It was reused on a later flight. So the older booster has already proven that it could be reused but this V3 has a lot of changes both to the booster and the separation process so it may be a while before they can catch one.

Yes. It proved the new hardware would be capable of getting to orbit if they hadn't deliberately launched into a suborbital trajectory, but so far not capable of getting back for reuse. Though it appears the heatshield suffered no obvious damage on this flight.

They were expected to explode. The first stage failed to restart its engines and the second stage lost an engine but otherwise it got to space with a largely redesigned engine and spacecraft. Which is way better than the V2 redesign did.

So not a great success but the next one probably will be. Then, hopefully the flight after can actually go to orbit.

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.

When I got my Starlink antenna I just put on the ground in the back yard pointing south, plugged it into a power outlet and connected to the Wifi and had it going in about five minutes. It's now mounted to the frame of the back yard swing with a battery pack and solar panel to power it.

I guess it's more of a problem if you don't want it to risk it getting stolen as then someone probably has to go up on a roof and run a power cable and possibly LAN cable to it. I doubt SpaceX are going to pay for someone to drive somewhere remote and do that for free.

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."

They solve the problem of people being able to drive wherever they want.

The future will be "I'm sorry Dave, I can't drive there."

> It's also a great place to build telescopes.

Radio telescopes probably, because the far side receives no interference from Earth. I can't see any reason to build optical or IR telescopes there rather than in orbit because in orbit you can point at anything wheres on the Moon you can only point at wherever is visible at the time.

Unless you're at the point where you can literally build them there from lunar resources. But that's quite some years away.

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.

They just doubled the standby price to $10 a month, which is what I used to pay for 10GB a month as a backup for working from home until they scrapped that plan. At full data rate it would burn through all that data in 20 minutes, but for remote development it could last a few days... that's not so easy on standby with only 500kbps.

In the 60s, NASA looked at recovering and reusing the first stage of the Saturn V but concluded that it would take sixty launches for the savings to exceed the development cost so they scrapped the idea.

The reason Falcon 9 is the cheapest launcher in its class is because it flies 100+ times a year and the most expensive part is reusable, and the reason it's reusable is that it flies 100+ times a year. And the reason it flies so often is to launch Starlink satellites.

To recover Starship's costs and make it cheap they have to fly hundreds of times a year. That means putting tens of thousands of tons of 'stuff' in space every year.

AI data centres in space are one option for what they can launch that will require tens of thousands of tons of 'stuff' every year.

It's worth remembering that most SpaceX launches are Starlink launches and those are presumably counted as a cost rather than revenue since SpaceX is its own customer for those launches. If they get Starship working, even if they can only reuse it a few times like the early Falcon 9s, that will likely reduce those costs significantly.