|
|
|
|
|
|
by Archelaos
1278 days ago
|
|
|
"We want AI agents that can discover like we can, not which contain what we have discovered." I wonder if we can even go beyond that and find ways to abstract "discoverability" so that AI agents can evolve into diverse species, perhaps entirely different from our specific ways of discovering. As an analogue, I think of the nervous system of octopuses, which is very different from ours, but capable of amazing feats. |
|
|
Combine "search" with "learning". Or content generation with content validation, and retrain on the clean outputs. Or, run many simulations such as AlphaGo, and learn from the outcomes.
In general the idea is to use lots of compute to generate interesting and hard to come by training data for the next iteration. This approach is necessary because we have exhausted most of the good training data and need a path forward. You can't copy money, but you can copy the model and data.