In this model the spec/scenarios are the code. These are curated and managed by humans just like code.
They say "non interactive". But of course their work is interactive. AI agents take a few minutes-hours whereas you can see code change result in seconds. That doesn't mean AI agents aren't interactive.
I'm very AI-positive, and what they're doing is different, but they are basically just lying. It's a new word for a new instance of the same old type of thing. It's not a new type of thing.
The common anti-AI trope is "AI just looked at <human output> to do this." The common AI trope from the StrongDM is "look, the agent is working without human input." Both of these takes are fundamentally flawed.
AI will always depend on humans to produce relevant results for humans. It's not a flaw of AI, it's more of a flaw of humans. Consequently, "AI needs human input to produce results we want to see" should not detract from the intelligence of AI.
Why is this true? At a certain point you just have Kolmogorov complexity, AI having fixed memory and fixed prompt size, pigeonhole principle, not every output is possible to be produced even with any input given specific model weights.
Recursive self-improvement doesn't get around this problem. Where does it get the data for next iteration? From interactions with humans.
With the infinite complexity of mathematics, for instance solving Busy Beaver numbers, this is a proof that AI can in fact not solve every problem. Humans seem to be limited in this regard as well, but there is no proof that humans are fundamentally limited this way like AI. This lack of proof of the limitations of humans is the precise advantage in intelligence that humans will always have over AI.
> Recursive self-improvement doesn't get around this problem. Where does it get the data for next iteration? From interactions with humans.
It wasn't true for AlphaGo, and I see no reason it should be true for a system based on math. It makes sense that a talented mathematician who's literally made of math, could build a slightly better mathematician, and so on.
AlphaGo was able to recursively self-improve within the domain of the game of go, which has an astonishingly small set of rules.
We're asking AIs to have data that covers the real physical world, plus pretty much all of human society and culture. Doing self-improvement on that without external input is a fundamentally different proposition than doing it for go.
> the real physical world, plus pretty much all of human society and culture
is only a tiny part of the problem (more data plus understanding more rules) and the main problem is "getting smarter".
You can get smarter without learning more about the world or human society and culture. I mean, that's allegedly how Blaise Pascal worked out a lot of mathematics in his teenage years.
My point is that the "getting smarter" part (not book-smart which is your physical world data, not street-smart which is your human culture data, but better-at-processing-and-solving-problems smart) is made of math. And using math to make that part better is the self-improvement that does not necessarily require human input.
I think you're strawmanning my math point from "if you're made of math and can make a trivial improvement in the math, you get a smarter n+1 program that can likely make another trivial improvement to n+2"... to "AI can solve all math" (which is not my point at all).
You seem to be generalizing item #3 from "there are limits to what AI can do with math", to "therefore, AI can't improve any math, and definitely not the very specific kind of math that is relevant to improving AI". That is a huge unjustified logical jump.
Has it ever happened on the path from Enigma to Claude Opus 4.6, that the necessary next step was to figure out a new nth Busy Beaver? Is Opus 4.6 a better Busy Beaver than Sonnet 3.5?
Or is that a mostly unrelated piece of math that is mostly irrelevant to making a "smarter" AI program from where we are today?
In this model the spec/scenarios are the code. These are curated and managed by humans just like code.
They say "non interactive". But of course their work is interactive. AI agents take a few minutes-hours whereas you can see code change result in seconds. That doesn't mean AI agents aren't interactive.
I'm very AI-positive, and what they're doing is different, but they are basically just lying. It's a new word for a new instance of the same old type of thing. It's not a new type of thing.
The common anti-AI trope is "AI just looked at <human output> to do this." The common AI trope from the StrongDM is "look, the agent is working without human input." Both of these takes are fundamentally flawed.
AI will always depend on humans to produce relevant results for humans. It's not a flaw of AI, it's more of a flaw of humans. Consequently, "AI needs human input to produce results we want to see" should not detract from the intelligence of AI.
Why is this true? At a certain point you just have Kolmogorov complexity, AI having fixed memory and fixed prompt size, pigeonhole principle, not every output is possible to be produced even with any input given specific model weights.
Recursive self-improvement doesn't get around this problem. Where does it get the data for next iteration? From interactions with humans.
With the infinite complexity of mathematics, for instance solving Busy Beaver numbers, this is a proof that AI can in fact not solve every problem. Humans seem to be limited in this regard as well, but there is no proof that humans are fundamentally limited this way like AI. This lack of proof of the limitations of humans is the precise advantage in intelligence that humans will always have over AI.