LINGUIS 265
Biological and Artificial Language
An introduction to a novel approach to language. Linguistics has predominantly focused onhuman language. Recent developments in machine learning and animal communication, however, require us to look beyond the humans. We'll extend the definition of language and model it as 'informative imitation'. This perspective enables us to examine biological and artificial communication systems in a novel way. The class will introduce a multifaceted approach with human, non-human, and artificial language acting as mutually informative systems. By learning about non-human language, we will better understand human language and its uniqueness.
Topics
- By the end of the term, students will be able to: 1. Implement and train a CNN- or GAN-based model on raw acoustic data. 2. Analyze learned representations with linguistically motivated probes/visualizations. 3. Design a computational experiment that tests a linguistic dependency. 4. Evaluate model behavior vs. human/animal data and articulate implications for acquisition. 5. Communicate results in a conference-style abstract and a 12-page research paper.
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027