I used to contribute regularly to Wikipedia and spent many hours editing articles each week.

The deletionists ended that.

Too much of my work was being deleted for stupid, arbitrary reasons.

If someone is willing to spend hours writing an article about Trudaeu's son, then it's reasonable to assume others are interested in reading it. Even if no one reads it, it does no harm by existing.

The deletions need to stop.

Efficiency of an electrical inverter: 90%

Efficiency of a gasoline generator: 20%

A gasoline generator might work as an emergency backup, but would not be economical for daily load leveling.

TFS says the inverter's extra hardware costs $650.

A 10kw gasoline generator would cost at least as much.

Though I'm not sure heavier fines or criminal charges is really going to fix this, it's too easy for executives to rationalize away any risks.

The real fix is more funding for researchers (university and government) so they have the resources to investigate these things on their own. We shouldn't have to rely on companies policing themselves.

Why? Is filling the Super Bowl blimp more important?

If nothing changed in 20 years, perhaps the looming helium crisis is overblown.

Nope. Warner Bros. was losing money before the merger and had accumulated $40B in net debt.

The headcount reduction was sorely needed, and the merger makes it even more necessary.

That's what the in-class exams and quizzes are for.

The in-class exams and quizzes solved that problem too.

That is extremely unlikely, but if it happens, people will switch to other AI engines.

I use Cursor for coding, which SpaceX now owns.

SpaceX (which may merge with Tesla soon) has deep pockets and an in-house need for AI, so it is even less likely to "close shop".

Even if Anthropic and OpenAI get in deep financial doo-doo, they won't close entirely. They might stop updating their models, but the existing models in static mode would be fine for coding. More likely, they would be acquired in a consolidation.

There are currently dozens of AI companies. I predict there will be fewer than five in a few years.

There are dumb TVs still being made.

That you don't even know that shows you are not a serious customer for such a product.

You can just click "Skip" when it asks you to connect to the Internet.

You can stop it, but most people can't or won't.

They don't need to block 100% of debloating.

99.9% is good enough.

The only ads I see are for video content like new movies or TV shows, and I don't mind those.

PG&E in California incentivizes me to reduce my power consumption from 2 to 7 pm, and gives me cheap off-peak electricity from 2 to 4 am, when I charge my EV.

I've been on this program for 11 years. It works well for me.

SpaceX needs Tesla's revenue to subsidize its AI efforts.

Tesla needs SpaceX for all the AI resources that were diverted from Tesla to SpaceX.

Elon Musk needs the merger in order to circumvent the performance goals in his compensation package.

That's why it is using it to look over drafts. You take the AI output and then go through its suggested edits or concerns. Essentially it functions as a very fast, half-way decent proof-reader. For example, my spouse looks over a lot of drafts of things I write, but if I give her a 15 page paper, there's good work on the first 8 or 9 pages, probably a joke on page 10 about my notation, one or two comments on the next page, and then a few comments on the bibliography. Claude is not as good a proof-reader as she is, but Claude doesn't poop out after a few pages. And Claude is much faster. So reliability goes up without having to rely on the AI being reliable itself.

And sometimes it catches subtle things. For example, not too long ago, I gave a paper draft to Claude, and it noted in the bibliography that the journal volume number seemed unlikely given the year; it turned out that the volume was correct, but I had actually written the wrong journal down in the entry. Another time, Claude noted that one entry in a bibliography was from a "borderline predatory" (its words but it wasn't wrong) journal, and since I wasn't using it for much other than as part of a list of "hey, here's prior literature" and the paper in question didn't prove anything interesting, I discussed just dropping it from the citation list. Claude then without me asking, pulled up preprint I already had on the arXiv and noted it saw that paper cited there and suggested we remove the paper there also. I didn't do so, but this is the sort of thing that it can suggest. One still has the human make the decisions, but the input itself is useful. And for the most part, verification by a human for most issues is much faster than finding the issues one self.

Not really. It turns out that specialized models do not do as well as large general models. This is essentially a variant of the Bitter Lesson https://en.wikipedia.org/wiki/Bitter_lesson. In this case, there's other reason for it also. There's a lot of reasoning science and math discussions in non-specialized training data. For example, one paper I wrote with two coauthors started as a Reddit discussion. But there's also just a lot of subtle interaction that specialized models don't necessarily get. Math is easier to understand if one has a good command of physics. Engineering is better if one has math and physics. Etc. And sometimes a technique gets directly pulled over. For example, in the large OpenAI paper drop, one of the graph theory problems relied on a trick involving a weighing on the graph that involved complex numbers designed to cancel, where the trick came from a physics context that did something similar. A math specialized model would have been unlikely to find and import that trick.

Right, and the line here isn't clear. One take on NFTs is that they are nominally a tech but functioned purely as a commodity bubble. Or that NFT is a tech but it was used to make commodities which had a bubble. And the line between tech and commodity was blurry historically. When Spain entered the "platinum age," that was based in part on how to make nearly pure platinum was a tech, but the platinum itself was a commodity.

Since per a token output seems to be getting better also for open-weight models, this suggests this is more than just a business decision. And some of it is specific changes; we know that larger models with more training data for example will often produce higher quality output. But also aside from scaling, there have been architectural improvements. For example, one insight that occurred was that having lower precision weights which allowed more weights was often a positive tradeoff. That lead to changes in both the software and hardware ends, as the GPUs were modified as a result. Similar, there's been changes in what functions are used for the feedforward function.

TFS also lists the Guardian as a source.

Like Chernobyl?

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