Learning to See is an ongoing collection of works that use 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.

An artificial neural network looks out onto the world, and tries to make sense of what it is seeing. 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 low level perceptual and phenomenological experience of vision, as well as the higher level cognitive act of making meaning, and constructing what we consider to be 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 each others’ point of view, driving social and political polarization. The interesting question isn’t only “when you and I look at the same image, do we see the same colors and shapes”, but also “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’m typing right now, I have no idea, what any of it means to you. It’s 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’ve ever read, seen everything that you’ve ever seen, and lived everything that you’ve ever lived.

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

https://www.memo.tv/works/gloomy-sunday/

https://www.memo.tv/works/learning-to-see/