No, those are the words of someone who has seen nothing but slop for more than a decade (OK, there were a couple of exceptions), and has reached the point where there is no expectation things will ever get better. I have also given up hope that Star Wars will ever be good again.

I expect your opinion, which I share, is a majority opinion. Star Wars is almost nothing but slop now.

I have no idea what your point is at all. Seriously, are you OK? Just to make it clear - I am against governments, I am against governments using any technology to make it easier for government to oppress people and this is one instrument of oppression.

AI video technology is still nowhere even remotely near just "click a button and take what it spits out". I don't know how to break this to anyone here, but you're not just going to go to some video generation site and turn out Woodnuts without extensive skill about AI video tools themselves and a wide range of traditional video production tools, and without spending weeks to months and significant financial expense on the project.

Even if / when this changes, video production is still always going to be limited by the human at hand. Most people's movie ideas, plotting, scripting, directing, etc frankly will be terrible. The slop in this case is the human, not the tool.

Unions only put other union members against eachother. Innovation stagnates but goodkuck with that

I'm surprised at appealing. That will likely cost more than the fine.
Still, not wanting the precedent is a thing.

So how can this be allowed if there is so much graft around this technology that is flowing through thousands of hands in the government offices?

Here is an example: https://www.fmcsa.dot.gov/news...

This here: https://simpler.grants.gov/opp...

Funding Opportunity Number: FM-MHP-26-002
Assistance Listing: 20.245
Funding Details: $52.7 million expected total amount to award

Executive Summary:
The objective of the HP-ITD program is to advance the
technological capability and promote the deployment of
intelligent transportation system applications for CMV
operations, including CMV, commercial driver, and carrier-
specific information systems and networks, and to
support/maintain CMV information systems and networks to
(i) link Federal motor carrier safety information systems with
State CMV systems; (ii) improve safety and productivity of
CMVs and commercial drivers; (iii) and reduce costs
associated with CMV operations and regulatory
requirements.

Eligible Applicants
1.1 General
The HP-ITD awards are available to States, the District of Columbia, the Commonwealth of Puerto
Rico, the Commonwealth of the Northern Mariana Islands, American Samoa, Guam, and the U.S. Virgin
Islands. FMCSA may award HP-ITD funds to eligible applicants that have an approved program plan as
outlined in the Fixing Americaâ(TM)s Surface Transportation (FAST) Act. Individuals and businesses are
not eligible to apply for HP-ITD funding.

This entire thing is premised on the idea that there will be *more* information available to the federal government to work with, not less. They are fully committed to using these ALPR cameras that are everywhere now to track everything all the time and to put every truck driver out of service for any inconsistency in their visual data and thus hand out more fines, more court time, more oppression.

This is just one single program, one example, there are so much more, there is so much money at stake, never mind the actual flock graft itself.

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.

NASA contracts out missions, they don't really do missions in-house. SpaceX is currently winning the launch and visit ISS missions by a mile. Competition like Boeing is more expensive and less reliable.

$500 hammer was actually a set of hammer, shovel, and pick. It had to be non-magnetic, non-sparking, yet durable enough for work. Intended use was for digging out old unstable explosives that the government had let sit around for far too long after WWII.

Pencil that could write in space - standard pencils use graphite, and shed graphite dust when used. Graphite is carbon, it is both conductive and flammable as a powder. Can one see why that might be bad in zero g constrained atmospheres?
The Fisher space pen was developed by Fisher, on his dime, and subsequently sold to both space agencies, US and Russia.

The USA is not "literally Russia". There might be the occasional commonality, but the problems Russia faces are far more severe and deeply run.

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