Google DeepMind Blog发布于 08/21 19:59
From Atari to EVE Online: Building on 15 Years of AI Research in Games
（翻译）从 Atari 到 EVE Online：以 15 年游戏 AI 研究为基石
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
Google DeepMind Blog发布于 08/06 23:06
WeatherNext: AI model achieves breakthrough in forecasting cyclones
（翻译）WeatherNext：AI模型在预测气旋方面取得突破
AWS Machine Learning Blog发布于 08/25 03:32
Introducing new Ray capabilities on SageMaker HyperPod
（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
AWS Machine Learning Blog发布于 08/24 23:53
AI-powered metadata correction and harmonization
（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
NVIDIA Technical Blog发布于 08/24 23:00
How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin
（翻译）NVIDIA Groq 3 LPX 如何在 NVIDIA Vera Rubin 上实现长上下文的超快交互
NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72…
AWS Machine Learning Blog发布于 08/22 00:59
Reduce RAG costs on Amazon Bedrock with query-aware compression
（翻译）通过查询感知压缩降低 Amazon Bedrock 上的 RAG 成本
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers
Hugging Face Blog发布于 08/14 01:16
Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets
（翻译）在一个地方记录、训练和部署：Strands Agents、LeRobot 与 Hugging Face Storage Buckets
A Blog post by Amazon on Hugging Face
Hugging Face Blog发布于 08/13 08:00
What We Learned by Reproducing 2,200 papers from ICML
（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.
AWS Machine Learning Blog发布于 08/21 05:23
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas
（翻译）使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码机器学习工作流——第2部分：使用Amazon SageMaker Canvas进行数据准备和模型构建
In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual transformations, and train an XGBoost fraud detection model. All without writing machine learning code, laying the groundwork for interactive dashboards
NVIDIA Technical Blog发布于 08/19 00:48
Run Massive-Scale UMAP in Minutes Using Multiple GPUs—Without Losing Accuracy
（翻译）使用多个 GPU 在几分钟内运行大规模 UMAP——且不损失精度
Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction.
arXiv cs.AI发布于 08/28 12:00
The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
（翻译）准确性与效率悖论：量化端侧能源预测中的净能量损失
Abstract page for arXiv paper 2608.26134: The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
AWS Machine Learning Blog发布于 08/29 00:22
How Decathlon runs demand forecasting at scale with Chronos-2
（翻译）迪卡侬如何利用 Chronos-2 进行大规模需求预测
Decathlon, one of the world's largest sporting goods retailers, forecasts weekly demand for tens of thousands of products across multiple continents. Learn how they deployed Chronos-2 on AWS to improve forecast accuracy by 11-15 points while cutting operational complexity and running weekly inferenc
arXiv cs.AI发布于 08/31 12:00
LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation
（翻译）LLM增强的因果发现：边存在性与方向性的概率融合
Abstract page for arXiv paper 2608.27472: LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation
arXiv cs.AI发布于 08/31 12:00
Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations
（翻译）物联网与深度学习用于茶园中Postelectrotermes militaris检测与严重程度评估的有效性
arXiv:2608.27480v1 Announce Type: new Abstract: Tea plantations are vulnerable to Postelectrotermes militaris, commonly known as the Upcountry Live Wood Termite (ULWT), which can cause substantial damage when infestations remain undetected. This study proposes an IoT-enabled acoustic monitoring fram
arXiv cs.AI发布于 08/31 12:00
A Framework for Object-Centric Predictive Monitoring of Collaborative Processes
（翻译）一种用于协作流程的以对象为中心的预测监控框架
Abstract page for arXiv paper 2608.27671: A Framework for Object-Centric Predictive Monitoring of Collaborative Processes