Feedback is 麻豆传媒鈥檚 popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com
Moon wanderings
Readers may have heard that the Artemis II crew successfully travelled around the moon and back to Earth this month. A lot has been said about the mission, some of it delightful and some of it baffling.
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A key aspect of Artemis II is that, at the most distant point of their journey, the astronauts were 406,771 kilometres from Earth, further than anyone has ever gone before. Reader Helen von den Steinen wrote in to tell us about an 鈥渁bsurd unit of measurement鈥� used by The New York Times to convey the scale of that gap. This unit was, of course, wiener dogs.
鈥淚f you took 22-inch dachshunds and laid them nose to tail, you鈥檇 need a very cooperative pack of almost 728 million dogs to cover the distance,鈥� we were informed. In case anyone hoped to check that, they also offered the important caveat that there are 鈥渙nly around 900 million dogs, of any breed, in existence鈥�.
Not content with that, the paper switched to dog walks. 鈥淚f you took one of the dachshunds on a brisk 3-mile-per-hour walk, you鈥檇 need to walk for more than 84,000 hours to get there鈥�, they write, which 鈥渢ranslates to nearly 10 years of continuous walking鈥�. Finally, they imagined constructing 鈥渁 chain of 2.37 billion Nathan鈥檚 Famous hot dogs to cover the distance鈥�. A competitive eater who can devour 76 hot dogs every 10 minutes would need to eat nonstop for almost 594 years to consume the entire chain, they write, eating over 700 billion calories in the process.
Helen admired 鈥渢he way they effortlessly transition between live dogs and hot dogs as if they are some comparable measurement鈥�. Feedback shares this admiration, and wonders how much dachshunds vary in length and if this was accounted for. Further to this, perhaps it might be helpful, when trying to convey immense distance, to start with something long like San Francisco鈥檚 Golden Gate Bridge and do multiples of that, but let鈥檚 not get too sensible about this.
We also note, without comment, the inevitable online remarks about the trip being faked, for example from writer James Delingpole, who wrote on X that the crew had been 鈥渟equestered in some ritzy hotel鈥� for the duration of the mission.
Moving swiftly on, we should like to finish by turning to the work of reader Richard Simmons. He was following up on a bit in these pages about the moon possibly being made of cheddar, in the sense of money, because of the allegedly growing lunar economy (11 April). Richard wondered exactly what kind of cheese the moon might be made of. After dismissing green cheese and other options, he settled on . This is a French variety, described as 鈥渁 close-textured disc coated in a dusting of charcoal ash鈥�. Based on the photos from Artemis II, Richard says, 鈥渋t has the correct colour and surface texture鈥�.
Shedloads of marathons
During a previous discussion about the exact size of a 鈥渟hedload鈥�, reader F. Ian Lamb introduced the concept of an 鈥渆ndogenous relative scaling unit鈥�, or ERS unit (28 March). This refers to a unit that isn鈥檛 absolute, but rather varies in size depending on context or even individual perception. Feedback wondered at the time if shedload was the only example of an ERS unit or if there might be more out there.
Reader Andrew Winkley suggests 鈥渕arathon鈥�. Clearly, in the context of long-distance running races, it has an unambiguous meaning, set in the 1920s: 42.195 kilometres, or 26.22 miles. But, as Andrew points out, it is also used to measure time, and here things get fuzzy. Consider a 鈥�24-hour dance marathon鈥�, 鈥渁 marathon study session down the library鈥� and 鈥渁 marathon booze-up鈥�. As Andrew says, 鈥渨hat constitutes a marathon in this context would depend on the activity鈥�. And, Feedback might add, the person鈥檚 tolerance for the activity in question.
Declassified
At this point, the limitations of AI are well-established, so savvy users are carefully choosing applications where its problems can be controlled or don鈥檛 matter.
Just kidding, someone wants to use it to classify government documents. Reporter Matthew Sparkes ran across a on arXiv called 鈥淩etrieval augmented classification for confidential documents鈥�. The authors note that classifying documents is a lot of work: 鈥渞equiring users to manually label each document鈥檚 confidentiality level is labor-intensive, disrupts work continuity, and often results in inconsistent or subjective labeling鈥�. Hence their proposal to use a large language model instead.
They tested their model on transcripts of US diplomatic cables published by WikiLeaks some years ago. Their best model was able to classify them as 鈥渦nclassified鈥�, 鈥渃onfidential鈥� and 鈥渟ecret鈥� with 96 per cent accuracy.
Matt identifies the immediate issue: if the tool is 96 per cent accurate, then, presumably, 鈥�4 per cent of top-secret info will be leaked鈥�. Feedback stared at this for a while and had some further thoughts. First, the researchers don鈥檛 compare the AI to expert humans, so we don鈥檛 know if it does a better or worse job.
Second, we found ourselves wondering: in which direction does the AI err? When you are classifying government documents, it may be best to err on the side of caution, to avoid, say, revealing the launch codes for all your country鈥檚 nuclear missiles. We couldn鈥檛 find any information about such asymmetric errors in the study.
Still, what could go wrong?
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