<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>可验证偏差 on AI 实战派 · 从技术到赚钱</title><link>https://guijiagi.com/tags/%E5%8F%AF%E9%AA%8C%E8%AF%81%E5%81%8F%E5%B7%AE/</link><description>Recent content in 可验证偏差 on AI 实战派 · 从技术到赚钱</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Fri, 09 Oct 2026 10:12:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E5%8F%AF%E9%AA%8C%E8%AF%81%E5%81%8F%E5%B7%AE/index.xml" rel="self" type="application/rss+xml"/><item><title>可验证性的阴影：当一切努力涌向「机器能判对错」的领域</title><link>https://guijiagi.com/posts/2026-10-09-13-verifiability-biased-research/</link><pubDate>Fri, 09 Oct 2026 10:12:00 +0800</pubDate><guid>https://guijiagi.com/posts/2026-10-09-13-verifiability-biased-research/</guid><description>OpenAI 用 Lean 可验证证明打了样后，研究资源有向「可形式化」领域集中的倾向。本文警告：可验证性是个好用的筛子，但它会系统性地冷落那些无法被机器盖章的认知领域。</description></item></channel></rss>