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I bet there are journals that have roughly the same sentiment for compiled languages vs assembler. and then again on assembler vs machine code. However just li

I bet there are journals that have roughly the same sentiment for compiled languages vs assembler. and then again on assembler vs machine code. However just like learning to drive, I think there is much scope for forcing the basics once in a while. The old programming blue shell, as it were.

aiono 2y ago
There is a difference: when you code in higher level languages most of the time (except some domains where you still need to understand assembly) you don't need to understand how generated assembly works. This is not the case with AI generated code. You still need to be able understand, modify and validate the generated code.
dartos 2y ago
> I bet there are journals that have roughly the same sentiment for compiled languages vs assembler Please find one. I highly doubt there are any at all. AI isn’t translating idea to code. It’s guessing as to what the most likely next code is, given a context. Compiling is exactly translating code to machine code, but hiding some (or a ton of) technical details. Those are two wildly different mechanisms to interact with. Assembly and compiled languages require the same precision of thought, just at different levels of technical detail.
- danielbln 2y ago
  LLMs can absolutely translate ideas to code. How is that even a topic of debate. It might not always be good or maintainable code, but the whole notion of "it's just a next token predictor" is so trite at this point.
  - dartos 2y ago
    You're misunderstanding what I mean. Sorry, I have trouble articulating this idea. "Idea" was probably not the best word, but I'm not sure of a better one. LLMs can, in effect, translate some ideas into code. Under the hood, though, they are not. They are predicting next tokens based on context. There are impressive and obviously useful emergent properties of how good they are at predicting tokens, but that is what they are doing. There's no question there. That's a fact. It's not a direct translation, however, as is the case with compiled languages. Compiled languaged directly translate the idea you communicate to them into machine/byte code in a deterministic, predictable, and importantly tractable way. LLMs do not do that. They aren't abstracting what the computer can do so that you can more effectively write instructions. as you say > It might not always be good or maintainable code What if your idea _was_ good and/or maintainable machine/byte code? What if you needed to examine the output code and ensure some quality of it. With a compiler, if you can communicate in a programming language what exactly what you want, it will _always_ produce machine/byte code that matches that. LLMs just don't do that. It's just not how statistical models work.

aiono 2y ago

There is a difference: when you code in higher level languages most of the time (except some domains where you still need to understand assembly) you don't need to understand how generated assembly works. This is not the case with AI generated code. You still need to be able understand, modify and validate the generated code.

dartos 2y ago

> I bet there are journals that have roughly the same sentiment for compiled languages vs assembler Please find one. I highly doubt there are any at all. AI isn’t translating idea to code. It’s guessing as to what the most likely next code is, given a context. Compiling is exactly translating code to machine code, but hiding some (or a ton of) technical details. Those are two wildly different mechanisms to interact with. Assembly and compiled languages require the same precision of thought, just at different levels of technical detail.

danielbln 2y ago
LLMs can absolutely translate ideas to code. How is that even a topic of debate. It might not always be good or maintainable code, but the whole notion of "it's just a next token predictor" is so trite at this point.
- dartos 2y ago
  You're misunderstanding what I mean. Sorry, I have trouble articulating this idea. "Idea" was probably not the best word, but I'm not sure of a better one. LLMs can, in effect, translate some ideas into code. Under the hood, though, they are not. They are predicting next tokens based on context. There are impressive and obviously useful emergent properties of how good they are at predicting tokens, but that is what they are doing. There's no question there. That's a fact. It's not a direct translation, however, as is the case with compiled languages. Compiled languaged directly translate the idea you communicate to them into machine/byte code in a deterministic, predictable, and importantly tractable way. LLMs do not do that. They aren't abstracting what the computer can do so that you can more effectively write instructions. as you say > It might not always be good or maintainable code What if your idea _was_ good and/or maintainable machine/byte code? What if you needed to examine the output code and ensure some quality of it. With a compiler, if you can communicate in a programming language what exactly what you want, it will _always_ produce machine/byte code that matches that. LLMs just don't do that. It's just not how statistical models work.