The second in a series of posts about how we think about coding and functional programming. The first was The Homunculus

Even though I've complained about the metaphor of the homunculus, it can be used as a useful device to explain the differences between programming paradigms to a non-technical audience …

Here are visualisations of the 205 scene categories in the MIT Places Database using a variant of the same technique used to generate the thousand faces of Caffenet.

These are rendered on random grayscale noise rather than a photograph, so they're purely a manifestation of what the neural net has …

Here are the thousand image categories in the ImageNet Challenge data visualisation context, rendered onto a webcam photo of me using the deepdraw technique.

This is the set of image classifications which the standard Deepdreams imagery is based on: as more than one hundred of the categories are dog breeds …

This is the first in a series of posts about the way we think about programming, the influence these ideas have on how we write code and what languages we choose, and why I like the functional programming paradigm in general and Haskell in particular.

Consumer notification: I've worked as …

The Deepdreams algorithm takes a deep neural net trained for image-recognition and uses a forced-feedback loop to induce a kind of machine-pareidolia or hallucination, with beautiful and often nightmarish results:

The original algorithm picks details from all of the neural net's training data, based on resemblances to the original image …