Passing question about LLMs and the Tech Singularity
I am currently reading my way thru Ted Chiang's guest column in the New Yorker, about why the predicted AI/Tech Singularity will probably never happen (https://www.newyorker.com/culture/annals-of-inquiry/why-computers-wont-make-themselves-smarter). ETA: I just noticed that article is almost 5 years old; the piece is still relevant, but worth noting.
Good read. Still reading, but so far, I find I disagree with his explicit arguments, but at the same time, he is also brushing up very closely to my own reasoning for why "it" might never happen. Regardless, it is thought-provoking.
But, I had a passing thought during the reading.
People who actually use LLMs like Claude Code to help write software, and/or, who pay close attention to LLMs' coding capabilities ... has anyone actually started experimenting with asking Claude Code or other LLMs that are designed for programming, to look at their own source code and help to improve it?
In other words, are we (the humans) already starting to use LLMs to improve their code faster than we humans alone could do?
Wouldn't this be the actual start of the predicted "intelligence explosion"?
Edit to add: To clarify, I am not (necessarily) suggesting that LLMs -- this particular round of AI -- will actually advance to become some kind of true supra-human AGI ... I am only suggesting that they may be the first real tool we've built (beyond Moore's Law itself) that might legitimately speed up the rate at which we approach the Singularity (whatever that ends up meaning).
The problem with that is LLMs don't have source code in the way that would be meaningful here. You have a program that provides an interface, of course, but the "running the LLM" part of that program consists of loading a really big array of numbers and doing math on them. The array is just data, not code, but that's the part where the "thinking" happens, to the extent these things think.
You can (and people definitely do) use the LLM to generate increasingly fancy wrappers around this process, but it doesn't make them smarter.
Noting my edit to my original post ... I get your point, 100%. Nonetheless, if it helps us move faster in the process of testing and experimenting in how to make LLMs smarter, isn't this still a leg up in the process?
Of course, I do not expect LLMs play any non-trivial role in the actual training process, which is where the "magic" is happening, and offhand, I don't see how they ever could. Perhaps those programmatic "wrappers" can only do so much, can only be optimized so much, and the LLM contribution to that work is also, ultimately, trivial.
IDK. Like I said, just a passing thought.