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...

The only thing they could possibly be covering up is that they have vast teams of ultra-specialised uber-gurus who have no meaningful cross-domain expertise (which is understandable, you can't be an ultra-specialised uber-guru if you do) but also that they've essentially nothing else and therefore nobody who can red-flag when a skill in one domain allows a person to exploit information that is released by another.

There is nothing wrong, at all, with having ultra-specialised uber-gurus for something like the NTSB, but 100% of their errors throughout history have come from not having additional teams that are cross-domain experts who can identify when accident issues aren't domain-specific (the 737 rudder control jams from a couple of decades ago and the 737-MAX automatic flight systems are examples of issues that was almost unsolvable through lack of cross-domain expertise) or when informational issues aren't domain-specific (as in this case).

You need the specialists, but relying on them alone is a great way to blunder. and the NTSB does not like admitting it blunders, which is why you're not seeing organisational changes, merely ad-hoc communication changes.

A spectrogram is basically a description of the sound and Daphne Oram pioneered technology for turning the informational sections of a spectrogram into sound back in 1958. That would be.... 68 years ago, by my reckoning.

Now, technology has moved on a great deal in 68 years. Exactly what you could do today, relative to what she did back then, is obviously significant. But this really should not have come as a shock.

The lack of understanding of this sort of stuff shows what happens when you have too many niche specialists and too few people who understand the broad technology.

We may need to tinker with individual laws -- but the bigger picture is as in my sig: "The biggest challenge of the 21st century is the irony of technologies of abundance in the hands of those still thinking in terms of scarcity."

The results from whatever the laws will likely remain problematical as long as we have a political mythology built around scarcity while we also have super-powerful computers which could be used for universal surveillance or all sorts of other problematical -- or beneficial -- things.

We need collectively to change the spirit behind the culture towards one recognizing and emphasizing abundance in all we do and legislate.

Related from: https://egreenway.com/taoism/t...
"When the Tao is forgotten, there is righteousness.
When righteousness is forgotten, there is morality.
When morality is forgotten, there is the law.
The law is the husk of faith,
and trust is the beginning of chaos."

So, we need to emphasize the "tao" (way, spirit) of abundance -- as otherwise instead of using technology to create egalitarian abundance for all, we may just digitize an inegalitarian status quo or worse.

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, but the problem is that the electronics have to run at those temperatures and not have solder joints start popping, or other fun failures.

As far as I know, it is just real-time. And it didn't even slow down at the flashing red light. So either it recognized that someone was waving it on or it didn't see the flashing light at all.

I *think* that number actually is counting them, though it's hard to be certain. I'm pretty sure servers are outnumbered by PCs by a large margin.

Cameras and LIDAR. I am not a self-driving car engineer, but from what I understand, it seems likely that it is possible to detect water with even just cameras, at least under the right circumstances, and with cameras plus LIDAR under a lot more circumstances. But doing so would require proper training data; it's not like there's a "Ooh, that's water" recognizer built into the hardware or whatever.

More to the point, they would have to train it how to recognize that some particular sensor return pattern (e.g. zero LIDAR reflections off the ground) is a problem, and do so in a way that doesn't over-correct for flooded potholes, an inch of water in the street, etc. Presumably, detecting water is the easy part; detecting the depth of water is the hard part unless you know exactly how high the curbs are.

I'm a little spooked when I see Tesla FSD beta demos where the car plows right on ahead through slightly flooded streets as though there's nothing there. It makes me wonder whether it saw it and ignored it or just didn't see it. It's the same feeling I got this morning when someone was signaling me to go ahead at a flashing red light and FSD beta (12.6.4) went right on ahead. I wonder if it somehow saw the hand gestures, or if it just didn't see the flashing red light at all.

That's what makes all this stuff fun.

Any hardware or device driver engineer will tell you that every hardware problem, once shipped, is a software problem. :-) (Translation: Hardware bugs, once the hardware is in the wild, have to be fixed with a software workaround.)

Nothing in self-driving cars is a quick fix other than geo-blocking something. You have to train a visual or LIDAR recognition model to recognize the problem situation and then train the path finding model to flag it as a bad path. How hard that is for this particular case, I have no idea. And there's probably other stuff beyond that; I'm not an expert in this area.

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.

Yeah, there's not enough crack in the world for that to make sense. The cost-to-orbit is well documented. Artemis 2: $4.1 billion for a launch that can lift 95 tons to LEO. SuperHeavy/Starship: $90 million for a launch that can lift 150 tons to LEO. Ignoring minor details (e.g. that nobody is willing to expend a SuperHeavy to do trans-lunar injection), the reality of the matter is that the cost per ton to orbit for NASA rockets is 72x what it is expected to cost for SpaceX rockets.

And that's consistent with Falcon Heavy costs, too, so you can't hide behind "Yeah, but SuperHeavy and Starship don't work yet", because unless your payload is huge, SpaceX is still almost two orders of magnitude cheaper.

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.