Researchers unveiled competition for the reigning large language model GPT-3. Four models collectively called Wu Dao were described by Beijing Academy of Artificial Intelligence, a research collective funded by the Chinese government, according to Synced Review.

ImageNet now comes with privacy protection.What’s new: The team that manages the machine learning community’s go-to image dataset blurred all the human faces pictured in it and tested how models trained on the modified images on a variety of image recognition tasks.

It’s well established that pretraining a model on a large dataset improves performance on fine-tuned tasks. In sufficient quantity and paired with a big model, even data scraped from the internet at random can contribute to the performance boost.

Some doctors don’t trust a second opinion when it comes from an AI system. A team at MIT and Regensburg University investigated how physicians responded to diagnostic advice they received from a machine learning model versus a human expert.

When you lack labeled training data, pretraining a model on unlabeled data can compensate. New research pretrained a model three times to boost performance on a medical imaging task.

A new system combines verbal descriptions and crude lines to visualize complex scenes. Google researchers led by Jing Yu Koh proposed Tag-Retrieve-Compose-Synthesize (TReCS), a system that generates photorealistic images by describing what they want to see while mousing around on a blank screen.

Self-attention is a key element in state-of-the-art language models, but it struggles to process images because its memory requirement rises rapidly with the size of the input. New research addresses the issue with a simple twist on a convolutional neural network.

Police are increasingly able to track motor vehicles throughout the U.S. using a network of AI-powered cameras — many owned by civilians. Flock, which sells automatic license plate readers is encouraging enforcers to use its network to monitor cars and trucks outside their jurisdiction.

Facebook’s management obstructed the architect of its recommendation algorithms from mitigating their negative social impact. The social network focused on reining in algorithmic bias against particular groups of users at the expense of efforts to reduce disinformation.

In training an image recognition model, it’s not uncommon to augment the data by cropping original images randomly. But if an image contains several objects, a cropped version may no longer match its label. Researchers developed a way to make sure random crops are labeled properly.

Facebook’s content moderation algorithms block many advertisements aimed at disabled people. The social media platform’s automated systems regularly reject ads for clothing designed for people with physical disabilities.

Models that interpret the interplay of words and images tend to be trained on richer bodies of text than images. Recent research worked toward giving such models a more balanced knowledge of the two domains.

AI may help revolutionize the human diet – or dessert, at least. Google applied AI engineer Dale Markowitz and developer advocate Sara Robinson trained a model to predict whether a recipe is...

U.S. government approval of medical AI products is on the upswing — but information about how such systems were built is largely unavailable. The U.S. Food and Drug Administration (FDA) has approved a a plethora of AI-driven medical systems.

Computer vision is alerting authorities the moment someone draws a gun. Several companies offer deep learning systems that enable surveillance cameras to spot firearms and quickly notify security guards or police.