Surprise Machines is a project of experimental museology that
sets out to visualize the entire image collection of the Harvard Art Museums,
with a view to opening up unexpected vistas on more than 200,000 objects usually
inaccessible to visitors. The project is part of the exhibition organized by
metaLAB (at) Harvard entitled Curatorial A(i)gents and explores the limits of
artificial intelligence to display a large set of images and create surprise
among visitors. To achieve this feeling of surprise, a choreographic interface
was designed to connect the audience’s movement with several unique views of the
collection.
Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences of choreography as well as tunable variations on input choreographic sequences. We use recurrent neural network and autoencoder architectures from a training dataset of movements captured as 53 three-dimensional points at each timestep. Sample animations of generated sequences and an interactive version of our model can be found at http: //www.beyondimitation.com.
Ritesh Singh and Douglas Duhaime have made an open-source web simulation to study 3D distribution of galaxies in the universe out to < 200 Mpc – and they want you to use it.
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