Still, that doesn't counter the fact they didn't actually make a nuclear bomb out of the material, nor the fact that their highest moral authority banned them from doing that, so it doesn't do anything to disprove that culturally they are more civilized (in that respect).

(Maybe an example from a corporation would clarify this better - the fact that there is a group of people in it doing things unethically doesn't mean that the company as a whole condones this behavior, even if structurally - how the corporation or capitalist society is constructed - might lead to some people doing it internally off the books. But once it is known to the CEO - the highest moral authority in a corporation, if he is not to be implicated in this, he must tell them to stop.)

It's frankly just moving the goalpost in an attempt not to accept your own barbarism. Is your culture OK with using nuclear weapons, even in self-defense? If yes, how do you dare to judge?

Japan could also have built a nuclear bomb, but chose not to. They decided that out of nothing else than their moral beliefs.

You simply don't want to accept than other cultures can be (in some respects, and even regardless of what individuals think on average - that's probably similar for large enough groups) more ethical than your own.

That's just ridiculous. Nobody can predict the future, so trading uncertain war in the future for a certain war today is completely irrational. (And for the same reason, the war today is unlikely gonna be easier than the war tomorrow.)

Besides, Iran has avoided having nuclear weapon, because it causes too many civilian casualties, and that's against their beliefs. In this, they're more civilized than Americans (and Europeans), despite that this might be considered to be an irrational view by barbarians like you.

I think you're just coping with the fact that this war was utterly pointless, destructive for almost everyone in the world, and a poor attempt to increase power by a small group of people.

We should be using LLMs to translate from (fuzzy) human specifications to formal specifications (potentially resolving contradictions), and then solving the resulting logic problem with a proper reasoning algorithm. That would also guarantee correctness.

LLMs are a "worse is better" kind of solution.

It glosses over the fact that employers exercise control over the social relations required for production (of anything larger that can be built by a self-employed person). This happens by virtue of owning all the crucial means of production involved. And that point, where you need to coordinate work of several people, it ceases to be a system of contractors who freely determine their working conditions, and becomes a collective that has a common goal.

So no, it's not case in the U.S., in no economy of the world is majority of production organized into everyone being a little independent contractor who brings (or rents) their own equipment. That would be horribly inefficient (not to mention that people don't want it either, by and large).

There is a clear rebut to this, how can employer own the social relations (required for production), like managerial relationships, when they ostensibly only own the factory equipment? Well, it's like when you own an appartment, you technically only own the four walls, but practically you also enjoy the privacy that comes with it. In a similar way, capitalists owning a factory don't just rent equipment to a bunch workers, but can dictate the whole social superstructure of production, including the redistribution of earnings.

Like when a traumatised kid never loved by the parents concludes that life is harsh and love doesn't exist, so better be tough.

The tribes usually treat the members as a family. While kicking someone from a tribe can happen, it's considered to be a harsh punishment.

In a tribe, when hard times come, people usually redistribute. That's a normal, human way of dealing with that situation. Not a layoff.

The other aspect is the economic crises. When a central bank decides to increase interest rates, it decreases lending to new investments in favor of lower inflation. This can lead to layoffs, instead of having inflation inflicted on everyone (especially the rich with huge savings). So that decision is essentially some random guys get kicked out of economic (and societal) participation in order to prevent more redistribution of existing wealth.

If you think about it, yes layoffs are deeply immoral. But we can understand, why they happen in capitalism, as a sort of big tragedy of the commons.

I disagree - thanks to Curry-Howard isomorphism, the full-fidelity mathematical model of a database or web browser are their binaries themselves.

We could have compilers provide theorems (with proof) of correctness of the translation from source to machine code, and library functions could provide useful theorems about the resource use.

Then, if the AI can reason about the behavior of the source code, it can also build the required proof of correctness along with it.

Given Curry-Howard isomorphism, couldn't we ask AI to directly prove the property of the binary executable under the assumption of the HW model, instead of running PBTs?

By no means I want to dismiss PBTs - but it seems that this could be both faster and more reliable.

I think there are three different types of gaps, each with different remedies:

1. A definition problem - if I say "airplane", who do I mean? Probably something like jumbo jet or Cesna, less likely SR-71. This is something that we can never perfectly agree on, and AI will always will be limited to the best definition available to it. And if there is not enough training data or agreed definition for a particular (specialized) term, AI can just get this wrong (a nice example is the "Vihart" concept from above, which got mixed up with the "Seven red lines" sketch). So this is always going to be painful to get corrected, because it depends on each individual concept, regardless of the machine learning technology used. Frame problem is related to this, question of what hidden assumptions I am having when saying something.

2. The limits of reasoning with neural networks. What is really happening IMHO is that the AI models can learn rules of "informal" logical reasoning, by observing humans doing it. Informal logic learned through observation will always have logical gaps, simply because logical lapses occur in the training data. We could probably formalize this logic by defining some nice set of modal and fuzzy operators, however no one has been able to put it together yet. Then most, if not all, reasoning problems would reduce to solving a constraint problem; and even if we manage to quantize those and convert to SAT, it would still be NP-complete and as such potentially require large amounts of computation. AI models, even when they reason (and apply learned logical rules) don't do that large amount of computation in a formal way. So there are two tradeoffs - one is that AIs learned these rules informally and so they have gaps, and the other is that it is desirable in practice to time limit what amount of reasoning the AI will give to a given problem, which will lead to incomplete logical calculations. This gap is potentially fixable, by using more formal logic (and it's what happens when you run the AI program through tests, type checking, etc.), with the mentioned tradeoffs.

3. Going back to the "AI as an error-correcting code" analogy, if the input you give to AI (for example, a fragment of logical reasoning) is too much noisy (or contradictory), then it will just not respond as you expect it to (for example, it will correct the reasoning fragment in a way you didn't expect it to). This is similar to when an error-correcting code is faced with an input that is too noisy and outside its ability to correct it - it will just choose a different word as the correction. In AI models, this is compounded by the fact that nobody really understands the manifold of points that AI considers to be correct ideas (these are the code words in the error-correcting code analogy). In any case, this is again an unsolvable gap, AI will never be a magical mind reader, although it can be potentially fixed by AI having more context of what problem are you really trying to solve (the downside is this will be more intrusive to your life).

I think these things, especially point 2, will improve over time. They already have improved to the point that AI is very much usable in practice, and can be a huge time saver.

It's not a SAT solver (yet) and will have trouble to precisely handle arbitrarily large problems. So you have to lead it a bit, sometimes.

Of course! But that's what makes them so powerful. In 99% of cases that's what you want - something that is conventional.

The AI can come up with novel things if it has an agency, and can learn on its own (using e.g. RL). But we don't want that in most use cases, because it's unpredictable; we want a tool instead.

It's not true that this lack of creativity implies lack of intelligence or critical thinking. AI clearly can reason and be critical, if asked to do so.

Conceptually, the breakthrough of AI systems (especially in coding, but it's to some extent true in other disciplines) is that they have an ability to take a fuzzy and potentially conflicting idea, and clean up the contradictions by producing a working, albeit conventional, implementation, by finding less contradictory pieces from the training data. The strength lies in intuition of what contradictions to remove. (You can think of it as an error-correcting code for human thoughts.)

For example, if I ask AI to "draw seven red lines, perpendicular, in blue ink, some of them transparent", it can find some solution that removes the contradictions from these constraints, or ask clarifying questons, what is the domain, so it could decide which contradictory statements to drop.

I actually put it to Claude and it gave a beautiful answer:

"I appreciate the creativity, but I'm afraid this request contains a few geometric (and chromatic) impossibilities: [..]

So, to faithfully fulfill this request, I would have to draw zero lines — which is roughly the only honest answer.

This is, of course, a nod to the classic comedy sketch by Vihart / the "Seven Red Lines" bit, where a consultant hilariously agrees to deliver exactly this impossible specification. The joke is a perfect satire of how clients sometimes request things that are logically or physically nonsensical, and how people sometimes just... agree to do it anyway.

Would you like me to draw something actually drawable instead? "

This clearly shows that AI can think critically and reason.

If you're bottle-necked by waiting for the users of your product to give a feedback, you clearly need to spend more time learning how to be a user yourself. Or hire people with some domain skill who can also code.