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The third generation of Apple Foundation Models — fruit of Apple’s collaboration with Google — introduces a variation on the mixture-of-experts architecture that runs on local devices.
Text-to-image generators that use diffusion or flow-matching typically compose a whole image at once (although they refine the whole image in steps).
Google added a music generator to Gemini and YouTube, putting a model that produces synthetic songs in front of hundreds of millions of users.
OpenAI plans to shut down its video generator Sora in a sudden retreat from the video market.
Research shows that diffusion image generators can train somewhat faster if they learn to reconstruct embeddings from a pretrained encoder that’s built for vision tasks like classification, segmentation, and retrieval — not image generation.
Image generators often mangle text. An open-weights model outperforms open and proprietary competitors in text rendering.
Current methods that produce 3D scenes from text or images are slow and produce inconsistent results. Researchers introduced a technique that generates detailed, coherent 3D scenes seconds.
Chain-of-thought reasoning can help autonomous vehicles decide what to do next.
A new image generator reasons over prompts to produce outstanding pictures.
The mixture-of-experts approach that has boosted the performance of large language models may do the same for video generation.
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.
Materials that have specific properties are essential to progress in critical technologies like solar cells and batteries. A machine learning model designs new materials to order.
Materials that have specific properties are essential to progress in critical technologies like solar cells and batteries. A machine learning model designs new materials to order.
Typical large language models are autoregressive, predicting the next token, one at a time, from left to right. A new model hones all text tokens at once.
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