Neural networks can describe in words what’s happening in pictures and videos — but can they make sensible guesses about things that happened before or will happen afterward? Researchers probed this ability.

The route to improving transformer-based language models like GPT-3 and Gopher, which are trained on immense quantities of text scraped from the web, has been to increase their size. But research shows that, given a processing budget, bigger doesn’t necessarily mean better.

Recent work showed that models for multilingual machine translation can increase the number of languages they translate by scraping the web for pairs of equivalent sentences in different languages. A new study radically expanded the language repertoire through training on untranslated web text.

Sentence pairs that have equivalent meanings in different languages — typically used to train machine translation systems — have been available in sufficient quantities for only around 100 languages. New work doubled that number and produced a more capable model.

Only a week ago, researchers unveiled a system that generates a few seconds of video based on a text prompt. New work enables a text-to-video system to produce an entire visual narrative from several sentences of text.

In spoken conversation, people naturally take turns amid interjections and other patterns that aren’t strictly verbal. A new approach generated natural-sounding audio dialogs without training on text transcriptions that mark when one party should stop speaking and the other should chime in.

Text-to-image generators like DALL·E 2, Midjourney, and Stable Diffusion are winning art contests and worrying artists. A new approach brings the magic of text-to-image generation to video.

Vision Transformers (ViTs) are overtaking convolutional neural networks (CNN) in many vision tasks, but procedures for training them are still tailored for CNNs. New research investigated how various training ingredients affect ViT performance.

A new method enables robots to respond helpfully to verbal commands by pairing a natural language model with a repertoire of existing skills.

Nvidia, known for chips designed to process AI systems, is providing access to large language models. Nvidia announced early access to NeMo LLM and BioNeMo, cloud-computing services that enable developers to generate text and biological sequences respectively.

The ability to update language models is essential to incorporate new information and correct undesirable behaviors. Previous methods are unwieldy and often fail as the amount of new data increases. New work offers a workaround.

A new approach alters transformers' self-attention mechanism to balance computational efficiency with performance on vision tasks.

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

A worldwide collaboration produced the biggest open source language model to date. BLOOM is a family of language models built by the BigScience Research Workshop, a collective of over 1,000 researchers from 250 institutions around the globe.

A new machine learning method attempts to account for biases that may be held by certain subsets of labelers.