Believe It Yourself™
automato.farm

Believe it Yourself‘ is a series of real-fictional belief-based computing kits to make and tinker with vernacular logics and superstitions. Tinker with cameras that can see luck, microphones that interpret your destiny, and compasses that can point you to harmony and balance.

‘The world’s first belief-based computing kit, that turns popular beliefs and superstitions into 0100110010.’

The fast-spreading and ease of access to Machine Learning and Cloud computing has brought to a boom of experiments and excitement around our ability to build machines that make sense, learn, measure and predict the world around us. With enough examples, we can train a tool or a ‘machine’ to recognize or quantify pretty much anything we want. ‘Beauty’, ‘Hotdog-ness’ or the more problematic ‘Criminal-ness’ and ‘Sexual orientation’ can be now measured within a few frames, based on a model, a probability, determined by a set of arbitrarily collected data. Subjective judgments and biased datasets can easily be turned into objective measures and potential truths, which will then be embedded in devices around us.

But what if we would train machines to measure even more unmeasurable, personal and culturally driven things? If we gather enough samples could we detect signs that prove and detect our superstitions? and can we use that to build tools and devices that reflect our own beliefs?

About automato.farm
automato.farm is a design and research group founded in 2015 based in Shanghai, Beijing and Rotterdam, exploring the implications of automation and machine intelligence leaking in the everyday through real and fictional products and experiences. They developed ethical algorithms for cars to deal with complex decision making on the road, created fictional future jobs that emerge as a bi-product of trainable machines, designed politically biased products, self-selling appliances addicted to their own use and more.

Believe It Yourself™ is a commission for ‘Uncanny Value’ at MAK Vienna for the 2019 Vienna Biennale.

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