Opening the AI Black Box
Terence Broad, researcher and artist
“I am not particularly interested in artworks autonomously made by machines, and artworks that are generated simply by using the most recent state of the art. AI model quickly get boring for me.” - Terence Broad.
In science and engineering, a black box is a device or system in which inputs and outputs can be viewed, but the internal operation is unknown. When Google engineer Alexander Mordvintsev designed DeepDream (2015) to analyze visual imagery and understand how images were processed, the dream-like hallucinogenic outputs began to be appreciated also for their artistic qualities. Then, the growing interest in computer vision pushed the development into Generative Adversarial Networks (GANs) with Ian Goodfellow and his colleagues.
Terence Broad is an artist and researcher who is currently completing a PhD at Goldsmiths in London. He is also a visiting researcher at the UAL Creative Computing Institute where he develops methods and tools to manipulate deep generative models.
From reversing and amplifying the uncanny valley of Mashairo Mori, to the research of novelty through the training of GANs without data, Broad’s approach is experimental, perhaps not always orthodox in the methods, research-driven and focused on exposing unseen aspects of the machine’s gaze.
[Fig.1] Video (un)stable equilibrium, Credits: Terence Broad. This is part of an ongoing series exploring the training of a machine without data.
Broad develops tools for artists and designers, and investigates the ways in which machine learning helps artists become differently creative.
“AI techniques are giving us very powerful tools for generation and representation, but there is a huge amount of scope for people to take these tools and do new and creative things with them, which is the approach I am taking with my artistic practice and my research.”
In Being Foiled, Broad approaches the uncanny valley in reverse and generates images that are less and less realistic until he reaches almost abstraction.
[Fig.2] Being Foiled — images where the uncanniness was most pronounced. Credits: Terence Broad.
[Fig. 3] Being Foiled— images at the end of the process where the results are almost total abstraction. Credits: Terence Broad.
In his own words: “Again, this work came out of the same desire to find ways of training models, such that the output was completely new and unlike any training data. I had been experimenting for some time with fine-tuning already trained GANs with different kinds of networks that had been frozen, forcing the output of them to change and converge into a new space […] I discussed this training process, and how the fine-tuning procedure, starting from realism and ending at complete abstraction, is a process of crossing the uncanny valley in reverse. Where normally the uncanny valley is a phenomena encountered when trying to create more realistic representations of people, this procedure encountered in the other direction, by deliberately producing representations that diverged more and more away from realism.”
Part of Broad’s research is centered on developing a method for analyzing a GAN to understand what the components inside are doing and discover how they work together. To know more about his work, read the full interview on my blog.
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