Exactly that. Many seem to have forgotten that hospitals were absolutely slammed early in the pandemic and that medical care in general suffered.

I think the obvious counter to an influx in bug reports / pull requests is to have the first layer of review of bug reports / pull requests to be AI as well.

Final remains humans.

I dunno, if that's how people vote, that's how it should be.

Though there is a big question of how they plan to run a vote with multiple options. I hope it's a Condorcet system to avoid the risk of watering down one side with similar competing options...

"It is clear Flock has aspirations far beyond ALPRs to become a digital platform for policing," [emphasis mine]

They misspelled, "Orwellian suveillance and harrassment."

Law enforcement officers are already abusing Flock's existing capabilities for stalking. This will not improve matters one iota.

Not to mention an increase in damaging severe weather.

Even people who like it warmer probably don't like having their house blown away to Oz.

Unfortunately, it has a way of becoming your problem as they strain medical resources, continue to drive global warming and run around spreading their infectious diseases.

The key is to use the AI more sensibly. Let it find things in code. Even let it look at possible bugs in the form of where does it "describe bug". Even let it propose changes. DO NOT let it make the changes, just show them. Do not just take it's word for it, take the shown code as a starting point but re-do it for maintainability after sanity checking it.

THEN it can be a useful tool.

Oh, but surely they'll win, they indicted [strike]a ham sandwich[/strike] this man! And the Trump administration has such a spectacular record with high-profile prosecutions thusfar...

That is not what they found. What they found is that if you spend 60 minutes doing the homework with AI, you learn approximately as much as someone who spends 60 minutes doing it without AI. If you spend 20 minutes doing it with AI, you learn as much as someone who spends 20 minutes doing it without AI. Etc. The only real difference is that the AI people get way better homework scores, and as a result, tend to stop spending as much time, and thus tend to learn less.

That's what this preprint study found.

100% this. Even a short 5-minute quiz with just a couple questions at the start of each day will do the trick. But you simply can't rely on homework scores to be a proxy of learning or time spent studying anymore. Brief daily quizzes on the previous day's material will force them to.

Also, It's not like students have the excuse of, "I couldn't study, I didn't have anyone to explain X to me" anymore. The very AI that they might otherwise use to cheat on their homework can also explain to them the things they missed in last year's algebra class that are blocking them from learning this year's calculus, or whatnot. Congrats - you now have an infinitely patient personal tutor who will spend hours dumbing things down for you until you get it. Now you have no excuse - learn the material. It becomes all about actually putting the time in.

Yeah. There's been widespread misrepresentation of this (preprint!) in the media. What they actually found: test scores correspond to how much time you spend doing homework, regardless of whether you use AI or not. Which should not be a surprising result! Of course, the AI people, esp. those who had been using AI for more than a few months, stopped spending much time on their homework, and then their test scores fell to match the non-AI users who didn't spend much time in their homework either.

The TL/DR is, in today's age, you cannot use test scores as a proxy of how much time a user is studying or knows the material.

There are of course some caveats, beyond the fact that it's just a preprint - notably that there's nothing to separate why the user started using AI to do their homework, so there's potential confounders in there. For example, if a student is in a poor family, maybe they had to get a job to help pay the bills. But working a job itself can lower test scores - for example, having to work late the night before an exam. So there are some confounder problems with the study. That said, I'm sure the general conclusion is correct: time spent doing the thing = amount of learning the thing (relatively independent of what tools you're using in the process).

I feel like I'm in a constant race to flee enshittification : Used to use Cursor (it's still my only option at work), but then Musk bought it, and they instantly went in on hard-sell tactics. Switched to vscode + copilot + OpenRouter, but was getting concerned about API costs. Just yesterday I set up a Tencent TokenHub account with their Max package, which required that I switch off copilot if I wanted to be able to use some models like Kimi K3 (had a temperature=1.0 requirement, copilot doesn't let you set temperatures and hardcodes them to very low), so now it's vscode + Pi + TokenHub.

Sigh. Please don't make me move again...

(Not that Tencent is in any way "the good guys"... but they're also providing a very generic product)

It's not the SCREEN that does the damage, it's the brain rot the screen is used to access. Also the amount of time speant with the screen. Moderation is good. This shouldn't surprise people, but since it is a nuanced understanding and not just screen bad!, it will.

Ah, the good 'ol "No True Communism" argument :)

"It suggests" if you ignore that their confounder control is terrible. First, sugar wasn't the only restricted food in post-war Britain - there was also rationing of eggs, butter, cheese and meats that ended during the same timeperiod. So you're not actually testing the effects of sugar consumption, now are you? Also, the early 1950s was a period of rapid change in the US, including the maturation and expansion of the NHS (founded in 1948), the introduction of new childhood vaccines, major environmental shifts (for example, the Great Smog of London in 1952 vs. the 1956 Clean Air Act). Saying "all changes = sugar" just based on birthdate seems absurd.

It's also worth mentioning that there's also survivorship bias - anyone who died before the study couldn't be enrolled, and there's also no actual linking to any maternal / childhood dietary logs. Also, the ratios of cancer differences they're describing aren't small, they're massive. Are we to attribute a lung cancer difference on the order of smoking vs. nonsmoking households as being to sugar, or due to, you know, smoking (or pollution) trends not accounted for in their cohort?