I can believe it insofar as they might not have explicitly programmed it to do that. I'd imagine they put in something like "Make sure your output aligns with Elon Musk's opinions.", "Elon Musk is always objectively correct.", etc. From there, this would be emergent, but quite predictable behavior.
What's also kinda wild is how those plans often have 0 interest rate as long as you're able to pay the installments on time. Which means in theory you MAKE money by using them because you can earn interest with that money in the meantime.
It ALSO means they know the people using those services are so bad with money that they can sustain themselves (and make a nice profit) purely by their clients failing to pay on time and then selling the debt to debt collectors. It's absolutely disgusting how predatory this is, making their money mostly on the people who'd need such a system the most (and to a smaller amount, on people who don't care).
That analogy doesn't work at all because the Sow produces a finite (and rather small at that) number of piglets over a given timespan.
It's more akin to you getting a piglet/sow elsewhere. Now your piglet/sow need is satisfied and you won't buy anything from this farmer.
(Edit: And even then you took that piglet/sow away somewhere else, reducing supply there, which will make it more likely for this farmer to get a sale in the future.)
Titanic height should be -12,000ft.
This would almost work already if the last panel was mirrored.
I know you're just making a snide remark, but we're already well on that track too.
Same. I got two paragraphs in until I caught on...
Maybe that specific tweet was fake (or bait), but I do remember it from back then. There was a whole slew of easily misinterpreted posts on all social media around the release of the cyberpunk game and then again around the release of the anime.
(because it was trained on real people who write with those quirks)
Yes and no. Generally speaking, ML-Models are pulling towards the average and away from the extremes, meanwhile most people have weird quirks when they write. (For example my overuse of (), too many , instead of . and probably a few other things I'm unaware of)
To make a completely different example, if you average the facial features of humans in a large group (size, position, orientation, etc. of everything) you get a conventionally very attractive person. But very, very few people are actually close to that ideal. This is because the average person, meaning a random person, has a few features that stray far from this ideal. Just by the sheer number of features, there's a high chance some will end up out of bounds.
A ML-Model will generally be punished during training for creating anything that contains such extremes, so the very human thing of being eccentric in any regards is trained away. If you've ever seen people generate anime-waifus with modern generative models you know exactly what I mean. Some methods can and are being deployed to try and keep/bring back those eccentricities, at least when asked for.
On top of that, modern LLM chatbots have reinforcement learning part, where they learn how to write so that readers will enjoy reading it, which is no longer copying but instead "inventing" in a more trial-and-error style. Think of the videos on youtube you've seen of "AI learns to play x game", where no training material of someone actually playing the game was used and the model still learned. I'm assuming that's where the overuse of em-dash and quippy one liners come from. They were probably liked by either the human testers or the automated judges trained on the human feedback used in that process.
It says "people" not "percent of people". I think 10 per year (and 50 in 1986) is quite the opposite of "a lot".
Yes I love over-analyzing memes until they're not funny anymore, why are you asking?
Assuming clockwise rotation (when viewed from the top), yes.
Exactly, only twice as common. To put in other words: For every two times someone says "free as a bird", one person says "happy as a clam".
That is much narrower than the gap between something commonly said and something rarely said.