Inceptionism: Going Deeper into Neural Networks

Am I the only person who is not entirely happy about the overuse of the pop-culture term 'inception' for everything that is remotely nested, recursive or strange-loop-like?

   In this paper, we will focus on an efficient deep neural network 
   architecture for computer vision, codenamed Inception, which derives 
   its name from the Network in network paper by Lin et al [12]
   in conjunction with the famous “we need to go deeper” internet meme [1]

I'll repeat what I posted on facebook because I thought it was clever: "Yes, but only if we tell them to dream about electric sheep."

So, tell the machine to think about bananas, and it will conjure up a mental image of bananas. Tell it to imagine a fish-dog and it'll do its best. What happens if/when we have enough storage to supply it a 24/7 video feed (aka eyes), give a robot some navigational logic (or strap it to someone's head), and give it the ability to ask questions, say, below some confidence interval (and us the ability to supply it answers)? What would this represent? What would come out on the other side? A fraction of a human being? Or perhaps just an artificial representation of "the human experience".

...what if we fed it books?

I think doing the same with videos would be far more interesting... and probably creepier.

Really cool. You could generate all kinds of interesting art with this.

I can't help but think of people who report seeing faces in their toast. Humans are biased towards seeing faces in randomness. A neural network trained on millions of puppy pictures will see dogs in clouds.

>If we choose higher-level layers, which identify more sophisticated features in images, complex features or even whole objects tend to emerge. Again, we just start with an existing image and give it to our neural net. We ask the network: “Whatever you see there, I want more of it!” This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird. This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere.

I would say that's a pretty good description of how many psychedelic hallucinations unfold. They start off as noise in your vision which turn into loose forms which turn into geometric patterns which turn into etc...