Because language is being treated as a thing complete in itself, as opposed to being related to an external world?
One of the issues in the 'Limitations' section was a difficulty with "common-sense physics", such as with the question "if I put cheese into the fridge, will it melt?"
To answer that question, you have to ask the right questions, such as "what is a fridge?" "what is a fridge for?" "What does it mean for cheese to melt?" "what is the cause of cheese melting?" Then one should consider the follow-on questions, such as "what are typical fridge temperatures?" "what are typical cheese melting points?" "what temperature is the cheese likely to be at initially?" (at which point, it helps to introduce the concept of room temperature, and note that it typically falls between the other two.) From facts such as the answers to these questions, one can deduce the probable outcome of putting cheese in a refrigerator, but none of the answers so far explicitly state it.
Is it plausible that any learning, solely from the structure of and correlations between examples of language use, could develop the sort of analytical/modeling approach that I have just outlined? Instinctively, I don't find it very plausible, but I am not very certain in that view.
Maybe there exists the following distinction. Modern language, as it's actually spoken most of the time, is like a higher level programming language. The structure of our brain, combined with our senses and the uber-simple way that we're taught as infants, is like lower level OS programming (parent points to hot food - look Johnny, it's hot! hot! - makes Johnny touch the food - food hot! - blows on the food to make it colder).
Some words like good/bad, hot/cold, and important/unimportant are underrepresented in everyday speech compared to the prevalence of the underlying concepts. That's why I'd categorize them as lower level word-concepts. This distinction, about variable levels of abstraction, might be important for true AI. Think about how many years it takes for humans to develop highly abstract cognition. That whole time our operating system is being coded. Maybe we need to approach AI in the same way.
It's not just AI that can benefit from better lower-level understanding. Seeing language in the above way, we can re-frame Ludwig Wittgenstein's philosophy and its normative implications for human communication. Our "programming" (communication) is on average too higher level. Excessively abstract instructions make it harder to decode and process in a precise and efficient manner.
One of the issues in the 'Limitations' section was a difficulty with "common-sense physics", such as with the question "if I put cheese into the fridge, will it melt?"
To answer that question, you have to ask the right questions, such as "what is a fridge?" "what is a fridge for?" "What does it mean for cheese to melt?" "what is the cause of cheese melting?" Then one should consider the follow-on questions, such as "what are typical fridge temperatures?" "what are typical cheese melting points?" "what temperature is the cheese likely to be at initially?" (at which point, it helps to introduce the concept of room temperature, and note that it typically falls between the other two.) From facts such as the answers to these questions, one can deduce the probable outcome of putting cheese in a refrigerator, but none of the answers so far explicitly state it.
Is it plausible that any learning, solely from the structure of and correlations between examples of language use, could develop the sort of analytical/modeling approach that I have just outlined? Instinctively, I don't find it very plausible, but I am not very certain in that view.