
“It’s very comparable to other object recognition algorithms for accuracy, but, we don’t need humans to be involved,” Lee said. “You don’t have to reinvent the wheel each time. You just run it.”
Professor Lee mentioned that the highly complicated algorithm may be used in a variety of tasks, from detecting invasive fish species to identifying flaws in produce such as apples on a production line. However, the complexity of the algorithm can go way beyond that.