ARS ELECTRONICA ARCHIVE – AI LAB

The European ARTificial Intelligence Lab (AI Lab) is a follow-up project to the European Digital Art and Science Network, a creative collaboration between scientific institutions, Ars Electronica and cultural partners throughout Europe that unites science and digital art. The European ARTificial Intelligence Lab follows on from this and addresses visions, expectations and fears that we associate with artificial intelligence. The consortium consists of 13 cultural institutions from Europe with Ars Electronica as coordinator. This online archive provides an overview of all activities carried out during the project's lifetime from 2018 to 2021. It also provides information about the network itself, the residency artists and juries, and the project partners involved. The AI Lab is co-funded by the EU program "Creative Europe (2014-2020)" and by the Federal Ministry of Arts, Culture, Civil Service and Sport.

Understanding AI Exhibition at Ars Electronica Center 2019

Understanding AI Exhibition at Ars Electronica Center

Understanding AI
Exhibition
Ars Electronica Center, Linz (AI)
27.05.2019 - ongoing

exhibited projects, comming from the framework of the European ARTificial Intelligence Lab:
Anatomy of an AI – Vladan Joler (RS), Kate Crawford (AU)
Gender Shades – Joy Buolamwini (US), Timnit Gebru (ETH)
Learning to See: Gloomy Sunday – Memo Akten (TR)
MegaPixels – Adam Harvey (US), Jules LaPlace (US)
Volumetric Data Collector – Hyun Parke (KR/US), Jinoon Choi (KR), Sookyun Yang (KR)
What a Ghost Dreams Of – h.o (INT)
• Info: Listed here are the project presented in the framework of the European ARTificial Intelligence Lab and co-funded by the Creative Europe Programme of the European Union.

Year of creation

2019

Urls

Exhibition Homepage: https://ars.electronica.art/center/en/exhibitions/ai/
AE Blog, 06.08.2019: https://ars.electronica.art/aeblog/en/2019/08/06/understanding-ai-futurelab-installations/

Start:

May 27, 2019

End:

Dec 31, 2021

Info:

• Internal Project: AI Lab Online Archiv

European ARTificial Intelligence Lab, Ars Electronica

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.

Credit: Magdalena Sick-Leitner

Anatomy of an AI System / Kate Crawford (US), AI Now Institute and Vladan Joler (RS) / SHARE Lab

“Anatomy of an AI System” consists of a large-scale map and a comprehensive treatise on the social, economic and ecological circles that draw on the use of digital assistants such as Amazon Echo. The focus is on making visible an extremely complex web that includes the consumption of resources and energy, the use of human labor and the framework conditions under which it occurs, AI applications, digital networks, and the security of our data.

Gender Shades / Joy Buolamwini, Timnit Gebru

Joy Buolamwini and Timnit Gebru investigated into the bias of AI facial recognition programs.

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.

Gender Shades / Joy Buolamwini (US) Timnit Gebru (ETH)

Joy Buolamwini and Timnit Gebru investigated 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. One 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.

Credit: Joy Buolamwini

Learning to See: Gloomy Sunday Memo Akten (TR)

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).

Credit: vog.photo

Learning to See: Gloomy Sunday / Memo Akten (TR)

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.

Credit: Ars Electronica / Martin Hieslmair

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.

Credit: Ars Electronica / Martin Hieslmair

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?

Credit: Ars Electronica / Martin Hieslmair

What a Ghost Dreams Of / h.o. (INT)