jm + dreams   2

Dormio: Interfacing with Dreams to Augment Human Creativity — MIT Media Lab
Using Dormio you fall asleep as you normally would, but the transition into stage 2 sleep is tracked and interrupted. This suspends you in a semi-lucid state where microdreams are inceptable, allowing direction of your dreams. 
dreaming  dreams  science  neuroscience  brain  sleep  lucid-dreaming  via:fp  dormio 
july 2018 by jm
Inceptionism: Going Deeper into Neural Networks
This is amazing, and a little scary.
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.

An enlightening comment from the G+ thread:

This is the most fun we've had in the office in a while. We've even made some of those 'Inceptionistic' art pieces into giant posters. Beyond the eye candy, there is actually something deeply interesting in this line of work: neural networks have a bad reputation for being strange black boxes that that are opaque to inspection. I have never understood those charges: any other model (GMM, SVM, Random Forests) of any sufficient complexity for a real task is completely opaque for very fundamental reasons: their non-linear structure makes it hard to project back the function they represent into their input space and make sense of it. Not so with backprop, as this blog post shows eloquently: you can query the model and ask what it believes it is seeing or 'wants' to see simply by following gradients. This 'guided hallucination' technique is very powerful and the gorgeous visualizations it generates are very evocative of what's really going on in the network.
art  machine-learning  algorithm  inceptionism  research  google  neural-networks  learning  dreams  feedback  graphics 
june 2015 by jm

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