Interestingly, it seems alphaevolve has already been in use for a year, and it is just now being publicly shown. The paper also mentions that it uses Gemini 2.0 (pro and flash), which creates a situation where Gemini 2.0 was used in a way to train Gemini 2.5.
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.
If you have the brain power, the compute and control the hardware, what is there to prevent the take off feedback loop? Deepmind is at this point in the timeline uniquely positioned.
> If you have the brain power, the compute and control the hardware, what is there to prevent the take off feedback loop?
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.
The fact that all computational problems have a best case complexity bound and there are generally diminishing marginal returns as algorithms approach that bound (i.e. lower hanging fruit are found first). E.g. no amount of intelligence is going to find an algorithm that can sort an array of any arbitrary Comparable type on a single CPU thread faster than O(n*log(n)). There's room for improvement in better adapting algorithms to cache hierarchy etc., but there's only a fixed amount of improvement that can be gained from that.
Running out of improvements after the first pass would prevent that. Who is to say this Alpha Evolve is not already obsolete, having already served its purpose?
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).
> which creates a situation where Gemini 2.0 was used in a way to train Gemini 2.5.
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.
It is really about autonomy. Can it make changes to itself without human review? If it does, what is the proof such changes won't just stop at some point? All I am seeing here is a coder assist tool, and unsure how helpful inexplicable solutions are in the long run. Could result in an obtuse code base. Is that the point?
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.