> The child does not have direct access to the "inputs" of linguistic rules or grammar; they learn primarily through observing and imitating the language output

> The child does not have direct access to the "inputs" of linguistic rules or grammar; they learn primarily through observing and imitating the language output of others around them. I would argue that that learning is always contextualized by visual and spatial information about the real world (which is what our language is meant to describe). And (equally importantly) the child gets real-world feedback on their decisions - some decisions achieve their desired goal and some don't. Some statements cause certain responses from people, some actions have certain consequences. > Moreover, the analogy to weather prediction or stock market analysis is somewhat misleading. Yes, these models benefit from input data (like air currents for weather, company fundamentals and CEO statements to the media for stocks). But these systems are fundamentally different from intelligence. > Intelligence, whether artificial or human, is about the ability to learn, adapt, and generate novel responses in a broad range of scenarios, not just about predicting specific outcomes based on specific inputs. "Intelligence" is kind of a nebulous term. If all it means is the ability to learn, adapt and generate novel responses, then sure, I think we could call almost any neural network intelligent. But I would argue that we usually do have some expectation that an intelligent system can produce "specific outcomes based on specific inputs". We want to be able to train a worker and have them follow that training so they do their job correctly.