[stoniejohnson]

I think the bitter lesson implies that if we could study/implement "how a machine with limitless cpu cycles would make our eyes see something we are currently thinking of" then it would likely lead to a better result than us using hominid heuristics to split things into sub-problems that we hand over to the machine.

[nuancebydefault]

The technology to probe brains and visual related neurons exists today. With limitless cpu cycles we would for sure be able to do make us see whatever we think about.

[stoniejohnson]

I'm not really familiar with that technology space, but if you take that as true, is your argument something like:

- We don't have limitless CPU cycles

- Thus we need to split things into sub-problems

If so that might still be amenable to the bitter lesson, where Sutton is saying human heuristics will always lose out to computational methods at scale.

Meaning something like:

- We split up the thought to vision problem into N sub-problems based on some heuristic.

- We develop a method which works with our CPU cycle constraint (it isn't some probe -> CPU interface). Perhaps it uses our voice or something as a proxy for our thoughts, and some composition of models.

Sutton would say:

Yeah that's fine, but if we had the limitless CPU cycles/adequate technology, the solution of probe -> CPU would be better than what we develop.

[nuancebydefault]

I think Sutton is right that if we had limitless cpu, any human split up would be inferior. So indeed since we are far away from limitless cpu, we divide and compose.

But i think we're onto something!

Voice to image indeed might give better results than text to image, since voice has some vibe to it (intonation, tone, color, stress on certain words, speed and probably even traits we don't know yet) that will color or even drastically influence the image output.