Understand the philosophical underpinnings of language-based artificial intelligence.
Learning objective
This graduate class, partly taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which is centered around statistical machine learning applied to natural language data.
Content
This graduate class, partly taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which is centered around statistical machine learning applied to natural language data. The course is a two-semester-long journey, but the second half (PLC II) does not depend on the first (PLC I) and thus either half may be taken independently. In each semester, we divide the class time into three modules. Each module focuses on a philosophical topic. After discussing logicist, structuralist, and generative approaches to language in PLC I, in this semester we will focus on language games, information theory, and critical perspectives on meaning. The modules will be four weeks long. Half of each module will be devoted to reading and discussing theoretical texts and supplementary criticism. In the other half, we will read recent NLP papers and discuss how they relate to philosophical insights into our conception of language—perhaps implicitly or unwittingly. The course is designed to foster fruitful exchanges between students from different disciplinary horizons, especially between the STEM and the humanities. As such, no prior knowledge of CS/AI/NLP or philosophy is assumed.
Literature
The literature will be provided by the instructors on the class website
Prerequisites / Notice
The course is designed to foster fruitful exchanges between students from different disciplinary horizons, especially between the STEM and the humanities. As such, no prior knowledge of CS/AI/NLP or philosophy is assumed.
Competencies
Subject-specific Competencies
Concepts and Theories
assessed
Method-specific Competencies
Analytical Competencies
assessed
Social Competencies
Communication
assessed
Cooperation and Teamwork
fostered
Sensitivity to Diversity
assessed
Personal Competencies
Adaptability and Flexibility
assessed
Creative Thinking
assessed
Critical Thinking
assessed
Integrity and Work Ethics
fostered
Self-awareness and Self-reflection
assessed
Performance assessment
Performance assessment information (valid until the course unit is held again)
Repetition only possible after re-enrolling for the course unit.
Additional information on mode of examination
The course will require a weekly reading of around 30 pages (with relatively high variance at times) and weekly free-form responses to the reading, which are to be completed online. The weekly tasks are short and not graded, but, in order to pass the class, at least 70% of the tasks must be completed. The final grade will be based on one class presentation and one term paper (around 5-10 pages) which is to be turned in at the end of the semester. The term paper ideally corresponds to one of the three modules and the students will be expected to explore the relation of the topics discussed in class to work not presented in the class, focusing on the connection between the philosophy of language and NLP. For example, discussing how three recent NLP papers implicitly assumed a Wittgensteinian or an information-theoretical perspective on language would be a good topic.
Performance assessment as a semester course