AI热点
追踪AI领域最新动态与趋势
北交所半年报启幕，多股预喜高景气赛道发力
7月31日，太湖雪与爱伦医疗率先交出上半年成绩单，成为北交所首批披露半年报的公司。两家企业均实现营收与净利润双增长，展现出不同赛道的增长韧性。与此同时，截至8月2日，已有国航远洋、安达科技、鸿仕达、汉维科技等多家北交所公司发布业绩预告，扭亏为盈与高增长案例频现，北交所半年报行情渐入佳境。在太湖雪率先发布正式半年报之前，北交所已有多家公司披露业绩预告，整体表现亮眼，扭亏为盈与高增长成为主旋律。同时，从已披露业绩的公司来看，业绩高增长或扭亏的公司集中在高景气赛道。（中证报）
合规约束叠加海外壁垒，“新三样”ESG竞争力加速兑现
A股ESG强制信披规则实施，叠加海外市场对进口产品设置严苛绿色准入门槛，倒逼“新三样”企业深化ESG合规管理、提质升级，整体成效显著。截至8月2日，新能源汽车、锂电池、光伏产业上市公司2025年度ESG报告披露率高于A股市场平均披露率，A级及以上高评级企业占比大幅领先市场整体水平。龙头公司通过持续完善供应链管理、搭建碳足迹管理体系，以标准化ESG建设拓展海外市场，构建全球化绿色竞争新优势。（中证报）
美联储9月加息25个基点的概率为73.6%
据CME“美联储观察”：美联储到9月维持利率不变的概率为26.4%，累计加息25个基点的概率为73.6%。美联储到10月维持利率不变的概率为19.9%，累计加息25个基点的概率为62.1%，累计加息50个基点的概率为17.9%。（财联社）
A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
In this tutorial, we design a complete GeoAI workflow for extracting building footprints from high-resolution NAIP aerial imagery. We begin by configuring the geospatial deep learning environment, downloading raster imagery and vector labels, and inspecting their spatial properties before generating georeferenced image chips and segmentation masks. We then train a U-Net model with a ResNet-34 […] The post A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Groundin
Sam Altman and AI’s decel debate
On the latest episode of Equity, we discuss why Sam Altman is calling on the industry to "pace the rate of AI development."
Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model
Inkling-Small matches Inkling at a quarter the size, and its NVFP4 checkpoint runs on one NVIDIA B300 GPU The post Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model appeared first on MarkTechPost.
NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework
Agentic RL research is constant algorithm modification, and in mainstream frameworks every change threads through trainer, distributed backend, and rollout glue. NVIDIA's Molt targets that cost with about 8.6K lines of RL code, composing Ray, vLLM, and NeMo AutoModel around one asynchronous loop. The agent stays ordinary Python, trajectories stay token-exact, and throughput comes out statistically comparable to a Megatron-based stack. The post NVIDIA AI Releases Molt: A PyTorch-Native Agentic Re
End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
In this tutorial, we build an advanced end-to-end time-series forecasting workflow with TimesFM 2.5. We begin by configuring the runtime, installing the required dependencies, detecting available hardware, and generating a realistic multi-store retail dataset with trend, seasonality, pricing, promotions, holidays, temperature effects, and random variation. We then load and compile the TimesFM 2.5 model, examine […] The post End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomal
Judge denies xAI’s request to block Minnesota ban on ‘nudify’ apps
Despite a lawsuit from xAI, a Minnesota ban on apps that allow users to “nudify” images can move forward.
YouTuber Hank Green says his AI usage is ‘not healthy’
Green offered a remarkable apology, saying that "the level of dopamine that I've been getting from interacting with LLMs ... is not healthy for me or good for the world."
AMD Releases Instella-MoE-16B-A3B: A Fully Open Mixture-of-Experts LLM With 2.8B Active Parameters Trained On Instinct GPUs
AMD released Instella-MoE-16B-A3B, a fully open Mixture-of-Experts language model trained from scratch on Instinct MI300X and MI325X GPUs. It holds 16B total parameters but activates only 2.8B per token, using Gated MLA and FarSkip-Collective. AMD published weights from every training stage, plus data mixtures, configs, and inference code. The post AMD Releases Instella-MoE-16B-A3B: A Fully Open Mixture-of-Experts LLM With 2.8B Active Parameters Trained On Instinct GPUs appeared first on MarkTec
Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking
Discover how to optimize transformer workloads using the NVIDIA Transformer Engine. This tutorial guides you through configuring fused GPU kernels, implementing FP8 delayed scaling, and benchmarking model performance. Learn to build and train efficient GPT-style causal language models in PyTorch with practical code examples and performance analysis. The post Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking appeared first on MarkTech