Transformer

Vision transformers have bested convolutional neural networks (CNNs) in a number of key vision tasks. Have CNNs hit their limit? New research suggests otherwise.

A tweak to diffusion models, which are responsible for most of the recent excitement about AI-generated images, enables them to produce more realistic output.

While Microsoft and Google scramble to supercharge their businesses with text generation, Meta has yet to launch a flagship generative AI service. Reporters went looking for reasons why.

A pretrained text-to-image generator enabled researchers to see — roughly — what other people looked at based on brain scans. Yu Takagi and Shinji Nishimoto developed a method that uses Stable Diffusion to reconstruct images viewed by test subjects...

A team in the United Arab Emirates, a seven-state federation on the Arabian Peninsula, built the latest top-performing open source large language model.

Text-to-image generators like DALL·E 2, Stable Diffusion, and Adobe’s new Generative Fill feature can revise images in a targeted way — say, change the fruit in a bowl from oranges to bananas — if you enter a few words that describe the change plus an indication of the areas to be changed.

When you’re looking for answers from a large language model, some prompts are better than others. So how can you come up with the best one? A new model automates the process.

Text from current language models can be useful as a rough draft, but that leaves the polishing to human writers. A language model learned how to generate and respond to editorial directions.

Both transformers and reinforcement learning models are notoriously data-hungry. They may be less so when they work together. Vincent Micheli and colleagues at the University of Geneva trained a transformer-based system to simulate Atari games using a small amount of gameplay.

Recent multimodal models process both text and images as sequences of tokens, but they learn to represent these distinct data types using separate loss functions. Recent work unifies the loss function as well.

Meta’s effort to make a large language model available to researchers ended with its escape into the wild. Soon after Meta started accepting applications for developer access to LLaMA, a family of trained large language models...

Get ready for the next wave of language-model mania. OpenAI introduced the latest in its GPT series of large language models to widespread excitement. The company showed statistics and examples designed to demonstrate...

As AI-generated text and images capture the world’s attention, music is catching up. Andrea Agostinelli, Timo I. Denk, and colleagues at Google and Sorbonne Université introduced MusicLM, a system that generates music from text descriptions.

Who would disagree that, if all people are mortal and Socrates is a person, Socrates must be mortal? GPT-3, for one. Recent work shows that bigger language models are not necessarily better when it comes to logical reasoning.

Amid speculation about the threat posed by OpenAI’s ChatGPT chatbot to Google’s search business, a paper shows how the search giant might address the tendency of such models to produce offensive, incoherent, or untruthful dialog.