[hansvm]

> In the end, the "AI" will be to create new "mixes" of different already used concepts... but I don't see how it would be able to create new concepts. The "AI" will - as much as I understand the technology - stay inside the space defined by it's knowledge base. If all the website of the knowledge base have only white or black background, the "AI" can't "think" to use a green background, because it doesn't have any inference mecanism to think of the background color as any color. It is limited to the background that was fed.

"AI" that does what we want is limited to whatever rules we impose on it. For a lot of problems the most efficient way to impose rules is to provide a set of samples and interpolate, but if we have some way to meaningfully define creativity (which I don't think will be feasible in general any time in the near future, maybe ever) then we can produce an architecture which matches that definition (and if we're hung up on the generative portion of that, a trivial though expensive way to accomplish generation is to enumerate outputs and check if they match our definition for creativity).

"AI" isn't limited to the samples it's given; it's limited to the biases we impose. We can explicitly impose a bias that says hue matters if we so desire.

[olivierduval]

IMHO, "creativity" is all about breaking existing rules, replacing them partially or totally with others. In math, it's a new axiom set, allowing/forbidding new inferences. And it's also using analogies with other domains to find new intuitions and new deductions.

However, the main problem with creativity is not reducing the knowledge base, it's finding new knowledges to extend the knowledge space consistently. Sometimes enumeration or automatic generation can help... but in that case, it's "only" a fixed set of meta-rules