Oh, but these are *preventative* wars. He gets a peace prize for every country he invades!

Venezuela was using fentanyl as a WMD. Iran was about to nuke us. Cuba might attack us with drones if someone provides them. Greenland might start a snowball fight, and make us look bad if we lose.

Presumably we've got all our best people on this, since they're obviously not on the UFO videos.

One of the "unidentifiable" objects in an earlier release (last year, I think) was obviously as hell two birds flying low over the ground.

I don't have a conspiracy theory to explain it, but this is all utter bullshit.

I switched to Ubuntu, but I know Redhat pushes their patches upstream like crazy.

Do I need to worry as well?

That's fine.

If we don't support our media we're going to lose our national identity.

Linux?

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...

Maltodextrin is easy to buy online and I imagine you could get it in stores that sell supplements for athletes (it's a common quick-energy booster, being a very rapidly-absorbed carbohydrate). The emulsifier and color you don't really need, though I suspect you could find them pretty easily if you wanted. The emulsifier is mostly to increase shelf life, and the color is for color.

I'm referring specifically to people who listen to certain artists so relentlessly the platform thinks they're super fans. Just buy the frigging albums. Not only do they support the artists with royalties but they own the content and save themselves renting it.

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.

... maybe they should buy the content instead of renting it from a shitty service like Spotify.

Blue Origin is far, far from having "caught up". They've had three launches, with a 33% mission failure rate. They are now where SpaceX was fifteen years ago, but with a much worse record (Falcon 9s first mission failure was launch 19, though it did have a partial failure on launch 4 -- primary payload successful, secondary payload failed). New Glenn has done better on booster recovery, but they weren't the ones learning how to do it.

And even if SpaceX never manages to make Starship fully-reusable, they can always punt, build a lighter, fully expendable second stage and have a launch platform that blows every other heavy lift vehicle in the world away.

The Chinese are moving pretty fast but they're also a generation behind.

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."

All true, but it's worth pointing out that if the Starship lander succeeds it will enable us to do a lot more, a lot faster. The whole "15 refueling flights for every moon trip" seems kind of crazy on its face, but if you look at the costs (assuming Starship works and become fully reusable), it makes the total cost per kilogram delivered to the surface of the moon insanely low and enables comparatively massive payloads to be delivered.

Big risk, big (potential) reward. Running both the Starship and Blue Moon projects in parallel is probably a good risk mitigation strategy, but if Starship succeeds completely, Blue Origin's lander will be a relic. Of course, it's also possible that Starship will just fail, or that it will succeed but be difficult to man-rate, in which case it may become the delivery service for lunar cargos, while people fly on Blue Moon.

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.