Jails are the inverse of this scenario. But still, plenty of safeguards in the procedures and technical aspects of incarceration exist precisely because that happened occasionally with human prisoners and guards, too.
Imagine we have all the worst people in history in a jail. Machiavellian murders that desire to kill as many as they can. Not only will they kill, they will manipulate as many other people as they can into killing also.
How many of these people can you afford to let out?
Under your premise it seems to be all of them. Under my premise even letting a single one out is a tragedy.
GP seems to imply that should our view be true, then human jails should be empty by definition, because all prisoners are intelligent beings and would've eventually talked their way of it.
But this doesn't account for the fact that modern incarceration has built-in safeguards and mitigations based on centuries of cases of people talking, bribing or forcing their way out of prison, as well as getting outside assistance in forms ranging from lawyers to raiding parties equipped for demolition works. There are now procedural and technological means to prevent such incidents for happening, applied proportionally to the degree of risk.
Meanwhile, with AI, we're still at the point where everyone is assuming they can just lock the agent in a sandbox and prompt nicely to not poke at it too hard, and things will be fine. There's no multi-layered structural and procedural safeguards, and there's no recognition for the fact that AI operates faster than humans, and that quite likely it'll be smarter at this than average "jailer".
Yes, that's what I was trying to snarkily say: we've got a way to confine intelligent agents (humans) while allowing them some interaction with the operators and a tiny bit with the outside world. It mostly works too -- real-life jailbreaks are rare enough to be big news.
You're right that a (as-of-yet hypothetical) AI has different abilities, but it also has different weaknesses. It's straightforward to accurately log all of its behaviors, and you can even re-run it to see what it would do in myriad situations.
It’s also the main reason those microwaves are so gross!
Since it’s relatively thick, boiling solution sputters everywhere and is—-at best—-partially cleaned up by harried scientists. Use a big container and try not to overshoot the temp.
These are bizarre assertions. GNSS is one way to navigate, and GNSS is one way to synchronize time-of-day. Communications networks are full of devices that don't have, or use, any GPS receivers at all. NTP, WiFi, DOCSIS, DSL, leased lines, servers, core routers, switches, desktops and laptops are all things that won't even notice that GPS was unavailable.
The paper was posted on the 18th of June. I would guess that means they started writing the first draft in the winter of 2026, if not earlier. 2026 data obviously wouldn't be available then, or even now---we're only in September!
In fact, most data products take a few months to compile and distribute. The US will release its Q2 estimate of GDP at the end of September, for example; Statistics Canada is on a pretty similar schedule. Thus, 2025 data might not have been available when they started writing either. It's certainly possible to estimate things on your own, but now you need to show that a) your method of estimating GDP (energy prices, whatever) is valid and b) any effects you see aren't simply due to using different metrics.
In short, it just takes a while to do and write up research.
The APIs it's relying on to show you that information do implement some anti-fingerprinting mechanisms - things like available memory will be put into rough buckets rather than returning the exact amount you have.
That panel reconstructs what a pixel would send from your own browser values, locally, and never makes the request. NextDNS seeing nothing is the tool working exactly as designed.
You build a model that describes how likely people are to get diagnosed with Alzhimer's as a function of sex: maybe 12 women out of 100 get it, while 8/100 men are diagnosed. You can do the same thing for age: almost nobody is diagnosed before 30, it's very rare before 40, and sadly common (~1 in 10) after 65 years of age. There are all sorts of mathematical tricks to include multiple variables, account for the fact that you can only be diagnosed once, or that data is "censored" (i.e., missing) at some ages because people have already died.
Based on that, you can then ask if the prevalence of Alzheimer's Disease among cab drivers is surprising, given their demographics. For example, if we know that they skew male and younger, we'd expect that number to be a bit lower than a naive estimate of 10%. In fact, we can calculate that number and then see if it's unexpected given the number we actually see. In practice, you'd actually do this by fitting two models: one containing job and one that doesn't, and see which one best describes the data and how, specifically, the job factor affects the outcome.
However, how well these adjustments work depends on the quality of your data and your modelling. Your model could be missing important factors or have the wrong structure: (e.g., you assume risk is directly proportional to age, but it actually increases more rapidly as you get older). Your data could have problems too: maybe women are more likely to go to the doctor (and thus get diagnosed), even if the actual prevalence is the same.
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