I used GPT-4 for an interview problem from leetcode out of curiosity. It got it right, very quickly, yay!
Then I asked it to modify it by eliminating one of the constraints on the problem. It did a very convincing "Ah, if we need [that] we need to do [this] and output a new version... that didn't actually work right.
I pointed out the specific edge case, it said "you are correct, for that sort of case we have to modify it" and then spit out exactly the same code as the last attempt.
The most interesting thing to me there isn't that it got it wrong - it's that spitting out exactly the same output without realizing it, while saying that you are going to do something different, is the clearest demonstration I've seen from it that it doesn't "understand" in human-like ways.
Extremely powerful and useful, but VERY important for users to know where it runs into the wall. Since it often won't tell you on its own.
These models are designed to produce a _plausible_ text output for a given prompt. Nothing more.
They are not designed to produce a _correct_ text output to a question or request, even if sometimes the output is correct. These proverbial stopped clocks might be correct more than twice a day, but that's just the huge training set speaking.
Well, I wasn't, but if you look at the top most comment of this thread [0] you'll see that considering the level of human reinforcement being demonstrated only reinforces my point.
Wow, reading this thread dispelled any doubt I might have had about the hedonistic treadmill.
Can you imagine having this conversation a year ago? And already there are pronouncements all over this thread that the current problems are 'intrinsic' to the approach. I'm not as readily convinced that the improvement is slowing down. Regularization is a powerful thing.
I was confused by the term, https://en.wikipedia.org/wiki/Hedonic_treadmill but it refers to the concept of humans basically adapting to anything and that the "new normal" can be an Overton Window away or more.
Couple in some Corn Pone Opinions (Twain) and constantly moving the Goal Posts (fallacy) or making grand proclamations without any evidence, even all of that is proof that we are living in interesting times.
Not to be a fence sitter, but things are moving so quickly that it is impossible to make predictions in anything but the current level of chaos. Anyone who makes predictions right now is suspect.
I didn't say anything about whether or not I expect it to get better (translation from English to code doesn't seem like an insurmountable task based on what these do so far), but I think that cuts both ways.
For every "leap to a conclusion that some things will never be fixed" there's a "leap to a conclusion that this is already some sort of more general intelligence it is."
And that's really key to my main point. The only way to avoid either of those is to actually use the things and see what does and doesn't work. That's a million times more interesting than just unrealistic hype or hate comments.
Cahtgpt is quite good for known problems before 2022, since the questions got into the training set. It's quite bad for new interview questions though.
Then I asked it to modify it by eliminating one of the constraints on the problem. It did a very convincing "Ah, if we need [that] we need to do [this] and output a new version... that didn't actually work right.
I pointed out the specific edge case, it said "you are correct, for that sort of case we have to modify it" and then spit out exactly the same code as the last attempt.
The most interesting thing to me there isn't that it got it wrong - it's that spitting out exactly the same output without realizing it, while saying that you are going to do something different, is the clearest demonstration I've seen from it that it doesn't "understand" in human-like ways.
Extremely powerful and useful, but VERY important for users to know where it runs into the wall. Since it often won't tell you on its own.