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.

We can bust on Microsoft all day and all night, and they deserve it, but the fact that their ability to find and fix these problems has greatly increased is a good thing. Software is incredibly complex, and no software more complicated than "10 GOTO 10" is free from the potential of security problems. Microsoft's QA has gone downhill in recent years, but now it's getting better apparently (even if it's after the fact). They are not going away, so this makes all our lives better.

It's bad, but Microsoft has a awfully large number of lines of code to run through Mythos or whatever (whether they need that many is a whole other discussion). A more useful metric on overall code quality would be how many bugs are being found per 10k lines of code compared to their peers (including FLOSS); e.g. if Microsoft ran 10m SLOC through Mythos to get those 570 bugs, and a smaller project ran 1m SLOC and got 57 bugs, then you could reasonably argue that their code quality is about on a par with the smaller project. It's still Apples to Oranges though, because some coding solutions are going to be much more challenging to code, and therefore more likely to contain bugs.

On the upside, we're probably going to get several months of this while everyone with access to Mythos et al runs their existing code through it and integrates into their release processes for new code, and the end result will be things being much harder for all the bad actors in the world. Even if you don't use the improved code yourself, that's hopefully going to have a significant impact on the number and size of all the botnets out there, and that's a net benefit to everyone apart from the bad actors.

Lenovo was never an "IBM brand." IBM sold them their client computing assets in 2005, and their x86 server assets in 2014, but Lenovo has never been a part of IBM.

Mmmm, sweet h2o!

I wrote a file searching utility in C++ about 30 years ago. About 15 years ago I ported it to Python and found it to be faster than using the Windows directory enumeration APIs, and I've been using that ever since. It just searches filenames, but that's all I need.

"NOTE: Experiences vary by region." says to me that Microsoft doesn't even know what their changes will do.

Bow down to me as your overlord. Fetch me those sugar.

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).

Where are the space ants? /s