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Filter results for [classification]
11/09/20

Javier Lloret Pardo - Annotators View Image Annotators constitute the hidden labour of AI vision. The current ubiquitous...

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On MIT’s Moments in Time (and Being Dead-Alive)

June 2020

I write this from my small New York apartment in my fourth month of isolation. The pandemic has required each of us to slow down and do less, and I keep thinking of a childhood friend who once told me, “We’re human beings, not human doings”. Even as a teenager, I knew this was an important paradigm shift: it meant that we could rethink how we define ourselves beyond endless production and consumption. Allowing oneself to be a human being seemed to resist the gig economy, workerism, the idea of “a calling”— all the ways that society has been structured to combine a person’s work into their core identity. The way busyness became a humblebrag. Human doings.

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Declassifier

April 2020

Philipp Schmitt's 'Declassifier' uses a computer vision algorithm trained on COCO, an image dataset developed by Microsoft in 2014. In the work, photographs from Schmitt’s series 'Tunnel Vision' are tested and overlaid with the images used to generate the algorithm in the first place. By doing so, Schmitt exposes the myth of magically intelligent machines; the visual data by which machine learning algorithms learn to make predictions is hardly ever shown, let alone credited. Part of the 'Data / Set / Match' programme.

Launch Project

Tunnel Vision

April 2020

When a computer vision algorithm recognises something in a picture, it soberly frames what it ‘sees’ in confetti-coloured rectangles, digital hues that contrast with the everyday shapes and colours that we see in a space with plain eye. Each neatly labelled with a single category, these annotations highlight answers but don't give explanations. To the uninitiated, it seems almost magical, or at least akin with some sort of intelligence.

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Where Did ImageNet Come From?

November 2019

In September 2019 the ImageNet creator Fei-Fei Li gave a talk at The Photographers' Gallery talking through the events and key people that led to the creation of visual datasets.

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An Introduction to Image Datasets

November 2019

In 2019 The Photographers' Gallery digital programme launched 'Data / Set / Match', a year-long programme that explores new ways to present, visualise and interrogate contemporary image datasets. This introductory essay presents some key concepts and questions that make the computer vision dataset an object of concern for artists, photographers, thinkers and photographic institutions.

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11/09/19

Strike (with) a Pose: Neural networks are easily fooled by strange poses of familiar objects Despite excellent performance on...

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28/08/19

An article from the NYT Privacy Project on the The Racist History of Facial Recognition. Starting with early scientific facial...

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19/07/19

We introduce natural adversarial examples – real-world, unmodified, and naturally occurring examples that cause classifier...

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20/06/19

The reason biases against women or people of colour appear in technology are complex. They’re often attributed to data sets...

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15/04/19

Layers of Abstraction: A Pixel at the Heart of Identity Shinji Toya and Murad Khan, 2019 This project centres around a...

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13/03/19

ImageNet Roulette (Trevor Paglen, 2019) uses a neural network trained on the “people” categories from the ImageNet dataset to...

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12/09/16

Generating Videos with Scene Dynamics Proof of concept computer science research from Carl Vondrick, Hamed Pirsiavash and...

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