Matt Maohoney wrote:
My point is that when AGI is built, you will have to trust its answers based
on the correctness of the learning algorithms, and not by examining the
internal data or tracing the reasoning.
Agreed...
I believe this is the fundamental
flaw of all AI systems based on structured knowledge representations, such as
first order logic, frames, connectionist systems, term logic, rule based
systems, and so on.
I have a few points in response to this:
1) Just because a system is "based on logic" (in whatever sense you
want to interpret that phrase) doesn't mean its reasoning can in
practice be traced by humans. As I noted in recent posts,
probabilistic logic systems will regularly draw conclusions based on
synthesizing (say) tens of thousands or more weak conclusions into one
moderately strong one. Tracing this kind of inference trail in detail
is pretty tough for any human, pragmatically speaking...
2) IMO the dichotomy between "logic based" and "statistical" AI
systems is fairly bogus. The dichotomy serves to separate extremes on
either side, but my point is that when a statistical AI system becomes
really serious it becomes effectively logic-based, and when a
logic-based AI system becomes really serious it becomes effectively
statistical ;-)
For example, show me how a statistical procedure learning system is
going to learn how to carry out complex procedures involving
recursion. Sure, it can be done -- but it's going to involve
introducing structures/dynamics that are accurately describable as
versions/manifestations of logic.
Or, show me how a logic based system is going to handle large masses
of uncertain data, as comes in from perception. It can be done in
many ways -- but all of them involve introducing structures/dynamics
that are accurately describable as "statistical."
Probabilistic inference in Novamente includes
-- higher-order inference that works somewhat like standard term and
predicate logic
-- first-order probabilistic inference that combines various heuristic
probabilistic formulas with distribution-fitting and so forth .. i.e.
"statistical inference" wrappedin a term logic framework...
It violates the dichotomy you (taking your cue from the standard
literature) propose/perpetuate.... But it is certainly not the only
possible system to do so.
3) Anyway, trashing "logic incorporating AI systems" based on the
failings of GOFAI is sorta like trashing "neural net systems" based on
the failings of backprop, or trashing "statistical learning systems"
based on the failings of linear discriminant analysis or linear
regression.
Ruling out vast classes of AI approaches based on what (vaguely
defined) terms they have associated with them ("logic", "statistics",
"neural net") doesn't seem like a good idea to me. Because I feel
that all these standard paradigms have some element of correctness and
some element of irrelevance/incorrectness to them, and any one of them
could be grown into a working AGI approach -- but, in the course of
this growth process, the apparent differences btw these various
approaches will inevitably be overcome and the deeper parallels made
more apparent...
-- Ben G
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