Real Machine Learning is the deductions-inferences that can be drawn from
machine known knowledge by the AI programs. Here lies the true learning to
learn. Rather than the acquisition of known knowledge.
Yes, I think the central issue is how an AGI can derive higher-level knowledge from existing knowledge or experience. For example, from seeing 10 red apples and concluding that all apples are
red. We should enumerate all the "inference rules" whereby new knowledge can be generated from existing knowledge.
The inference rules can be fixed. What is flexible is the set of knowledge in the AGI. This set can get increasingly abstract as the AGI repeatedly derive new knowledge from the existing base.
yky
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