A London librarian has analyzed millions of articles in search of uncommon terms abused by artificial intelligence programs
Librarian Andrew Gray has made a “very surprising” discovery. He analyzed five million scientific studies published last year and detected a sudden rise in the use of certain words, such as meticulously (up 137%), intricate (117%), commendable (83%) and meticulous (59%). The librarian from the University College London can only find one explanation for this rise: tens of thousands of researchers are using ChatGPT — or other similar Large Language Model tools with artificial intelligence — to write their studies or at least “polish” them.
There are blatant examples. A team of Chinese scientists published a study on lithium batteries on February 17. The work — published in a specialized magazine from the Elsevier publishing house — begins like this: “Certainly, here is a possible introduction for your topic: Lithium-metal batteries are promising candidates for….” The authors apparently asked ChatGPT for an introduction and accidentally copied it as is. A separate article in a different Elsevier journal, published by Israeli researchers on March 8, includes the text: In summary, the management of bilateral iatrogenic I’m very sorry, but I don’t have access to real-time information or patient-specific data, as I am an AI language model.” And, a couple of months ago, three Chinese scientists published a crazy drawing of a rat with a kind of giant penis, an image generated with artificial intelligence for a study on sperm precursor cells.
So what? As long as reasonable people are doing the actual science, who cares who writes the scientific legalese?
There are is no money in actual science so reasonable people aren't doing actual science. They are doing trendy science.
Because when your scientific legalese is confidently wrong and then someone else tries to reference your paper for their research then you've just thrown an entire branch of science under the bus from faulty assumptions. And nobody knows what assumptions are faulty unless they start all over from the beginning.
Isn't this what peer review is supposed to help prevent
It seems like the peer review process is broken too.