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Full-Text Articles in Artificial Intelligence and Robotics
From Ai With Love: Reading Big Data Poetry Through Gilbert Simondon’S Theory Of Transduction, Andrew Klobucar
From Ai With Love: Reading Big Data Poetry Through Gilbert Simondon’S Theory Of Transduction, Andrew Klobucar
Electronic Literature Organization Conference 2020
Computation initiated a far-reaching re-imagination of language, not just as an information tool, but as a social, bio-physical activity in general. Modern lexicology provides an important overview of the ongoing development of textual documentation and its applications in relation to language and linguistics. At the same time, the evolution of lexical tools from the first dictionaries and graphs to algorithmically generated scatter plots of live online interaction patterns has been surprisingly swift. Modern communication and information studies from Norbert Weiner to the present-day support direct parallels between coding and linguistic systems. However, most theories of computation as a model of …
Why Are We Like This?: Exploring Writing Mechanics For An Ai-Augmented Storytelling Game, Max Kreminski, Melanie Dickinson, Michael Mateas, Noah Wardrip-Fruin
Why Are We Like This?: Exploring Writing Mechanics For An Ai-Augmented Storytelling Game, Max Kreminski, Melanie Dickinson, Michael Mateas, Noah Wardrip-Fruin
Electronic Literature Organization Conference 2020
Why Are We Like This? (WAWLT) is a playful, co-creative, AI-augmented, improvisational storytelling game in which one or more players explore and influence an ongoing simulation which they then glean for narrative material. It uses the recently developed simulation technology of story sifting (the recognition of microstories in a chronicle of simulation events), via the Felt library, to afford a new kind of playful, social, and creative writing experience. In this paper, we discuss our primary design goals: (1) using computation and interaction design to support casual player creativity, and (2) foregrounding character subjectivity as a driver for …
Towards Robust Artificial Intelligence Systems, Sunny Raj
Towards Robust Artificial Intelligence Systems, Sunny Raj
Electronic Theses and Dissertations, 2020-
Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a …