I'm sure it varies, but personally I have a very prosaic reason that I would still drive myself in most scenarios: If someone else is driving I tend to get motion sickness.
I think Google basically _is_ the standards committees, at this point. Not in the sense of having majority control just by themselves, but in the sense of (1) the cartel being argued over here (browsers funded by Google) having that or close to it, and (2) Chrome being the main source of new features getting implemented, so that the job of the standards committees is mostly to play catch-up with Chrome.
> Aren’t coding copilots based on tokenizing programming language keywords and syntax?
No, they use the same tokenization as everyone else. There was one major change from early to modern LLM tokenization, made (as far as I can tell) for efficient tokenization of code: early tokenizers always made a space its own token (unless attached to an adjacent word.) Modern tokenizers can group many spaces together.
Merging like that doesn't work -- it will tend to overestimate the number of distinct elements.
This is fairly easy to see, if you consider a stream with some N distinct elements, with the same elements in both the first and second halves of the stream. Then, supposing that p is 0.5, the first instance will result in a set with about N/2 of the elements, and the second instance will also. But they won't be the same set; on average their overlap will be about N/4. So when you combine them, you will have about 3N/4 elements in the resulting set, but with p still 0.5, so you will estimate 3N/2 instead of N for the final answer.
I have a thought about how to fix this, but the error bounds end up very large, so I don't know that it's viable.
Robbery/burglary? SWATing? The possibilities are delightful and endless. The former is a major concern for people who are known to be rich; the latter for people who are infamous online (and has the [dis]advantage that it can be carried out by anybody, anywhere in the world, typically repeatedly, and with usually zero repercussions.)
Thanks for linking the graph, that's kind of wild. I agree with you that the lowest datapoint seems crazy. I can think of a few explanations.
- Random bad luck.
- As you say, failing to control for something -- although, if you then treat the lowest datapoint as being effectively the default risk, this would suggest support for radiation hormesis (that people who got a bit more than background radiation actually did better.)
- Some kind of data collection artifact. Perhaps the people with the absolute lowest dose, in a radiation-worker dataset, are selected for being ones who are not getting an accurate measurement (i.e. sloppy about wearing dose badges or something), and those people genuinely do have worse outcomes.
I'm not a statistician, but I think there's a bit in your excerpt that is actually a concerning display of poor statistical literacy.
If you're fitting a function which grows asymptotically (i.e. is monotonically increasing at least past a certain point), the best (polynomial) fit absolutely cannot have a negative quadratic as the leading term. If your model gives one, it is 100% guaranteed to be an artifact. Treating it as "suggesting some downward curvature" is a pretty bad misunderstanding.
If you have doubts about this, consider what would happen if we added datapoints at higher doses. Every single datapoint we add to the right side of the graph will make the fit of a negative quadratic significantly worse. Ultimately, if you continue the graph indefinitely to the right, the fit of a negative quadratic is guaranteed to be infinitely bad. Any hint to the contrary is inherently an artifact of the limited dataset.
(It may well be the case that, under certain conditions with a range-restricted dataset like this, such a finding might indeed be more likely if the true function has some downward curvature. But that's not statistics, it's voodoo. All the associated statistical parameters, p-value, likelihood ratio, etc., are absolutely meaningless nonsense.)
As I understand it, Schnorr signatures are the "natural" (and simplest) construction for a digital signature in elliptic curve cryptography. The reason ECDSA exists is simply https://patents.google.com/patent/US4995082 (now expired), which made Schnorr illegal to use, so something worse had to be invented.
According to the comments, the author has only just today learned that Substack added this new popup without asking him, after he got them to disable the previous popup. So hopefully you're right that it's possible for him to turn off.
I got something like that not long ago, and it was diagnosed as acute macular neuroretinopathy. Luckily, in many cases it apparently resolves on its own, and mine did. Was the spot slightly off-center in your vision? That's where it tends to happen.
As I recall, it's fairly random and fairly rare, but apparently it's been seen a bit more during COVID.
Their docs say (somewhat recently updated, I think) that even 0 is not perfectly deterministic in all cases (though it's very close). Some people had previously observed this and speculated that it was some kind of floating point roundoff issue, when two outputs have almost identical scores.
"Tinfoil wrapped around complications" is definitely going in my quotes file. Reads like James Mickens! (If that's what you were going for, well done. If not, go read his stuff immediately.)
As far as I'm aware/recall, European privacy laws consider any connection back to a telemetry server to count as "collecting" IP addresses, since the telemetry server learns it (even if they pinky swear not to write it down.)