Learning to See | Onassis Foundation

An artificial neural network looks out onto the world and tries to make sense of what it sees. But it can only see through the filter of what it already knows.

Just like us.

Because we too, see things not as they are, but as we are.

In this context, the term “seeing”, refers to both the lower-level perceptual and phenomenological experience of vision, as well as the higher-level cognitive act of making meaning and constructing what we consider truth. Our self-affirming cognitive biases and prejudices define what we see, and how we interact with each other as a result, fuelling our inability to see the world from the others’ point of view, driving social and political polarization. The interesting question is not “when you and I look at the same image, do we see the same colors and shapes?” but “when you and I read the same article, do we see the same story and perspectives?”.

Everything that we see, read or hear, we try to make sense of by relating to our own past experiences, filtered by our prior beliefs and knowledge.

In fact, even these sentences that I am typing right now, I have no idea what any of it means to you. It is impossible for me to see the world through your eyes, think what you think, and feel what you feel, without having read everything that you have ever read, seen everything that you have ever seen, and lived everything that you have ever lived.

Empathy and compassion are much harder than we might realize, and that makes them all the more valuable and essential.

“Learning to See” is an ongoing series of works that use state-of-the-art machine learning algorithms to reflect on ourselves and how we make sense of the world. The picture we see in our conscious mind is not a mirror image of the outside world, but is a reconstruction based on our expectations and prior beliefs.

Title: Learning to See

Medium: video installation

Artist: Memo Akten

Year: 2017

Location: On display at Pedion tou Areos

Part of: You and AI: Through the Algorithmic Lens

Glossary: Neural Network, Machine Learning