Self improving AI runs into the same issue as prefect comprehension, you can’t get arbitrarily better at everything.
The idea AI can get better at everything at the same time is a holdover from deeply flawed science fiction not some realistic goal.
The idea AI can get better at everything at the same time is a holdover from deeply flawed science fiction not some realistic goal.
If the time between advances is a + b and a is the proportion of the period that can be improved by advances then you won't reduce to a gap of nothing between advances, you reduce to a gap of b.
Assume the invention of the plow and the invention of the sword is 500,100 units and a was the 500,000, you wouldn't even know the 100 as in there. Maybe we're at a=2000 now and b is still siting at 100.
Assuming we'll reach infinity because we're dividing by the only variable we see and it is decreasing in size seems nuts if the reason we might not see other variables is because of the size of the variable we can see.
But that’s beside the point, being arbitrarily bad at everything isn’t a problem. The diminishing returns as you apply the ceiling is problematic for self improving AI.
Diminishing returns aren't necessarily a problem if the rate of increase in resources is faster. In other words, if Gen 2 takes twice the resources but Gen 1 figured out a way to triple compute efficiency then there is no ceiling.
There has been some fundamental advancements in LLM training and efficiency. But good luck actually finding non toy models of exactly the same size, hardware, and training one from now and other from 2001 where the newer model is dramatically better at literally everything.
Most of the real world advances are from throwing ever more resources at the problem.
> Gen 2 takes
Diminishing returns are not a question of a single generation. Gen 2, 3, 4, 5… would also need to have the same 3x return on 2x resources or you don’t have an exponential curve.