> 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....
Well, then your architecture would be monolithic and not modular. I think
it's a good choice to break the AGI into n < 5 modules, with separate
learning mechanisms.
The "monolithic vs. modular" distinction, as you pose it, is
insufficiently refined...
The Novamente design is modular, in two senses:
1) there is a high-level architecture consisting of a network of
functionally specialized lobes -- a lobe for language processing, a
lobe for visual perception, a lobe for general cognition etc.
2) each lobe contains a set of agents, each one carrying out a
particular cognitive process on the knowledge store (e.g. reasoning,
declarative-to-procedural knowledge conversion, pattern mining, etc.)
But, the actual cognition carried out by each agent is done using a
particular customization of the same generalized learning algorithm.
In the monolithic way, implementing algorithms would
be unnecessarily complex.
Actually, I believe it is simpler to have a single generalized
learning approach underlying the various cognitive agents.
What is the advantage of a single learning
mechanism?
The primary advantage is that it allows the different agents to
interact and interoperate with each other much more sensitively than
if they all used qualitatively different internal methods. This is
critical because the most essential aspects of intelligence are those
that emerge from the appropriate interaction of multiple cognitive
agents.
-- Ben G
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