Dr. Jennifer Hill, Director, Office of Assessment
This course examines assessment in higher education – its traditions, practices, and decisions – through the lens of epistemology. How do members of our learning community obtain and trust information as they navigate difficult questions and problems facing higher education in the U.S.? As a tradition of inquiry, assessment connects organizational decision-making with evidence, and our sources of evidence increasingly are influenced by artificial intelligence. AI has the capacity to transform how individuals and groups cultivate bodies of evidence, but it also can amplify existing informational biases and introduce new complications to the utilization of that information. The course explores the opportunities and impacts in several educational domains: student learning and academic integrity, efficiencies and limitations in applied educational research, and the prudent utilization of evidence in organizational decision-making and compliance.
Mary Osborne, Senior Product Manager of Natural Language Processing, SAS Institute, Inc.; Assistant Visiting Professor in Computational Linguistics
General purpose models of natural language processing provides new opportunities for linguistic theoretical approaches to provide disciplinary perspectives to the toolbox of these models, linguistic theories in generating and utilization of text, narrative & dialogue in accessible open source AI. Linguistic theories inform AI modeling includes generative grammar, cognitive linguistic theory, empirical language models, code & meaning. Foundations of NLP will be explored. Encoder only, decoder only and encoder-decoder models (including BARD, Llama 2, BERT, Chat GPT, GPT-NEO) will be used in examining LLMs. The role of humans in labeling the data, understanding domains, how to formulate strong prompts and prompt engineering and how to strengthen the multilingual development of LLMs.
Dr. Henry Pickford, Professor of German Studies
The idea of artificial or machine intelligence raises fundamental epistemological questions about the nature of mind and mental activities. What does it mean to claim that a machine thinks and understands? After a brief introduction to current conceptions of artificial intelligence, we will read seminal articles and excerpts from classical thinkers with the idea that, in better understanding some epistemological puzzles about artificial intelligence, we will better understand puzzles about our knowledge of others, and ultimately knowledge of ourselves. Topics may include: symbolic and connectionist AI, strong and weak AI, the computational theory of mind, intentionality, mental content, embodied cognition, functionalism, behaviorism, consciousness, and self-knowledge. Authors may include: Descartes, Turing, Searle, Dennett, Putnam, Strawson, Nagel, Dreyfus, Kant, Heidegger, and Wittgenstein. Students will also gain familiarity and practice with philosophy as an activity by interpreting, evaluating, and constructing arguments. No background in computer science is expected.
