About half the inhabitants of Gaza are under 18 years old, so 1/3 of the dead being children corresponds to a ratio of two civilians killed for every combatant. This is not out of the ordinary for urban warfare conducted in a manner intended to reduce civilian casualties.

I don’t understand why browsers support this “functionality”.

mostly useful for suppressive fire

I think the concern is about a shooter firing into a dense crowd (like the Las Vegas attack) which is generally an application that would not come up during military use.

My worst review said that my paper was technically sound but my entire specialty was a “cottage industry” generating computational models with no real-world relevance and therefore the paper should be rejected. The editor offered the opportunity to rebut but what could I say to something like that?

(The reviewer still lives, as far as I know.)

On the plus side, this meant that I was rejected by PNAS but then published in BJ.

But who can stop an oven with a gun?

Why do people do that? I mean, if they intend to abandon the dog poop, why would they bag it first?

He doesn’t look particularly concerned.

There’s already a genetic mutation that does that.

Myostatin-related muscle hypertrophy is not known to cause any medical problems, and affected individuals are intellectually normal.

And it makes you look like this:

That’s a house-cat, and it looks like that without having to lift weights. Some people have this mutation too, and it’s particularly dramatic in children who would otherwise never be that muscular. (I’d post pictures but I’m not sure about the ethics of sharing photos of other people’s swole toddlers even when they’re already available online.)

And Picard is nine feet tall. I guess that’s why he’s in charge.

Species with internal fertilization tend to have females care for the young because the male is the one who can leave first. He can maximize his reproductive success by searching for other females while the female is left with the choice between putting a lot more time and energy into caring for the offspring or leaving them to die.

Species with external fertilization usually have the opposite dynamic. The female lays unfertilized eggs and then leaves. The male is the one stuck caring for them because they would die otherwise. However, the males of some species have evolved to be too clever for this:

Among the maternal mouthbrooding cichlids, it is quite common … for the male to fertilise the eggs only once they are in the female’s mouth.

Removed by mod

I understand that that’s the intent. The problem is the methodology, which is as I said just multiplication by five. Calling it a gold standard implies that there’s actually some sophisticated analysis going on, and there isn’t.

The “gold standard in the field” is apparently to multiply the Hamas numbers by five. I’m not kidding. That’s where the 186,000 number comes from. This is low-effort bullshit.

Edit: Also this article is just wrong about what the 335,500 number is claimed to be. It is what you get if you extrapolate the 186,000 number to the end of the year, not to September.

The Egyptians weren’t always shy, but there do appear to be traces of green paint.

In case anyone is curious: the ancestors of house spiders lived in caves. They’re adapted to live in very dry environments, which is why they can survive indoors while most other spiders can’t.

My naming convention for C++ is that custom types are capitalized and instances aren’t. So I might write User user;.

So far “more data” has been the solution to most problems, but I don’t think we’re close to the limit of how much useful information can be learned from the data even if we’re close to the limit of how much data is available. Look at the AIs that can’t draw hands. There are already many pictures of hands from every angle in their training data. Maybe just having ten times as many pictures of hands would solve the problem, but I’m confident that if that was not possible then doing more with the existing pictures would also work.* Algorithm design just needs some time to catch up.

*I know that the data that is running out is text data. This is just an analogy.

What occasions are you referring to? I know people claim that Israeli use of white phosphorous munitions is illegal, but the law is actually quite specific about what an incendiary weapon is. Incendiary effects caused by weapons that were not designed with the specific purpose of causing incendiary effects are not prohibited. (As far as I can tell, even the deliberate use of such weapons in order to cause incendiary effects is allowed.) This is extremely permissive, because no reasonable country would actually agree not to use a weapon that it considered effective. Something like the firebombing of Dresden is banned, but little else.

Incendiary weapons do not include:

(i) Munitions which may have incidental incendiary effects, such as illuminants, tracers, smoke or signalling systems;

(ii) Munitions designed to combine penetration, blast or fragmentation effects with an additional incendiary effect, such as armour-piercing projectiles, fragmentation shells, explosive bombs and similar combined-effects munitions in which the incendiary effect is not specifically designed to cause burn injury to persons, but to be used against military objectives, such as armoured vehicles, aircraft and installations or facilities.

The issue I have with referring to the current situation as a bubble is that this isn’t just hype. The technology really is amazing, and far better than what people had been expecting. I do think that most current attempts to commercialize it are premature, but there’s such a big first-mover advantage that it makes sense to keep losing money on attempts that are too early in order to succeed as soon as it is possible to do so.

Multiple studies are showing that training on data contaminated with LLM output makes LLMs worse, but there’s no inherent reason why LLMs must be trained on this data. As you say, people are aware of it and they’re going to be avoiding it. At the very least, they will compare the newly trained LLM to their best existing one and if the new one is worse, they won’t switch over. The era of being able to download the entire internet (so to speak) is over but this means that AI will be getting better more slowly, not that it will be getting worse.