Learning to see: Hello, World! (2017)
A deep neural network opening its eyes for the first time, and trying to understand what it sees. Training live through surveillance cameras.
“Learning To See” is an ongoing series of works that use state-of-the-art Machine Learning algorithms as a means of reflecting on ourselves and how we make sense of the world. The picture we see in our conscious mind is not an exact reflection of the outside world, but is a reconstruction based on our expectations and prior beliefs. In “Learning To See”, an artificial neural network loosely inspired by our own visual cortex, looks through cameras and tries to make sense of what it sees. Of course it can only see what it already knows. Just like us.
The work is part of a broader line of inquiry about self affirming cognitive biases, our inability to see the world from others’ point of view, and the resulting social polarization.
This work examines the process of learning, and understanding. A deep neural network opening its eyes for the first time, and trying to understand what it sees. Training on live surveillance cameras.
This neural network has not been trained on anything. It starts off completely blank. It is literally opening its eyes for the first time and trying to understand what it sees. In this case ‘understanding’ means trying to find patterns, trying to find regularities in what it’s seeing, so that it can efficiently compress and organise incoming information in context of its past experience and make accurate and efficient predictions of the future.
But the network is training in realtime, it’s constantly learning, and updating its ‘filters’ and ‘weights’, to try and improve its compressor, to find more optimal and compact internal representations, to build a more ‘universal world-view’ upon which it can hope to reconstruct future experiences. Unfortunately though, the network also ‘forgets’. When too much new information comes in, and it doesn’t re-encounter past experiences, it slowly loses those filters and representations required to reconstruct those past experiences.
These ideas are not behaviours which I have explicitly programmed into the system. They are characteristic properties of deep neural networks which I’m exploiting and exploring.
* One might liken this to a new born baby’s brain. This comparison may work metaphorically at a higher level; however, it’s not entirely accurate. A new born baby’s brain has had hundreds of millions of years of evolution shaping its neural wiring, and arguably the baby is born with already many synaptic connections in place. In this work however, this artificial neural network ‘starts life’ with full architecture in-tact, but all connections are initialised randomly. So at a lower level the details are a quite different.
Originally loosely inspired by the neural networks of our own brain, Deep Learning Artificial Intelligence algorithms have been around for decades, but they are recently seeing a huge rise in popularity. This is often attributed to recent increases in computing power and the availability of extensive training data. However, progress is undeniably fueled by multi-billion dollar investments from the purveyors of mass surveillance – technology companies whose business models rely on targeted, psychographic advertising, and government organizations focussed on the War on Terror. Their aim is the automation of Understanding Big Data, i.e. understanding text, images and sounds. But what does it mean to ‘understand’? What does it mean to ‘learn’ or to ‘see’?
Created during my PhD at Goldsmiths, funded by the EPSRC UK.