Machine Learning Research

Large language models excel at processing text but can’t interpret images, video, or audio directly without further training on those media types. Researchers devised a way to overcome this limitation.

Hugging Face has made a name by providing open AI models. Now it’s providing an open robot.

OpenAI refreshed its roster of models and scheduled the largest, most costly one for removal.

Researchers built a model that’s more robust to noisy inputs like misspellings, smarter about character-level information like the number of R's in strawberry, and potentially better able to understand unfamiliar languages that might share groups of letters with familiar languages.

OpenAI embraced Model Context Protocol, providing powerful support for an open standard that connects large language models to tools and data.

Google’s new flagship model raised the state of the art in a variety of subjective and objective tests.

If you have a collection of variables that represent, say, a cancer patient and you want to classify the patient’s illness as likely cancer or not, algorithms based on decision trees, such as gradient-boosted trees, typically perform better than neural networks.

Alibaba’s latest open-weights system raises the bar for multimodal tasks in a relatively small model.

Meta updated its popular open-weights models, claiming performance superior to closed competitors in three size classes.

Even without explicit training in reasoning, large language models “think” in ways that may be more deliberate than previously understood.

AI systems designed to generate animated 3D scenes that include active human characters have been limited by a shortage of training data, such as matched 3D scenes and human motion-capture examples. Generated video clips can get the job done without motion capture.

Researchers updated the highly responsive Moshi voice-to-voice model to discuss visual input.

Diffusion transformers learn faster when they can look at embeddings generated by a pretrained model like DINOv2.

Diffusion models usually take many noise-removal steps to produce an image, which takes time at inference. There are ways to reduce the number of steps, but the resulting systems are less effective. Researchers devised a streamlined approach that doesn’t sacrifice output quality.

Google updated its open-weights family of large language models to include versions that handle image and video inputs.