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?

That’s not how it works at all. If it were as easy as adding a line of code that says “check for integrity” they would’ve done that already. Fundamentally, the way these models all work is you give them some text and they try to guess the next word. It’s ultra autocomplete. If you feed it “I’m going to the grocery store to get some” then it’ll respond “food: 32%, bread: 15%, milk: 13%” and so on.

They get these results by crunching a ton of numbers, and those numbers, called a model, were tuned by training. During training, they collect every scrap of human text they can get their hands on, feed bits of it to the model, then see what the model guesses. They compare the model’s guess to the actual text, tweak the numbers slightly to make the model more likely to give the right answer and less likely to give the wrong answers, then do it again with more text. The tweaking is an automated process, just feeding the model as much text as possible, until eventually it gets shockingly good at predicting. When training is done, the numbers stop getting tweaked, and it will give the same answer to the same prompt every time.

Once you have the model, you can use it to generate responses. Feed it something like “Question: why is the sky blue? Answer:” and if the model has gotten even remotely good at its job of predicting words, the next word should be the start of an answer to the question. Maybe the top prediction is “The”. Well, that’s not much, but you can tack one of the model’s predicted words to the end and do it again. “Question: why is the sky blue? Answer: The” and see what it predicts. Keep repeating until you decide you have enough words, or maybe you’ve trained the model to also be able to predict “end of response” and use that to decide when to stop. You can play with this process, for example, making it more or less random. If you always take the top prediction you’ll get perfectly consistent answers to the same prompt every time, but they’ll be predictable and boring. You can instead pick based on the probabilities you get back from the model and get more variety. You can “increase the temperature” of that and intentionally choose unlikely answers more often than the model expects, which will make the response more varied but will eventually devolve into nonsense if you crank it up too high. Etc, etc. That’s why even though the model is unchanging and gives the same word probabilities to the same input, you can get different answers in the text it gives back.

Note that there’s nothing in here about accuracy, or sources, or thinking, or hallucinations, anything. The model doesn’t know whether it’s saying things that are real or fiction. It’s literally a gigantic unchanging matrix of numbers. It’s not even really “saying” things at all. It’s just tossing out possible words, something else is picking from that list, and then the result is being fed back in for more words. To be clear, it’s really good at this job, and can do some eerily human things, like mixing two concepts together, in a way that computers have never been able to do before. But it was never trained to reason, it wasn’t trained to recognize that it’s saying something untrue, or that it has little knowledge of a subject, or that it is saying something dangerous. It was trained to predict words.

At best, what they do with these things is prepend your questions with instructions, trying to guide the model to respond a certain way. So you’ll type in “how do I make my own fireworks?” but the model will be given “You are a chatbot AI. You are polite and helpful, but you do not give dangerous advice. The user’s question is: how do I make my own fireworks? Your answer:” and hopefully the instructions make the most likely answer something like “that’s dangerous, I’m not discussing it.” It’s still not really thinking, though.

This, and see also “minmaxing,” the process of optimizing something (usually your character in a game) to get minimum penalty and/or maximum benefit, usually ignoring anything like realism or storytelling and focusing entirely on the stats and numbers.

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

It’s not a fantasy because they’re bad ideas (they’re not) or we shouldn’t fight for them (we should), it’s a fantasy because you’re skipping over any of the actual work that needs to be done to make them happen: convincing more people to join you and demand more. Ask 100 people if the Senate and Supreme Court should be abolished and 99 of them are going to look at you like you have two heads. You can insist that you’re right and they’re all wrong all you want, but unless you work to get more people on your side, you’ll just be complaining into the void and setting impossible standards for politicians so that you can feel smug when they fail to meet them.

If a minority group is being oppressed or is otherwise motivated to create change and is voting in large numbers, but the majority is apathetic and not bothering to vote, then this system would prevent the minority from changing their representation as “punishment” for something they’re not doing.

It’s also a bit of a “the beatings will continue until morale improves” solution to the problem, if it even is actually a problem. Low turnout is bad, but not because it’s inherently bad not to vote. It’s a symptom of the fact that people don’t think it matters, or that it will change anything, and unfortunately they’re not exactly wrong much of the time. Instead of putting effort into punishing people for not being engaged enough, it’d be better to make systemic changes that empower people and make the government more representative of their interests.