Unless I'm mistaken, this uses "standard deviation" to refer to standard error throughout. They differ by a factor of sqrt(num_samples).
This is actually much more commonly useful than the t distribution, in my experience. You can squint at a histogram (or some summary stats), eyeball the stdev, approximate the stderr in your head, and get a pretty good sense of confidence.
I most often find myself doing this for the Bernoulli distribution, where it's also handy to know that the stdev is sqrt(p(1-p)), or "about 1/2 if p is middling, or sqrt(p) when it's small" (and you can flip the polarity to handle p→1).
The map contains a bunch of references to America, the West Indies, Guiana, and Mexico. (Often with a connotation of "faraway exotic place" or "exciting new international development".)
He may not have written about the British colonies but the New World was clearly at least somewhat present in his mind and his audience's minds.
The most far-flung pins on the map are further away than Shakespeare or his audience likely had in mind.
"America" looks like it's at the centroid of the modern continental USA, but Shakespeare was surely thinking of somewhere in the Caribbean. "Asia" is shown somewhere in Mongolia/Kazakhstan, but the quotes suggest Turkey or the Middle East, and Shakespeare surely would have said "Cathay" or "India" if he meant to go that far. Likewise "Russia" is shown in Siberia, but everyone in Russia lives near the European borders thousands of km west.
That said, the references to Ethiopia, India, and the Indies are very clear and can only be where they are shown on the map.
(Don't take any of this as criticism! The map is very cool, it just shows the limits of what a fully automated approach can do. A human approach would be limited by the human's biases instead.)
> Likewise "Russia" is shown in Siberia, but everyone in Russia lives near the European borders thousands of km west.
Yes, and "Love's Labour's Lost" specifically pairs/ contrasts "Russians" with "Muscovites": the "Russia" of St Petersburg is pretty far west of "Moscow."
> These reactors can be made safer, but they all still have a foundational design flaw which means the ultimate goal should be replacing rather than continually spending money reinforcing.
This was about the Fukushima reactors that were completely destroyed? In response to a discussion of Belgian reactors that are completely different?
> the underlying working principles are the same as GPT-2
I don't think anyone was claiming otherwise. Sonnet is still better at writing code than GPT-2, and worse than Opus. Workflows that work with Opus won't always work with Sonnet, just as you can't use GPT-2 in place of Sonnet to do code autocomplete.
It's really not. Leaving aside Android because that has a very different userspace, Chromebooks have excellent battery life even when not running ChromeOS, because they have proper drivers and someone actually bothered optimizing for power efficiency. And there are plenty of cases of people switching a machine from Windows to some Linux distro and significantly improving battery life (see elsewhere in this comment section). So no, if a given laptop is described as the vendor as "Linux first" and has poor battery life, that's on them.
And the eye cones not are sharp filter, they overlap ranges with mid-low sensibility. That must be nought to someone with Tetrachromacy to percibe something different on a RGB screen.
> More precisely, she had an additional cone type L′, intermediate between M and L in its responsivity, and showed 3 dimensional (M, L′, and L components) color discrimination for wavelengths 546–670 nm (to which the fourth type, S, is insensitive).
Source: Wikipedia
Is that so? Our color perception is weird. It's one dimension split in three overlapping sectors. Adding a fourth sector may add information that makes it easier to distinguish colors.
Something I think about often is an oliver sacks book about an ethnic group that has a particularly high rate of true monochromacy. And the people with no color perception at all are particularly adept at spotting certain plants based on some characteristic of their leaves that is obscured by color. So even removing information can change perception in surprising ways.
OTOH sacks seems to have fabricated a lot of shit over the years so who knows if this is even real. Another thing I think about a lot now.
We do have four sectors, 3 color perception and then the brightness perception that is used in the dark. In mid darkness you get a mix of all of those, although the fourth is not really perceived as a color so it can be a bit hard to use.
Brightness is another dimension, not a "sector" (as I dubbed it) on the color spectrum. But it would be equal for all subjects in a test, so it can't add information.
Deep neural networks can generalize well even when they're far into the overparametrized regime where classical statistical learning theory predicts overfitting. This is usually called "double descent" and there are many papers on it.
If you have read the blog post it's a difference between the chemical symbol Ge and Gr, which as I understand is what you would refer to as a "semantic error".
How would the reader know the writer intended Ge instead of Ga? More importantly: why should the burden of figuring that out fall on the reader instead of the writer? Especially when considering that every publication normally has a lot more readers than writers.
In this case, chemistry of Ga and Ge are a bit different, and the Cr compound that was misstated is part of a family of materials that rely heavily on the coordinating chemistry of Ge and its mates in the same period. So it makes more sense. If indeed it were Ga, that would be an interesting compound that probably wouldn't look anything like the material families being discussed by these authors.
I think the reader and the writer share the burden of accurate communication. The reader should ideally come prepared and the writer should provide as best they can. A prepared reader makes quick work of this typo.
Thanks for replying, I understand your original reasoning now in a way that I didn't when I last responded. I was only considering how it would appears to people who don't recognize Gr isn't an element, I agree that it's a syntactic mistake to those who know chemical symbols well.
This is actually much more commonly useful than the t distribution, in my experience. You can squint at a histogram (or some summary stats), eyeball the stdev, approximate the stderr in your head, and get a pretty good sense of confidence.
I most often find myself doing this for the Bernoulli distribution, where it's also handy to know that the stdev is sqrt(p(1-p)), or "about 1/2 if p is middling, or sqrt(p) when it's small" (and you can flip the polarity to handle p→1).