Abstract: Multimodal brain tumor segmentation (BraTS), integrated with surgical robots and navigation systems, enables accurate surgical interventions while maximizing the preservation of surrounding ...

A generalized architectural blueprint for building efficient MLLMs. This template achieves efficiency through a combination of component choices and data flow optimization. Key strategies include: (1) ...

For the past three years, AI’s breakout moment has happened almost entirely through text. We type a prompt, get a response, and move to the next task. While this intuitive interaction style turned ...

Ray's innovative disaggregated hybrid parallelism significantly enhances multimodal AI training efficiency, achieving up to 1.37x throughput improvement and overcoming memory challenges. In a ...

An unexpected revisit to my earlier post on mouse encoder hacking sparked a timely opportunity to reexamine quadrature encoders, this time with a clearer lens and a more targeted focus on their signal ...

In the early stages of AI adoption, enterprises primarily worked with narrow models trained on single data types—text, images or speech, but rarely all at once. That era is ending. Today’s leading AI ...

Modern multimodal AI models can recognize objects, describe scenes, and answer questions about images and short video clips, but they struggle with long-form and large-scale visual data, where ...

A new AI model from NYU Abu Dhabi predicts solar wind days in advance with far greater accuracy than existing methods. By analyzing ultraviolet solar images, it could help protect satellites, ...

Artificial intelligence data annotation startup Encord, officially known as Cord Technologies Inc., wants to break down barriers to training multimodal AI models. To do that, it has just released what ...

Credit: Image generated by VentureBeat with Gemini 2.5 Flash (nano banana) AI models are only as good as the data they're trained on. That data generally needs to be labeled, curated and organized ...