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PaperclipPerfector  7d ago (text post)   27928 thread views

Culture War Roundup for the week of December 16, 2024

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Apparently this AI is ranked as the 175th best coder on Earth. I think we’ve reached the point, where anyone working in software needs to either pivot to developing AIs themselves or else look for an exit strategy. It looks like humans developing “apps”, websites and other traditional software development has 1-3 years before they’re in a similar position to horse and buggy drivers in 1920.

I think that says more about the grading metrics than anything else.

Also is this the "Dreaded Jim" from the LessWrong/SSC days?

anyone working in software needs to either pivot to developing AIs themselves or else look for an exit strategy.

I will start panicking when I will see AI-generated code working correctly and requiring no changes. For three simple cases in row, that I needed to implement.

Right now AI is powerful tool but in no danger whatsoever to replace me.

Though yes, progress is scary here.

working in software

why this field would be at unusually high risk? Of all things it is field where minor mistakes and inconsistencies may take down entire system. And for now AIs are failing at being consistent at large projects.

I find that frontier LLMs tend to be better than I am at writing code, and I am pretty good but not world class at writing code (e.g. generally in the first 1% but not first 0.1% of people to solve each day of advent of code back when I did that). What's missing tends to be context, and particularly the ability to obtain the necessary context to build the correct thing when that context isn't handed to the LLM on a silver platter.

Although a similar pattern also shows up pretty frequently in junior developers, and they often grow out of it, so...

LLM is great at writing code in area utterly unfamiliar to me and often better than reading documentation.

But nearly always rewrite/tweaking/fixing is needed, for anything beyond the most trivial examples.

Maybe I am bad at giving it context.

Maybe I am bad at giving it context.

You, me, and everyone else. Sarah Constantin has a good post The Great Data Integration Schlep about the difficulty of getting all the relevant data together in a usable format in the context of manufacturing, but the issue is everywhere, not just manufacturing.

Obtaining the data is a hard human problem.

That is, people don’t want to give it to you.

[...]

Data cleaning doesn’t seem intellectually challenging, but it is surprisingly difficult to automate [...] Part of the issue is that the “reasonable” thing to do can depend on the “real-world” meaning of the data, which you need to consult a human expert on. For instance, are these two columns identical because they are literal duplicates of the same sensor output (and hence one can safely be deleted), or do they refer to two different sensors which happened to give the same readings in this run because the setting that would allow them to differ was switched off this time? The answer can’t be derived from the dataset, because the question pertains to the physical machine the data refers to; the ambiguity is inherently impossible to automate away using software alone.

There's a reason data scientists are paid the big bucks, and it sure isn't the difficulty of typing import pandas as pd.

Considering that people already thought LLMs could write code well (they cannot in fact write code well), I'm not holding my breath that they are right this time either. We'll see.

they cannot in fact write code well

My brother in Christ, the 174th best coder on Earth is literally an LLM.

What is your theory on why that LLM is not working at OpenAI and creating a better version of itself? Can that only be done by the 173rd best coder on Earth?

... why do you think LLMs are not meaningfully increasing developer productivity ar openai? Lots of developers use copilot. Copilot can use o1.

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.

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My brother in Christ, the 174th best coder on Earth is literally an LLM.

No, it is ranked as 175th in a specific ranking. That is with access to all analysis, answers of this existing questions. Solving question is distinctively easier if you seen the answer.

Make no mistake, LLM are much better at coding than I would predict 10 years ago. Decade ago I would laugh at anyone predicting such progress, and in fact I mocked any idea of AI generating code that is worth looking at. And trawling internet for such solutions is extremely powerful and useful. And ability to (sometimes) adapt existing code to novel situation still feels like magic.

But it is distinctively worse at handling novel situations and taking any context into account. Much worse than such ranking suggest. Leaving aside all cheating of benchmarks and overfitting and Goodhart's law and all such traps.

If this AI would be really 174th best coder on Earth then they would be already releasing profitable software written by it. Instead, they release PR stuff. I wonder why? Maybe at actual coding it is not so great?

My brother in Christ, up until now (can't speak for this one) LLMs frequently get things wrong (because they don't actually understand anything) and can't learn to do better (because they don't actually understand anything). That's useless. Hell, it's worse than useless - it's actively harmful.

Perhaps this new one has surpassed the limitations of prior models before it, but I have my doubts. And given that people have been completely driven by hype about LLMs and persistently do not even see the shortcomings, saying it's "the 174th best coder on earth" means very little. How do I know that people aren't just giving into hype and using bad metrics to judge the new model just as they did the old?

o3 is approximately equivalent to the #175 best human in competitive programming on CodeForces.

That tweet you linked does not mean what you say it means.

My brother in Christ, the 174th best coder on Earth is literally an LLM.

Competitive programming is something that fits LLM's much better than regular programming. The problems are well defined, short and the internet is filled with examples to learn from. So to say that it equals regular programming is not accurate at all.

Are LLM's decent (and getting better) at regular programming? Yes, especially combined with an experienced programmer dealing with something novel (to the programmer, but not the programming community at large), in roughly the same way (but better) that stackoverflow helps one get up to speed with a topic. In the hands of a novice programmer chaos occurs, which might not be bad if it leads to the programmer learning. But humans are lazy.

Will LLM's replace programmers? Who knows, but given my experience working with them, they struggle with anything that is not well documented on the internet very quickly. Which is sad, because I enjoy programming with them a lot.

Another thing to add is that I think the low hanging fruit is currently being picked dry. First it was increasing training for as long as it scaled (gpt4), then it was run time improvements on the model (have it re-read it's own output and sanity check it, increasing the cost of a query by a lot). I'm sure that there are more improvements on the way but like most 'AI' stuff, the early improvements are usually the easiest. So saying that programming is dead in X amount of years because "lllllook at all this progress!!!" is way too reactive.

Says who? What’s the evidence? I see these claims but they don’t seem backed up by reality. If they are so great, why haven’t we practically fired all coders?

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