Category: Artificial Intelligence
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Transfer Learning
This print illustrates the process of transfer learning, a machine learning technique when a model trained on one task is reused –usually to save time and computing energy– for another related task. A series of uncanny images reveals a machine learning model’s transition from a training dataset to a primary one.
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Tiny Datasets
The intimacy of a tiny dataset approach is in contrast with big data and its tendency to produce homogenized results; the Tiny Dataset series celebrates its local, limited, situated, chaotic, and precise results in alliance with Donna Haraway’s critique of “The God Trick.”
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When Artistic Authorship Meets Scientific Bias
“Team Picks” contrasts desirable artistic authorship and undesirable scientific bias. It confronts the destiny of any new technology – that it will eventually become an old, discarded one.
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Five Guide Rails for Artists Working with Machines and A.I.
While attending the July Center for Machine Arts residency, surrounded by so many seductive artmaking technologies, I had to spend my time efficiently as we prepared for a fast-approaching exhibition. This is not an unfamiliar process for me as I routinely evaluate how machine learning serves my art practice; sometimes it does and other time…
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Art Works in Conversation with Climate Change, A.I., Innovation, and Local Knowledge
This spring, artists were front and center, engaged in conversations about artificial intelligence, climate change, innovation, and situated knowledge.
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Vermont Creative AI: Symposium on Art + Artificial Intelligence, and more!
With Co-Organizer Chris Thompson and Burlington City Arts, I’m excited to share that registration opened this week for the Vermont Creative AI: Symposium on Artificial Intelligence + Art. Other upcoming programs include a AI Ethics panel. Additionally, we’ve developed programming for young adults and families. Join Burlington City Arts and me for an Earth Day activity on Saturday,…
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The Three Generations of the Damaged Leaf Dataset (DLD)
Over 800 leaves from the Damaged Leaf Dataset were shown in floor to ceiling wallpaper at Burlington City Arts this spring. All three generations were represented, DLD III (created with AI) were shown as engravings on silicon wafers. Project Description: The leaf architectures of the Damaged Leaf Dataset (DLD) are ruins and creations. Collected in…
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Co-Created: The Artist in the Age of Intelligent Machines
Opening Reception February 10, 2023 5-7pm Burlington City Arts Center, Burlington, Vermont Damaged Leaf Dataset Table II, 2023 (Detail) Today, intelligent machines merge with our lives in ways unimaginable just a decade ago. From recognizing our faces and interacting via speech to mapping travel routes and delivering news to our feeds, machine learning algorithms are now our constant,…
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The Backstory of the Damaged Leaf Dataset
Lymantria dispar, also known as the spongy moth, is considered one of the most destructive invasive insects in the northeastern United States, responsible for defoliating millions of acres in North America. Its caterpillar prefers Oak trees though it feasts on 300 species of deciduous and evergreen trees and is known to parachute into cropping fields…
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The Second Falling of the Season
Back to collecting for the Damaged Leaf Dataset! Leaves munched by #spongymoth caterpillars in #vermont are falling for a second time this year, and this time in full autumn colors. the first set of leaves fell in June and were mostly green.
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New Architectures of Climate Change
Damaged Leaf Dataset Update September 7, 2022 A drought in VT prompted a Spongy Moth caterpillar outbreak. On our property in Colchester, VT, Oaks that lost their first set of leaves to hungry caterpillars generated a second set of leaves. Now the second generation of leaves is starting to fall, making evident the difference in…