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Aphex Twin – T69 Collapse

WARNING: CONTAINS STROBING. Taken from Aphex Twin’s ‘Collapse’ EP out 14 September on Warp. Video by Weirdcore.

Order ‘Collapse’ EP:…

Order on Bleep:…

Stream ‘T69 Collapse’:

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Could There Be Other Explanations?

Could There Be Other Explanations? from Julius Horsthuis on Vimeo.

Make your way through a fractal agnostic temple in glorious 3K.
I tried to keep the post effects to a minimum to give you a pure mandelbulbilicious experience.
Music: “Nomasi” by Osanno

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Fractalicious 3

Fractalicious 3 from Julius Horsthuis on Vimeo.

Every 6 months or so Julius Horsthuis bundles the best parts of his Fractal short films into a showreel-style piece. This is the third installment of the Fractalicious series. rendered in Mandelbulb3D.
music: “Attica” by Vessels

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Journey through the layers of the mind

Journey through the layers of the mind from Memo Akten on Vimeo.

first tests playing with #deepdream #inceptionism

A visualisation of what’s happening inside the mind of an artificial neural network.

In non-technical speak:

An artificial neural network can be thought of as analogous to a brain (immensely, immensely, immensely simplified. nothing like a brain really). It consists of layers of neurons and connections between neurons. Information is stored in this network as ‘weights’ (strengths) of connections between neurons. Low layers (i.e. closer to the input, e.g. ‘eyes’) store (and recognise) low level abstract features (corners, edges, orientations etc.) and higher layers store (and recognise) higher level features. This is analogous to how information is stored in the mammalian cerebral cortex (e.g. our brain).

Here a neural network has been ‘trained’ on thousands of images – i.e. the images have been fed into the network, and the network has ‘learnt’ about them (establishes weights / strengths for each neuron). (NB. This is a specific database of images fed into the network known as ImageNet )

Then when the network is fed a new unknown image (e.g. me), it tries to make sense of (i.e. recognise) this new image in context of what it already knows, i.e. what it’s already been trained on.

This can be thought of as asking the network “Based on what you’ve seen / what you know, what do you think this is?”, and is analogous to you recognising objects in clouds or ink / rorschach tests etc.

The effect is further exaggerated by encouraging the algorithm to generate an image of what it ‘thinks’ it is seeing, and feeding that image back into the input. Then it’s asked to reevaluate, creating a positive feedback loop, reinforcing the biased misinterpretation.

This is like asking you to draw what you think you see in the clouds, and then asking you to look at your drawing and draw what you think you are seeing in your drawing etc,

That last sentence was actually not fully accurate. It would be accurate, if instead of asking you to draw what you think you saw in the clouds, we scanned your brain, looked at a particular group of neurons, reconstructed an image based on the firing patterns of those neurons, based on the in-between representational states in your brain, and gave *that* image to you to look at. Then you would try to make sense of (i.e. recognise) *that* image, and the whole process will be repeated.

We aren’t actually asking the system what it thinks the image is, we’re extracting the image from somewhere inside the network. From any one of the layers. Since different layers store different levels of abstraction and detail, picking different layers to generate the ‘internal picture’ hi-lights different features.

All based on the google research by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer