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The past seen through artificial eyes

How does artificial intelligence (AI) view a digital archive? Does it see connections that we don’t see? What patterns does it reveal? Can AI be a guide, or a curator? Does it know what we’re looking for better than we do ourselves? And is it a tool, or an oracle? Together with Het Nieuwe Institiuut and VPRO Tegenlicht, designer and artist Richard Vijgen has been investigating these questions over the past few months. Vijgen researched the possible role of AI in opening up digital heritage collections. Here, he describes how he did this using Het Nieuwe Instituut’s National Collection for Architecture and Urban Planning and the 20-year archive of Tegenlicht (Backlight), a documentary TV programme.

Generative adversarial network

As a final experiment, the study uses the work of two architects, Piet Blom and Theo van Doesburg, to train a generative adversarial network. Two neural networks are played off against each other. One network tries to recognise the work of a particular architect optimally, and the other tries to produce an image from scratch that resembles the work of the architect in question. At first, this does not work; the images are random, and the recognition algorithm rejects them. After a while, however, it is better able to generate an image that, for example, can pass as a drawing by Van Doesburg, even though it is not. The outcome is visually interesting and raises all kinds of defining questions.

Who is the author of this image? Is it a new work by the algorithm or the architect, or is it a visual summary of his work?

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The term artificial intelligence is called into question. Is it not better to talk about supplementary intelligence? Is artificial intelligence a false promise? What is its intelligence? Does it have a genuine understanding?

The techniques used in this study all use pattern recognition. Large amounts of information can train a neural network in such a way that it becomes statistically increasingly likely that it can accurately recognise an image.

Although this is more akin to Pavlovian reaction than intelligence and is not a question of understanding, it does, however, lead to useful applications. Moreover, it offers ample scope for further research.

One example is the possibility of determining a building’s architect based on a photographic image.

To a computer scientist, it comes as no surprise that the training model determines what an AI can recognise. Nevertheless, it makes sense to make this visible and insightful in public applications. How does an algorithm see, and how is it trained? To what extent does it accommodate uncertainty when classifying? Variables that can lead to very different outcomes. Variables that a user can select. This would make an AI less of a black box and more of a tool. It would help demystify the relationship between man and machine. Visualising intermediate steps, such as class activation maps, could also be useful.

A generative adversarial network may seem to generate a new design by Van Doesburg, but what is the significance of this? Is it actually a new work? Who is the author? Who is the copyright holder? Again, it would be a misconception to attribute a creative power to the algorithm. It could be regarded as an attempt to make variations on the architect’s work by distilling an essence.

Artificial intelligence will play a role in how the public experiences the archive of the future. Whether this is in the form of an oracle or a tool is a question of design.

Neural networks are intrinsically stratified and diffuse. A user-friendly interface carries the risk of mystifying the technology and placing the user in a passive role. AI as a tool requires more effort on the part of the user, because it places them in a more active and intelligent role.