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European ARTificial Intelligence Lab, Ars Electronica

Works

Anatomy of an AI
Vladan Joler (RS), Kate Crawford (AU)
In the 21st century, we are seeing a new kind of mining for raw materials that drills deep into the biosphere. This enables AI technologies that are having a profound effect on the cognitive and affective layers of human nature. The resources for producing systems such as Amazon Echo, a speech controlled, Internet-based personal assistant, go beyond the technical aspects of data modelling, hardware, servers, and networks and extend much further into the realms of work, capital, and nature. The true costs – social, ecological, economic, and political – remain mostly hidden. Anatomy of an AI uses the example of Amazon Echo to show the countless components and factors behind the production of artificial intelligence systems. But this process is so complex that its full extent can hardly be comprehended.

Authors: Kate Crawford and Vladan Joler
Maps and design: Vladan Joler and Kate Crawford
Published by: SHARE Lab, SHARE Foundation and The AI Now Institute, NYU
https://anatomyof.ai/

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Gender Shades
Joy Buolamwini, Timnit Gebru
Joy Buolamwini and Timnit Gebru investigated into the bias of AI facial recognition programs. The study reveals that popular applications that are already part of the programming display obvious discrimination on the basis of gender or skin color. A further reason for the unfair results can be found in erroneous or incomplete data sets on which the program is being trained. In things like medical applications, this can be a problem: simple convolutional neural nets are already as capable of detecting melanoma (malignant skin changes) as experts are. However, skin color information is crucial to this process. That’s why both of the researchers created a new benchmark data set, which means new criteria for comparison. It contains the data of 1,270 parliamentarians from three African and three European countries. Thus Buolamwini and Gebru have created the first training data set that contains all skin color types, while at the same time being able to test facial recognition of gender.

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Learning to See: Gloomy Sunday
Memo Akten (TR)

„We see things not as they are, but as we are“

Learning to See is an ongoing series of works that use the latest machine learning algorithms to reflect on how we understand the world. What people see is a reconstruction based on our expectations and previously held beliefs. Learning to See is an artificial neural network loosely inspired by the human visual cortex. It looks through cameras and also tries to understand what it sees. Of course it can only see what it already knows — the same as us. This work is part of a broader line of research about the difficulty of seeing the world through the eyes of others. Learning to See: Gloomy Sunday is a video and an interactive installation where the recordings taken by a live camera aimed at a table covered with objects are analyzed by a series of neural networks trained on different data sets (ocean, fi re, clouds, and flowers).

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MegaPixels
Adam Harvey (US), Jules LaPlace (US)
MegaPixels is an independent art and research project that investigates the ethics, origins, and individual privacy implications of face recognition image datasets and their role in the expansion of biometric surveillance technologies. The project aims to provide a critical perspective on machine learning image datasets, one that might otherwise be overlooked by academic and industry-funded artifi cial intelligence think tanks. Each dataset presented on this site undergoes a thorough review of its images, intent, and funding sources.

Credits: megapixels.cc

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Volumetric Data Collector
Hyun Parke (KR/US), Jinoon Choi (KR), Sookyun Yang (KR)
Volumetric Data Collector is based on the idea of using a LiDAR sensor— a 3D laser sensor often used in autonomous vehicles—as an expanded sensory organ for the human body. The team of developers packed a LiDAR sensor, a display monitor as visual output, and accessory equipment into a portable unit. The device can capture a 3D point cloud of the area around the wearer, which is then translated into visual data. For example, the Seoul LiDARs collected three-dimensional information from historical locations in Seoul, South Korea. The goal is to use technical expansion of human senses to investigate how spaces—for example, urban environments—can be differently defined or perceived. Here, visitors have an opportunity to conduct their own experiment with a portable LiDAR unit.

Credits: Sookyun Yang (KR), Jinoon Choi (KR), Hyun Parke (KR/US)
With support from ZER01NE, Seoul LiDARs

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What a Ghost Dreams Of
h.o (INT)
What is a “ghost”? Generally it is understood as an inner “soul” and a mysterious outward appearance. What a Ghost Dreams Of grapples with a new “ghost” of our time: digital surveillance in our society. Here in the Main Gallery, visitors are observed by a large “eye” when they come in. Everyone who passes by is fed by computer vision directly into a “ghost” that creates new digital faces of people who do not exist in the real world. What do we humans project into the digital counterpart we are creating with AI? It is getting to know our world without prior knowledge and generating data that never existed. What are the effects of using AI to produce works of art? Who holds the copyright? And what is AI, the “ghost,” dreaming about, and what does that mean for us as human beings?

AI System: John Brumley
Surveillance Application: Hiroshi Chigira
Technical Direction: Hiroshi Chigira, John Brumley, Taizo Zushi
Art Direction, Concept: Hideaki Ogawa, John Brumley, Hiroshi
Chigira, Emiko Ogawa, Taizo Zushi
Eye Blinks Editing / Directing: Martina Sochor
Eye Blinks Cinematography: Jonatan Salgado Romero
Eye Blinks Model: Andressa Miyazato
Photography: Florian Voggeneder
Face Photo Booth: Ali Nikrang
This project utilizes the AI algorithm StyleGAN (Karras et al. 2018)
About h.o: www.howeb.org/about

Understanding AI Exhibition

What is artificial intelligence? And what do we actually know about human intelligence? How intelligent can artificial intelligence be in comparison? And more importantly: what effects will the advances in this field have on our society?

Artificial intelligence offers plenty of room for speculation about the future. What is certain is that this technology has already changed our everyday life in far-reaching ways and will continue to do so. We have already been supported by algorithms in a wide range of areas such as autonomous driving, security technology, marketing or social media for a long time. Artificial intelligence is even used to create works of art. But how many of our tasks do we want to outsource to machines? No other development in our time poses such a clear question about how we want to employ our technological means in a societal context.

Social transformation due to artificial intelligence is already in full swing. In order to get our bearings with it, we need a basic understanding of this technology. Understanding AI presents the most important technical aspects of artificial intelligence as well as concrete examples of how they are used. Here visitors can discover how machines and their sensors “perceive” the world in comparison to humans, what machine learning is, or how automatic facial recognition works, among other things. They can also learn about various social and ethical issues such as deep fakes (deceptively genuine-seeming pictures or videos made automatically using neural networks), the effects of using digital methods for profiling, and the hidden side of our everyday electronic devices such as smartphones. New creative applications made possible by artificial intelligence are also on display for visitors to experience. There are no easy answers about how to use artificial intelligence or what its dangers are, but Understanding AI provides a broad basis of information to help us navigate this complex field.