Étiquette : deep learning (Page 4 of 11)

CNET Is Experimenting With an AI Assist. Here’s Why

“The goal: to see if the tech can help our busy staff of reporters and editors with their job to cover topics from a 360-degree perspective. Will this AI engine efficiently assist them in using publicly available facts to create the most helpful content so our audience can make better decisions? Will this enable them to create even more deeply researched stories, analyses, features, testing and advice work we’re known for?
I use the term « AI assist » because while the AI engine compiled the story draft or gathered some of the information in the story, every article on CNET – and we publish thousands of new and updated stories each month – is reviewed, fact-checked and edited by an editor with topical expertise before we hit publish. That will remain true as our policy no matter what tools or tech we use to create those stories. And per CNET policy, if we find any errors after we publish, we will publicly correct the story.
Our reputation as a fact-based, unbiased source of news and advice is based on being transparent about how we work and the sources we rely on. So in the past 24 hours, we’ve changed the byline to CNET Money and moved our disclosure so you won’t need to hover over the byline to see it: « This story was assisted by an AI engine and reviewed, fact-checked and edited by our editorial staff. » We always note who edited the story so our audience understands which expert influenced, shaped and fact-checked the article.”

Source : CNET Is Experimenting With an AI Assist. Here’s Why – CNET

DALL·E: Introducing Outpainting (reminder)

https://no-flux.beaude.net/wp-content/uploads/2022/12/girl-with-a-pearl-earring.jpeg

“DALL·E’s Edit feature already enables changes within a generated or uploaded image — a capability known as Inpainting. Now, with Outpainting, users can extend the original image, creating large-scale images in any aspect ratio. Outpainting takes into account the image’s existing visual elements — including shadows, reflections, and textures — to maintain the context of the original image.”

Source : DALL·E: Introducing Outpainting

Intel Introduces Real-Time Deepfake Detector

real time deepfake detector

“As part of Intel’s Responsible AI work, the company has productized FakeCatcher, a technology that can detect fake videos with a 96% accuracy rate. Intel’s deepfake detection platform is the world’s first real-time deepfake detector that returns results in milliseconds.
Most deep learning-based detectors look at raw data to try to find signs of inauthenticity and identify what is wrong with a video. In contrast, FakeCatcher looks for authentic clues in real videos, by assessing what makes us human— subtle “blood flow” in the pixels of a video. When our hearts pump blood, our veins change color. These blood flow signals are collected from all over the face and algorithms translate these signals into spatiotemporal maps. Then, using deep learning, we can instantly detect whether a video is real or fake.  ”

Source : Intel Introduces Real-Time Deepfake Detector

NeRF Research Turns 2D Photos Into 3D Scenes

“When the first instant photo was taken 75 years ago with a Polaroid camera, it was groundbreaking to rapidly capture the 3D world in a realistic 2D image. Today, AI researchers are working on the opposite: turning a collection of still images into a digital 3D scene in a matter of seconds. Known as inverse rendering, the process uses AI to approximate how light behaves in the real world, enabling researchers to reconstruct a 3D scene from a handful of 2D images taken at different angles. The NVIDIA Research team has developed an approach that accomplishes this task almost instantly — making it one of the first models of its kind to combine ultra-fast neural network training and rapid rendering.”

Source : NeRF Research Turns 2D Photos Into 3D Scenes | NVIDIA Blog

Predicting sex from retinal fundus photographs using automated deep learning

Figure 2

“Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center.”

Source : Predicting sex from retinal fundus photographs using automated deep learning | Scientific Reports

“There have been several AI breakthroughs to-date, including AI agents that have mastered arcade games, complex strategy games such as chess, shogi and Go as well as other real-time, multiplayer strategy games. GT Sophy takes game AI to the next level, tackling the challenge of a hyper-realistic simulator by mastering real-time control of vehicles with complex dynamics, all while operating within inches of opponents.”

Source : PROJECT | Gran Turismo Sophy

which face is real

“Which Face Is Real has been developed by Jevin West and Carl Bergstrom at the University of Washington as part of the Calling Bullshit project. All images are either computer-generated from thispersondoesnotexist.com using the StyleGAN software, or real photographs from the FFHQ dataset of Creative Commons and public domain images. License rights notwithstanding, we will gladly respect any requests to remove specific images; please send the URL of the results pages showing the image in question.”

Source : Which Face Is Real?

“Harness the power of AI to quickly turn simple brushstrokes into realistic landscape images for backgrounds, concept exploration, or creative inspiration. 🖌️ The NVIDIA Canvas app lets you create as quickly as you can imagine.”

via NVIDIA Studio

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