AI的十万个为什么
The 100k Whys of AI

原始链接: https://lcamtuf.substack.com/p/the-100000-whys-of-ai

关于AI生成的文本是否与人类写作难以区分的争论,往往基于一个前提,即大语言模型(LLM)能完美复刻人类的统计规律。然而,这忽略了这些模型“准确定性”的本质。当许多用户输入相似的提示词时,AI往往会产生功能上完全相同、重复的输出。 一个典型的例子是亚马逊上“AI垃圾内容”的激增,无数非虚构类书籍有着几乎一模一样的封面、标题和主题。这种缺乏真正创造性差异的表现——或者说是“幻觉出相同的陈词滥调”——正是AI独特的标志。尽管AI可以模仿人类的措辞,但它总是会反复回到同样的有限模式中,从而产生一种可识别的、同质化的信号。 归根结底,在一个生产内容远比消费内容容易的时代,相信自己的直觉正成为一项至关重要的技能。如果你的自动输出结果看起来就像那种批量生产、平庸乏味的《十万个为什么》,那么这种作品的人工合成性质将无法掩盖。

```Hacker News新消息 | 过往 | 评论 | 提问 | 展示 | 招聘 | 提交登录AI的十万个为什么 (lcamtuf.substack.com)14 分,由 surprisetalk 发布于 45 分钟前 | 隐藏 | 过往 | 收藏 | 1 条评论 帮助 dlenski 2 分钟前 [–] 这很好地说明了 LLM 回应的同质性。描述这种效应的另一种方式是……如果你要求人类写 1,000 本书,你是在要求 1,000 个有着不同经历、不同技能、不同情绪(等等)的个体去创作。但如果你要求 LLM 写 1,000 本书,你可能最多只是在和 3 到 5 个不同的模型对话。而且它们都是在相同或相似的数据上训练的,其回应方式也大同小异。LLM 在“人生阅历”或“技能”方面并没有太大区别,而且它们确实也不具备独立于你所给提示词之外的所谓“情绪”。回复 指南 | 常见问题 | 列表 | API | 安全 | 法律 | 申请 YC | 联系 搜索: ```
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原文

One of the most painful arguments I keep having with fellow techies is the question of whether you can distinguish between human-written and AI-generated text.

Their skepticism is rooted in reason: at their core, LLMs are state-of-the-art statistical models of how humans talk. If so, the output from the model should be almost by definition indistinguishable from human language under any statistical test.

I don’t think this is always argued in good faith; at least some of the debates are started by folks who wish to maintain deniability for their own underhanded use of the tech. But if you sincerely hold this belief, I present you the following collage:

The image shows about 150 Amazon book covers that appear if you search the site for “100000 whys” (link). Some of these books are category bestsellers in children literature. You can view a zoomable, full-resolution version here.

There’s nothing inhuman about any of these titles or covers. At the same time, I probably don’t need to convince you that you’re staring at the purest form of AI slop that now fills up many nonfiction book categories on Amazon. More specifically, what we’re seeing here is the artifact of the tools being quasi-deterministic: if a hundred “authors” give their favorite AI tool a similar prompt — say, “generate a reference book for children” — the model will produce functionally identical output perhaps 80% of the time.

The similarities in the collage go far beyond the choice of titles: for example, all the covers in the top row feature a roaring dinosaur in the top left corner of the design. There are many other clusters in the data, too. Look for a recurring red-and-white cartoon rocket, a golden retriever, a lion, and so forth.

This is precisely what makes LLM writing distinctive: it’s not that the models’ individual mannerisms are different from ours. It’s that they resort to the same, complex set of mannerisms in response to almost any normal prompt. This is a fuzzy signal, so you shouldn’t fire your intern when they say “it’s not this — it’s that”. But in more casual settings, it’s OK to trust your gut. In fact, these instincts are becoming increasingly important because traditional models of online interactions fall apart if it takes much less effort to produce content than to engage with it.

PS. If you’re using an LLM to automate blogging: yes, the tech is amazing, but chances are, your publication could be renamed to “100,000 Whys”.

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