Search Results for author: Morgan R. Frank

Found 7 papers, 1 papers with code

The Resume Paradox: Greater Language Differences, Smaller Pay Gaps

no code implementations17 Jul 2023 Joshua R. Minot, Marc Maier, Bradford Demarest, Nicholas Cheney, Christopher M. Danforth, Peter Sheridan Dodds, Morgan R. Frank

This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity.

Art and the science of generative AI: A deeper dive

no code implementations7 Jun 2023 Ziv Epstein, Aaron Hertzmann, Laura Herman, Robert Mahari, Morgan R. Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld, Hany Farid, Neil Leach, Alex Pentland, Olga Russakovsky

A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation.

Exposure of occupations to technologies of the fourth industrial revolution

no code implementations25 Oct 2021 Benjamin Meindl, Morgan R. Frank, Joana Mendonça

Our work not only allows analyses of the impact of 4IR technologies as a whole, but also provides exposure scores for more than 300 technology fields, such as AI and smart office technologies.

Industrial Topics in Urban Labor System

no code implementations18 Sep 2020 Jaehyuk Park, Morgan R. Frank, Lijun Sun, Hyejin Youn

It is therefore important to recognize that classification system are not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented.

Classification General Classification

Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts

3 code implementations5 Aug 2020 Ryan J. Gallagher, Morgan R. Frank, Lewis Mitchell, Aaron J. Schwartz, Andrew J. Reagan, Christopher M. Danforth, Peter Sheridan Dodds

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content.

Human language reveals a universal positivity bias

no code implementations15 Jun 2014 Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth

Using human evaluation of 100, 000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage.

Cultural Vocal Bursts Intensity Prediction Translation

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