They sure do:

https://aws.amazon.com/federal...

Oh, sorry, linked the wrong company.

As such, they remain functional because nobody is weaponizing their state of indebtedness.

No weaponization necessary. For domestic debt, as long as your citizens keep buying bonds you're fine, and Japan's citizens keep buying bonds. If they stopped then you'd have to cut back government services. It's kind of a tax, maybe.

Foreign debt requires foreigners to keep buying your bonds. If they stop then you have to cut back not their government services, but those of your own citizens. It's not entirely unlike the sitation Saudi Arabia is in, except with debt instead of oil.

The US has the additional issue that a decent amount of that foreign debt is held by countries they have declared to be their enemies, which does add the possibility of hostile action. Most of it is held by allies they have decided to attack though, which I think in American baseball is called an "unforced error."

$207 billion over five years is one Northrop revenue.

Ah, clever. Would you also care to argue that rocks can't do arithmetic, which is what I actually said?

I'm not sure how any of that makes "it right though." It rather sounds like you're arguing against the author's apparent point that such things emerge out of whole cloth from the magic that is human intelligence.

I don't think we're having the same conversation. The OP asked about how not buying stuff decreases productivity. I explained that "productivity" in this sense is GDP / capita and not buying stuff decreases GDP. I'm not discussing social policy and certainly have not "missed the wealth gap." If you would like to discuss social policy, there are lots of Slashdot articles where such things happen.

You know, Slashdot has this great feature where messages are shown in threads. The message I relied to, for example, is shown immediately before mine. If you read the message (the one that mine is a reply to), and engage that vaunted human thinking ability, it might make more sense.

My comment was about a logical error made by someone I assume is human. I'm not sure that, plus your own apparent error, is quite the evidence for human intelligence over machine that you think it is.

Yes, that's the story you and the article author probably learned in grade school. It's not true, of course, but it certainly appeals to the lone genius personality cult cognitive bias.

Yeah, that's not much better.

Einstein, for example, conceived of relativity before any empirical evidence confirmed it.

WTF does that mean? How can you confirm something before anybody conceives of it? If we assume its just clumsy language then its just not true. Maxwell's electrodynamics were known to be in conflict with Galilean relativity (among other things) and the physics community had spent decades working on the problem including Lorentz, Lamor, Poincaire and others working out the necessary transform to replace the Galilean one. Einstein, who was a PhD student at the time, wrote a very nice paper tying it all together.

As for metaphors, or vocabularies or whatever, Einstein was notably not a fan of the metaphors that are currently most associated with general relativity.

There's also a nice little paper by some physicists where they train a small neural network (much, MUCH smaller than any LLM) on various types of observations and show that it learns symmetries of the physical system. One of their examples is learning the invariance of the spacetime interval.

Perhaps drunk too?

I doubt it. The summary (I didn't read the article, I meant it when I said "stop reading") sounds like a typical opinion piece from someone with lots of opinions and not much concern for accuracy. To pick the same example again, the Einstein myth is pretty overwhelming but this guy takes it to the next level with the "didn't like the metaphor" stuff. Is that the 2025 version of "the narrative?"

The Arduino bootloader does indeed make using ATMegas nicer, but the ESP32 uses its own bootloader, which is burned in, not modifiable. The RP2040 too, and I expect most or all of the RISC-V chips as well.

The Arduino toolchain also contributes a lot but, except for the ATMega, it's also created and maintained by not-Arduino.

Sure do :) I can provide more if you want, but start there, as it's a good read. Indeed, blind people are much better at understanding the consequences of colours than they are at knowing what colours things are..

The congenitally blind have never seen colours. Yet in practice, they're practically as efficient at answering questions about and reasoning about colours as the sighted.

One may raise questions about qualia, but the older I get, the weaker the qualia argument gets. I'd argue that I have qualia about abstracts, like "justice". I have a visceral feeling when I see justice and injustice, and experience it; it's highly associative for me. Have I ever touched, heard, smelled, seen, or tasted an object called "justice"? Of course not. But the concept of justice is so connected in my mind to other things that it's very "real", very tangible. If I think about "the colour red", is what I'm experiencing just a wave of associative connection to all the red things I've seen, some of which have strong emotional attachments to them?

What's the qualia of hearing a single guitar string? Could thinking about "a guitar string" shortly after my first experience with a guitar string, when I don't have a good associative memory of it, sounding count as qualia? What about when I've heard guitars play many times and now have a solid memory of guitar sounds, and I then think about the sound of a guitar string? What if it's not just a guitar string, but a riff, or a whole song? Do I have qualia associated with *the whole song*? The first time? Or once I know it by heart?

Qualia seems like a flexible thing to me, merely a connection to associative memory. And sorry, I seem to have gotten offtopic in writing this. But to loop back: you don't have to have experienced something to have strong associations with it. Blind people don't learn of colours through seeing them. While there certainly is much to life experiences that we don't write much about (if at all) online, and so one who learned purely from the internet might have a weaker understanding of those things, by and large, our life experiences and the thought traces behind them very much are online. From billions and billions of people, over decades.

Language does not exist in a vacuum. It is a result of the thought processes that create it. To create language, particularly about complex topics, you have to be able to recreate the logic, or at least *a* logic, that underlies those topics. You cannot build a LLM from a Markov model. If you could store one state transition probability per unit of Planck space, a different one at every unit of Planck time, across the entire universe, throughout the entire history of the universe, you could only represent the state transition probabilities for the first half of the first sentence of A Tale of Two Cities.

For LLMs to function, they have to "think", for some definition of thinking. You can debate over terminology, or how closely it matches our thinking, but what it's not doing is some sort of "the most recent states were X, so let's look up some statistical probability Y". Statistics doesn't even enter the system until the final softmax, and even then, only because you have to go from a high dimensional (latent) space down to a low-dimensional (linguistic) space, so you have to "round" your position to nearby tokens, and there's often many tokens nearby. It turns out that you get the best results if you add some noise into your roundings (indeed, biological neural networks are *extremely* noisy as well)

As for this article, it's just silly. It's a rant based on a single cherry picked contrarian paper from 2024, and he doesn't even represent it right. The paper's core premise is that intelligence is not lingistic - and we've known that for a long time. But LLMs don't operate on language. They operate on a latent space, and are entirely indifferent as to what modality feeds into and out from that latent space. The author takes the paper's further argument that LLMs do not operate in the same way as a human brain, and hallucinates that to "LLMs can't think". He goes from "not the same" to "literally nothing at all". Also, the end of the article isn't about science at all, it's an argument Riley makes from the work of two philosophers, and is a massive fallacy that not only misunderstands LLMs, but the brain as well (*you* are a next-everything prediction engine; to claim that being a predictive engine means you can't invent is to claim that humans cannot invent). And furthermore, that's Riley's own synthesis, not even a claim by his cited philosophers.

For anyone who cares about the (single, cherry-picked, old) Fedorenko paper, the argument is: language contains an "imprint" of reasoning, but not the full reasoning process, that it's a lower-dimensional space than the reasoning itself (nothing controversial there with regards to modern science). Fedorenko argues that this implies that the models don't build up a deeper structure of the underlying logic but only the surface logic, which is a far weaker argument. If the text leads "The odds of a national of Ghana conducting a terrorist attack in Ireland over the next 20 years are approximately...." and it is to continue with a percentage, that's not "surface logic" that the model needs to be able to perform well at the task. It's not just "what's the most likely word to come after 'approximately'". Fedorenko then extrapolates his reasoning to conclude that there will be a "cliff of novelty". But this isn't actually supported by the data; novelty metrics continue to rise, with no sign of his suppossed "cliff". Fedorenko argues notes that in many tasks, the surface logic between the model and a human will be identical and indistinguishable - but he expects that to generally fail with deeper tasks of greater complexity. He thinks that LLMs need to change architecture and combine "language models" with a "reasoning model" (ignoring that the language models *are* reasoning - heck, even under his own argument - and that LLMs have crushed the performance of formal symbolic reasoning engines, whose rigidity makes them too inflexible to deal with the real world)

But again, Riley doesn't just take Fedorenko at face value, but he runs even further with it. Fedorenko argues that you can actually get quite far just by modeling language. Riley by contrast argues - or should I say, next-word predicts with his human brain - that because LLMs are just predicting tokens, they are a "Large Language Mistake" and the bubble will burst. The latter does not follow from the former. Fedorenko's argument is actually that LLMs can substitute for humans in many things - just not everything.