Vision


'Nerd,' 'Nonsmoker,' 'Wrongdoer': How Might A.I. Label You?

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Facial recognition and other A.I. technologies learn their skills by analyzing vast amounts of digital data. Drawn from old websites and academic projects, this data often contains subtle biases and other flaws that have gone unnoticed for years. ImageNet Roulette, designed by the American artist Trevor Paglen and a Microsoft researcher named Kate Crawford, aims to show the depth of this problem. "We want to show how layers of bias and racism and misogyny move from one system to the next," Mr. Paglen said in a phone interview from Paris. "The point is to let people see the work that is being done behind the scenes, to see how we are being processed and categorized all the time."


5 Significant Object Detection Challenges and Solutions

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Objects of interest occupying minority classes, therefore, receive more significance and see improved accuracy. Object detection is customarily considered to be much harder than image classification, particularly because of these five challenges: dual priorities, speed, multiple scales, limited data, and class imbalance. Researchers have dedicated much effort to overcome these difficulties, yielding oftentimes amazing results; however, significant challenges still persist. Basically all object detection frameworks continue to struggle with small objects, especially those bunched together with partial occlusions. Real-time detection with top-level classification and localization accuracy remains challenging, and practitioners must often prioritize one or the other when making design decisions. Video tracking may see improvements in the future if some continuity between frames is assumed rather than processing each frame individually. Furthermore, an interesting enhancement that may see more exploration would extend the current two-dimensional bounding boxes into three-dimensional bounding cubes. Even though many object detection obstacles have seen creative solutions, these additional considerations–and plenty more–signal that object detection research is certainly not done!


Researchers are training image-generating AI with fewer labels

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Generative AI models have a propensity for learning complex data distributions, which is why they're great at producing human-like speech and convincing images of burgers and faces. But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply. The solution might lie in an approach proposed by researchers at Google and ETH Zurich. In a paper published on the preprint server Arxiv.org These self- and semi-supervised techniques together, they say, can outperform state-of-the-art methods on popular benchmarks like ImageNet.


See How AI Stereotypes You

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Computers think they know who you are. Artificial intelligence algorithms can recognize objects from images, even faces. But we rarely get a peek under the hood of facial recognition algorithms. Now, with ImageNet Roulette, we can watch an AI jump to conclusions. Some of its guesses are funny, others…racist.


Artificial Intelligence based app ImageNet will classify you from selfie

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As facial recognition software gets inevitable in everyday life, the developers behind a new internet app-slash-art job want to show people exactly how they look in the view of Artificial Intelligence –and the revelations are jarring. At first glance, ImageNet Roulette seems like just another viral selfie app. Want to understand what you'll look like in 30 years? There is an app for that. Would you be In the event that you were a dog what breed?


New AI Face Anonymization Model Protects Privacy

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Was George Orwell right, is Big Brother watching us? Undoubtedly many are alarmed by the ever-increasing level of computer-driven surveillance, particularly involving facial recognition technologies. In the past few months, San Francisco and Oakland, California, and the US state of Massachusetts have all banned police from using facial recognition tech. Meanwhile, in Europe, The General Data Protection Regulation (GDPR) introduces restrictive rules about privacy preservation in data processing. A team of researchers from the Norwegian University of Science and Technology recently proposed a new architecture that can anonymize faces in images automatically while the original data distribution remains uninterrupted.


Predict Age and Gender Using Convolutional Neural Network and OpenCV

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Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. Age and gender, two of the key facial attributes, play a very foundational role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications, such as access control, human-computer interaction, law enforcement, marketing intelligence and visual surveillance, etc. Recently I came across Quividi which is an AI software application which is used to detect age and gender of users who passes by based on online face analyses and automatically starts playing advertisements based on the targeted audience. Another example could be AgeBot which is an Android App that determines your age from your photos using facial recognition.


Mercury ViewPoint -

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The Mercury ViewPoint SmartVisor doesn't just magnify, with the touch of a button it reads out to you as well. ViewPoint SmartVisor is a breakthrough in technology for anyone suffering from restricted sight. It also works great for people with central vision loss e.g. ViewPoint SmartVisor sits comfortably on the head giving clear reproduced natural and enhanced images in the magnification of your choice. See everything clearly in full colour, enhanced full colour or with different coloured foregrounds and backgrounds.


Frontier technologies at the Olympic Games Tokyo 2020 Frontier Enterprise

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The Olympic Games Tokyo 2020 promise to exhibit not only the highest standards in human endurance and physical ability, but also some wild, cutting-edge technology never visible (or invisible) before at a public event of this size. Here are some of the most interesting technologies on display featuring AI and VR to artificial shooting stars, among others. In October 2017, the NTT Group established a consortium comprising six companies with SoftBank, Facebook, Amazon, PLDT, and PCCW Global to begin constructing "JUPITER", a large-capacity optical submarine cable system linking the United States, Japan, and the Philippines. Construction is currently scheduled for completion in March 2020. "JUPITER" has the speed to transmit approximately six hours of high vision images (about three full movies) in one second.


The Compelling Challenges of Engineering Computer Vision - PROPRIUS

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Of all the interesting obstacles slowing down the advancement of artificial intelligence, computer vision may be the most compelling. This is due to the multifaceted challenge of programming a machine with enough inductive reasoning to extrapolate information from observations and come up with plausible and accurate conclusions. Of course, this is the end goal of artificial intelligence research – endowing a computer with the power and ability to think, at least within reason. When it comes to translating flexible human thought processes into more structured machines, there are a handful of problems that slow down the computer's mastery. While we move around the world and throughout our daily routines, we see an uncountable number of images that our brain parses through and then separates into different classifications.