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> This code is incorrect though, as it doesn't account for ... Isn't this the fundamental problem with code generated by a statistical text generation algorith

> This code is incorrect though, as it doesn't account for ... Isn't this the fundamental problem with code generated by a statistical text generation algorithm? In other words, "code" is short for encoding a solution. And to have a solution to encode is to understand the problem to solve. Without understanding, statistical code generation is little more than a popularity contest.

roywiggins 2y ago
The model doesn't "understand" anything, but if you, the programmer, understands it well enough, that can be enough to direct it to find a solution. You can do a lot with ChatGPT or Claude if wrong code is easy to spot (which will obviously depend on what you're working on). If you can easily spot mistakes these things can often come up with a fix once you point it out. I've had some real success converting small-scale production C++ code into Python using Claude. Stuff that isn't really deep or complicated, but it's still faster and less annoying using an LLM to assist. I am sure there are large domains where it's crap, but for relatively simple stuff (CRUD logic, simple file parsing) it does remarkably well.
- AdieuToLogic 2y ago
  > The model doesn't "understand" anything, but if you, the programmer, understands it well enough, that can be enough to direct it to find a solution. This is exactly my point. Whether a programmer uses past experience exclusively to author source code or a statistical code generator (LLM) and then their learned ability is orthogonal to my original premise. Without understanding, statistical code generation is little more than a popularity contest.
im3w1l 2y ago
I disagree with everything you said. Code is not short for encoding a solution. Code is any kind of program whether correct or not. You can have a solution without understanding it. You can for instance know the rough shape a solution should have and try to guess at the details. And get it correct some of the time. And current models do have some form of understanding, although it is sometimes incomplete. They are clearly able to solve many problems after all.

roywiggins 2y ago

The model doesn't "understand" anything, but if you, the programmer, understands it well enough, that can be enough to direct it to find a solution. You can do a lot with ChatGPT or Claude if wrong code is easy to spot (which will obviously depend on what you're working on). If you can easily spot mistakes these things can often come up with a fix once you point it out. I've had some real success converting small-scale production C++ code into Python using Claude. Stuff that isn't really deep or complicated, but it's still faster and less annoying using an LLM to assist. I am sure there are large domains where it's crap, but for relatively simple stuff (CRUD logic, simple file parsing) it does remarkably well.

AdieuToLogic 2y ago
> The model doesn't "understand" anything, but if you, the programmer, understands it well enough, that can be enough to direct it to find a solution. This is exactly my point. Whether a programmer uses past experience exclusively to author source code or a statistical code generator (LLM) and then their learned ability is orthogonal to my original premise. Without understanding, statistical code generation is little more than a popularity contest.

im3w1l 2y ago

I disagree with everything you said. Code is not short for encoding a solution. Code is any kind of program whether correct or not. You can have a solution without understanding it. You can for instance know the rough shape a solution should have and try to guess at the details. And get it correct some of the time. And current models do have some form of understanding, although it is sometimes incomplete. They are clearly able to solve many problems after all.