Comment by echelon

> Its not a sure bet what is most optimal is what you see.

I wouldn't ever make "most optimal" a criteria. We're looking for measurable improvements, not a jump to god emperor or apex predator.

> you may only be in a localized threshold of fitness stability that is not necessarily optimal, but separated from another optimal configuration by having suboptimal intermediary steps that need more activation energy to overcome before falling into a state with lower entropy (or more optimal fitness).

Optimization is like that. But unlike genetics, where we can't re-route the recurrent laryngeal nerve or change fundamental biochemistry, these are engineered systems where we can set up wildly different experiments at any time. Just to cite one of many different research threads, there's now research now going into developing models from small scale training data.

> you can never be sure if synthetic data is any good or if what things gravitate toward are really most optimal.

We can know if the synthetic data is better. We have objective measures, a scientific process, and we'll always be striving for improvement.