Recently, Zhejiang University and Alibaba DAMO Academy jointly released a remarkable study aimed at creating high-quality multimodal textbooks from educational videos. This innovative research not only provides new ideas for training large-scale language models (VLMs) but may also change the way educational resources are utilized. With the rapid development of artificial intelligence technology, the pre-training corpus of VLMs mainly relies on visual-text pairs and visually intertwined data. However, much of this current data comes from the web, where the correlation between text and images is weak, and the knowledge density is relatively low.

"MedTrinity-25M" is a large-scale multimodal medical dataset released by the UCSC-VLAA team, containing 25 million medical images along with detailed annotations. This dataset features multi-granularity annotations, supports the training of multimodal large medical models, and involves a complex construction process that includes data processing, metadata extraction, region of interest localization, and the collection of medical knowledge. Detailed descriptions are generated using large-scale language models to enhance data usability. The dataset was officially released on July 21, 2024, accompanied by a pre-trained model.

Nucleus AI has released a large-scale language model with 22 billion parameters, aiming to utilize AI technology to optimize agricultural supply and demand. This model is available under the open-source MIT license and can be fine-tuned for various generation tasks and products. Nucleus plans to release more versions of this model to enhance speed and reduce costs. The company's long-term goal is to establish an intelligent operating system for agriculture to optimize supply and demand relationships and reduce uncertainty for farmers.