Quanta Magazine发布于 08/20 22:04

Are We Thinking Correctly About AI Intelligence?

(翻译)我们对 AI 智能的思考正确吗?

Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.

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Microsoft AI Blog发布于 06/09 23:05

Why don’t cancer medicines work the same for everyone?

(翻译)为什么癌症药物对每个人的效果不同?

Why do cancer drugs work for some people but not others? New AI research looks at how tumor cells behave to better match treatments to patients.

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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

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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.

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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 1: Setting up your Snowflake environment

(翻译)使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码机器学习工作流——第1部分:设置Snowflake环境

Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for

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arXiv cs.AI发布于 08/26 12:00

A survey detection channel overrides the pixels in an astronomical foundation model, and biases tomographic mean redshifts

(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.

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arXiv cs.AI发布于 08/26 12:00

Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes

(翻译)审计合成回忆录:对照其所描述生活的文档记录度量LLM生成自传中的场景级虚构

Abstract page for arXiv paper 2608.23640: Auditing the Synthetic Memoir: Measuring Scene-Level Confabulation in LLM-Generated Autobiography Against the Documented Record of the Life It Describes

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arXiv cs.AI发布于 08/26 12:00

A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification

(翻译)审计可解释人工智能鲁棒性与保真度的形式化方法论框架:从应用到信任认证

Abstract page for arXiv paper 2608.23817: A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification

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