ToolGrad: Efficient tool-use dataset generation with textual "gradients"
Machine Intelligence
AI NEWS JOURNAL
研究与论文
沿着一个主题，追踪新的事实与后续进展。
为金融服务引入ChatGPT
采用ChatGPT用于金融服务,将内置金融数据和GPT-6 Astra用于研究、建模和客户准备的材料相结合.
对AI和青少年发展新研究的供资赠款
现在申请OpenAI500万美元的赠款计划.
研究加速:OpenAI内部的视图
在OpenAI内部,编码代理正在重塑AI研究. 探索有关剂用量,实验速度,任务复杂性,研究加速的早期数据.
Mapping global methane emissions from space with deep learning
Climate & Sustainability
医疗保健组织现在可以将EHR和更多的产业数据与ChatGPT连接起来
ChatGPT现在可以连接到可信赖的保健数据,帮助临床医生安全地获取病人的背景,医学研究等等.
GlucoFM: Foundation model for continuous glucose monitoring
Health & Bioscience
AgentHands: Generating interactive hand gestures for spatially grounded agent conversations in XR
Human-Computer Interaction and Visualization
From Atari to EVE Online: Building on 15 Years of AI Research in Games
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
How mobility gives language models a deeper understanding of place
Algorithms & Theory
Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence
General Science
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction. We face a new epoch in computing. Hardware is changing rapidly — not just faster GPUs, but a growing range of chips from different vendors, each with its own architecture and often tailored to specific AI workloads. Software is changing just as fast, and AI coding tools now generate in minutes what took months of effort a few years ago. With so much of computing now centered on AI, GPU kernels are a crucial component of its success. These are the low-level programs that run inside the GPU, and writing efficient ones is far from obvious — it takes years of expertise to get right. Transferring a kernel from o