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Microsoft says its facial recognition technology is less biased

Mashable

Microsoft claims its facial recognition technology just got a little less awful. Earlier this year, a study by MIT researchers found that tools from IBM, Microsoft, and Chinese company Megvii could correctly identify light-skinned men with 99-percent accuracy. But it incorrectly identified darker-skinned women as often as one-third of the time. Now imagine a computer incorrectly flagging an image at an airport or in a police database, and you can see how dangerous those errors could be. Microsoft's software performed poorly in the study.


These glasses trick facial recognition software into thinking you're someone else

#artificialintelligence

Facial recognition software has become increasingly common in recent years. Facebook uses it to tag your photos; the FBI has a massive facial recognition database spanning hundreds of millions of images; and in New York, there are even plans to add smart, facial recognition surveillance cameras to every bridge and tunnel. But while these systems seem inescapable, the technology that underpins them is far from infallible. In fact, it can be beat with a pair of psychedelic-looking glasses that cost just $0.22. Researchers from Carnegie Mellon University have shown that specially designed spectacle frames can fool even state-of-the-art facial recognition software.


AI claims to be able to thwart facial recognition software, making you "invisible"

#artificialintelligence

A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.


Facial Recognition: the Advent of a New Era in Non-Digital Marketing?

#artificialintelligence

The Facial Recognition Technology Is Known to Have Gained a Foothold in Many Industry Verticals and It Keeps on Continuously Charting New Ground. Facial Recognition has gained so much traction in an entire host of verticals and applications (according to Variant Market Research, its market is expected to be worth some $ 15.4 billion by 2024) that most anyone, regardless of the kind of business they are in, should look into whether the technology could come in handy in reaching their business objectives. In part, this is owing to the ability of the Facial Recognition technology to better equip and advance the field of expertise known as Marketing, - something universal and of the utmost importance to most industries. Moreover, Face Recognition can make a dent in precisely those areas of Marketing, in which the now rampant Digital Marketing falls short, or is, simply, irrelevant. What are those areas, how much headway has been made already and what are the potentialities one should be aware of?


Image Recognition: A peek into the future

#artificialintelligence

Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?