OrCam, a company that makes products to aid accessibility for the visually impaired, has won a CES innovation award for its glasses-mounted MyEye Pro device. It aids the blind and visually impaired by reading out printed and digital text, recognizing people, identifying products, and more. OrCam took the prize in both the CES innovation accessibility and health and wellness categories. "We are living in uncertain times, yet... our users' challenges related to access have not stopped during the pandemic. If anything, they have intensified," said OrCam co-founder and co-chairman Prof. Amnon Shashua in OrCam's blog post.

As concerns over privacy and data security on social networks grow, U of T Engineering researchers led by Professor Parham Aarabi and graduate student Avishek Bose have created an algorithm to dynamically disrupt facial recognition systems. "Personal privacy is a real issue as facial recognition becomes better and better," says Aarabi. "This is one way in which beneficial anti-facial-recognition systems can combat that ability." Their solution leverages 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 working to identify faces, and the second working to disrupt the facial recognition task of the first.

It's become a custom for some protesters to cover their faces during public demonstrations. Now, it seems, technology could outwit them: a team of engineers has created an algorithm that can identify faces that are partially covered. The algorithm identifies faces using angles at 14 different points on the face, according to a paper published on the preprint server arXiv to be presented at the IEEE International Conference on Computer Vision Workshops in October. The researchers trained and validated the algorithm, which relies on a form of artificial intelligence called deep learning, using a dataset of 1500 images of 25 human faces. Each face was partially obscured by one or more of ten disguises (such as sunglasses, a face scarf, or a hat) and eight complex backgrounds to simulate real-world photos.

This involves grouping different data points (customers, products, movies, etc.) Hierarchical clustering is based around organizing data points into a set of similar clusters, then recursively grouping clusters together until you are left with a single cluster. Because the algorithm has to run through every data point and compare groups of data points to other groups of data points, the run time increases dramatically. Usually the algorithm progresses by randomly assigning data points as centroids, followed by assigning data points to the appropriate clusters.

Just 200 face cells are required to identify a face, the biologists say. After discovering how its features are encoded, the biologists were able to reconstruct the faces a monkey was looking at just by monitoring the pattern in which its face cells were firing. The finding needs to be confirmed in other laboratories. But, if correct, it could help understand how the brain encodes all seen objects, as well as suggesting new approaches to artificial vision. "Cracking the code for faces would definitely be a big deal," said Brad Duchaine, an expert on face recognition at Dartmouth.

Google's Hartmut Neven demonstrates his visual-search app by snapping a picture of a Salvador Dali clock in his office building. Google and other tech companies are racing to improve image-recognition software Computers can recognize some objects in images, but not all Google's engineering director predicts the technology will fully mature in 10 years Google's engineering director predicts the technology will fully mature in 10 years Santa Monica, California (CNN) -- Computers used to be blind, and now they can see. Thanks to increasingly sophisticated algorithms, computers today can recognize and identify the Eiffel Tower, the Mona Lisa or a can of Budweiser. Still, despite huge technological strides in the last decade or so, visual search has plenty more hurdles to clear. At this point, it would be quicker to describe the types of things an image-search engine can interpret instead of what it can't.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind.

Researchers fed the software this picture of actor J.K. Simmons, among others, to teach it to recognize specific faces. Since 1989, Cops has famously aired footage of suspected criminals, many with their faces blurred out to protect their privacy. Ever since then, blurred or pixelated faces have become standard fare for concealing the identity of individuals who prefer not to be recognized in the media. YouTube got in the game a few years ago, offering a facial blurring tool to help protect protestors against retribution from law enforcement or employers. But machine learning researchers at Cornell Tech and the University of Texas at Austin have developed software that makes it possible for users to recognize a person's concealed face in photographs or videos.

In today's world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind. This challenge (and opportunity) is not limited to certain industries.