Spyke

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science

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Death rates among 25–49-year-olds 42% higher in the North than the South of England

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They also mention “people under 75”, so I assume they’re just using quarter-century ranges: 0–24; 25–49; 50–74; and 75–99.

Actually, here's a figure from the original study that uses 5-year bands, and you can see the anomaly covers those specific bands (with the under-25 bands looking more like random noise, which I assume is a statistical effect of the low number of total deaths in that range).

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Why did no one ever think about automating most of the diagnosis in healthcare, instead of being dependant on a doctor?

This idea goes all the way back to ancient Greece, where the Methodic school of medicine argued that illnesses could be treated by selecting treatments automatically based on a checklist of symptoms:

The Methodic school takes it to be that once a doctor has recognized the disease a patient has for what it is, the treatment that should follow is inherently obvious. It is not a matter of inference or observation, but of an immediate knowledge.

But the mainstream consensus has always been that each disease can manifest differently in each individual, and physicians need to rely on intuition derived from experience with previous diseases to diagnose each new case. And removing the physician from the loop—even in cases where final prescription would be the same—reduces the physician's ability to make observations of the patient and the disease that could help inform them if there are future developments.

science

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Computers Made of Human Brain Tissue Identify Braille Letters

Liu emphasized that this was an open-loop experiment, meaning that the organoids only received initial inputs without getting new feedback about their performance on the tasks. For this reason, the study does not prove that the organoids actually learned Braille, or were altered in any permanent way, just that they could classify Braille letters after receiving the electrical inputs.

So all they established is that the organoids respond in distinct ways to distinct signals out of the box?

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“We were looking at it wrong”: The explosive claim that’s upended the AI world

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It does diddly squat in addressing the ‘confidently incorrect’ hallucination / Bullshit-generator problem fundamental to LLMs

Eh, I can see some potential relevance. If an AI starts by dividing objects into “pointy things” and “round things” where a human would divide them into “animals” and “artifacts”, the AI would be more likely to mistake a cat for a car where a human would mistake a cat for a dog. Neither mistake is objectively worse than the other; but to a human, the former is a bizarre hallucination while the latter is an excusable misperception.