Transformer

An architectural innovation improves upon transformers — up to 2 billion parameters, at least...

Nvidia’s latest chip promises to boost AI’s speed and energy efficiency.

Three researchers decoded scrolls that had gone unread since they were turned into charcoal by the eruption of Mount Vesuvius in the year 79.

A neural network makes music for unaccompanied vocal tracks. Chris Donahue, Antoine Caillon, Adam Roberts, and colleagues at Google proposed SingSong, a system that generates musical accompaniments for sung melodies. You can listen to its output here.

Google unveiled Gemini, its bid to catch up to, and perhaps surpass, OpenAI’s GPT-4. Google demonstrated the Gemini family of models that accept any combination of text (including code), images, video, and audio and output text and images. The demonstrations and metrics were impressive...

Researchers proposed a way for robots to find objects in households where things get moved around. Andrey Kurenkov and colleagues at Stanford University introduced Node Edge Predictor, a model that learned to predict where objects were located in houses.

The transformer architecture is astonishingly powerful but notoriously slow. Researchers have developed numerous tweaks to accelerate it — enough to warrant a look at how these alternatives work, their strengths, and their weaknesses.

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.