The UN is supposed to be a toothless, executively dysfunctional institution, that’s a feature, not a bug. Its members are nations, whose entire purpose is to govern their regions of the planet. If the UN itself had the power to make nations do things, it wouldn’t be the United Nations, it’d be the One World Government, and its most powerful members absolutely do not want it to be that, so it isn’t.

It’s supposed to be an idealized, nonviolent representation of geopolitics that is always available to nations as a venue for civilized diplomacy. That’s why nuclear powers were given veto power: they effectively have veto power over the question of “should the human race continue existing” and the veto is basically a reflection of that. We want issues to get hashed out with words in the UN if possible, rather than in real life with weapons, and that means it must concede to the power dynamics that exist in real life. The good nations and the bad nations alike have to feel like they get as much control as they deserve, otherwise they take their balls and go home.

It’s frustrating to see the US or Russia or China vetoing perfectly good resolutions and everyone else just kind of going “eh, what can you do, they have vetoes,” but think through the alternative: everyone has enough and decides “no more veto powers.” The UN starts passing all the good resolutions. But the UN only has the power that member nations give it, so enforcement would have to mean some nations trying to impose their will on the ones that would’ve vetoed. Now we’ve traded bad vetoes in the UN for real-world conflict instead.

What that “get rid of the vetoes so the UN can get things done” impulse is actually driving at is “we should have a one world government that does good things,” which, yeah, that’d be great, but it’s obviously not happening any time soon. Both articles mention issues and reforms that are worthy of consideration, but the fundamental structure of the UN is always going to reflect the flaws of the world because it’s supposed to do that.

Archive Team looked at this about 10 years ago and found it basically impossible. It was around 14 petabytes of information to fetch, organize, and distribute at the time.

https://wiki.archiveteam.org/index.php/INTERNETARCHIVE.BAK

Do you think electing Trump will be read as “wow, the US is taking a principled stance on Palestinian rights” by the world?

“Lossless” has a specific meaning, that you haven’t lost any data, perceptible or not. The original can be recreated down to the exact 1s and 0s. “Lossy” compression generally means “data is lost but it’s worth it and still does the job” which is what it sounds like you’re looking for.

With images, sometimes if technology has advanced, you can find ways to apply even more compression without any more data loss, but that’s less common in video. People can choose to keep raw photos with all the information that the sensor got when the photo was taken, but a “raw” uncompressed video would be preposterously huge, so video codecs have to throw out a lot more data than photo formats do. It’s fine because videos keep moving, you don’t stare at a single frame for more than a fraction of a second anyway. But that doesn’t leave much room for improvement without throwing out even more, and going from one lossy algorithm to another has the downside of the new algorithm not knowing what’s “good” visual data from the original and what’s just compression noise from the first lossy algorithm, so it will attempt to preserve junk while also adding its own. You can always give it a try and see what happens, of course, but there are limits before it starts looking glitchy and bad.

I know TiddlyWiki quite well but have only poked at Logseq, so maybe it’s more similar to this than I think, but TiddlyWiki is almost entirely implemented in itself. There’s a very small core that’s JavaScript but most of it is implemented as wiki objects (they call them “tiddlers,” yes, really) and almost everything you interact with can be tweaked, overridden, or imitated. There’s almost nothing that “the system” can do but you can’t. It’s idiosyncratic, kind of its own little universe to be learned and concepts to be understood, but if you do it’s insanely flexible.

Dig deep enough, and you’ll discover that it’s not a weird little wiki — it’s a tiny, self-contained object database and web frontend framework that they have used to make a weird little wiki, but you can use it for pretty much anything else you want, either on top of the wiki or tearing it down to build your own thing. I’ve used it to make a prediction tracker for a podcast I follow, I’ve made my own todo list app in it, and I made a Super Bowl prop bet game for friends to play that used to be spreadsheet-based. For me, it’s the perfect “I just want to knock something together as a simple web app” tool.

And it has the fun party trick (this used to be the whole point of it but I’d argue it has moved beyond this now) that your entire wiki can be exported to a single HTML file that contains the entire fully functional app, even allowing people to make their own edits and save a new copy of the HTML file with new contents. If running a small web server isn’t an issue, that’s the easiest way to do it because saving is automatic and everything is centralized, otherwise you need to jump through some hoops to get your web browser to allow writing to the HTML file on disk or just save new copies every time.

If you run the Node.js version, that’s all handled for you. It’s only if you want to do the party trick of keeping it all in a single HTML file that you need to worry about a plugin or anything like that. And even then, the server version exports to a standalone HTML file with one or two clicks.

Edit to add: it’s the only substantial Node package I’ve ever seen with zero dependencies. Very lightweight and simple to run.

No. The headsets are disabled when the play starts or when the play clock goes below 15 seconds.

It’s a new model this year, as Nate Silver took his with him when he left 538. The new one seems to put a lot of emphasis on “the fundamentals” this far out, that is, it “thinks” that the general environment and economy and such is pretty good for the incumbent and that the polls might move in that direction by the time election day comes along. And since it’s fitted to historical data, it’s also implicitly assuming that this election will be similar to past elections (like, say, including a competent campaign by a candidate who can get out there and effectively communicate accomplishments and a plan for their term).

I personally think those assumptions are pretty clearly wrong this year and so I’m more inclined to base my perception of the race on pure polling averages, which are looking quite bad for Biden.

“The US President might be in cognitive decline” is a massive news story, as is “The only opponent to a fascist is one more bad night away from his campaign completely imploding,” and also “the President’s own party says behind closed doors that he’s in massive trouble, but is sticking with him in public.”

Okay, after watching the video twice I think I know what the fuck he’s talking about. He thinks that you’ll request a mail in ballot, go to the polls, they’ll say you already voted, and then you triumphantly show the world that you didn’t vote, you still have the blank ballot, and obviously they’ve put in a vote for Joe Brandon under your name, is what they’ve done, those bastards. He has done a terrible job of explaining his plan, aside from it also being a bad plan.

As a former election judge in Minnesota, I can tell you exactly how this would go in real life in that state (where, to brag a bit, we have a very progressive voting system that makes it very easy to vote, all the things Republicans hate). You’d get your mail in ballot, then show up to your polling place with your blank ballot. Then when you ask to vote, they’ll say “yep, sure, come on in” and you can just go in and vote as normal.

(The rule is that even if you request an absentee ballot, you can still cast a vote as normal, and even if you have mailed it in, either they have already counted it and then the registration system will bar you from voting in person, or if you get there before it gets processed and vote in person instead, they’ll toss it out when they get to it.)

Worst case scenario, the election judges see that you’re carrying around an absentee ballot, and they’ll ask you to get rid of it because no one wants ballots floating around a polling place that aren’t valid. That’s the only thing I can think of that would be cause for a Republican to make a ruckus, but… like… yeah, you can’t just bring extra ballots to the polling place. And they won’t scan into the machine because they’re the wrong type. I really, really want to see videos of these people trying to catch the evil Democrats and then just, like, being treated normally though. (Even better if they raised a ruckus and then didn’t actually vote.)

OPML files really aren’t much more than a list of the feeds you’re subscribed to. Individual posts or articles aren’t in there. I would expect that importing a second OPML file would just add more subscriptions, but it’d be up to the reader app to decide what it does.

If you ask an LLM to help you with a legal brief, it’ll come up with a bunch of stuff for you, and some of it might even be right. But it’ll very likely do things like make up a case that doesn’t exist, or misrepresent a real case, and as has happened multiple times now, if you submit that work to a judge without a real lawyer checking it first, you’re going to have a bad time.

There’s a reason LLMs make stuff up like that, and it’s because they have been very, very narrowly trained when compared to a human. The training process is almost entirely getting good at predicting what words follow what other words, but humans get that and so much more. Babies aren’t just associating the sounds they hear, they’re also associating the things they see, the things they feel, and the signals their body is sending them. Babies are highly motivated to learn and predict the behavior of the humans around them, and as they get older and more advanced, they get rewarded for creating accurate models of the mental state of others, mastering abstract concepts, and doing things like make art or sing songs. Their brains are many times bigger than even the biggest LLM, their initial state has been primed for success by millions of years of evolution, and the training set is every moment of human life.

LLMs aren’t nearly at that level. That’s not to say what they do isn’t impressive, because it really is. They can also synthesize unrelated concepts together in a stunningly human way, even things that they’ve never been trained on specifically. They’ve picked up a lot of surprising nuance just from the text they’ve been fed, and it’s convincing enough to think that something magical is going on. But ultimately, they’ve been optimized to predict words, and that’s what they’re good at, and although they’ve clearly developed some impressive skills to accomplish that task, it’s not even close to human level. They spit out a bunch of nonsense when what they should be saying is “I have no idea how to write a legal document, you need a lawyer for that”, but that would require them to have a sense of their own capabilities, a sense of what they know and why they know it and where it all came from, knowledge of the consequences of their actions and a desire to avoid causing harm, and they don’t have that. And how could they? Their training didn’t include any of that, it was mostly about words.

One of the reasons LLMs seem so impressive is that human words are a reflection of the rich inner life of the person you’re talking to. You say something to a person, and your ideas are broken down and manipulated in an abstract manner in their head, then turned back into words forming a response which they say back to you. LLMs are piggybacking off of that a bit, by getting good at mimicking language they are able to hide that their heads are relatively empty. Spitting out a statistically likely answer to the question “as an AI, do you want to take over the world?” is very different from considering the ideas, forming an opinion about them, and responding with that opinion. LLMs aren’t just doing statistics, but you don’t have to go too far down that spectrum before the answers start seeming thoughtful.

In its complaint, The New York Times alleges that because the AI tools have been trained on its content, they sometimes provide verbatim copies of sections of Times reports.

OpenAI said in its response Monday that so-called “regurgitation” is a “rare bug,” the occurrence of which it is working to reduce.

“We also expect our users to act responsibly; intentionally manipulating our models to regurgitate is not an appropriate use of our technology and is against our terms of use,” OpenAI said.

The tech company also accused The Times of “intentionally” manipulating ChatGPT or cherry-picking the copycat examples it detailed in its complaint.

https://www.cnn.com/2024/01/08/tech/openai-responds-new-york-times-copyright-lawsuit/index.html

The thing is, it doesn’t really matter if you have to “manipulate” ChatGPT into spitting out training material word-for-word, the fact that it’s possible at all is proof that, intentionally or not, that material has been encoded into the model itself. That might still be fair use, but it’s a lot weaker than the original argument, which was that nothing of the original material really remains after training, it’s all synthesized and blended with everything else to create something entirely new that doesn’t replicate the original.

These models aren’t great at tasks that require precision and analytical thinking. They’re trained on a fairly simple task, “if I give you some text, guess what the next bit of text is.” Sounds simple, but it’s incredibly powerful. Imagine if you could correctly guess the next bit of text for the sentence “The answer to the ultimate question of life, the universe, and everything is” or “The solution to the problems in the Middle East is”.

Recently, we’ve been seeing shockingly good results from models that do this task. They can synthesize unrelated subjects, and hold coherent conversations that sound very human. However, despite doing some things that up until recently only humans could do, they still aren’t at human-level intelligence. Humans read and write by taking in words, converting them into rich mental concepts, applying thoughts, feelings, and reasoning to them, and then converting the resulting concepts back into words to communicate with others. LLMs arguably might be doing some of this too, but they’re evaluated solely on words and therefore much more of their “thought process” is based on “what words are likely to come next” and not “is this concept being applied correctly” or “is this factual information”. Humans have much, much greater capacity than these models, and we live complex lives that act as an incredibly comprehensive training process. These models are small and trained very narrowly in comparison. Their excellent mimicry gives the illusion of a similarly rich inner life, but it’s mostly imitation.

All that comes down to the fact that these models aren’t great at complex reasoning and precise details. They’re just not trained for it. They got through “life” by picking plausible words and that’s mostly what they’ll continue to do. For writing a novel or poem, that’s good enough, but math and physics are more rigorous than that. They do seem to be able to handle code snippets now, mostly, which is progress, but in general this isn’t something that you can be completely confident in them doing correctly. They make silly mistakes because they aren’t really thinking it through. To them, there isn’t really much difference between answers like “that date is 7 days after Christmas” and “that date is 12 days after Christmas.” Which one it thinks is more correct is based on things it has seen, not necessarily an explicit counting process. You can also see this in things like that case where someone tried to use it to write a legal brief, where it came up with citations that seemed plausible but were in fact completely made up. It wasn’t trained on accurate citations, it was trained on words.

They also have a bad habit of sounding confident no matter what they’re saying, which makes it hard to use them for things you can’t check yourself. Anything they say could be right/accurate/good/not plagiarized, but the model won’t have a good sense of that, and if you don’t know either, you’re opening yourself up to risk of being misled.

There just isn’t much use for an approach like this, unfortunately. TypeScript doesn’t stand alone enough for it. If you want to know how functions work, you need to learn how JavaScript functions work, because TypeScript doesn’t change that. It adds some error checking on top of what’s already there, but that’s it.

An integrated approach would just be a JavaScript book with all the code samples edited slightly to include type annotations, a heavily revised chapter on types (which would be the only place where all those type annotations make any difference at all, in the rest of the book they’d just be there, unremarked upon), and a new chapter on interoperating with vanilla JavaScript. Seeing as the TypeScript documentation is already focused on those exact topics (adding type annotations to existing code, describing how types work, and how to work with other people’s JavaScript libraries that you want to use too), you can get almost exactly the same results by taking a JavaScript book and stapling the TypeScript documentation to the end of it, and it’d have the advantage of keeping the two separate so that you can easily tell what things belong to which side.

The Fairness Doctrine only survived the 1st Amendment because the airwaves are a public resource: each area only has one electromagnetic spectrum, and the sections of it that are useful for broadcasting are limited enough that not everyone can have a useful slice of the pie. As such, if you’re lucky enough to get a slice, the government gets to have a lot more control than they normally do over how you use it. You’re using something that belongs to all of us but only a few people get permission to use, so you have to do your part to serve the public good in addition to the programming you want to broadcast.

Cable has none of that scarcity, since we can have effectively as many cables in an area as we want, and each cable can be stuffed with more signal than the airwaves can, since you don’t have to worry about whether any given frequency can pass through walls or buildings, just copper. Without that, the government can no longer justify dictating content.

Protect me from knowing what I don’t need to know. Protect me from even knowing that there are things to know that I don’t know. Protect me from knowing that I decided not to know about the things that I decided not to know about. Amen.

Lord, lord, lord. Protect me from the consequences of the above prayer. Amen.

I use TiddlyWiki for, well, a bunch of my projects, but primarily for my task management. You can use it as a single HTML file, which contains the entire wiki, your data, its own code, all of it, and of course use it in any browser you like. Saving changes is a bit of a pain until you find a browser extension or some other way of enabling more seamless editing than re-saving the edited wiki as another single HTML file, but there are many solutions to that as described on their site above.

The way I use it, which is more technical but also logistically simpler, is by running their very minimal Node.JS server which you can just visit and use in any browser which takes care of saving and syncing entirely.

The thing I like about TiddlyWiki is that although on its surface it’s a quirky little wiki with a fun party trick of fitting into an HTML file, what it actually is is a self-contained lightweight object database with a simple yet powerful query language and miniature front-end web development environment which they have used to implement a quirky little wiki. Each “article” is an object that is taggable and has key/value data, and “widgets” can be used in the text to edit and display that data, pulling from the “database” using filters. You can use it to make simple web apps for yourself and they come together very quickly once you know what you’re doing, and the entire thing is a demonstration of a complex web app that is also possible. The wiki’s implemented entirely using those same tools, and everything is open for you to tweak and edit to your liking.

I moved a Super Bowl guessing/fake gambling game that I run from a form and spreadsheet to a TiddlyWiki and now I can share an online dashboard that live updates for everyone and it was decently easy to make and works really well. With my task manager, I recently decided to add a feature where I can set an “agenda” value on any task, and they all show up in one place, so I could set it as “Boss” and then quickly see everything I wanted to bring up in our next 1 on 1 meeting. It took just a few minutes to add the text box to anything that gets tagged “Task” and then make another page that collected them all and displayed them in sections.

When 66 to 72 million years old you reach, look as good you will not, hmm?

This is the key with all the machine learning stuff going on right now. The robot will create something, but none of them have a firm understanding of right, wrong, truth, lies, reality, or fiction. You have to be able to evaluate its output because you have no idea if the robot’s telling the truth or not at that moment. Images are pretty immune to this because everyone can evaluate a picture for correctness or realism, and even if it’s a misleading photorealistic image, well, we’ve already had Photoshops for a long time. With text, you always have to keep in mind that the robot might be low quality or outright wrong, and if you aren’t equipped to evaluate its answers for that, you shouldn’t be using it.