CAD Seminar Series: Haiyong Liu, How AI learns to say 'yes' or 'no' or recognize a chair in Chinese
This event is in the past.
When:
April 2, 2025
2:30 p.m. to 3:30 p.m.
2:30 p.m. to 3:30 p.m.
Where:
Event category:
Seminar
Hybrid
Speaker: Haiyong Liu, Linguistics, WSU
Time: Wednesday, April 2, from 2:30 pm to 3:30 pm
Location for in-person participants: 1146 FAB
Zoom link for online audience: https://wayne-edu.zoom.us/j/92845590121?pwd=CpRA5Wa5gzSMn2xiVkR2abD83O5nrH.1
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wayne-edu.zoom.us
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Title: How AI learns to say 'yes' or 'no' or recognize a chair in Chinese
Abstract: AI is causing spine-chilling effects in translation and simultaneous interpretation, with a fraction of the cost but tripling the output speed. It can accumulate needed factual and cultural knowledge in a matter of days, which takes a decade for a human translator or interpreter to learn. Besides efficiency, it is making progress in demonstrating affect and human touch in its work; for example, it has been used in the translation of literature as well, requiring only a human proof-reader before the final print, who is paid a third of what they were before AI. Allegedly, AI makes fewer mistakes regarding political correctness. Nevertheless, AI is still referred to with the inanimate pronoun “it”, when pronouns of the high-animacy sentient humans deserve gender distinction as in English “he” and “she” or pronoun-worthiness as in Chinese. What’s more, can AI translation ever implement cultural appropriateness as humans do? Can AI purposely leave room for ambiguity in its translation? Can AI figure out subtle grammatical rules that linguists have not been able to completely generalize? For instance, Chinese does not have pronouns for non-humans or Chinese speakers answer yes-no questions with verbs instead of the English counterparts of “yes” or “no”. Once AI learns one rule, can it automatically learn or generate another related rule through the cluster effects or implicational hierarchy? My talk will explore these questions with anecdotes and examples from my own research.
Bio: Dr. Haiyong Liu is a professor of Chinese linguistics in the department of Classical and Modern Languages, Literatures, and Cultures (CMLLC) and the Linguistics Program. His research focuses on topics in Chinese syntax; for example, how Chinese quantifies its nouns, how Chinese pronouns refer to inanimate entities, and how Chinese speakers realize counter-factuality etc., He has published his works on some of them top-notch journals in his field. Recently he was awarded the Marilyn Willamson Endowed Distinguished Faculty Fellowship by the Humanities Center of Wayne State, for his project on how the Chinese language answers yes-no questions, when it has neither the exclusive lexical item for "yes" nor "no".
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CAD Seminar Series: Advancing Knowledge, Innovation, and Collaboration in Computation, AI, and Data Science (CAD)
The CAD Seminar Series is a dedicated platform for advancing knowledge, fostering innovation, and promoting collaboration across the fields of Computation, Artificial Intelligence, and Data Science. This series brings together leading experts, researchers, and professionals to explore the latest developments, tackle emerging challenges, and drive forward-thinking solutions at the convergence of these critical disciplines.
Objectives:
• Advance Knowledge: Share cutting-edge research and insights that push the boundaries of what is known in CAD.
• Foster Innovation: Encourage the development of novel ideas and solutions through interdisciplinary dialogue and creative thinking.
• Promote Collaboration: Unite expertise across disciplines and build bridges between academia, industry, and government to address complex problems and create opportunities for joint ventures.
Target Audience: The CAD Seminar Series is designed for a diverse audience, including faculty, researchers, students, and professionals in Computation, AI, Data Science, and related fields. It serves as a forum for exchanging ideas, networking, and contributing to the growth of these rapidly evolving areas. We highly recommend in-person attendance to enhance engagement and networking opportunities with speakers and fellow participants.
Call for Participation: We welcome contributions from researchers, practitioners, and students. Whether presenting your work, participating in discussions, or attending as a learner, your involvement is crucial to the success of this collaborative initiative.