April 11, 2024, 4:42 a.m. | Bernard J. Koch, David Peterson

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.06647v1 Announce Type: cross
Abstract: Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and technology, and hindered AI from overcoming long-standing limitations with respect to explainability, ethical harms, and environmental efficiency. Drawing on qualitative interviews and computational analyses, our three-part history of AI research traces the creation of this "epistemic monoculture" back to a radical reconceptualization of scientific progress that occurred in the 1990s. …

abstract ai research arxiv benchmarking building cs.ai cs.cy cs.lg deep learning ethical ever explainability limitations research science science and technology set stage technology type unlocked

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Software Engineer, Generative AI (C++)

@ SoundHound Inc. | Toronto, Canada