最近通过社交媒体的一卷卷卷子让我想到信任。 我一半的饲料都充斥着AI slop，聪明的用户会喊出是机器生成的，常常会让那些不知不觉地传播它的红宝石感到尴尬
还记得GPT-3的好时光吗？当我们可以问聊天组什么，它几乎会立刻非常自信地吐出一个可信但虚假的答案？这时，适用于大赦国际的答复的 " halluciation " 一词进入了词汇表。 尽管Claude或Gemini经常为我做有用的工作，这才是当前AI：信任但核实的问题。 并证明你必须。
应用这个格言来维持科学记录的完整性，你对同行评审奖学金的出版商所面临的挑战有一个想法。 在“我用AI来伪造一份科学论文”中，应用数学的自由撰稿人和博士Benjamin Skuse讲述了人类守门人试图通过试图通过IEEE的辩护获得自己由AI产生的论文来击退AI slop的上升趋势的故事。
" 他基本上击中了`运行中的实验 ' 和`写纸'。 他却什么都没做。 -伊丽莎·斯德克兰
正如一些特征和AI击败编辑Eliza Strickland告诉我的，Skuse惊讶地发现，她的称表示，“然后，他只把发布打入了出版引擎的maw".
Skuse发现，IEEEE的人文审查过程和研究-集成性检查程序相结合，目前正在跟上速度，这相当于对使用自由工具的人进行猫摸游戏。 但是，更先进的LLMs不再像自由版那样犯同样愚蠢的错误。 这就使得出版商陷入了一场军备竞赛之中，这种竞争越来越令人信服，而那些制度旨在探测这种竞争。
学者的出版商并非唯一试图持之以恒，全球人类知识的保存者维基百科也是如此。 对于本月的"五个问题"，斯德克兰采访了WikiProject AI Cleanup的创始成员Ellia Lebleu，该志愿者努力从平台上检测并删除AI生成的材料。 Lebleu的团队正面临与商业出版社同样的问题
称 Strickland。 “谁知道他们能做多久呢？”
如果我们不能集体地假设科学记录是值得信赖的，或者说关于世界的基本事实是准确的，那么我们对什么才是真正的解脱而成为不确定性的理解。 也许这能让量子科学家和骗子高兴，他们在没有共同现实的信息环境中蓬勃发展。 但是，这种不确定性最终会破坏人们对现有机构的信任，这些机构将管理人类知识以及促进科学和技术的进步以造福人类。
人类是否相信产出并不重要。 史特克兰(Strickland)说，“我们有时会为其他AI写AI，我们只是被抛在一边。
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本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion.
Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must.
Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses.
“He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland
As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.”
Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it.
Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools.
“Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.”
If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity.
Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”
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