| >an expert model will always outperform a general purpose one, even if it's a metatransformer It's an interesting question as it begs questions of conceptual "boundaries." The sense-plan-do process requires a search and filter process for task switching, assuming an agent can do more than one thing. So assuming you have a robotic/autonomous agent that is a collection of systems (locomotion, dexterous gripper, visual perception, etc...), if each system could be represented as an "expert module", say for example the dexterous manipulator, then so long as a discriminator can appropriately switch states using the sensor/system inputs, then it's conceptually possible that there is a canonical "expert module" that everyone uses and therefore "general purpose" would apply to the agent as a whole while expert model would apply to the dexterous manipulator. You can walk that reasoning up the abstraction layers then to conclude that (as usual with these turtle stacks) the distinctions come as each sub system/module specializes more granularly for the environment they operate in. I think that it's probably forever and always true that any system designed to explore/exploit a bounded environment with comprehensive observations, will always outperform a system that is required to adapt it's sense-plan-do components to the bounded environment without similar observations. A system would either have to generate different observations than the native agent, or change the boundaries of the environment in a way that is unavailable to the native agent in order to outperform it. |