Shitposting saves jobs

You can't really blame the amount of stupidity online.

The problem is that ChatGPT (and other LLM) produce content of the average quality of its input data. AI is not limited to LLM.

For chess we were able to build AI that vastly outperform even the best human grandmasters. Imagine if we were to release a chess AI that is just as good as the average human...

unexpected heroes what a plot twist

from what i understand, the big change in chat-gpt4 was that the model could “ask for help” from other tools: for maths, it knew it was a maths problem, transformed it to something a specialised calculation app could do, and then passed it off to that other code to do the actual calculation

same thing for a lot of its new features; it was asking specialised software to do the bits it wasn’t good at

My guess is that it's more a result of overfitting for alignment. Fine-tuning for "safety" (rather, more corporate-friendly outputs).

That is, by focusing on that specific outcome in training the model, they've compromised its ability to give well-"reasoned" "intelligent" sounding answers. A tradeoff between aspects of the model.

It's something that can happen even in simple statistical models. Say you have a scatter plot of data that loosely follows some trend, and you come up with two equations to describe that trend. One is a simple equation that loosely follows it but makes a good general approximation, and the other is a more complicated equation that very tightly fits the existing data. Then you use those two models to predict future data. But you find that the complicated equation is making predictions way off the mark that no longer fit the trend, and the simple one still has a wide error (how far its prediction is from the actual data) but still more or less accurately fits the general trend. In the more complicated equation, you've traded predictive power for explanatory power. It describes the data you originally had but it's not useful for forecasting data that follows.

That's an example of overfitting. It can happen in super-advanced statistical models like GPT, too. Training the "equation" (or as it's been called, spicy autocorrect) to predict outcomes that favor "safety" but losing the model's power to predict accurate "well-reasoned" outcomes.

If that makes any sense.

I'm not a ML researcher or statistician (I just went through a phase in college), so if this is inaccurate I'm open to corrections.

Yep.

Standard VC bullshit.

Burn money providing a lot for nothing to build brand recognition. Then cut free service before bringing out "premium" that at first works better than the original.

Until a bunch of people starting paying and the resources aren't scaled up to match.

They probably laid off the guy behind the curtain.

Is it really? It seems like it would be excellent at that. I have a little hand held device from the 1990's that can play 20 questions and is almost always right. It seems that if that little device can win, ChatGPT most certainly should be able to.

Edit: I just played and it guessed what I was thinking of in 13 questions. But then it kept asking questions. I asked why it was asking questions still since it already guessed it and it said "oh, you are absolutely correct, I did guess it correctly!". Lol, ChatGPT is funny sometimes.

This has nothing to do with that. They already have all the data they could ever need to train the model.

I mean, whose to say they aren't? But also, the fediverse is worthless compared to the big players. The entirety of the fediverses content to date is like a days worth of twitter or reddit content.