Well the thing is, the code analogy works really well for things like algorithms, math, and factual knowledge. Really, the things you are talking about that cannot be modularized are things that require large amounts of training. However, the training algorithm may be able to be modularized and encoded thus enabling the AI to learn these things. This is the goal of machine learning.
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Well the thing is, the code analogy works really well for things like algorithms, math, and factual knowledge. Really, the things you are talking about that cannot be modularized are things that require large amounts of training. However, the training algorithm may be able to be modularized and encoded thus enabling the AI to learn these things. This is the goal of machine learning.
A training algorithm selects a hypothesis from a hypothesis space.
What happens if the hypothesis space does not include the true hypothesis?
Defining the hypothesis space is tricky - though the training/search part is also tricky :-).