I think the intersection of games and LLMs is the way to solve alignment. Imagine a 3D UI for an LLM, this way we can democratize interpretability research.

Basically to train another model, that will take the LLM and convert it into 3D shapes and put them in some 3D world that is understandable for humans.

Simpler example: represent an LLM as a green field with objects, where humans are the only agents:

You stand near a monkey, see chewing mouth nearby, go there (your prompt now is “monkey chews”), close by you see an arrow pointing at a banana, father away an arrow points at an apple, very far away at the horizon an arrow points at a tire (monkeys rarely chew tires).

So things close by are more likely tokens, things far away are less likely, you see all of them at once (maybe you’re on top of a hill to see farther). This way we can make a form of static place AI, where humans are the only agents