Whats to say if you gave an LLM a pre prompt conditioning for things like: Be curious, Explore the domain you exist in, Learn about your environment like your l

Whats to say if you gave an LLM a pre prompt conditioning for things like: Be curious, Explore the domain you exist in, Learn about your environment like your life depends on it, You are a social creature so when you find out information you like to share it, Being a social creature, the most practical tool for sharing ideas is language (these notions among many others are similar pre-conditions for humans behaviour, largely I imagine for survival, but in our case initially informed by emotion rather than language) And then just feed it endless continuous video of the world from a persons perspective (ie, not just random jumps around scenes) How is that any different to the way humans developed our own theories and understanding on the universe we found ourselves in? Obviously a video feed isn't quite the same as having a myriad of senses like we have for interfacing with the world. But theres no reason to say newtons laws need touch or smell to be figured out (maybe that is a bad example for not requiring touch, being about mass and force, but you get the point). Learning language and understanding of the world from scratch might depend on having physcial interaction with the world, sure, but thats again just different sets of input training data that a multimodal model can make connections between if there were pressure to do so. Then you simply prompt the LLM to define the laws it has observed in a way that can use language to transmit the idea. In the human equivalent, a "prompt" doesn't need to be (but can be) a direct question being asked. It could just be our urge to share ideas, an emotion perhaps that moves us to action. I don't want to go too far on this thought as it is pure speculation without serious scientific backing, but for the sake of the thought experiment with all this in mind you could even say that brains are much like an LLM that is in a constant state of training & updating, but also being "prompted" from our environment and biological feedback loops being fed in. I don't feel like this idea is even that controversial or remotely original. The difference is that models of today are already trained on the corpus of human knowledge so they already know all of this instead of having to figure it out. But I don't really see an argument for why an LLM wouldn't be able to crystallise observed patterns into mathematical formulae if it had the appropriate influences that would encourage it to do so. Its just that we don't care to train them inefficiently. I imagine this will be tried in the near future though.