（翻译）大脑压缩嘈杂世界的新框架
An updated view of categorization reflects the modern understanding that the nervous system is more of a prediction engine than a filing cabinet.
作者丨 张璐 编辑丨 岑峰 幸丽娟 &n
作者丨 幸丽娟 编辑丨岑 峰  
作者丨 幸丽娟 编辑丨岑 峰  
（翻译）WeatherNext：AI模型在预测气旋方面取得突破
（翻译）在 SageMaker HyperPod 上推出新的 Ray 功能
Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMaker Studio, all with o
（翻译）AI 驱动的元数据修正与协调
Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus gover
（翻译）GPU加速的大规模金融工具聚类
Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters…
（翻译）生成式推荐器如何重新定义大规模推荐系统
Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and…
（翻译）我们通过复现 2,200 篇 ICML 论文学到了什么
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
（翻译）OlmoEarth 嵌入介绍：从 OlmoEarth Studio 导出自定义嵌入用于下游分析
A Blog post by Ai2 on Hugging Face
（翻译）使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码ML工作流——第3部分：使用Amazon Quick Sight可视化洞察
In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick Sight, build interactive dashboards, use generative BI to answer questions in natural language, and publish AI-generated executive summaries for stake
（翻译）量化感知修复：性能超越全精度原版的压缩 4 比特模型
A Blog post by Multiverse Computing on Hugging Face
（翻译）巡天检测通道覆盖天文学基础模型中的像素，并使层析平均红移产生偏置
arXiv:2608.23626v1 Announce Type: new Abstract: Foundation models for astronomy are trained on survey pixels together with the catalogue products derived from those pixels. Those catalogues are incomplete at a measurable rate, and a model trained on both inherits that incompleteness as a systematic.
（翻译）LLM 能理解限价订单簿动态吗？
Abstract page for arXiv paper 2608.23706: Do LLMs Understand Limit Order Book Dynamics?
