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Comment by koboll | Hacker News Reader

It honestly seems sort of like a straight path toward an LLM that can hold a whole codebase in context and can produce new functionality on command. Fundamentally, software is just taking inputs and producing outputs; we will almost certainly be able to define those and let the LLM do everything in between within a year or two.

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And brain surgery is just cutting someone's head open with a scalpel. Programming languages are stupidly information dense. I'm highly skeptical an AI will be useful for non-trivial boiler plate any time soon, they lack the reasoning powers to get the details right even occasionally for common tasks and basically never for uncommon ones.

While I take your meaning here, I would suggest also that natural languages are even more stupidly information-dense. And while the rules of their logic are not as hard and immutable as the semantics of programming languages, they do have plenty.

I don't agree that natural language is more information dense than most programming languages, there's a bunch of shared context, work, and assumptions that must be understood before effective use of both, but natural language is typically pretty loose and relies heavily on context for real meaning, which is still subjective. Conversely, PLs have no subjectivity once executed/compiled. Natural language is very verbose when trying to express the concepts of a PL with the same specificity, just look at how long entry-level tutorials for writing hello world in any PL are. Or try explaining all the details of even a basic function call in C to a CS101 class. You need to say a lot of words to describe the same exact thing.

PLs benefit from being targeted at a specific domain dramatically that NL just can't.

Programming languages are stupidly information dense indeed, but if that's the most major roadblock, I don't see why throwing more compute at the problem won't eventually solve it. GPT-4 is already handling a very stupid amount of information density.

My point isn't the information density, it's the fact that every detail matters. LLMs aren't great at details or facts and therefore aren't really suited for writing software right now. It's not a computational limit, it's intrinsic to how LLMs work.

Something a lot of people aren't considering is the added power the LLM will have once we are building systems from the ground up specifically for the AI to use. Right now it's doing a decent job with languages and tools that are built for human usability. I imagine soon we'll have languages, systems, and tools specifically built to be extremely usable by AI.

I seen that said a few time and I struggle to see why a language would be more AI friendly than à other ?

> I imagine soon we'll have languages, systems, and tools specifically built to be extremely usable by AI.

Those are called programming languages, we have quite a lot of them.

Consistency is still a huge problem. I tried to build a D&D character with Chatgpt the other day. Absolute nightmare. For example, it said the character gets a racial bonus to two attributes. I told it to give me my attributes taking into account racial bonuses. It applied the bonus to one attribute but not the other.

Today I got the first case of something I could not find in Google, that chatGPT found.

It was more an indexing question and a factor of me remembering the concept of a theater play, but nothing else.

I was really impressed when the LLM found the answer right away. Until I realized it was a lie.

I had to made it print a list of 50 plays, and I found what I was looking for.

I realize it’s not a good usage for a LLM but I think it’s how it’s gonna be with those

Maybe at some stage software will also have limits to, I know that is hard to imagine but maybe why software has stagnated a little is because we’re running out of actual things it can do for us from a societal and economic standpoint?

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