What is real creativity? Creativity is just random noise converted into patterns. Is the computer variety of creativity not real enough?
This is not a consensus definition. Creativity doesn't actually seem to be very random at all according to the people who study it.
Humans are not magically creative as much as they'd like to be.
If i ask you to think of a random number, you don't just pull it out of thin air, It can be based on tens to hundres of things: -Should i do a relaly low or high number? -People always use round numbers that end in 0 or 5, maybe i shouldn't do that, or should i to make it seem truer -what other large "random" numbers have a heard? -i remember seeing a number recently, maybe try a modification of that -you used {x} as a random number last time, go similar to that?
All this adds up in that under a second thought you have when i asked you to think of a random number. the literal same thing goes into all creative works, the output is a function of the input.
Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into everything the brain does.
Some neural nets can map real images into a Gaussian, and back. That means they disentangle the factors of the image into a mix of independent factors that map into the standard deviation. Any set of random numbers could be converted back into an image, by the reverse process.
You're acting like the brain is some kind of simple algorithm, more goes into a painting or a composition than just a bit of simple logic. A composer is not sitting there at 2am on her Piano going "Hm, I like round numbers, so I might make this note a F because it's the fourth note in the C major scale".
I might be wrong but from my understanding, we don't even really understand how neural nets are able to make certain decisions or generate certain pictures yet, correct?
It is the interaction between the relations and components that drives the kind of analogical reasoning humans exhibit: traversing networks of relations and thinking up new ways of combining various components.
Without a large base of knowledge to draw from, a creator simply does not have enough data to do anything more than copying. They would not have learned the rich relations nor a diverse enough set of components and operations on them. They would also be unable to know which combinations are truly novel nor have enough experience to predict what might be meaningful or impactful. This is why good creators must continually sample a large set of works.
The limit to this is that humans will generally struggle to wander far from experience. Computers on the other hand, can cover more of a space to make connections humans would find difficult. And the less random the decisions, the more interesting the founds structures are likely to be.
Where computers struggle and humans shine is the selection process. Ashby defines selection as: "problem solving" is largely, perhaps entirely, a matter of appropriate selection. Take, for instance, any popular book of problems and puzzles. Almost every one can be reduced to the form: out of a certain set, indicate one element".
In this work for example, the excellent results are a better grasp of structure induced by having the GAN's networks condition on an input image. This architecture does have the downside of essentially limiting the generative capacity. Humans can generate many variations conditioned on an input but these networks: "Despite the dropout noise, we observe very minor stochasticity in the output of our nets. ...GANs that produce stochastic output, and... capture the full entropy of the conditional distributions they model, is an important question left open by the present work." AGI more and more looks like it will be about striking the right balance between generation and selection.
For an artist, selection ability also plays a large part in taste. Having not just a good enough understanding to create novel combinations, but also a good enough model of people to predict what they are likely to find somehow compelling.
Computers are good at generating, humans at selection. It is for this reason I disagree with those who believe tools like these herald the end of creativity nor can I agree with those who believe this heralds the end of creators. The quality floor of average art will rise but there will be less evidence of selection talent as the ceiling of what counts as great art must also rise.
Joule for Joule, it is unlikely that anything for the forseeable future will beat teams of machine and man.
> AGI more and more looks like it will be about striking the right balance between generation and selection.
I was thinking AGI is essentially reinforcement learning on top of rich, predictive models of the world. But you could see it as the balance between generation and selection.
While this is a highly philosophical topic, I can say, on the difficulty of defining creativity from a scientific point of view, is that more and more what I think is considered "true creativity" is fundamentally a social concept, and as such, even though computers are good at reproducing patters and variations on patterns, they will never be "truly creative" until they become independent members of society, whatever that means. So as cool as machine learning is getting these days, this would require a leap in AI that is still rather far away (imho).
That's why I think it's a little optimistic to label something as complex as creativity as being "just" anything. Creativity is social, insofar as it is defined and recognized by human society, and machines are.. not. Yet.
