最近通过社交媒体的卷轴让我想到了信任。 通常会让那些不知不觉地传播讯息的红宝石感到尴尬，有时也会让那些有意分享讯息的海报感到愉快，因为他们觉得这很有趣
记得GPT-3的好时光吗？当我们可以问到聊天机什么，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 " hallucination " 一词进入词汇表。 尽管Claude或Gemini经常为我做有益的工作，这是当前AI：信任但核实的问题。 并证明你必须。
应用这个标准来维持科学记录的完整性你了解同行评审奖学金的出版商所面临的挑战。 在“我用AI来伪造一份科学论文”中，应用数学的自由撰稿人兼博士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制作的论文来击退AI崩溃的上升趋势的故事。
" 他基本上打了`运行的实验 ' 和`写纸'。 而他却什么都没做。 - 伊丽莎·斯特里克兰
Eliza Strickland告诉我，Skuse很惊讶的是，她的称说，“然后，他只用发布进入了出版引擎的maw".
Skuse发现，IEEEE的人体审查过程与研究-完整性检查程序相结合，目前保持了速度，相当于对使用自由工具的人进行猫捉鼠游戏。 但是，更先进的LLMs不再像自由版那样犯同样愚蠢的错误。 这使得出版商陷入了一场军备竞赛之中，这种军备竞赛越来越令人信服，与旨在侦测这种军备竞赛的系统相互竞争。
学者的出版商并非唯一试图持之以恒的反对者。 全球人类创造的知识库维基百科也是如此对于本月的"五个问题"，斯特里克兰采访了维基项目AI Cleanup的创始成员埃莉娅·勒布卢(Ellia Lebleu)，该志愿者努力从平台上检测和删除AI生成的材料。 Lebleu的团队正面临与商业出版商相同的问题
称 Strickland。 “维基百科被称为人类生成的信息库，“谁知道他们能做多久呢？”
如果我们不能共同假定科学记录可信，或世界的基本事实准确，也许这能让量子科学家和在信息环境中兴旺的骗子高兴，因为信息环境没有共同的现实。 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，促进科学和技术的进步，造福人类。
或许在AI做科学、写论文的世界里，史崔克兰(Strickland)指出，“我们有时会为其他AI写AI，我们只会被抛在一边。
- 维基百科编辑如何保持在线百科全书Human-Written QQ
- AI Boosts研究生涯，但Flattens科学发现
- 我用AI来伪造一份科学论文情况如下：
本文由 AI图鉴自动抓取并翻译，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.”
- How Wikipedia’s Editors Keep the Online Encyclopedia Human-Written ›
- AI Boosts Research Careers but Flattens Scientific Discovery ›
- I Used AI to Fake a Scientific Paper. Here’s What Happened ›
Auto-fetched and translated by AI Atlas. All rights belong to the original author.