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kuhewa

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This made me wonder - do any cloud compute systems have an option to time jobs or use physical resources geographically based on surplus power availability to minimise emissions?

I reckon it might incidentally happen if optimising for cost of power depending how correlated that is to carbon intensivity of power generation, which admittedly I haven't thought through.

Modern sailing vessels always sail into the wind, because they're always going faster than the wind blows.

Maybe state of the art hydrofoiling boats, cats and some quite large monohulls, but maybe that's what you meant by modern. Most sailboats built today have a pretty low top speed (due to hull speed limitations) relative to wind speed - monohulls often max out around 5-6 kts aren't going to be faster than the wind pretty much ever

You don't tend to hear about it and not that there isn't still progress to be made, but there has been tonnes of progress on fisheries interactions with protected bycatch species. For ex the infamous dolphin problem in the eastern tropical Pacific purse seine tuna fishery is down 99.8% from its peak to the point populations are recovering, despite the fishery intentionally setting on dolphin schools to catch > 150,000 t of yellowfin tuna per year.

Pelagic gillnets are probably the gear that still have the most issues with dolphin bycatch, and acoustic pingers that play a loud ultrasonic tone when they detect an echolocation click are already used to reduce interactions in some fisheries.

We had one on a yacht I crewed that did ecotour sorta sails. When we'd catch a small tuna, after the trips I'd butterfly it and George Foreman it. No added oil just right on the Teflon cooking surface and texture and taste would come out sorta like fried chicken. It was great

I'm not sure it's that unique of a situation though, most prey organisms have some antipredator adaptations but rarely are they foolproof, just like rarely are predatory adaptations like mimicry/camouflage/crypsis absolutely foolproof.

Oh yeah, 'charging up' your wetsuit is great. The replacement is fairly limited with a good suit especially if you have booties and a hood, but who cares, people pay a lot of money for skin care products with urea in them.

I bet all of these researchers involved had a long list of candidates they'd like to test and have a very good idea what the lowest hanging fruit are, sometimes for more interesting reasons than 'it was used successfully as an inhibitor for X and hasn't been tried yet in this context' — not that that isn't a perfectly good reason. I don't think ideas are the limiting factor. The reason attention was paid to this particular candidate is because google put money down.

I'm sure the scientists involved had a wish list of dozens of drug candidates to repurpose to test based on various hypotheses. Ideas are cheap, time is not.

In this case they actually tested a drug probably because Google is paying for them to test whatever the AI came up with.

That's incredible. Looks like they leased a building to the hostelers and it was shut down because it violated sanctions, but amazing that the $42,000 a month lease is worth it to dabble in for NK. Then again they were subletting parking spots and rooms previously, perhaps if the order is to milk any property or opportunity overseas it adds up.

'could' is doing a whole lot of work in that sentence, I'm being charitable. Reality is LLMs are being crammed in places where it isn't very sensible under thin justifications, just like the last few big ideas were (c.f. blockchain)

require a restart or get stuck on some uninterruptible process and requires forcing killing it at least once a day.

Yes, I think I've been trained by crashes to subconsciously limit interactions with the RStudio GUI while something is running, e.g resizing a window seems to be surefire way to cause a crash.

by and large, the processes people are scrambling to place LLMs in are ones that typical machines struggle or fail

I'm pretty sure they are scrambling to put them absolutely anywhere it might save or make a buck (or convince an investor that it could)

Writing is complex, LLMs once had subhuman performance,

And now they can easily replace mediocre human performance, and since they are tuned to provide answers that appeal to humans that is especially true for these subjective value use cases. Chip design doesn't seem very similar. Seems like a case where specifically trained tools would be of assistance. For some things, as much as generalist LLMs have surprised at skill in specific tasks, it is very hard to see how training on a broad corpus of text could outperform specific tools — for first paragraph do you really think it is not dubious to think a model trained on text would outperform Stockfish at chess?

One camp is a high specificity camp, the other is a high sensitivity camp. You definitely know what camp you are in if you are only making the argument you can detect true cases of LLM use without many false positives- I've already admitted less blatant LLM use goes undetected so I am not arguing for high sensitivity.

And we have plenty of evidence of hallmarks of LLM use, we can even replicate the LLM resume generation process if we wanted. There is plenty of useful "training data" available even if you don't have a validated set of resumes submitted for this type of role at this type of company from this demographic of applicants.

Basically what you are trying to argue is that you can't have confidence that the animals you see people walking down the street on leashes are dogs unless you ask the owners whether they are dogs are not... AND that it doesn't matter that dogs are highly distinct from other domestic pets AND that we've seen many verified dogs before in other contexts, AND have even bred different varieties of dogs on our own.

I highly doubt that you maintain that standard for inductive inference across the board in your own practice. Life would be very difficult if you refused to make inferences about novel things (with any confidence) based on generalised patterns derived from other, similar cases.

The makeup is a great analogu. I can bet with 99.9% that when I say a woman is wearing makeup that I'm correct (or it is tattoed on or similar). When it's obvious, it's obvious. However I don't detect makeup on women quite often.

The economists is not not as good of an analogy, almost converse of the makeup example as that is a high rate of false positives.

Let's make one of your breakfast foods eggs. Sometimes you won't notice from the walk if they just had normal eggs. However, when they are also rotten and were over indulged in, you can tell from the person's walk that they ate them for breakfast and due to the noxious sulfur smell emanating from the diarrhoea in the person's business casual slacks, you can tell with a high degree of confidence it wasn't the rotten milk cornflakes, but one of a very few number of sulfur-rich foods, probably eggs.

Bad human resume text and overuse of unmodified LLM output are both detectable, but they are detectable because they are bad in quite different ways.

Regarding the original resume reader's notion that they can detect LLM text with a high degree of accuracy, it is not their LLM output detection specificity I would take issue with (similarly, despite stating validation is critical, I would bet you, too, are pretty confident when you see an entire page of blogspam or marketih copy that you regard as LLM generated despite it rarely being marked as such). Rather, it is their sensitivity, as I am sure occasional use and especially slightly modified output from LLMs gets by them now and again without them knowing.

Probably not entirely fair. e.g. After enough sentences it is trivially easy to identify LLM output. So you repeatedly get the opportunity to test a sentence or two, guess the provenance and then realise it is the first sentence in several paragraphs of generated output.

"cyclometric"

it's cyclotopic, a term they coined. I suggest the intro section juxtaposing trigonometry vs 'circular' approaches might best be read as guidance as to how interested high school students (their past selves?) might think about the topic rather than a necessary preface for their paper.

arguments being made here

Being made where though? I google '65% of women children gaza' and among the front page of results, all but one reporting that figure do not indicate that women and children were selected for. Civilian infrastructure yes (e.g., "Israeli military has relentlessly targeted infrastructure indispensable to civilian survival." which is true, considering 14 hospitals were hit directly). The exception is the State of Palestine ("The Israeli aggression continues to target civilians in Gaza Strip" [1]), and I don't believe the wording is even incorrect — when you bomb a hospital knowing there are civilians inside (whether or not there were militants), you have targeted civilians.

Certainly, the bare statement that 65% of the victims of the war are women and children is intended to make people think that.

Consider that is a subjective interpretation, others might find it indicates a strikingly indiscriminatory approach such that targeting is moot. That was my impression reading that figure. Let that be an answer to your initial question in the parent comment

I suspect that this is about to begin going in circles since no new arguments or evidence are being presented for your claims. So to conclude: I will reiterate that this is a matter that can be informed by empirical data, and the only data that has been provided in this thread with which we can interrogate the norms and outcomes of warfare (numbers from the example you invoked of Ukraine and other recent bloody and heavily urbanised conflicts in the Oxfam link) weighs heavily against assuming that mortalities should reflect the civilian population's demographics in a military action It strikes me as an undercooked, and insofar as it reflects reality, an appalling assumption.

Even assuming every adult male is a militant, they are killing two women/children per 1 militant. Killing that indiscriminately and ineffectively is, indeed, alarming: it does not matter if the goal of the dropped bombs is to preferentially kill women and children.

[1] https://www.pcbs.gov.ps/post.aspx?lang=en&ItemID=4614#_edn1

Urban conflict in general produces civilian casualties with women, men, and children dying in proportions matching the demographics.

You are begging the question still. Citation needed.

In the Russia-vs-Ukraine war there are relatively few dead children because the demographics of both counties skews towards adults — not because of the military doctrine of either side. Children make up less than 20% of the population and hence less than 20% of the civilian deaths.

That dog won't hunt. ~58k Ukranian soldiers killed + 12k civilians = 70k total [1]. 633 Ukrainian children killed [2]. 20% of the population is children but they make up < 1% or those directly killed in the conflict. You will probably take issue with the degree of urbanisation etc., but it was your example.

Fifty percent of civilian deaths are children not because Israel is targeting children (as if they were cartoon villains!)

I suggest you are strawmanning the argument here — I don't think Israel is actively targeting children is an accurate representation of the concerned 'side' overall (I'm sure you can find a tweet making it). But it is plain their actions are pretty indiscriminate wrt the combatant:civilian kill ratio.

they just a normal amount of evil.

You haven't supported this claim, and its a considerable leap from 'Gaza has a pyramid-shaped age structure'. Yet, there is data available on recent urbanised conflicts and what the 'normal amount' is.

[1] https://en.wikipedia.org/wiki/Casualties_of_the_Russo-Ukrain...

[2] https://www.savethechildren.org.au/media/media-releases/chil...