[hansvm]

> So it's obviously NOT able to evaluate "creative" capabilites because... well... creativity is about being DIFFERENT and not the same.

Neural nets can approximate any function as accurately as you'd like. Blindly shoving whole sites prelabled by their creativity into a convnet seems likely to end poorly, but that doesn't mean a better training method can't be applied which more faithfully represents the problem domain (no clue what the inside of this particular tool looks like, just commenting on the general infeasibility claim).

> So a Facebook designed clone will have no interest, even if it looks like Facebook.

Doesn't this site just claim that appearances also matter, not that a polished turd will be successful?

[olivierduval]

> just commenting on the general infeasibility claim

I agree: the main problem of "AI" is the knowledge base used. And the biais associated. Which mean that you can correct any biais by changing the reference... but introducing other biaises.

It would be possible to use such a tool either to focus on smaller tasks (let's say: focusing only on photo gallery of professional photographer websites) and be more specific (but less creative) or to grow the knowledge base to include paintings, architecture, object design to exact more general design principles (gestalt and so...)

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.

[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