The algorithm seems to have been failing to scale correctly, yes. On closer inspection, it looks like the billing software treated the bytes used as K used.

Did AWS use Grok to generate the billing sheets or the code for handling billing?

I'm serious. The errors reported look suspiciously like AI hallucinations or a signed integer being treated as unsigned.

Hmmmm... interesting approach. Is the license "permission" to watch it from sony during the duration of their contract, or do I "own" the movie, but not how it's presented through Sony, in this case.

If it's the latter, copy/rip the media.

A short aside:

I have a fairly large audible account. I noticed about 10 years ago, I still have books in my queue, but I'm unable to download/listen to them. Audible explained the licence to me, told me that it should appear back in my "library" eventually but might take years (and maybe never), but here, have a free "credit" (which is basically, another free book).

Had that happen about 6 times over the last 10 years (the most recent was about a year ago. Each time, I got a credit -- and with the exception of the most recent, the book re-appeared in my library. If the pattern continues, I should have it back in my library within 2 years from when it was removed.

I can live with that.

"an awful lot of DVDs only last a decade or two."

If only there was some way to take the data on a DVD and move it to a computer. Maybe "brute force it". We could use the term "rip"!

Then backups with redundancy would turn a decade or two in to a generation or ten. ...if only...

Yeah, there's two main problems:

1) People entering the wrong fields. For example, medicine really needs workers, at all levels, but not enough people are going into it.

2) Certain manual labour fields, like field work and home construction, because... well, I think we all know why there's a shortage of workers in those fields.

Good question. Their POWER series of CPUs were not insignificant in capability, their chip designers were clearly technically sophisticated, and GPUs are just specialised vector processors with a few extra bells and whistles - stuff IBM is extremely familiar with.

It would not have been difficult to release a GPU or other LLM-specific processor to go along with the POWER11. They'd been working on the POWER11 for 4 years, they knew in 2020 that LLMs had a strong potential to be significant for Big Data processing - an area you use big iron for, they're not rank amateurs, they have plenty of reserve, they could have assembled an emergency team to build a vector processor that was custom-designed for just LLM work, and released an LLM processor card that could run circles around nVidia.

They didn't. Because, as has happened before, their management is simply too stupid and too slow.

Or let's put this another way. Show of hands - how many of you "spicy autocorrect" / "stochastic parrot" people had "AI will start mass-solving Erdos problems" on your forecast list a couple years back? Huh, none of you? Fascinating!

Take some time to reassess your priors. And while you do so, understand that, yes, they are doing logic / reasoning.

They weren't discovered by an LLM. They were known conjectures that were proven by an automated solving language that was linked to an LLM.

I'll take "Things That Didn't Happen For $200", Alex.

Only a handful of meaningful proofs have ever been done by automated formal theorem solvers (the Four Colour Theorem being the most noteworthy example - but its proof is so long that humans can't verify it). By contrast, AI tools have been solving Erdos problems en masse. The majority of them just bog-standard commercial models. In case you need help, the only ones on that list that were hybrid (AI / non-AI) in the actual solving phase are:

1) AlphaProof / DeepMind Prover Agent / AlphaProof Nexus
2) Aristotle (Harmonic)
3) Seed Prover / Seed Prover 1.5 (ByteDance)
4) AxiomProver (Axiom Math)

In each of the above, LLMs come up with the lemmas / strategies but then use Monte Carlo search ("brute force") or likewise to investigate what they came up with. These are a minority. In the "AI Standalone" category, these "hybrid" tools made up only ~20% of attempts and successful proofs. Hybrid tools actually made more of a contribution in the "AI Alongside Literature" (related literature found afterward) and even more of the "AI Building On Literature" (related literature known beforehand) categories, which is the opposite of what people like you expect.

And even with the hybrid tools, it's still the AI doing the heavy lifting when it comes to strategy. Non-AI theorem solvers, again, don't have a spectacular record for churning out novel proofs to unsolved problems. Tools like Lean are more about mathematical rigour - a passive environment that requires a driver (a human or AI) to feed it actual strategies, lemmas, and proof steps. And no, you cannot brute force "strategy" in the vast majority of cases, which is, again, why automated theorem solvers don't have much of a track record with unsolved mathematical problems.

Let's take a random example: the disproof of the unit distance conjecture. It was solved purely by a general purpose commercial GPT model, not custom-trained to mathematics, with no external tools. Read what the various mathematicians reviewing / commenting on it have to say (sections #3 and onward). Seriously, don't skip reading them, actually read them. This was one of Erdos's favourite problems. He mentioned it commonly in his lectures. Essentially every mathematician working in complex geometry has thought about this problem. The approach that the model came up with was highly novel approach, based on CM-fields and class field towers.

I know you don't want to accept this reality, but it is the reality, so you better improve your ability to accept it,. The field of mathematics is already doing so.

Dang, link didn't post.

Ah, good 'ol "Model Collapse" theory that people have been pounding on for years now, predicting an imminent collapse in model capabilities. How has that been working out for you?

Except what the US is actually facing, at least in the near term, is just the opposite, a worker shortage.

You can't pay royalties to the entire internet. That's not realistic.

What is practical are things like taxes to fund public benefits, or requirements of returning things to the commons (for example, open models).

What if...

Someone (say someone who was familiar with doxygen and GCC) developed number of comment types, where some stipulated preconditions that must be true for the function to run correctly, postconditions that must be true once the function has run, kernel facilities that the function definitely needs, and kernel facilities that the function definitely doesn't need. These would all be optional for any given function.

A static checker could then validate if the code meets the behaviour expected by the programmer. This is precisely what is done in SPARK, a fork of Ada for high-reliability code. Combined with existing static checker capabilities, this would greatly increase the number of bugs that could be caught with all kinds of tools, AI included.

It could ALSO build a full fine-grained mapping for any fine-grained mandatory access controls system. You'd also want includes that you could import for precompiled libraries. This would allow someone to verify if the code was making unanticipated/undesirable calls but would also make SELinux possible to develop for at the application level.

It would not be trivial. If it was trivial, it would have been done simply because it already IS done in other languages and that makes it "obvious" to anyone who has been programming for a while. However, it should not be massively complicated, simply because you can use AI as the static checker. Once it has a definite set of bounda that must be satisfied, it should be much more capable of knowing what paths would violate those bounds. Which means that the checker stage essentially is trivial today, leaving only the markup stage.

You're the one making the claim that modern frontier models still make this mistake (they don't), so how about you go to a frontier model, reproduce what you're claiming happens, and click the "share" button and post the link here?

Don't worry, I'll wait.

Your inability to reproduce the thing that you claim happens is duly noted.