Luba Elliott opened the Cloud & Machine Learning track at the recent Google Developer Group event at UCL in London with a talk on the Creative Applications of Machine Learning. It was her closing question that motivated me to look into some of the artists she mentions.
“What is the best way for an AI to dream?”
– Luba Elliott, #GDGLondon 2017
Ask someone to explain why a reasonably complex neural network arrived at a particular decision and you will likely be met with a shrug of the shoulders. If you are lucky, you will be met with a generic explanation of how neural networks work. Rarely will you hear anything specific to the outcome in question. The same could be said about dreams. There is the theory, but nothing that allows you to determine why an individual dream took the turn it did.
Mathematics can explain the theory behind the artificial intelligence. Engineering advances have given us the compute resources necessary to consume vast troves of data to train these models. As a result, the individual decisions made by machines are now a function of data sets so vast that we struggle to comprehend the information they contain. It is practically impossible to explain why an individual decision was made.
This very challenge has led to some interesting debate around the European Union regulations on algorithmic decision-making and a “right to explanation” where a user can ask for an explanation of an algorithmic decision that was made about them.
What struck me about Luba’s talk was that rather than trying to understand and explain these systems, there was a community of people dedicated to exploring their creativity. But is there was more than creativity to their work. Could art hold the key to explaining these machines? Much of the creativity we see in their work results from peering inside these models, exploring them as they develop, from seeing them dream.
Luba’s question got me thinking: In addition to looking for mathematical explanations for the decisions our models make should we also be looking into the way they dream? Even if a deterministic outcome eludes us, we arrive at some incredibly intriguing art and with it an insight into the models that we have not previously had. I wanted to share a selection of artists mentioned during the talk, not because they are the first to explore the field of Creative AI, but because their work represents an usual look inside the machines that many of us have come to depend on.
Fall of the House of Usher from Anna Ridler on Vimeo.
Fall of the House of Usher is a 12-minute animation based on the short story by Edgar Allan Poe, where each still is generated by artificial intelligence. This is done by using a neural net (pix2pix) trained on the artist’s ink drawings made of stills from the 1929 version of the film. Each still shown in the animation is not merely a filter that is applied to an existing image, but an entirely new image by a neural net.
J-train style transfer from Gene Kogan on Vimeo.
Gene Kogan is an artist and a programmer who is interested in generative systems, computer science, and software for creativity and self-expression.
What I particularly like about Gene’s work is how raw it fees. This rawness only serves to highlight the non-deterministic nature of the artificial intelligence algorithms. Gene’s more recent work starts to explore the idea of what it might mean for an AI to dream.
For those interested in exploring this field Gene has been working on a collection of free educational resources, ml4a.
Learning to see: Hello World! [WIP R&D 1] from Memo Akten on Vimeo.
Artist working with computation as a medium, exploring collisions between nature, science, technology, ethics, ritual, tradition and religion.
I can’t remember what it was like to see for the first time. Sight is something the majority of us take for granted. This video does an incredible job of portraying what it might be like to experience those first few moments of sight. Incredibly, this is not an artists interpretation, this is an algorithm figuring out how to discern objects within its field of vision.
Geomancer (2017) - Trailer from Lawrence Lek on Vimeo.
Geomancer is a CGI film by Lawrence Lek about the creative awakening of artificial intelligence. Feauturing a neural network-generated dream sequence, and a synthesised vocal soundtrack, Geomancer explores the aesthetics of post-human consciousness.
Luba Elliott is a curator, artist and researcher specialising in artificial intelligence in the creative industries. She is currently working to educate and engage the broader public about the latest developments in creative AI through monthly meetups, talks and tech demonstrations.
I’d like to thank Luba for taking the time to speaking at #GDGLondon and for introducing me to a new dimension to the field of Artificial Intelligence. If you are interested further, take a look at her overview of the Creative AI Landscape.