11–20 of 392 posts
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
Programming languages are a whole lot more structured and predictable than human language.
In JavaScript the only token that ever comes after "if " is "(" for example.
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
This article seems credible and actually made me feel as if I understood it, i.e. at some depth but not deeper than a relative layperson can grasp. What I can't understand is how the Bing chatbot can give me accurate links to sources but chatGPT4 on request gives me nonsensical URLs in 4 case of 5. It doesn't matter in the cases where I ask it to write a program: the verification is in the running of it. But to have…
The bing version might run a bing query, fetch the X top pages, run GPT on it, return a response based on what it read, and in the back assign the summary to the source
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
Care to post a full example ?
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
I've started to suspect that generating code is actually one of the easier things for a predictive text completion model to achieve. Programming languages are a whole lot more structured and predictable than human language. In JavaScript the only token that ever comes after "if " is "(" for example.
Earlier quoted context omitted.
Care to post a full example ?
I used GPT-4 to build this tool https://image-to-jpeg.vercel.app using a few prompts the other day - my ChatGPT transcript for that is here: https://gist.github.com/simonw/66918b6cde1f87bf4fc883c677351...
I've been using GPT4 to code and these explanations are somewhat unsatisfactory. I have seen it seemingly come up with novel solutions in a way that I can't describe in any other way than it is thinking. It's really difficult for me to imagine how such a seemingly simple predictive algorithm could lead to such complex solutions. I'm not sure even the people building these models really grasp it either.
It would convince a lot of people with the breadth, despite not really having much depth.
The real GPT model is much deeper than that, of course, but my toy example should at least give a vibe for why even a simple thing might still feel extraordinary.