Image-to-Image Translation with Conditional Adversarial Nets

Networks with selective attention already exist, but what if they can learn about themselves? Right now they cannot create any notion of self or "body" (defined as the boundary between the environment that can be predicted vs that which cannot), because their outputs have no causal effect on their inputs. There are no differences within the network that make a difference to itself, there is no intrinsic perspective.

Could this change, if, for example

- inputs are augmented with the network state (or derived version thereof)

- previous outputs of the network / external memory are fed back?

This seems to be the kind of self reference self awareness requires.

Also, do asynchronous networks have fundamental advantages over synchronous networks? What about static vs dynamic networks?

What's your point actually? Honest question.

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?

Whenever this topic comes up someone with a very engineering-centric attitude jumps on the opportunity to downplay the complexity of the concept of "creativity". Often using the word "just".. I would argue that something like creativity is far too difficult to even define, let alone model and synthesize, to ever be adorned with the modifier "just".

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.

> You're acting like the brain is some kind of binary computer

It's not binary but it is a computer. The alternative is to believe in magic.

> we don't even really understand how a computer is able to make certain decisions or generate certain pictures yet, correct?

I don't think that's correct, no. We understand how the process works. We may not understand the weights a specific neural net ends up with, but that's an issue with just having so much data to deal with. Similarly we don't "understand" how a web page ends up with a specific PageRank. We understand the process, but we can't manually reproduce the result because it's just too much data.

While I do not accept ragebol's notion of real creativity, I very much doubt your definition is correct. The use of 'just' is too strong, excluding the possibility of influence from other inputs. When humans learn, they do so by learning a large set of arbitrary relationships which further trigger complex associations (this is blue, it is used for X, it is like Y, which is like Z). These relationships are further broken down in terms of human meaningful components that are updateable with new knowledge with almost surgical precision.

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.