There have been multiple things which have gone wrong with AI for me but these two pushed me over the brink. This is mainly about LLMs but other AI has also not been particularly helpful for me.
Case 1
I was trying to find the music video from where a screenshot was taken.
I provided o4 mini the image and asked it where it is from. It rejected it saying that it does not discuss private details. Fair enough. I told it that it is xyz artist. It then listed three of their popular music videos, neither of which was the correct answer to my question.
Then I started a new chat and described in detail what the screenshot was. It once again regurgitated similar things.
I gave up. I did a simple reverse image search and found the answer in 30 seconds.
Case 2
I wanted a way to create a spreadsheet for tracking investments which had xyz columns.
It did give me the correct columns and rows but the formulae for calculations were off. They were almost correct most of the time but almost correct is useless when working with money.
I gave up. I manually made the spreadsheet with all the required details.
Why are LLMs so wrong most of the time? Aren’t they processing high quality data from multiple sources? I just don’t understand the point of even making these softwares if all they can do is sound smart while being wrong.
LLMs are not designed to give you objective factual answers. They’re designed to guess what you want to hear, like a middle school student writing a book report for a book they never read.
I don’t think it considers what the user wants to hear. It is concerned about what the data it has trained on would consider a logical answer.
What the user wants to hear is usually biased in the question. “Why are vaccines good” will have a different response from “Why are vaccines bad”
Both may or may not include factual information (again, middle school student guessing at a reading assignment analogy), but they’re shaped by the questioner to reaffirm your own biases.