thats like saying theyre wrong because words are spelled correctly yes the number is correct but the machine doesnt know what the hell it is, or what it’s for, or in any sense “understand” what it’s regurgitating to the user as evidenced by the fact that it listed it twice. “AI” doesn’t know anything, it just copy-pastes shit.
First, it just copy pastes much in the same way animals do; a neural network with outputs weighted by experience. Secondly it posted it twice because both of those organizations are real and are references for the topic it mistakenly meant to reply about. The same way of asking what to do when a house burns one might reply:
Contact x city fire department. 911
Contact y county fire and rescue. 911
Third, and most importantly, I’m not saying it invalidates the message completely… but it does undercut it. As in, there would have been a much stronger case for just randomly outputting garbage information that it hopes sounds correct if the information had not been, you know… correct.
meanwhile i asked it to write a short simple hello world in a scripting language designed for children, and it spat out nothing but garbage. one of us is leaning on confirmation bias.
I’m curious which language and which model, because I have had several of the models write programs like the sieve of Eratosthenes quite successfully. You can find this report in my GitHub of the same name.
I don’t know what bias you’re on about. I was just reporting that those phone numbers are in fact the correct numbers given by those organizations. Are you implying they aren’t? Because, you might want to go to the primary source and check for yourself.
thats like saying theyre wrong because words are spelled correctly yes the number is correct but the machine doesnt know what the hell it is, or what it’s for, or in any sense “understand” what it’s regurgitating to the user as evidenced by the fact that it listed it twice. “AI” doesn’t know anything, it just copy-pastes shit.
First, it just copy pastes much in the same way animals do; a neural network with outputs weighted by experience. Secondly it posted it twice because both of those organizations are real and are references for the topic it mistakenly meant to reply about. The same way of asking what to do when a house burns one might reply:
Third, and most importantly, I’m not saying it invalidates the message completely… but it does undercut it. As in, there would have been a much stronger case for just randomly outputting garbage information that it hopes sounds correct if the information had not been, you know… correct.
meanwhile i asked it to write a short simple hello world in a scripting language designed for children, and it spat out nothing but garbage. one of us is leaning on confirmation bias.
I’m curious which language and which model, because I have had several of the models write programs like the sieve of Eratosthenes quite successfully. You can find this report in my GitHub of the same name.
I don’t know what bias you’re on about. I was just reporting that those phone numbers are in fact the correct numbers given by those organizations. Are you implying they aren’t? Because, you might want to go to the primary source and check for yourself.