English(EN) While it is obviously true that not having verifiable domains makes training models in those spaces difficult... it is also true that models are also getting mu

Ethan Mollick：AI模型在不可验证领域取得进步

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-03 04:16

Ethan Mollick 在 Bluesky 上发文指出，尽管可验证域名给 AI 模型训练带来了挑战，但模型在处理不可验证领域方面的能力正在迅速提高。他观察到，AI 发展的边界是不均衡的，但其受到的可验证性问题的限制比预期的要小。 AI

影响表明 AI 模型正在克服数据可验证性相关的限制，有可能拓宽其应用范围。

排序理由一篇由知名 AI 领域人士就 AI 能力发表的观点文章。

在 Bluesky Jetstream — AI desk 阅读 →

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AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Bluesky Jetstream — AI desk TIER_1 English(EN) · emollick.bsky.social · 2026-07-03 04:16

While it is obviously true that not having verifiable domains makes training models in those spaces difficult... it is also true that models are also getting mu

While it is obviously true that not having verifiable domains makes training models in those spaces difficult... it is also true that models are also getting much better at non-verifiable domains. The frontier is jagged, but less so than I'd have expected from verifiability alone

报道来源 [1]

While it is obviously true that not having verifiable domains makes training models in those spaces difficult... it is also true that models are also getting mu

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