Dogs love pumpkin. Dogs love cinnamon.

That's because you're the victim of a crime: extortion

He was one month shy of his 16th birthday when he died. Now he'd have to wait a month or get his learner's permit before being allowed to get killed by a car on an e-bike.

Based on what they've done in the past, they're going to write it off on their taxes when though the titles aren't even games that they own.

In terms of abilities, there's problems other people can solve that I never will even with years of study and training, and there are problems I can solve that my immediate peers never would, even with years of study and training.

In terms off knowledge, everybody you meet knows something you don't. (They might not have the ability to help you find what it is though)

In terms of skills, behaviors it seems like nobody ever considers trying and finding out for themselves, which is endemic across all levels of academia, government, business, and profession, and in that matter they are all as dumb as a bunch of rocks.

Whether or not you use downvotes doesn't really matter.

If what you like is well represented by the Boba drinkers and the Boba drinkers disproportionally don't like Cofee then Cofee will be disproportionally excluded from the top of your results. Unless you explore deeper the Cofee results will be pushed to the bottom of your results. And any that happen to come to the top will have arrived there from broad appeal and will have very little contribution to thinking you like Cofee.

If you don't let the math effectively push things away that are disliked by the people who like similar things as you then everything will saturate at maximum appeal and the whole system does nothing.

There's two problems. The first is that those other things you might like will be rated lower than things you appear to certainly like. That's the "easy" problem and has solutions where a learning agent is forced to prefer exploring new options over sticking to preferences to some degree, but becomes difficult when you no longer know what is explored or unexplored due to some abstraction like dimension reduction or some practical limitation like a human can't explore all of Lemmy like a robot in a maze.

The second is that you might have preferences that other people who like the same things you've already indicated a taste for tend to dislike. For example there may be other people who like both Boba and Cofee but people who like one or the other tend to dislike the other. If you happen to encounter Boba first then Cofee will be predicted to be disliked based on the overall preferences of people who agree with your Boba preference.

No, not as simply as that. That's the basic idea of recommendation systems that were common in the 1990s. The algorithm requires a tremendous amount of dimensionality reduction to work at scale. In that simple description it would need a trillion weights to compare the preferences of a million users to a million other users. If you reduce it to some standard 100-1000ish dimensions of preference it becomes feasible, but at the low end only contains about as much information as your own choices about subscribed to or blocked communities (obviously it has a much lower barrier of entry).

There's another important aspect of learning that the simple description leaves out, which is exploration. It will quickly start showing you things you reliably like, but won't experiment with things it doesn't know you'd like or not to find out.

Carbon Catch and Release

Air cooling is just vacuum cooling with extra steps.