Dreaming of Electric Sheep (Flock 0x0) (2019)

# Dreaming of Electric Sheep: From Artificial Artificial Intelligence to Artificial Intelligence

More than 12 years ago, Amazon coined the term artificial artificial intelligence for processes outsourcing tasks to humans, especially those tasks computers are (or were?) lousy at, e.g., identifying objects in a photograph, writing short product reviews, transcribing podcasts, watching a short film and then describing the emotions it elicits.

Crowdsourcing emerged as a new form of labor on demand [2] and Amazon Mechanical Turk (MTurk) provided the mechanisms to harness the power of a global workforce to complete tasks at scale. It was in this context where Aaron Koblin’s project, The Sheep Market, was born.

The Sheep Market is a collection of 10,000 sheep created by workers on Amazon’s Mechanical Turk. Each worker was paid $0.02 (USD) to complete the HIT (human intelligence task): “draw a sheep facing left.”

Back to now, AI/ML is currently showing impressive performance in fields such as computer vision and natural language processing. AI/ML is expected to impact many industries, helping us in our tedious tasks or taking our jobs, or something in between.

This collection is an experiment that illustrates how certain crowdsourced tasks, e.g., drawing a sheep using MTurk’s artificial artificial intelligence HITs, can now be completed by machines achieving close to human level performance, which potentially could start taking away those micro-payments from human workers. Workers that, paradoxically, have helped machines to learn from the datasets created by completing micro tasks.

For example, in creating these pieces the machine, an AI/ML model, is trained using a dataset of sheep sketches crowdsourced from real people [1] and then is able to generate original sheep drawings on its own.

##TL;DR of our Approach

The Dataset. As training set we use the sheep dataset from The Quick, Draw! experiment.

Architecture. We use a Deep Convolutional Generative Adversarial Networks or DCGAN to implement our approach.

Deep Learning Tools. We use Keras with TensorFlow backend for the model implementation and JupyterLab for prototyping.

Models. After the Generator and Discriminator models are trained, we save them independently.

From random noise to sheep. We then use the Generator to produce sheep drawings from random noise. The results are as good (or as bad and sketchy) as the ones produced by humans 😉

## References
[1] Quick, Draw! Sheep Dataset. Data made available by Google, Inc. under the Creative Commons Attribution 4.0 International license. https://quickdraw.withgoogle.com/data/sheep

[2] The Rise of Crowdsourcing. Jeff Howe. 2016 https://www.wired.com/2006/06/crowds/

[3] Draw me an Electric Sheep. Libre AI. https://medium.com/libreai/draw-me-an-electric-sheep-9a3e0b5fe7d5