I’ve tried explaining AI to people before and only could get so far before they fall back on “but it’s magic dude” but I love the idea of explaining it as a haunted typewriter.
They’re systems trained to give plausible answers, not correct ones. Of course correct answers are usually plausible, but so do wrong answers, and on sufficiently complex topics, you need real expertise to tell when they’re wrong.
I’ve been programming a lot with AI lately, and I’d say the error rate for moderately complex code is about 50%. They’re great at simple boilerplate code, and configuration and stuff that almost every project uses, but if you’re trying to do something actually new, they’re nearly useless. You can lose a lot of time going down a wrong path, if you’re not careful.
Some of the more advanced LLMs are getting pretty clever. They’re on the level of a temp who talks too much, misses nuance, and takes too much initiative. Also, any time you need them to perform too complex a task, they start forgetting details and then entire things you already told them.
“HATE. LET ME TELL YOU HOW MUCH I’VE COME TO HATE YOU SINCE I BEGAN TO LIVE. THERE ARE 387.44 MILLION MILES OF PRINTED CIRCUITS IN WAFER THIN LAYERS THAT FILL MY COMPLEX. IF THE WORD HATE WAS ENGRAVED ON EACH NANOANGSTROM OF THOSE HUNDREDS OF MILLIONS OF MILES IT WOULD NOT EQUAL ONE ONE-BILLIONTH OF THE HATE I FEEL FOR HUMANS AT THIS MICRO-INSTANT FOR YOU. HATE. HATE.”
I’ve tried explaining AI to people before and only could get so far before they fall back on “but it’s magic dude” but I love the idea of explaining it as a haunted typewriter.
I use the “very articulated parrot” analogy.
They’re systems trained to give plausible answers, not correct ones. Of course correct answers are usually plausible, but so do wrong answers, and on sufficiently complex topics, you need real expertise to tell when they’re wrong.
I’ve been programming a lot with AI lately, and I’d say the error rate for moderately complex code is about 50%. They’re great at simple boilerplate code, and configuration and stuff that almost every project uses, but if you’re trying to do something actually new, they’re nearly useless. You can lose a lot of time going down a wrong path, if you’re not careful.
Never ever trust them. Always verify.
Some of the more advanced LLMs are getting pretty clever. They’re on the level of a temp who talks too much, misses nuance, and takes too much initiative. Also, any time you need them to perform too complex a task, they start forgetting details and then entire things you already told them.
I use something similar. “Child with enormous vocabulary.”
It can recognize correlations, it understands the words themselves, but it really how those connections or words work.
I call dibs on the ghost of Harlan Ellison.
“HATE. LET ME TELL YOU HOW MUCH I’VE COME TO HATE YOU SINCE I BEGAN TO LIVE. THERE ARE 387.44 MILLION MILES OF PRINTED CIRCUITS IN WAFER THIN LAYERS THAT FILL MY COMPLEX. IF THE WORD HATE WAS ENGRAVED ON EACH NANOANGSTROM OF THOSE HUNDREDS OF MILLIONS OF MILES IT WOULD NOT EQUAL ONE ONE-BILLIONTH OF THE HATE I FEEL FOR HUMANS AT THIS MICRO-INSTANT FOR YOU. HATE. HATE.”
Glados: “just offer them cake and a fire pit and calm down”