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PaperclipPerfector  7d ago (text post)   27913 thread views

Culture War Roundup for the week of December 16, 2024

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If his claim was correct, LLM's wouldn't be a tool that help OpenAI developers boost their productivity, LLM's would literally be writing better and better versions of themselves, with no human intervention.

Stackoverflow is better than most programmers at answering any particular programming question, and yet stackoverflow cannot entirely replace development teams, because it cannot do things like "ask clarifying questions to stakeholders and expect that those questions will actually be answered". Similarly, an LLM does not expose the same interface as a human, and does not have the same affordances a human has.

Stackoverflow is better than most programmers at answering any particular programming question, and yet stackoverflow cannot entirely replace development teams

And that's why we don't call Stack Overflow things like "the 175th best coder on Earth".

I expect that "Stack Overflow" (i.e. a chat containing many SO users) could collectively place 175th in most programming competitions, and by that token be "the 175th best coder on earth, as measured by performance on competition-type problems".

Writing code is almost never the hard part of delivering value using code though.

"Several (thousands? tens of thousands?) actual human programmers, working together, could end up ranking as the 175th best coder on Earth" is not exactly a mind blowing take, and changing definitions mid-conversations isn't a productive way to have a conversation.

Writing code is almost never the hard part of delivering value using code though.

Fine, if an LLM was actually the 174th best coder on Earth, and writing code is not the hard part of delivering value through code, we should be seeing LLMs being improved by people with next to no knowledge of programming, using LLMs to assist them.

Fine, if an LLM was actually the 174th best coder on Earth, and writing code is not the hard part of delivering value through code, we should be seeing LLMs being improved by people with next to no knowledge of programming, using LLMs to assist them.

Consider the following argument:

If my Lamborghini was actually the 175th-fastest-accelerating car on Earth, and accelerating to 60mph from a stop is not the slow part of my commute through gridlocked traffic, we should be seeing my commute become much faster because I have a fast-accelerating car.

This argument does not make sense, because accelerating from 0 to 60 is not a meaningful bottleneck on a commute through gridlocked traffic. Similarly, "being able to one-shot extremely tricky self-contained programming problems at 99.9th percentile speed" becoming cheap is not something that alleviates any major bottleneck the big AI labs face.

The basic algorithm underlying LLMs is very simple. Here's GPT-2 inference in 60 lines of not-very-complicated code. The equivalent non-optimized training code is similar in size and complexity. The open-source code that is run in production by inference and training providers is more complicated, but most of that complexity comes from performance improvements, or from compatibility requirements and the software and hardware limitations and quirks that come with those.

The thing about performance on "solve this coding challenge" benchmarks is that coding challenges are tuned such that people have a low success rate at solving them, but most valuable work that people do with code is actually solving problems where the success rate is almost 100%. "Our AI has an 80% solve rate on problems which professionals can only solve 10% of the time" sounds great, but if the AI system only has a 98% solve rate on problems which professionals can solve 99.9% of the time, that will sharply limit the usefulness of that AI system. And that remains true even if the reason that the AI system only has a 98% solve rate is "people don't want to give it access to a credit card so it can set up a test cluster to validate its assumptions".

That limitation is unimportant in some contexts (e.g. writing automated tests where, where if the test passes and covers the code you expect it to test you're probably fine) and absolutely critical in other contexts (e.g. $X0,000,000 frontier model training runs).

Also, alternative snarky answer

we should be seeing LLMs being improved by people with next to no knowledge of programming, using LLMs to assist them.

LLMs derive their ability to do stuff mostly from their training data, not their system architecture. And there are many, many cases of LLMs being used to generate or refine training data. Concretely, when openai pops up their annoying little "which of these two chatgpt responses is better" UI, the user answering that question is improving the LLM without needing any knowledge of programming.

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