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Meta is rolling out AI-powered upgrades to its social platforms. Meta announced a chat interface, image generator, and celebrity tie-ins for Facebook, Instagram, Messenger, and WhatsApp. The new capabilities take advantage of LLaMa 2 and an unnamed image generator, presumably CM3leon.

ViTDet, a new system from Facebook, adds an object detector to a plain pretrained transformer.

Vision models can be improved by training them on several altered versions of the same image and also by encouraging their weights to be close to zero. Recent research showed that both can have adverse effects that may be difficult to detect.

Researchers have shown that it’s possible to train a computer vision model effectively on around 66 percent of the pixels in each training image. New work used 25 percent, saving computation and boosting performance to boot.

Regulators are forcing Meta (formerly Facebook) to display certain advertisements more evenly across its membership. The United States government compelled Meta to revise its ad-placement system to deliver ads for housing to members regardless of their age, gender, or ethnicity.

Researchers took a step toward achieving a longstanding goal: One model that performs a whole lot of very different tasks. Scott Reed, Konrad Żołna, Emilio Parisotto and a team at DeepMind announced Gato.

When you’re training a neural network, it takes a lot of computation to optimize its weights using an iterative algorithm like stochastic gradient descent. Wouldn’t it be great to compute the best parameter values in one pass? A new method takes a substantial step in that direction.

Facebook’s parent company is staking its future on a new compute cluster. Meta unveiled AI Research SuperCluster (RSC), which is designed to accelerate training of large models for applications like computer vision, natural language processing, and speech recognition.

Large models pretrained on immense quantities of text have been proven to provide strong foundations for solving specialized language tasks. My biggest hope for AI in 2022 is...

In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

A prominent online streaming service is using a machine learning model to identify trolls who try to get around being banned.

Transformers have matched or exceeded earlier architectures in language modeling and image classification. New work shows they can achieve state-of-the-art results in some reinforcement learning tasks as well.

Facebook released a chatbot that summarizes dialog on the fly and uses the summary to generate further repartee.

Chatbots often respond to human input with incorrect or nonsensical answers. Why not enable them to search for helpful information?