一位Mastodon用户批评了人工智能模型使用的数据收集方法，认为逐个提交地抓取存储库和解析HTML是一种效率低下的方法。该用户建议，更有效的策略将涉及直接克隆存储库并分析提交历史以进行训练。 AI
排序理由 该条目是一篇批评人工智能数据收集方法的社交媒体帖子，缺乏新闻价值或明确的事件。
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人工智能数据收集方法因效率低下而受到批评
本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Meme
- 该条目是一篇批评人工智能数据收集方法的社交媒体帖子，缺乏新闻价值或明确的事件。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- other
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- Standard
- On-topic for AI-industry coverage; kept in the public index.
- Story freshness
- Breaking (< 6h)
- Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.
完整方法见我们的编辑标准。
报道来源 [1]
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你可能会认为，所谓的“人工智能”应该以最高效的方式使用我们的数据进行训练，对吧？Clo
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