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The world of artificial intelligence (AI) has recently seen significant advancements in generative models, a type of machine-learning algorithms that "learn" patterns from sets of data in order to generate new, similar sets of data. Generative models are often used for things like drawing images and natural language generation – a famous example are the models used to develop chatGPT. Generative models have had remarkable success in various applications, from image and video generation to composing music and to language modeling. The problem is that we are lacking in theory when it comes to the capabilities and limitations of generative models; understandably, this gap can seriously affect how we develop and use them down the line. One of the main challenges has been the ability to effectively pick samples from complicated data patterns, especially given the limitations of traditional methods when dealing with the kind of high-dimensional and complex data commonly encountered in modern AI applications.

Frank Pavich is the director of "Jodorowsky's Dune," a documentary about the Chilean filmmaker Alejandro Jodorowsky's attempt to film a version of "Dune" in the mid-1970s. I was recently shown some frames from a film that I had never heard of: Alejandro Jodorowsky's 1976 version of "Tron." The actors, unfamiliar to me, looked fantastic in their roles. The costumes and lighting worked together perfectly. The images glowed with an extravagant and psychedelic sensibility that felt distinctly Jodorowskian.

Frank Pavich is the director of "Jodorowsky's Dune," a documentary about the Chilean filmmaker Alejandro Jodorowsky's attempt to film a version of "Dune" in the mid-1970s. I was recently shown some frames from a film that I had never heard of: Alejandro Jodorowsky's 1976 version of "Tron." The actors, unfamiliar to me, looked fantastic in their roles. The costumes and lighting worked together perfectly. The images glowed with an extravagant and psychedelic sensibility that felt distinctly Jodorowskian.

The most common type of Autoencoder is an Undercomplete Autocencoder which squeezes (encodes) data into fewer neurons (lower dimension) while removing "unimportant" information. It achieves that by training an encoder and decoder simultaneously, so the output neurons match inputs as closely as possible.

Researchers from NVIDIA, Aalto University, and MIT have developed an AI that can remove noise from grainy photos and automatically enhance them. This technology can be beneficial in several real-world situations where it is difficult to obtain clear image data like MRI scans, astronomical imaging, and more. Existing noise-reduction AI systems require both noisy and clean input images, but NVIDIA's AI can restore images without being shown what the noise-free image looks like. It just needs to look at examples of corrupted images. The researchers trained the AI on 50,000 images and the deep-learning algorithm was able to produce impressive results.

American visual computing company Nvidia, along with researchers from Aalto University and MIT, have come up with an artificial intelligence (AI) program that removes defects from images such as grain, text and watermarks, without studying how clean photos look like. "Our AI program shows significant benefits that can be reaped by removing the need for potentially strenuous collection of clean data (images)," the team said. The AI program is expected to find use in the medical field to eliminate extensive post-processing that is deployed to remove noise from MRI (magnetic resonance imaging) images. "There are several real-world situations where obtaining clean training data are difficult: Low-light photography (for example, astronomical imaging) and MRI," the team said. Previously, a neural network could cut out noise from images only after being trained on example pairs of unclean and clean images.

There is a new and apparently better way to fix grainy photos, and it uses AI. Artificial Intelligence, one must say, coupled with lots of computing power, but that's growing day after day. Artificial Intelligence and machine learning will never cease to surprise us, apparently. Each new day, there's some new announcement, covering different fields: AI can render 3D hair in real time, smell illnesses in human breath, assess infrastructure quality in Africa or help transform audio into music playing avatars. Now it can also help photographers get rid of noise in their photos.

But Nvidia could make protecting photos online much harder with a new advancement in artificial intelligence that can automatically remove artifacts from a photograph, including text and watermarks, no matter how obtrusive they may be. In previous advancements in automated image editing and manipulation, an AI powered by a deep learning neural network is trained on thousands of before and after example photos so that it knows what the desired output should look like. But this time, researchers at Nvidia, MIT, and Aalto University in Finland, managed to train an AI to remove noise, grain, and other visual artifacts by studying two different versions of a photo that both feature the visual defects. Fifty-thousand samples later, the AI can clean up photos better than a professional photo restorer. Practical applications for the AI include cleaning up long exposure photos of the night sky taken by telescopes, as cameras used for astrophotography often generate noise that can be mistaken for stars.