Google has activated a safety feature that lets minors under 18 request that images of themselves be removed from search results, The Verge has reported. Google first announced the option back in August as part of a slate of new safety measures for kids, but it's now rolling out widely to users. Google said it will remove any images of minors "with the exception of case of compelling public interest or newsworthiness." The requests can be made by minors, their parents, guardians or other legal representatives. To do so, you'll need to supply the URLs you want removed, the name and age of the minor and the name of the person acting on their behalf.

In late February 2020, the European Commission published a white paper on artificial intelligence (AI)a and an accompanying report on the safety and liability implications of AI, the Internet of Things (IoT), and robotics.b In the white paper, the Commission highlighted the "European Approach" to AI, stressing "it is vital that European AI is grounded in our values and fundamental rights such as human dignity and privacy protection." In April 2021, the proposal of a Regulation entitled "Artificial Intelligence Act" was presented.2 This Regulation shall govern the use of "high-risk" AI applications which will include most medical AI applications. Referring to the above-mentioned statement, this Viewpoint aims to show European fundamental rights already provide important legal (and not merely ethical) guidelines for the development and application of medical AI.7 As medical AI can affect a person's physical and mental integrity in a very intense way and any malfunction could have serious consequences, it is a particularly relevant field of AI in terms of fundamental rights.

Scientists from Skoltech, Philips Research, and Goethe University Frankfurt have trained a neural network to detect anomalies in medical images to assist physicians in sifting through countless scans in search of pathologies. Reported in IEEE Access, the new method is adapted to the nature of medical imaging and is more successful in spotting abnormalities than general-purpose solutions. Image anomaly detection is a task that comes up in data analysis in many industries. Medical scans, however, pose a particular challenge. It is way easier for algorithms to find, say, a car with a flat tire or a broken windshield in a series of car pictures than to tell which of the X-rays show early signs of pathology in the lungs, like the onset of COVID-19 pneumonia.

Google's artificial intelligence and machine-learning algorithms have in the past been criticized for how they deal with darker skin tones, including mistakenly tagging photos of Black people as gorillas. The company apologized and said it would fix its software. Now, it's using AI to power what it calls "the world's most inclusive camera." The goal of the Real Tone image processing in Google's new Pixel 6 and Pixel 6 Pro smartphones is to "more accurately highlight the nuances of diverse skin tones," especially darker complexions, according to the company's website. The new phones, which begin shipping Thursday, are the first to come with Real Tone.

I have always been fascinated by Science fiction. It was made using AI Text-to-Image generation, which is itself futuristic technology, putting in key words and keyframing the motion, creating an infinite loop.

The entertainment industry has yet to regulate the use of deepfakes and voice cloning. On September 29, the Emmy for interactive documentary went to'In Event of Moon Disaster', a film that uses artificial intelligence (AI) to create a fake video featuring former US President Richard Nixon. The film shows him delivering a speech that was prepared in case the Apollo 11 mission failed, leaving astronauts Neil Armstrong and Buzz Aldrin to die on the moon. The multimedia project was created by the Massachusetts Institute of Technology's Center for Advanced Virtuality, with a bit of help from a Ukrainian voice-cloning startup, Respeecher, which worked on Nixon's voice. The increasing scale of AI is raising the stakes for major ethical questions.

Finally, DeiT relies on data regularization techniques like stochastic depth. Ultimately, strong augmentations and regularization are limiting ViT's tendency to overfit in the small data regimes. Overall architecture of the proposed Pyramid Vision Transformer (PVT).

NoC interconnect IP to be dataflow backbone for image signal processors providing enhanced sensitivity, high-resolution HD imaging through low current, low power in a single-chip solution for the security/surveillance market. Eyenix's imaging solution provides a step-function advance over their previous product, replacing a 3rd-party artificial intelligence (AI) function with a superior capability developed in-house for super-resolution imaging. This is provided in a tightly integrated system design, including functions for image stabilization for mobile usage and image dewarping for wide-angle camera correction. The first application is destined for surveillance camera applications. Eyenix chose Arteris IP on-chip interconnect technology as a part of Eyenix's proprietary image processing chip because it enables Eyenix to design and integrate a complete and superior Eyenix imaging solution without dependency on external IP blocks for the AI function.

But with the help of technology in this visual age, images, videos are becoming more and more prevalent in today's lives. In the early days, social media was predominantly text-based but now technology has started to adapt according to the needs of people with impaired vision too. All thanks to modern technologies for their design, making navigating social media for giving a wonderful experience to also the visually impaired. Let's look at such one technology called image recognition which made life easier for people with impaired vision. Here are the 7 ways it can aid people.

OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture, and analysis including features like face detection and object detection. Here we are focused on face detection(frontal face). Before that, u may have some confusion between what are computer vision and image processing...Let me explain The input and output of image processing are both images. Computer vision is the construction of explicit, meaningful descriptions of physical objects from their image.