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.”

I’m an Australian father of four, living in the US. Something of a corner case. Nevertheless, getting my kids Australian citizenship by descent, and then Australian passports so we can fly home legally, has kicked off a carnival of errors with a very dark side. Australia is generally thought of as a developed western liberal country with relatively high state capacity. And I have generally found that at least state government services, now administered through centralized offices, have become more efficient over time. Passport applications for children living overseas, however, remain a time machine with a difficult security overhang. Australian …

Researchers created a dataset of photos of thousands of paper-thin slices of a male and female cadaver to create one of the most detailed datasets about the human body.

In a remote corner of the driest state in the country, Anne Heggli's team has been keeping watch over some of Earth’s oldest beings. Since 02012, Heggli’s NevCAN network has recorded conditions at the Nevada Bristlecone Preserve every eight minutes. The result is more than 120 million data points and a high-resolution portrait of how a bristlecone pine thrives. Paleoclimatologist Adam Csank showed how bristlecone microcores reveal the droughts and floods these trees survived long before us, a history that fine-tunes models of what's ahead. Artist and philosopher Jonathon Keats went further. “The most accurate clock,” he said, “is a tree.” His monumental artwork Centuries of the Bristlecone keeps bristlecone time, a clock calibrated to the trees' own growth over millennia. Together, Heggli, Keats and Csank made the case for long science: patient, continuous observation that will help us answer future urgent questions. “Data at this resolution,” Csank said, “is like a Rosetta Stone” for understanding nature’s resilience. At fourteen years young, the bristlecone record is just beginning, Heggli said, but it already holds the key to questions like these: How will bristlecones adapt? What happens when the droughts and floods they've weathered for millennia arrive faster and more frequently? “We can't answer the questions if we're not watching,” said Csank. “We are the stewards of this data,” Heggli concluded. “This is why we need long science.”

Prende y se apaga sola, sale después de hora. CHARLY GARCÍA La máquina de ser feliz I’ve learned to tolerate them, and sometimes even like them, but Zoom meetings are weird. Even in familiar contexts: I have weekly check-ins with … Continue reading →