IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Document Type
Poster
Publication Date
Fall 2019
Abstract
Sophisticated generative models have been some of the most obviously impressive achievements of AI in the past decade. Indeed, AI has approached or achieved human-level performance in a variety of domains, with a particular focus placed on artistic disciplines. Because writing a value function for this kind of model is effectively impossible, these models are notoriously difficult to evaluate. My aim was to create such a model, and in doing so elucidate the characteristics of successful generative AI and explore how generative models can be adapted successfully to new domains. And more broadly, I hoped to create a model capable of producing good sculpture, although I have no intention of evaluating what it means for a sculpture to be good.
Recommended Citation
Downey, Nick, "Generating 3d Sculptures Using a Recurrent Neural Network with Long Short-Term Memory" (2019). IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound. Paper 21.
https://digital.kenyon.edu/dh_iphs_ai/21
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