hiw3c 日报精选
IEEE Spectrum AI 网页快照

守护知识之门

原文标题 · Guarding the Gates of Knowledge

最近刷了一圈社交媒体,让我开始思考信任问题。我的信息流里大概有一半都是AI垃圾内容——精明的用户会指出这些是机器生成的,常常让那些在不知情的情况下转发了内容的愣头青尴尬不已,有时也会让特意分享这些内容的发帖人暗自得意,就因为他们觉得这些内容好笑,或者用来制造混乱挺有用。还记得GPT-3的那个美好旧时光吗?那时我们可以向聊天机器人问任何问题,它几乎瞬间就会非常笃定地吐出一条看似合理但其实是假的答案。

来源:IEEE Spectrum AI · spectrum.ieee.org

正文为英文,可一键机器翻译(仅首次需要等待)

The November issue of IEEE Spectrum is here!

Topics

Sections

More

For IEEE Members

For IEEE Members

IEEE Spectrum

Follow IEEE Spectrum

Support IEEE Spectrum

Access Thousands of Articles — Completely Free

Create an account and get exclusive content and features: Save articles, download collections, and post comments — all free! For full access and benefits, subscribe to Spectrum .

Guarding the Gates of Knowledge

AI can fake a plausible paper at the push of a button. Can detection tools and peer review keep up?

Harry Goldstein is Editor in Chief of IEEE Spectrum.

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 ›

The Vicious Spiral of AI Slop: Uncurated machine-generated content threatens research integrity and trust in science, ultimately harming all of us - PMC ›

Harry Goldstein is Editor in Chief of IEEE Spectrum .

What Would a U.S. Telecommunications Act 2.0 Look Like?

Consumer Appetite for Personal AI Goes Mainstream

When Electric Cars Were for Women

Related Stories

AI Models Are Watermarking Text—Will You Notice?

IEEE Spectrum's Top 6 AI Stories of 2025

This Compact AI Model Runs on Consumer Devices