Thank you for your solid reply. I think ANN is the real thing, and most of the other "AI" solutions that we have give the impression of intelligence, but are simply algorithms that solve problems we have in very well defined ways.
ANN is a general intelligence that gets shaped by experience and adapts figure out its own solutions. This is the path that will lead to our concept of AI presented in sci-fi and what the general public actually thinks of. It isn't some coder banging on the keyboard trying to replicate what a human would do under a specific situation, it is something that truly learns.
We have a long way to go before this becomes a reality, but it will happen.
That's very romantic, but I think instead we're going to learn that intelligence is not such a well-defined idea as to be engineered into anything imagined by science fiction.
Further, there are plenty of techniques which aren't ANNs but still are "shaped by experience" and "figure out their own solutions".
I side pretty strongly with the idea of building tools to bring the powerful modes of computerized learning close to the powerful modes of human learning. I think we're inordinately far away from replicating one with the other.
> I think ANN is the real thing, and most of the other "AI" solutions that we have give the impression of intelligence, but are simply algorithms that solve problems we have in very well defined ways...ANN is a general intelligence that gets shaped by experience and adapts figure out its own solutions...It isn't some coder banging on the keyboard trying to replicate what a human would do under a specific situation, it is something that truly learns.
It's not ANNs on the one hand and "some coder banging on the keyboard trying to replicate what a human would do" on the other.
Supervised learning is the most common use case for an ANN, and means learning from labelled training data during a training phase and then not changing; it can be done with a number of techniques, including Support Vector Machines, Decision Trees, Bayesian methods, and so on.
Reinforcement Learning on the other hand, is continuous learning from trying things and making mistakes -- experience, in other words. It too can be done a number of ways, like with Evolutionary Algorithms, Markov Decision Processes, Inductive Logic Programming, etc. Hell, PG's spam classifier learns from experience, and it's nothing more than a Naive Bayes classifier ( http://www.paulgraham.com/spam.html ).
Without going on for much longer, my point is that there really is no reason to exalt ANNs the way you have.
Tansey wrote a good, solid reply, but I dislike worshipping at the altar of mysteriousness. When a human or better GAI finally gets built, it won't be entirely hand-coded (although the seed for it might be). But the better we understand how our AI works, the better we can make sure it's doing what we want it to; and not, say, crashing the stock market with millions of erroneous transactions carried out too fast for humans to correct.
ANN is a general intelligence that gets shaped by experience and adapts figure out its own solutions. This is the path that will lead to our concept of AI presented in sci-fi and what the general public actually thinks of. It isn't some coder banging on the keyboard trying to replicate what a human would do under a specific situation, it is something that truly learns.
We have a long way to go before this becomes a reality, but it will happen.