That’s true; I am assuming that the age distribution of dead civilians matches the overall age distribution of civilians. Maybe efforts to minimize child casualties skew the actual distribution one way, or maybe children’s greater frailty skews it the other way. I don’t know but I think that my assumption is reasonable as a rough estimate.

Let me try to explain it another way.

We know that 1/3 of the dead are children, according to the headline. We also know that children make up about half the population of Gaza. We assume that none of the combatants are children.

If a person is killed, that person is either an adult combatant, an adult civilian, or a child civilian. Since child civilians make up 1/3 of the dead and there are as many adult civilians as child civilians in Gaza, adult civilians therefore make up another 1/3 of the dead. That adds up to 2/3 of the dead being civilians. 2/3 civilian dead and 1/3 combatant dead is a 2:1 ratio of civilians to combatants killed.

That’s not what I’m saying - I don’t have a term that represents “#deadKids/#allCivilians”.

If I were to use your notation, I would write:

#deadKids/#allDead = #deadCivilians/#allDead * #allKids/#allCivilians

I recognize that it’s macabre to treat this as a word problem, but the math works out if you do. If out of 100 dead people, 33 are combatants and 67 are civilians (the 2:1 civilian to combatant ratio I have calculated) and half of the dead civilians are children, then there are 33 dead children, which is the “one third” in the headline.

Nothing can fix things because teenagers will not cooperate. If Instagram could identify all its teenage users, those users would move to a platform that couldn’t. The only thing the restrictions achieve is a reduction in the market share of the platform with the restrictions.

That’s not what I am assuming. My assumptions are only that none of the dead combatants are children and that the age distribution of dead civilians matches the age distribution of the civilian population.

If we assume that (1) the civilian population is 50% children and (2) none of the combatants are children then:

(fraction of the dead that is children) = (fraction of the dead that is civilians) * (fraction of the civilians that is children)

(1/3) = (fraction of the dead that is civilians) * (1/2)

(fraction of the dead that is civilians) = (1/3) ÷ (1/2) = (2/3)

This is where my 2:1 civilians to combatants number comes from.

The fact that among the dead, the ratio of civilians to combatants equals the ratio of adults to children is a coincidence.

Many people seem to think so but the evidence doesn’t support their argument. A 2:1 ratio of civilians to combatants killed isn’t particularly low but it is far closer to the best that Western armies have been able to accomplish than it is to the ratio seen from armies that are not trying to reduce civilian casualties. For example, Russia’s ratio in Mariupol is approximately 8:1 and that was against Ukrainian soldiers in uniform who weren’t deliberately hiding among civilians. Urban warfare always involves heavy civilian casualties.

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.

The fact that it won’t have any record of calls I missed while the phone was off or didn’t have reception, although actually that’s probably the fault of the service provider. They can send me texts I missed. Why can’t they send me a list of missed calls?

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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.

FourPacketsOfPeanuts has already given a good answer specifically about Israel’s situation, but I want to say something about international law in general. Law may be written based on moral principles, but law is still not the same thing as morality. In our daily lives, we follow our moral principles because that’s what we believe is right, and we follow the law because otherwise cops will put us in jail.

The situation for a sovereign country is different - there are no cops and there is no jail. If other countries wanted to take hostile action, they would even if there was no violation of international law, and if they did not want to take hostile action, the wouldn’t even if there was a violation. Morality still exists (although morality at the scale of countries is necessarily not the same as morality at the scale of individuals) but the law might as well not exist because it is not enforced. It’s just pretty language that may be quoted when a country does what it was going to do anyway.

I’m not trying to imply that I think that Israel is violating international law. I’m saying that discussing whether it is or not is a purely intellectual exercise with no practical relevance. If I support Israel but you convince me that it is technically breaking some law, I’m still not going to change my mind. If you oppose Israel but I convince you that it is technically obeying every law to the letter, you’re still probably not going to change your mind.

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.

Not really questionable - hospitals explicitly lose their protection if they are used for military activity.

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.

I don’t disagree, but before the recent breakthroughs I would have said that AI is like fusion power in the sense that it has been 50 years away for 50 years. If the current approach doesn’t get us there, who knows how long it will take to discover one that does?

It would be odd if AI somehow got worse. I mean, wouldn’t they just revert to a backup?

Anyway, I think (1) is extremely unlikely but I would add (3) the existing algorithms are fundamentally insufficient for AGI no matter how much they’re scaled up. A breakthrough is necessary which may not happen for a long time.

I think (3) is true but I also thought that the existing algorithms were fundamentally insufficient for getting to where we are now, and I was wrong. It turns out that they did just need to be scaled up…