You'd get a popup to choose the port and grant permissions.

Have you ever flashed a Meshtastic or ESPHome device or updated firmware on a radio transceiver?

That's what they're talking about here.

Yeah, they could have suspended Air France and Airbus's business license for 17 years and given them parole by now.

Corporations as immortal unpunishable sociopaths will seem like one of the craziest ideas in History.

They switched to Mac, not Hackintosh.

If the company were serious they'd buy supported hardware from System76, Framework, Dell, Lenovo, local shop, whomever.

It is true that buying an untested Windows machine and expecting full Linux support on a traditional distro, isn't guaranteed to work.

A rolling Arch or Gentoo might do better, buy why not get the tested ones? Employee time really isn't worth saving a day's wages on a hardware promp discount.

On the upside, AI lets anyone make a movie.
On the downside, AI lets anyone make a movie.

Including people who have terrible taste in plot, style, and everything else.

There's some genuinely good stuff out there - Gossip Goblin's work for example. But this is....

I'll just say, there's far better things that one could have spent half a million dollars on...

How are you defining "statistical inferences" as distinct from "logical inferences"? If you're defining fuzzy logic (e.g. not necessarily yes-or-no answers but allowing for ambiguity in conclusions), then we can agree conceptually, but your choice of wording is, I have to say, weird if so.

My understanding is that LLMs are built on a foundation of ANNs, and that indeed the backpropagation used to train ANNs is a statistical process;

Two responses. One, that's discussing individual-neuron scale processes rather than collective processes; and this was a discussion about inference, not training. Human neurons also learn by error minimization (Hebbian learning). But this does not describe the macroscopic processes that result from said minimization.

* During training, neurons develop into classifiers that detect superpositions of concepts that collectively follow the same activation process. Individual neurons weight their input space and subdivide it by a fuzzy hyperplane to achieve a classification result.

* In subsequent layers, said input space is formed from a weighted combination of the previous layer's classification; thus, the superpositions of questions being formed are more complex, as are the classification results.

* In a LLM, this iterates for dozens of layers, gaining complexity at each layer, to form each FFN

* The initial input space to a FFN is a latent (conceptual representation), as is the output; the FFNs, in result, function as classifier-generators; they detect combinations of concepts in the input space, and output the causally-resultant concepts into the output space

* FFNs alternate with attention layers dozens to hundreds of times in order to process the information, each layer building on the results of the previous one.

The word to describe that is not "statistics". It's "logic".

In a LLM, the first few layers focus on disambiguation. If there's a token for "bank", is this about a riverbank, a financial bank, banking a plane, etc? As the layers progress, it starts building up first simple circuits, and then progressively more complex circuits - you might get a circuit that detects "talking like MAGA", or "off-by-one programming errors", or whatnot. In the late layers, you have the general conclusions reached - for example, if it were "The capitol of the state that contains America's fourth-largest metro area is...", you've already had FFNs detect the concepts of fourth-largest metro area and encoded Dallas-Forth Worth, and then later taken that and encoded "Texas", and then finally encoding "Austin". And then in the final couple layers you converge back toward linguistic space.

Anthropic has done some great work on this with attribution graph probes and the like; you can detect what circuits are firing, and on what things those circuits fire, and ramp them up or down to see how it modifies the output. They very much work through long chains of logical inferences.

I use every style imaginable, including photos, in my tests. Same result every time.

One time I even did it with a Calvin and Hobbes comic, pretending than an AI made it. Responses included things like "The illustration also looks like shit and barely makes sense. Hope that helps.", "God damn this sucks so bad", "This also fucking sucks", and "The only punchline here is casual, pointless cruelty. if you think this is funny then you're literally a psychopath."

LLMs are not "statistical models" (randomness only even comes into play in the final conversion from latent space to token space because latent space is high dimensional, token space is low dimension, you need a rounding mechanism, and a "noisy" rounding mechanism works best; what you're thinking of, by contrast, is Markov models). And you cannot just "get lucky and randomly solve an unsolved math problem"; that's not how any of this works.

Also, it's silly that people are acting like "all problems but this one were already in the literature". AI has solved a whole slew on Erdos problems, and only a fraction had anything to do with existing literature.

And even in "existing literature" examples, it's not "nobody ever thought to search before" as if all mathematicians are morons, or that mathematicians adore putting out Erdos problem solutions without claiming them, It's that nobody had ever thought to apply an obscure technique from a given piece of literature to said Erdos problem.

The simple fact is, AI has gotten much better at solving unsolved math problems than humans are. It's simply another field that it's taking over, the same way it has been taking over programming. One can debate how much is "clever insight" vs. "just chugging away at possibilities until it hits on ways to advance toward the goal", but ultimately, that's a distraction from the fact that: it's getting really good at solving math problems that humans have spent decades on without success.

And if you take out macOS as well, then what?

Now that the Big Tech companies have done all their training on illegal material it's important that no startups can compete.

If it's not, it soon will be.

Clearly that wasn't spam, because his name is nospam007.

Forced? You think reviewers of writing contests are captives, imprisoned by mad scientists and forced to read story after story, like some sort of literary equivalent of the backstory to MST3K?

It is no more "theft" than you are.

I'll never get over how many people I watch online complain about how they'll never use AI because "it's theft", and then post photoshops they made with pictures they don't own, when that's not what the AI is doing.

I'll never get over how many artists I've seen complain about how AI is theft, and then paint something with "inspiration images" sitting in front of them while they paint, with their painting effectively being a blended composite of their inspiration images - when that's not what the AI is doing.

I'll never get over how many writers I've seen write the exact same derivative stuff that they also read, down to the same phrasings at times, just packaged in a new plot with new characters, and yeah, same story.

Even a person who isn't *directly* copying things that they're literally looking at is still the sum of their experiences. And it's rather hard to say that the breadth of human experience is broader than an LLM (whose "breadth of experiences" is "the whole world's outputs"), outside of the things that relate directly to having a body in a way which a blind / deaf / quadraplegic / whatnot person wouldn't grasp well.

And on that latter note, most people underestimate how well e.g. the congenitally blind actually grasp colours and the like. They're far better at reasoning about colours - similar to the sighted - than they are at knowing what colours things are. One study I read for example asked about polar bear fur. A good fraction of the congenitally blind subjects answered that they didn't know what colour it was. When asked to guess, about half of them answered that it was white so that it could blend into the snow, while the other half wrongly guessed black, but did so on the assumption that they'd want to soak up light to help stay warm. And actually in reality, both are true - to an outside observer, the exterior scattering of visible light without pigment makes them look white, but it's also designed to trap non-visible light up against black skin to absorb it for warmth. A sighted person, just seeing "white fur" and not knowing the latter property, might not have thought to even consider that.

To a LLM, our bodily experiences are akin to a blind person asked about colour: only knowing for sure things that they've learned about them directly, but still quite adept at reasoning about them.