In the specific context of improving our AI hardware, for example, it's not as simple as coming up with a good idea -- hardware companies hire thousands of people to improve their designs. Prototypes need to be implemented, verified, quantified, compared thoroughly with the alternatives, then the idea is approved for production, which again leads to a cascade of implementation, verification, etc. until they can reach consumers. In order to make these improvements reach the consumer significantly faster you need to accelerate all of the steps of the very simplified pipeline mentioned earlier.
More generally, an argument can be made that we have been in that take off feedback loop for hundreds of years; it's just that the rate of improvement hasn't been as spectacular as we may have hoped for because each incremental step simply isn't that big of a deal and it takes quite a bit of time to reach the next one.
Not to sound metaphysical or anything, but dependency on artificial intelligence seems to be something you would find at the peak of Mount Stupid (where the Darwin Awards are kept).
I am late for a chess game, l8r sk8rs.
"DeepMind AI Reduces Google Data Centre Cooling Bill by 40%"
https://deepmind.google/discover/blog/deepmind-ai-reduces-go...
The use of synthetic data from prior models to create both superior models and distilled models has been going on since at least OpenAI's introduction of RLHF, and probably before that too.
That’s distinct from those prior models providing actual code to improve the next model
I don't know if I would call this the fabled "self improving feedback loop", but it seems to have some degree of it. It also begs the question if Alphaevolve was being developed for a year, or has been in production for a year. By now it makes sense to hold back on sharing what AI research gems you have discovered.