[tluyben2]

It is fundamentally different in mathematical foundations; some functions are proven formally verified and therefor will execute 100% perfectly (I guess you are talking about actual bugs like hardware issues?); what gpt3 does is not even close to that; if you put the same input to gpt3 multiple times it comes up with different answers. That is nowhere close to a computer executing an algorithm.

[wyattpeak]

I'm not talking about GPT-3, I'm discussing the theoretical question raised by the grandparent of my comment: How is predicting the output of a function fundamentally different from executing the code?

We call computers deterministic despite the fact that they don't with perfect reliability perform the calculations we set them. The probability that they'll be correct is very high, but it's not 1. So the requirement we have for something to be considered deterministic is certainly not "perfectly a hundred percent of the time", as the parent to my comment suggested.

[danans]

> if you put the same input to gpt3 multiple times it comes up with different answers. That is nowhere close to a computer executing an algorithm.

It's a non-deterministic algorithm, of which many kinds exist. Producing different answers that are close-ish to correct is in fact what a Monte Carlo algorithm does. Not that you'd use GPT3 as a Monte Carlo algorithm though, but it's not that different.

[tluyben2]

Sure, but if you have something as clear as some of the actual deterministic python code from the article, this doesn’t fly.

Close-ish to correct makes sense for some problems and makes no sense at all for others.