this post was submitted on 22 Jul 2023
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I tried it for a couple months and it was alright but eventually it got too frustrating. I did love how well it did some really repetitive things. But rarely did it actually get anything complex 100% right. In computing, "almost right" is wrong. But because it was so close, it was hard to spot the mistakes.
There were cases where my IDE knew the right answer but Copilot did not. Realizing that Copilot was messing up my IDE enhancements to produce code I was painfully babysitting, I cancelled it.
This is the most insidious conundrum related to AI usage. At the end of the day, a LLM's top priority is to ensure that your question is answered in a way that satisfies that model. The accuracy of its answers are a secondary concern. If forced to choose between making up BS so it can have a response that looks right versus admitting it doesn't have enough information to answer, it can and often will choose the former. Thus the "hallucination" problem was born.
The chance of getting your answer lightly sprinkled with made up stuff is disturbingly high. This transfers the cognitive load of the AI user from "what is the answer" to "I must repeatedly go verify everything in this answer because I can't trust it".
Not an insurmountable obstacle, and they will likely solve it sooner rather than later, but AI right now is arguably the perfect extension of the modern internet - take absolutely everything you read with at least a grain of salt... and keep a pile of salt cubes close by.