The Encode organization has recently requested court support against OpenAI's plans to transition into a for-profit entity. Encode is a nonprofit organization and a co-sponsor of California's Artificial Intelligence Safety Act. The organization's lawyers stated in a brief that OpenAI's transformation would "undermine" its original mission to develop and deploy artificial intelligence technologies in a safe and beneficial manner for the public. Encode's founder and president, Sneha Revanur, accused Open

Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

The domestic open source video generation model CogVideoX-5B has been officially released in the Mota ModelScope community, significantly improving the quality and visual effects of video generation. Based on the large-scale DiT model, this model utilizes a 3D causal variational autoencoder and expert Transformer technology, achieving spatio-temporal joint modeling through 3D-RoPE positional encoding and a 3D full attention mechanism. The use of progressive training techniques allows the model to generate long videos with distinct motion features, coherence, and high quality.

Zhipu AI announces the open source of its video generation model CogVideoX, aiming to promote the rapid development and application of video generation technology. The CogVideoX model is based on advanced large model technology and is capable of meeting commercial-grade application requirements. The currently open sourced CogVideoX-2B version requires only 18GB of GPU memory for inference at FP-16 precision and 40GB for fine-tuning, allowing inference with a single 4090 GPU and fine-tuning with a single A6000 GPU.

Scientists at ShanghaiTech University have developed CLAY, an AI model that can generate detailed 3D objects from text or 2D images, outperforming earlier technologies in quality, diversity, and generation speed. At its core, CLAY includes a multi-resolution variational autoencoder and a diffusion transformer, allowing direct processing of 3D content without conversion. By training on over 500,000 3D models, CLAY can produce objects ranging from everyday items to complex creatures and achieve precise control through additional inputs, generating urban scenes and reconstructing 3D models from sketches.

Recently, significant progress has been made in the research and application of multimodal large models. Foreign companies such as OpenAI, Google, and Microsoft have introduced a series of advanced models, and domestic institutions such as Zhipu AI and Jietu Xingchen have also made breakthroughs in this field. These models typically rely on visual encoders to extract visual features and integrate them with large language models, but they face issues such as visual inductive bias due to training