That’s cool but you’re describing the models from 2 years ago and also not considering harnesses, which account for most of the progress of the last year or so.
You’re entirely right. This won’t replicate to other fields like writing and creative arts in general, but software engineering is just not that hard and can basically be brute forced with a good harness.
It’s a done deal and there is no world where people will write professional code by hand. I like it cause it really separates coding (the job) from coding (the art form). People will code by hand for aesthetic reasons just like people learn the violin instead of using a synth and we’ll have a generation of lovingly crafted stuff. But boring software will be entirely automated, if not generated on the fly based on immediate needs.
it’s precisely because software engineering is hard (not difficult, but rigid - objective) that makes it a good fit for LLM agent execution
Yes i think we’re actually in agreement here. I said “hard” (not difficult) as a reference to “hard problems”, a term that comes from complexity theory but is now commonly used to describe problems which can’t be reduced to an algorithm or evaluated objectively, and thus can’t readily be “solved”.
Squishy subjects like music and sociology are full of hard problems, while solid subjects like math and coding are full of easy problems. Now the change introduced by LLMs is that as long as a problem is “easy”, it can no longer be so laborious as to be impossible. Every software problem is solvable, modulo the effort/computing power you can spend on it.
Your job as a software architect / engineer is actually to find the easiest workable solution(s).
You also get bonus point if your solution is average (standard, unsurprising etc…), which makes it particularly soluble in LLMs which, by definition, can only produce output that is within the distribution of their training set.
As for relative difficulty, the “soft” fields have a very sliding scale for that with a lot of allowance given to newbies that isn’t accepted “at the highest levels”
That’s not where i would put the difference. If you take a field like music, the problem is that it can’t “just work”. A nostalgic song may move the masses today but you can’t say “okay we’ve solved nostalgia let’s get to serenity next”. Soon enough you’ll need a new nostalgic song and by definition it will be out of distribution. You can’t find it in a high dimensional representation of past music, and, well, you can’t train on future data, so there is no way an LLM finds it and recognizes it for what it is.
with a high bar of standards required before publication and release, LLMs are a powerful tool that can accelerate many creative processes
I still believe they’ll never amount to much regarding artistic processes, and not just for the reasons i already mentioned. To make something good you need to sit with it and walk with it and spend some time in it doing all the tedious little tasks until it really feels like home and you can express yourself in it. You can’t achieve that if a machine speedruns all the little tasks for you.
Eh, agree to disagree. My use of the phrase might be easier to parse if you think about the “hard problem of consciousness” as opposed to the “easy problem of consciousness”. Also, more remotely, the notion of NP-hard vs NP-easy in computational complexity. I guess we are arguing about semantics aren’t we ?
I’m fairly certain that’s not a strict definition - LLMs also seem very heavily influencable via their context window inputs, though those are more limited and transient than the training set.
While LLMs don’t simply “statistically predict the next token” (common over-simplification which bears no relation to what is actually going on), the output they produce does exhibit the same statistical distribution as their training data. That’s how you get intelligible language, code that compiles, chains of thought that makes sense etc… The context input will steer the output towards a certain subset of the corpus (highly optimized C functions have slightly different distribution than throw-away Python scripts), but it can never direct the model fully out of distribution as those out of distribution vectors cannot be expressed within the model’s embedding space.
she was a commodity - as talented as you could ask, more talented than many national acts, but not unique
Yes exactly, if music was solvable then the most skilled people would systematically outclass the least skilled ones which is obviously not the case.
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They Don’t pass 4/5.
They pass 0/5 because they are 80% (that number is way too optimistic btw) accurate to human output on every one of the five attempts.
They also can’t be forced to learn and retake the trial because they don’t have any contextual awareness, they just guess the next word in a sequence.
Even if a machine made 4 self edits sucessfully, it would be permanently disfigured by the one failure and no longer be capable of making good edits.
That’s cool but you’re describing the models from 2 years ago and also not considering harnesses, which account for most of the progress of the last year or so.
You’re entirely right. This won’t replicate to other fields like writing and creative arts in general, but software engineering is just not that hard and can basically be brute forced with a good harness.
It’s a done deal and there is no world where people will write professional code by hand. I like it cause it really separates coding (the job) from coding (the art form). People will code by hand for aesthetic reasons just like people learn the violin instead of using a synth and we’ll have a generation of lovingly crafted stuff. But boring software will be entirely automated, if not generated on the fly based on immediate needs.
Removed by mod
Yes i think we’re actually in agreement here. I said “hard” (not difficult) as a reference to “hard problems”, a term that comes from complexity theory but is now commonly used to describe problems which can’t be reduced to an algorithm or evaluated objectively, and thus can’t readily be “solved”.
Squishy subjects like music and sociology are full of hard problems, while solid subjects like math and coding are full of easy problems. Now the change introduced by LLMs is that as long as a problem is “easy”, it can no longer be so laborious as to be impossible. Every software problem is solvable, modulo the effort/computing power you can spend on it.
You also get bonus point if your solution is average (standard, unsurprising etc…), which makes it particularly soluble in LLMs which, by definition, can only produce output that is within the distribution of their training set.
That’s not where i would put the difference. If you take a field like music, the problem is that it can’t “just work”. A nostalgic song may move the masses today but you can’t say “okay we’ve solved nostalgia let’s get to serenity next”. Soon enough you’ll need a new nostalgic song and by definition it will be out of distribution. You can’t find it in a high dimensional representation of past music, and, well, you can’t train on future data, so there is no way an LLM finds it and recognizes it for what it is.
I still believe they’ll never amount to much regarding artistic processes, and not just for the reasons i already mentioned. To make something good you need to sit with it and walk with it and spend some time in it doing all the tedious little tasks until it really feels like home and you can express yourself in it. You can’t achieve that if a machine speedruns all the little tasks for you.
Removed by mod
Eh, agree to disagree. My use of the phrase might be easier to parse if you think about the “hard problem of consciousness” as opposed to the “easy problem of consciousness”. Also, more remotely, the notion of NP-hard vs NP-easy in computational complexity. I guess we are arguing about semantics aren’t we ?
While LLMs don’t simply “statistically predict the next token” (common over-simplification which bears no relation to what is actually going on), the output they produce does exhibit the same statistical distribution as their training data. That’s how you get intelligible language, code that compiles, chains of thought that makes sense etc… The context input will steer the output towards a certain subset of the corpus (highly optimized C functions have slightly different distribution than throw-away Python scripts), but it can never direct the model fully out of distribution as those out of distribution vectors cannot be expressed within the model’s embedding space.
Yes exactly, if music was solvable then the most skilled people would systematically outclass the least skilled ones which is obviously not the case.
Removed by mod