> It's a fact known to anyone who's done practical AI work that a more
> specialized approach to a given problem is going to be more efficient than a
> more general approach, in almost all cases. Building a real AI thus
> requires a very delicate balance between generality and specialization. One
> needs, in fact a general intelligence mechanism that can also serve as a
> sort of "mind OS" on which numerous specialized intelligence mechanisms can
> run. But you've heard this spiel from me before...
It is really an application of the bias-variance dilemma in learning
theory: a learning system will make mistakes due to inherent biases and
variance due to lack of learning. A system with no bias will have a
large variance and requires a lot of training to become useful, but a
carefully selected bias (= prior knowledge of the domain) can reduce the
variance quite a bit. The price is of course that now the system is
biased towards a certain domain and certain mistakes.
The right balance here is of course another learning problem. In nature
it is defined by the fitness value of an individual and can be learned
by evolution.
-- ----------------------------------------------------------------------- Anders Sandberg Towards Ascension! asa@nada.kth.se http://www.nada.kth.se/~asa/ GCS/M/S/O d++ -p+ c++++ !l u+ e++ m++ s+/+ n--- h+/* f+ g+ w++ t+ r+ !y
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