Sam Altman is one of the dullest, most incurious and least creative people to walk this earth. This is, after all, the person who once tweeted 'i am a stochastic parrot and so are u', in response to Emily Bender's (entirely incisive and absolutely brilliant) critique of what his large language models are *actually doing*.
“Almost nothing” is not the same as “actually useless”. The former is saying the applications are limited, which is true.
LLMs are fine for fictional interactions, as in things that appear to be real but aren’t. They suck at anything that involves being reliably factual, which is most things including all the stupid places LLMs and other AI are being jammed in to despite being consistely wrong, which tech bros love to call hallucinations.
They have LIMITED applications, but are being implemented as useful for everything.
To be honest, as someone who’s very interested in computer generated text and poetry and the like, I find generic LLMs far less interesting than more traditional markov chains because they’re too good at reproducing clichés at the exclusion of anything surprising or whimsical. So I don’t think they’re very good for the unfactual either. Probably a homegrown neural network would have better results.
Agreed, our chat server ran a Markov chain bot for fun.
In comparison to ChatGPT on a 2nd server I frequent it had much funnier and random responses.
ChatGPT tends to just agree with whatever it chose to respond to.
As for real world use. ChatGPT 90% of the time produces the wrong answer. I’ve enjoyed Circuit AI however. While it also produces incorrect responses, it shares its sources so I can more easily get the right answer.
All I really want from a chatbot is a gremlin that finds the hard things to Google on my behalf.
“Almost nothing” is not the same as “actually useless”. The former is saying the applications are limited, which is true.
LLMs are fine for fictional interactions, as in things that appear to be real but aren’t. They suck at anything that involves being reliably factual, which is most things including all the stupid places LLMs and other AI are being jammed in to despite being consistely wrong, which tech bros love to call hallucinations.
They have LIMITED applications, but are being implemented as useful for everything.
To be honest, as someone who’s very interested in computer generated text and poetry and the like, I find generic LLMs far less interesting than more traditional markov chains because they’re too good at reproducing clichés at the exclusion of anything surprising or whimsical. So I don’t think they’re very good for the unfactual either. Probably a homegrown neural network would have better results.
Agreed, our chat server ran a Markov chain bot for fun.
In comparison to ChatGPT on a 2nd server I frequent it had much funnier and random responses.
ChatGPT tends to just agree with whatever it chose to respond to.
As for real world use. ChatGPT 90% of the time produces the wrong answer. I’ve enjoyed Circuit AI however. While it also produces incorrect responses, it shares its sources so I can more easily get the right answer.
All I really want from a chatbot is a gremlin that finds the hard things to Google on my behalf.
GPT-2 was peak LLM because it was bad enough to be interesting, it was all downhill from there
Absolutely, every single one of these tools has got less interesting as they refine it so it can only output the platonic ideal of kitsch.