Classical AI vs. connectionism

Eliezer S. Yudkowsky (
Mon, 14 Sep 1998 11:07:12 -0500

Emmanuel Charpentier wrote:
> On the other side, we can try to copy the (natural) neural network.
> The artificial neural networks that we can program nowadays are so
> simple (and yet so effective in some tasks) that we can easily predict
> the emergence of many more features. Memory, analogy, imagination,
> semantics, intuition... etc.

The idea that all computers work by manipulating semantically charged computational tokens may be summarized as "classical AI". In other words, symbols are the basic units of computation, manipulated by the high-level rules of abstract thought.

"Connectionism" is the idea that you cannot explicitly program intelligence; it has to be caught in a neural net.

Both paradigms are flat wrong. The battle between them has been refought many times; I do not intend to refight it. The seed AI in _Coding_ is based on neither principle. Thought does not magically materialize from abstract manipulation, or inside a neural network. Thought has to be programmed BY HAND with LOTS OF HARD WORK.

> How do you program analogy?

See Hofstadter's Copycat for an excellent demonstration of the basic principle involved. You program analogy by reducing "analogy" to bonds, groups, distinguishing descriptors... The basic cognitive elements underlying our perception of an analogy.

> One more thing, natural neural network can hold conflicting
> beliefs: I can believe that the earth is flat (sitting atop of four
> giant elephants atop a great turtle) and try to calculate its radius
> using angles of the sun shadows in deep wells. No problem. -I/we- can
> be unconsistant! (and so easily) And it's a great feature, because
> finally, when you look at science, it's only a set of beliefs, some of
> which might conflict between each other (until better beliefs come
> into play).

Any system that's more than a first-order-logic game can hold conflicting beliefs. You're fighting the wrong war. The seed AI in _Coding_ isn't classical AI. I think I may have even put in an explicit explanation of how probabilities are estimated given conflicting ideas.

> So, why do you want to program a perfect AI? And how do you
> manage unconsistency and/or uncompleteness (not having all/enough data)?

I don't want to program a perfect AI. I want to program an AI that has the capability to consciously direct the execution of low-level algorithms. The high-level consciousness is no more perfect than you or I, but it can handle inconsistency and incompleteness. The low-level algorithm can't handle inconsistency or incompleteness and it's downright stupid, but it's very, very fast and it's "perfect" in the sense of not having the capacity to make high-level mistakes.

Again: The _high_level_ is not classical, but it has the capability to _use_ algorithmic thought, in addition to all the _other_ capabilities. Let me explain it your way: The high level is not connectionistic, but it has all the magical capabilities you attribute to neural nets, and it can direct evil classical AI in the same way that you can write a computer program. It doesn't use classical AI for anything other than, say, arithmetic or chess programs. And when I say "use", I don't mean it like "the brain uses neural networks", I mean it like "Emmanuel uses a pocket calculator".

I don't know how much of _Coding_ you've read. If you read the whole thing, you should hopefully understand that _Coding_ is not remotely near to classical AI.

--         Eliezer S. Yudkowsky

Disclaimer:  Unless otherwise specified, I'm not telling you
everything I think I know.