ToolGrad: Efficient tool-use dataset generation with textual "gradients"
Machine Intelligence
AI NEWS JOURNAL
研究与论文
Follow the latest facts and developments on one topic.
Introducing ChatGPT for Financial Services
Introducing ChatGPT for Financial Services, combining built-in financial data and GPT-6 Astra for research, modeling, and client-ready materials.
Funding grants for new research into AI and teen development
Apply now for OpenAI’s $5 million grant program supporting independent research on how generative AI affects teen development, well-being, and safety.
Research acceleration: The view inside OpenAI
Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration.
Mapping global methane emissions from space with deep learning
Climate & Sustainability
Healthcare organizations can now connect EHR and additional industry data to ChatGPT
ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more.
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
Advancing AMIE towards expert-level audio-visual clinical consultations
Health & Bioscience
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