Fri, 03 Jan 2003 20:04:54 -0800
Well, in Novamente we are not coding *specific knowledge* that is learnable... but we are coding implicit knowledge as to what sorts of learning processes are most useful in which specialized subdomains...
I don't know, from where I sit this distinction is artificial. Learning is generally defined as projected compression, complexity of methods to achieve it can be sequentially increased as long as it produces positive additional compression minus the expense,- until it matches complexity of the inputs. In other words, optimal methods themselves should be learned.
The Novamente design is mathematically formulated, but not mathematically derived. That is, individual formulas used in the system are mathematically derived, but the system as a whole has been designed by intuition (based on integrating a lot of different ideas from a lot of different domains) rather than by formal derivation.
In my view, we are nowhere near possessing the right kind of math to derive a realistic AI design from definitions in a rigorous way.
To select formulas you must have an implicit criterion, why not try to make it explicit? I don't believe we need complex math for AI, complex methods can be universal, - generalization is a reduction. What we need is a an autonomously scalable method.
Juergen Schmidhuber's OOPS system is an attempt in this direction, but though I like Juergen's work, I think this design is too simplistic to be a functional AGI.
Thanks, I am looking at it. I noticed that he starts with a known probability distribution, to me that suggests that the problem is already solved ;-)
I don't think you're right about the reason human learning is so slow. It is not just hardware inefficiency,
Of course, that's only part of it.
it is the fact that a lot of trial-and-error-based algorithms are used in the brain.
I call it search.
The human brain has many flaws and is not a perfect guide for AGI, but it has far more general intelligence than any existing computer program, and so it is certainly worth carefully studying when designing a would-be AGI system.
How about using it? ;-)