We will explore the relationship between language and technology from the invention of writing systems to current natural language and speech technologies, and especially Large Language Models. Topics include script decipherment, machine translation, automatic speech recognition and generation, dialog systems, computational natural language understanding and inference, as well as language technologies that support users with language disabilities. We will also trace how science and technology are shaping language, discuss relevant artificial intelligence concepts, and examine the ethical implications of advances in language processing by computers. Students will have the opportunity to experience text analysis with relevant tools. This is an interdisciplinary course and technical background is not required.

This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text. The course will have relevance to various disciplines in the humanities, sciences, computational, and technical fields. We will discuss problems that involve different components of the language system (such as meaning in context and linguistic structures). Students will additionally collaborate in teams on modeling and implementing natural language processing and digital text solutions. Students will program in Python and use a variety of relevant tools. Expected: Programming skills, demonstrated via coursework or instruction approval.

This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Large language models and machine learning, including standard and deep neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools and LLMs or related foundation models. Expected: Programming skills, demonstrated by coursework or instructor approval.