classification – Artificial Intelligence https://ars.electronica.art/ai/en Ars Electronica Festival 2017 Tue, 28 Jun 2022 13:43:24 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.6 hananona https://ars.electronica.art/ai/en/hananona/ Tue, 08 Aug 2017 05:47:11 +0000 https://ars.electronica.art/ai/?p=1787

STAIR Lab. (JP) collaborating with Surface & Architecture Inc, Kyoko Kunoh, Tomohiro Akagawa, Tanoshim Inc., mokha Inc. and Tokyo Studio Co. Ltd. (JP)

The latest AI research makes it possible to teach computers the names of things by showing them many examples. The key is a large amount of training data and deep learning software. By leveraging this, the artists have developed an AI capable of classifying 406 kinds of flower by using over 300,000 flower pictures.

hananona is an interactive work that visualizes how AI classifies a flower. When it sees a flower, it identifies its name and shows its class on a visual “flower map”—a visualization of the inside of the AI brain. This is a group of image clusters, each of which is a cluster of flower photos learned as belonging to the same class. By looking at them, users can see how AI classifies the flowers.

Users are encouraged to challenge hananona with their own flower photos, or with other materials such as pictures, paintings, flower-like objects etc. so that they can observe how the AI reacts to different abstraction levels of flowers.

Credits

STAIR Lab., Chiba Institute of Technology

Creative direction, design: Surface & Architecture Inc.

Art direction: Kyoko Kunoh
Interaction design, programming: Tomohiro Akagawa
Programming: Tanoshim Inc.
Server programming: mokha Inc.
Furniture production, site setup: Tokyo Studio Co., Ltd.

]]> flyAI https://ars.electronica.art/ai/en/flyai/ Tue, 08 Aug 2017 05:39:26 +0000 https://ars.electronica.art/ai/?p=1783

David Bowen (US)

This installation creates a situation where the fate of a colony of living houseflies is determined by the accuracy of artificial-intelligence software.

The installation uses the TensorFlow machine-learning image-recognition library to classify images of live houseflies. As the flies fly and land in front of a camera, their image is captured. The captured image is classified by the image-recognition software and a list of guessed items is ranked one through five. Each of the items is assigned a percentage based on how likely the software thinks the listed item is what it sees. If “fly” is ranked number one on the list, a pump delivers water and nutrients to the colony based on the percentage of the ranking. If “fly” is not ranked number one the pump does not deliver water and nutrients to the colony. The system is set up to run indefinitely with an indeterminate outcome.

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