This only proves some of them can’t solve all complex problems. I’m only claiming some of them can solve some complex problems. Not only by remembering exact solutions, but by remembering steps and actions used in building those solutions, generalizing, and transferring them to new problems. Anyone who tries using it for programming, will discover this very fast.
PS: Some of them were already used to solve problems and find patterns in data humans weren’t able to get other ways before (particle research in CERN, bioinformatics, etc).
You’re referring to more generic machine learning, not LLMs. These are vastly different technologies.
And I have used them for programming, I know their limitations. They don’t really transfer solutions to new problems, not on their own anyway. It usually requires pretty specific prompting. They can at best apply solutions to problems, but even then it’s not a truly generalised thing, even if it seems to work for many cases.
That’s the trap you’re falling into as well; LLMs look like they’re doing all this stuff, because they’re trained on data produced by people who actually do so. But they can’t think of something truly novel. LLMs are mathematically unable to truly generalize, it would prove P=NP if they did (there was a paper from a researcher in IIRC Nijmegen that proved this). She also proved they won’t scale, and lo and behold LLM performance is plateauing hard (except in very synthetic, artificial benchmarks designed to make LLMs look good).
They don’t really transfer solutions to new problems
Lets say there is a binary format some old game uses (Doom), and in it some of its lumps it can store indexed images, each pixel is an index of color in palette which is stored in another lump, there’s also a programming language called Rust, and a little known/used library that can look into binary data of that format, there’s also a GUI library in Rust that not many people used either. Would you consider it an “ability to transfer solutions to new problems” that it was able to implement extracting image data from that binary format using the library, extracting palette data from that binary format, converting that indexed image using extracted palette into regular rgba image data, and then render that as window background using that GUI library, the only reference for which is a file with names and type signatures of functions. There’s no similar Rust code in the wild at all for any of those scenarios. Most of this it was able to do from a few little prompts, maybe even from the first one. There sure were few little issues along the way that required repromting and figuring things together with it. Stuff like this with AI can take like half an hour while doing the whole thing fully manually could easily take multiple days just for the sake of figuring out APIs of libraries involved and intricacies of recoding indexed image to rgba. For me this is overpowered enough even right now, and it’s likely going to improve even more in future.
That’s applying existing solutions to a different programming language or domain, but ultimately every single technique used already exists. It only applied what it knew, it did not come up with something new. The problem as stated is also not really “new” either, image extraction, conversion and rendering isn’t exactly a “new problem”.
I’m not disputing that LLMs can speed up some work, I know it occasionally does so for me as well. But what you have to understand is that the LLM only remembered similar problems and their solutions, it did not at any point invent something truly new. I understand the distinction is difficult to make.
I understand what you’re having in mind, I’ve had similar intuitions about AI in early 2000s.
What exactly is “truly new” is an interesting topic ofc, but it’s a separate topic.
Nowadays I’m trying to look at things more empyrically, without projecting my internal intuitions on everything.
In practice it does generalize knowledge, use many forms of abstract reasoning and transfer knowledge across different domains.
And it can do coding way beyond the level of complexity of what average software developer does at everyday work.
This only proves some of them can’t solve all complex problems. I’m only claiming some of them can solve some complex problems. Not only by remembering exact solutions, but by remembering steps and actions used in building those solutions, generalizing, and transferring them to new problems. Anyone who tries using it for programming, will discover this very fast.
PS: Some of them were already used to solve problems and find patterns in data humans weren’t able to get other ways before (particle research in CERN, bioinformatics, etc).
You’re referring to more generic machine learning, not LLMs. These are vastly different technologies.
And I have used them for programming, I know their limitations. They don’t really transfer solutions to new problems, not on their own anyway. It usually requires pretty specific prompting. They can at best apply solutions to problems, but even then it’s not a truly generalised thing, even if it seems to work for many cases.
That’s the trap you’re falling into as well; LLMs look like they’re doing all this stuff, because they’re trained on data produced by people who actually do so. But they can’t think of something truly novel. LLMs are mathematically unable to truly generalize, it would prove P=NP if they did (there was a paper from a researcher in IIRC Nijmegen that proved this). She also proved they won’t scale, and lo and behold LLM performance is plateauing hard (except in very synthetic, artificial benchmarks designed to make LLMs look good).
Lets say there is a binary format some old game uses (Doom), and in it some of its lumps it can store indexed images, each pixel is an index of color in palette which is stored in another lump, there’s also a programming language called Rust, and a little known/used library that can look into binary data of that format, there’s also a GUI library in Rust that not many people used either. Would you consider it an “ability to transfer solutions to new problems” that it was able to implement extracting image data from that binary format using the library, extracting palette data from that binary format, converting that indexed image using extracted palette into regular rgba image data, and then render that as window background using that GUI library, the only reference for which is a file with names and type signatures of functions. There’s no similar Rust code in the wild at all for any of those scenarios. Most of this it was able to do from a few little prompts, maybe even from the first one. There sure were few little issues along the way that required repromting and figuring things together with it. Stuff like this with AI can take like half an hour while doing the whole thing fully manually could easily take multiple days just for the sake of figuring out APIs of libraries involved and intricacies of recoding indexed image to rgba. For me this is overpowered enough even right now, and it’s likely going to improve even more in future.
That’s applying existing solutions to a different programming language or domain, but ultimately every single technique used already exists. It only applied what it knew, it did not come up with something new. The problem as stated is also not really “new” either, image extraction, conversion and rendering isn’t exactly a “new problem”.
I’m not disputing that LLMs can speed up some work, I know it occasionally does so for me as well. But what you have to understand is that the LLM only remembered similar problems and their solutions, it did not at any point invent something truly new. I understand the distinction is difficult to make.
I understand what you’re having in mind, I’ve had similar intuitions about AI in early 2000s. What exactly is “truly new” is an interesting topic ofc, but it’s a separate topic. Nowadays I’m trying to look at things more empyrically, without projecting my internal intuitions on everything. In practice it does generalize knowledge, use many forms of abstract reasoning and transfer knowledge across different domains. And it can do coding way beyond the level of complexity of what average software developer does at everyday work.