I'm just wondering, is there something we can get out of assembly theory that allows us to rebuild current machine learning architectures more cheaply? Yeah. You also want to have, say, goal-directed behavior in neural networks, then assembly theory is a good framework for doing that. I think it's a really interesting idea that you can map concepts from how neural networks learn to thinking about goal- directed behavior as a learning process.