2. Ben raised the issue of learning. I think we should divide learning
into 3 parts:
(1) linguistic eg grammar
(2) semantic / concepts
(3) generic / factual.
This leaves out a lot, for instance procedure learning and
metalearning... and also perceptual learning (e.g. object recognition)
... and a lot more...
I think the learning mechanisms in (1)-(3) are quite distinct, for example,
learning that "fish cat eat" is ungrammatical, and learning that cats like
to eat fish, are quite distinct matters.
I disagree, I think that the same learning algorithms can be made to
carry out all sorts of learning.... It's all just pattern recognition
;-) ... it's true that different domains of pat. rec. demand different
types of inductive bias, but a sufficiently flexible general learning
algorithm can be made to incorporate appropriate domain-specific
biases ...
In Novamente, the synthesis of probabilistic logical inference and
probabilistic evolutionary learning is to be used to carry out all of
the above kinds of learning you mention, and more....
-- Ben
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