BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Center for Governance and Markets - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Center for Governance and Markets
X-ORIGINAL-URL:https://cgm.pitt.edu
X-WR-CALDESC:Events for Center for Governance and Markets
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230419T130000
DTEND;TZID=America/New_York:20230419T143000
DTSTAMP:20260518T150912
CREATED:20251009T225502Z
LAST-MODIFIED:20251023T210826Z
UID:1137-1681909200-1681914600@cgm.pitt.edu
SUMMARY:Textual Speculations: How Generative AI Predicts the Next Word
DESCRIPTION:The current discourse around generative AI is steeped in speculation: how effective can large language models get? How will they affect employment and education? And are they leading to artificial general intelligence (AGI)? But beyond the discourse\, the models themselves are built on speculation: drawing from a giant dataset of natural language in text\, they predict the next word in a sequence. Earlier approaches to natural language generation (such as Markov models) also predicted the next word\, but recent large language models (LLMs) combine more complicated algorithms\, concepts of attention\, and larger datasets to conceal their predictive nature and produce far more coherent and plausible natural language. Yet AI writing detectors operate on this idea that AI writing is more predictable than that of humans: humans tend to write with greater “burstiness” and “perplexity.” \nWith the contrast between human and AI writing as a framing device\, this talk traces the ways that prediction has operated in generative AI and other historical attempts to automate writing. Attendees of the talk will come away with an understanding of: current Large Language Models driving generative AI writing and how they differ from earlier models; how AI models do and don’t replicate human writing; and the practical effects of generative AI in writing and pedagogy. \nAnnette Vee is Associate Professor of English and Director of the Composition Program at the University of Pittsburgh\, where she teaches writing and digital composition. She is the author of Coding Literacy (MIT Press\, 2017) and has published on computer programming\, digital literacy\, blockchain technologies\, intellectual property\, and AI-based text generators. \n  \nZoom Recording
URL:https://cgm.pitt.edu/event/textual-speculations-how-generative-ai-predicts-the-next-word/
LOCATION:Wesley W. Posvar Hall\, 230 S Bouquet St\, Pittsburgh\, PA\, 15213\, United States
ATTACH;FMTTYPE=image/png:https://cgm.pitt.edu/wp-content/uploads/2025/10/genai.png
END:VEVENT
END:VCALENDAR