Seeing Machines

Once upon a time, the US army decided to develop a computer system for detecting camouflaged tanks. They built a neural network — a kind of artificial brain — and trained it on hundreds of photos of tanks hidden among trees, and hundreds of photos of trees without any tanks, until it could tell the difference between the two types of pictures. And they saved another few hundred images which the network hadn’t seen, in order to test it. When they showed it the second set of images, it performed perfectly: correctly separating the pictures with tanks in them from the ones without tanks. So the researchers sent in their network — and the army sent it straight back, claiming it was useless.

Upon further investigation, it turned out that the soldiers taking the photos had only had a tank to camouflage for a couple of days, when the weather had been great. After the tank was returned, the weather changed, and all the photos without a tank in them were taken under cloudy skies. As a result, the network had not learned to discriminate between tanks, but between weather conditions: it was very good at deciding if the weather in the photograph was sunny or overcast, but not much else. The moral of the story is that machines are great at learning; it’s just very hard to know what it is that they’ve learned. – James Bridle

James Bridle - Activations [1][2]

Hito Steyerl - How Not to be Seen: A Fucking Didactic Educational [3]

Sterling Crispin – data masks [4]

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Paul Virilio – the vision machine 1994 [5]

Trevor Paglen – Is photography over? [6]

Seeing machines

{HOG}

{CNN}

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{Deepfish}

glimp of A.I. Histogram of Oriented Gradients (HOG) Deformable Parts Model (DMP) Convolutional Neural Network (CNN)