Machine Learning Research

Researchers devised a way to cut the cost of training video generators. They used it to build a competitive open source text-to-video model and promised to release the training code.

Mistral AI launched two models that raise the bar for language models with 8 billion or fewer parameters, small enough to run on many edge devices.

Text embedding models are often used to retrieve text, cluster text, determine similarity between texts, and generate initial embeddings for text classifiers. A new embedding model comes with adapters that specialize it to each of these use cases.

Researchers probed the black market for AI services that are designed to facilitate cybercrime.

OpenAI launched a suite of new and updated tools to help AI developers build applications and reduce costs.

Meta upped the ante for text-to-video generation with new systems that produce consistent characters and matching soundtracks.

Large language models can process small spreadsheets, but very large spreadsheets often exceed their limits for input length. Researchers devised a method that processes large spreadsheets so LLMs can answer questions about them.

Meta extended its Llama family of models into two new categories: vision-language and sizes that are small enough to fit in edge devices.

A robot that plays table tennis beats human beginners and entertains experts.

The parade of ever more capable LLMs continues with Qwen 2.5.

Studies have established that large language models can memorize the text passages they’ve been trained on repeatedly and regurgitate them when prompted in adversarial and, though rarely, in benign ways.

Preliminary versions of OpenAI’s new model family were trained explicitly to think step-by-step, yielding outstanding marks in math, science, and coding — but users can’t see their reasoning steps.

Traditionally, building 3D meshes for gaming, animation, product design, architecture, and the like has been labor-intensive. Now the ability to generate 3D meshes from a single image is widely available.

Datasets that were scraped from the web tend to be unbalanced, meaning examples of some classes (say, cats) are plentiful while examples of others (say, caterpillars) are scarce.

Researchers have probed the inner workings of individual layers of large language models. A new tool applies this approach to all layers.