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I doubt that any algorithms would be able to find a global maximum in such a large multidimensional non-convex space

Models that are strong enough to improve themselves without a human (I.e. ai researcher) in the loop

Improvement means being able to do more complicated things more reliably. Relatedly, it means being able to learn to do new things with fewer and fewer examples. We are running out of easily verifiable or simulation-friendly or data-rich domains for LLMs to conquer. (Note that I didn't say "simple" or "easy" domains.)

I’ve had this question on my mind at least once a month for what feels like years at this point.

I don’t feel like monitoring Twitter to see how it’s going though.

If a self improving model can't produce enough value to pay the electric bill then who cares? Unplug it.

Obviously the doomers will tell you that the future looks like the Matrix, because most of what they predict is based on extrapolation from sci fi movies.

The alternative is to switch to far more efficient models and have aggressive optimization of efficiency as part of the process of improvement.

There are at least two resources here that have a Pareto optimal front: time and energy. And effort spent to change the shape of that may pay off more than efforts spent purely on improving intelligence and agency.

Money can be exchanged for goods and services. I think the real constraint is actually physics, nominally energy, which is what a big chunk of the operational cost comes down to. I'm already basically assuming the next 20 years of fab time is set aside to feed the beast, so the capital cost is "give me all the processing and memory you have"

def self_improve_more():

     if self_improved():

          self_improve_more()

     else:

          see_tony_robins_and_buy_tapes()

The human brain operates at better levels of intelligence than the best LLMs, at 20 watts of power. We are a long way to that kind of efficiency, it will probably take both bespoke hardware and algorithmic improvements to catch up to nature.

Only if you think in terms of perceived raw intelligence, but self-update is a form of valuable self-improvement that could benefit current models a lot, if they could commit facts from context into their weights cheaply and reliably.

I think the idea is fundamentally improved architectures. For example, transformer-based models were an incredible stepwise improvement. Self improvement would be a model discovering a stepwise improvement similar to the transformer. And presumably the improved models from that would be more likely to make further advances still.

Learning from training data is technically self-improvement but not the sort that is typically meant in this context.

> AIs already improve themselves via training

Marginally. Model collapse is still a problem. Continuous learning is still a problem.

For AI to make a big leap we need a big break through.

Thing is, it depends on whether llms + reinforcement can self-improve in principle. Learned recently that cognitive scientists, before the transformer & llms, were studying the possibility that thinking and learning might be based on some kind of prediction, i.e. something similar to token prediction, and I quite suddenly became less skeptical about the possibilities of llms. (Some will say I’m late to the party of course.) But if knowledge to date has been accumulated in a process quite like “chain of thought” in llms, then I don’t see any reason that computers won’t self-improve in the near future.