“In a world where surveillance technology is being deployed everywhere from airports and stadiums to public schools and hotels and raising a plethora of privacy concerns, it’s perhaps inevitable that farms on land and at sea would find ways to exploit it to improve productivity. Just this year, American agribusiness giant Cargill Inc. said it was working with an Irish tech start-up on a facial-recognition system to monitor cows so farmers can adjust feeding regimens to enhance milk production. Scanners will allow them to track food and water intake and even detect when females are having fertile days. Salmon farming may be next in line. As fish vies with beef and chicken as the global protein food of choice, exporters like Norway, the world’s biggest producer of the pinkish-orange fish, have become the focal point for radical marine-farming methods designed to help the $232 billion aquaculture industry feed the world.”
“Powered by the Quadro RTX 6000, this demo shows off production-quality rendering and cinematic frame rates, enabling users to interact with scene elements in real time”.
via NVIDIA (YouTube)
“Thanks to the technology supported by the architecture, including the new DLAA anti-aliasing tech, Turing can improve rendering times of real-time ray tracing over Pascal “by a factor of six.””
“Creating slow-motion footage is all about capturing a large number of frames per second. If you don’t record enough, it becomes choppy and unwatchable as soon as you slow down your video — unless, that is, you use artificial intelligence to imagine the extra frames”.
«We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class».
«Each of these images took about 18 days for the computers to generate, before reaching a point that the system found them believable»
«Arsenal’s smart assistant AI suggests settings based on your subject and environment. It uses an advanced neural network to pick the optimal settings for any scene (using similar algorithms to those in self driving cars). Like any good assistant, it then lets you control the final shot. Here’s how it works…»
«Can we use AI to increase empathy for victims of far-away disasters by making our homes appear similar to the homes of victims?»
Source : Deep Empathy
«Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions».
«Developed out of the Max Planck Institute for Intelligent Systems in Germany, researchers propose a new approach to traditional (and usually disappointing) single-image super-resolution (SISR) technology on the market».