In this work-from-home era, who hasn’t spent a video conference wishing they could read an onscreen document without turning their eyes from the person they’re talking with? Or simply hoping the stream wouldn’t stutter or stall? Deep learning can fill in the missing pieces.

Trained on a small dataset, generative adversarial networks (GANs) tend to generate either replicas of the training data or noisy output. A new method spurs them to produce satisfying variations.

Documentary filmmakers often shield the identities of people who might be harmed for speaking out. But typical tactics like blurring faces and distorting voices can make it hard for audiences to connect emotionally. A new

Trying on new makeup is a hassle — apply, inspect, wash off, repeat. Not to mention the tribulation of visiting brick-and-mortar stores during the pandemic. Augmented reality is helping people try on all the makeup they want without leaving home.

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

Convolutional neural networks are good at recognizing disease symptoms in medical scans of patients who were injected with iodine-based dye, that makes their organs more visible. But some patients can’t take the dye. Now synthetic scans from a GAN are helping CNNs learn to analyze undyed images.

Deepfakes have gone mainstream, allowing celebrities to star in commercials without setting foot in a film studio. A new method helps determine whether such endorsements — and other images produced by generative adversarial networks — are authentic.

GANs are adept at mapping the artistic style of one picture onto the subject of another, known as style transfer. However, applied to the fanciful illustrations in children’s books, some GANs prove better at preserving style, others better at preserving subject matter.

Covid shut down the tennis tournament at Wimbledon this year, but a new model simulates showdowns between the sport’s greatest players. Stanford researchers developed Vid2Player, a system that simulates the footwork, positioning, and strokes of tennis pros like Roger Federer.

Transformer networks are gaining popularity as a high-accuracy alternative to recurrent neural networks. But they can run slowly when they’re applied to long sequences.

A strategy manifesto from one of China’s biggest tech companies declares, amid familiar visions of ubiquitous AI, that deepfakes are more boon than bane.

People granted early access to OpenAI’s latest language model are raving about its way with words — and more. Beta testers of GPT-3 are showing off the model’s ability to write business memos, craft blogs, pen tweets, and even generate computer code.

A new report details the role of AI in China’s effort to fight the coronavirus. Researchers at Synced, a China-based AI publication, describe how nearly 90 machine learning products have contributed to the country’s pandemic response.

The same technology that has bedeviled Hollywood stars and roiled politics is easing corporate communications. Synthesia generates training and sales videos featuring photorealistic, synthetic talking heads that read personalized scripts in any of 34 languages.

Which headline was written by a computer? A: FIFA to Decide on 2022 World Cup in March B: Decision in March on 48-team 2022 World Cup, Says Infantino