Artificial neural networks have been the model for "machine learning" for quite a while now. It is essentially just computer code inspired by the way the central nervous system works in animals (brains). For more information on artificial neural networks, you can go to: (https://en.wikipedia.org/wiki/Artificial_neural_network)
Well, Google recently created a "Deep Learning" algorithm based off research done with Artificial Neural Networks and "trained" it with huge databases of pictures of mammals, people, buildings, cars, etc. For example, the network is shown a thousand pictures of a duck and told repeatedly that this is a duck. When it learns the million things that make a duck distinctly a duck, they move on to other objects. Rinse, repeat and rinse, repeat. When a large database of information is established they can move on with the network and ask it to carry out different tasks with its newly-acquired information. To quote Google's blog here: (http://googleresearch.blogspot.ie/2015/06/inceptionism-going-deeper-into-neural.html)
"Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations. 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."
Now recently, they decided to release the code, (http://googleresearch.blogspot.ie/2015/07/deepdream-code-example-for-visualizing.html) so people could see what the trained neural networks were seeing on any image that they wanted.
Inevitably, the internet has done awesome things with it so far, and this is what this post is about. This app (https://dreamscopeapp.com/create) created by a user on reddit allows users to upload any image they want, and run it through the "Deep Dream" algorithm. The results are creepy, awesome, trippy, and eerily resemble what MANY LSD users report when on Acid (seriously, they seem amazed by the similarity). I took the liberty to run a few pictures through the algorithm and posted the results. Feel free to do the same for your own pictures, or simply just discuss the topic at hand.
Are our brains on LSD essentially treating visual input exactly how this neural network perceives still images? Is this computer taking a picture and letting its imagination go wild? Similar to how a child looks at clouds in the sky? Are our brains actually just an advanced artificial intelligence?
I'm sure it will be a good time. Enjoy. Happy Off-Season.