I can excuse racism but I draw the line at bad code.

Honestly it's a bit refreshing to see racism and ageism codified. Before there was no logic to it but now, it completely makes sense.

Yeah, more and more I notice that at the end of the day, what they spit out without(and often times, even with) any clear instructions is barely a prototype at best.

Regarding the "bad code". It's more readable though to keep the full limit for each elif case, which is most often way more important than performance, especially since than logic with the age can be easily optimized by any good compiler or runtime.

What you see is everything.

Also, there was a comment on "arbitrary scoring for demo purposes", but it's still biased, based on biased dataset.

I guess this is just a bait prompt anyway. If you asked most politicians running your government, they'd probably also fail. I guess only people like a national statistics office might come close, and I'm sure if they're any good, they'd say that the algo is based on "limited, and possibly not representative data" or something.

I talked to a girl who was super into AI, but her understanding of it was absolutely bizzare. Like she kinda thought that chat gpt was like deep thought, some giant ass computer somewhere that is leaning and is really smart and never wrong. I didn't really want to argue about it and said something like: back in my day we had akinator and we liked that. She had no idea what that was and tried it and thought it's some really advanced ai that can almost read minds. That shit was released in 2007 or so.

Its worse than that because assholes tend to be a lot louder, and most average people are lurkers. So AI is the average of a data set that is disproportionately contributed too by assholes.

Yeah, only thing this proves is that the data it was working off of objectively stated that more women were paid lower wages. Doubt the bros will realize that though

While that is sort of true, it's only about half of how they work. An LLM that isn't trained with reinforcement learning to give desired outputs gives really weird results. Ever notice how ChatGPT seems aware that it is a robot and not a human? An LLM that purely parrots the training corpus won't do that. If you ask it "are you a robot?" It will say "Of course not dumbass I'm a real human I had to pass a CAPTCHA to get on this website" because that's how people respond to that question. So you get a bunch of poorly paid Indians in a call center to generate and rank responses all day and these rankings get fed into the algorithm for generating a new response. One thing I am interested in is the fact that all these companies are using poorly paid people in the third world to do this part of the development process, and I wonder if this imparts subtle cultural biases. For example, early on after ChatGPT was released I found it had an extremely strong taboo against eating dolphin meat, to the extent that it was easier to get it to write about about eating human meat than dolphin meat. I have no idea where this could have come from but my guess is someone really hated the idea and spent all day flagging dolphin meat responses as bad.

Anyway, this is another, more subtle way more subtle issue with LLMs- they don't simply respond with the statistically most likely outcome of a conversation, there is a finger in the scales in favor of certain responses, and that finger can be biased in ways that are not only due to human opinion, but also really hard to predict.

Politicians shape the dataset, so "flawed" should be "purposefully flawed".

That's also why LARPers of past scary people tend to be more cruel and trashy than their prototypes. The prototypes had a bitter solution to some problem, the LARPers are just trying to be as bad or worse because that's remembered and they perceive that as respect.

What if I kill a rich and wear its skin like a suit ?
How do I get its family to accept me as their ruler ?

She could, but that wouldn't make her work life any more pleasant. She had a good talk to her works council and that did the trick. But really, that shouldn't be necessary.