I don't believe in combining different methods
because cognition deals with the unknown, - we can't a priori split it into
different areas, except to the extent that they're sensor/hardware specific,
or levels, except that syntactic complexity of inputs should be sequentially
like to distinguish between *functional specialization* and *integrated
Novamente (my own AI system) has a mix of cognitive algorithms, which
work together to provide overall cognitive functionality. The exact
mixture of algorithms is determined by a bunch of parameters. This is
one example of "integrated cognition".
Functional specialization has to do with there being modules of an
intelligent system devoted to particular areas like language processing,
vision processing, social interaction, etc. In the Novamente design,
each functionally specialized lobe has its own parameter values which
determine the specific mix of cognitive algorithms operating within it.
(We haven't gotten to experimenting with this yet, now we're just
experimenting with mixing cognitive algorithms.)
Generally, a mixture of cognitive algorithms is just as capable of
dealing with the unknown as a single cognitive algorithm. Sometimes more
the other hand, functional specialization biases one's system to deal with
some parts of the space of the unknown better than others.
is a plus and a minus, obviously. Human cognition deals with the truly
unknown very slowly and awkwardly. The human brain
is specialized not only based on its sensors and actuators, but also for
linguistic processing, social interaction, temporal event processing, etc.
etc. etc. This means that it would not work as well taken outside of its
ordinary social and physical situations. But it means that its limited
resources are generally well deployed within its usual