BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21041@www.clsp.jhu.edu DTSTAMP:20240328T101247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?\nBiography\nAndrew Piper is Professor and William Dawson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n_\,\n a l aboratory for cultural analytics\, and editor of the /Journal of Cultural Analytics/\, an open-access journal dedicated to the computational study o f culture. He is the author of numerous books and articles on the relation ship of technology and reading\, including /Book Was There: Reading in Ele ctronic Times/(Chicago 2012)\, /Enumerations: Data and Literary Study/(Chi cago 2018)\, and most recently\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data/(Cambridge 2020). DTSTART;TZID=America/New_York:20211112T120000 DTEND;TZID=America/New_York:20211112T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Andrew Piper (McGill University) ” How can we use machine learning to understand narration?” URL:https://www.clsp.jhu.edu/events/andrew-piper-mcgill-university-how-can- we-use-machine-learning-to-understand-narration/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?
\nBiography
\n< p>Andrew Piper is Professor and William D awson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n\na laboratory for cultural ana lytics\, and editor of the /Journal of Cultural Analytics/\, an open-acces s journal dedicated to the computational study of culture. He is the autho r of numerous books and articles on the relationship of technology and rea ding\, including /Book Was There: Reading in Electronic Times/(Chicago 201 2)\, /Enumerations: Data and Literary Study/(Chicago 2018)\, and most rece ntly\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data /(Cambridge 2020).
\n X-TAGS;LANGUAGE=en-US:2021\,November\,Piper END:VEVENT BEGIN:VEVENT UID:ai1ec-23894@www.clsp.jhu.edu DTSTAMP:20240328T101247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.\nBiography\nMark Dredze is the John C Malone Professor of Computer Science at Johns Hopkins University and the Director of Research (Foundations of AI) for the JHU AI-X Foundry. He deve lops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.\nProf. Dredze is affiliated with the Malone Center for Engineering in Healthcare\, the Cent er for Language and Speech Processing\, among others. He holds a joint app ointment in the Biomedical Informatics & Data Science Section (BIDS)\, und er the Department of Medicine (DOM)\, Division of General Internal Medicin e (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009. DTSTART;TZID=America/New_York:20230918T120000 DTEND;TZID=America/New_York:20230918T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Dredze (Johns Hopkins University) “BloombergGPT: A Large Langu age Model for Finance” URL:https://www.clsp.jhu.edu/events/mark-dredze-johns-hopkins-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.
\nBiography
\nMark Dredze is the John C Malone Professor of Computer Science at Jo hns Hopkins University and the Director of Research (Foundations of AI) fo r the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.
\nProf. Dredze is affiliated with the Malone Center fo r Engineering in Healthcare\, the Center for Language and Speech Processin g\, among others. He holds a joint appointment in the Bio medical Informatics & Data Science Section (< span class='il'>BIDS)\, under the Department of Medicine (DOM)\, Di vision of General Internal Medicine (GIM) in the School of Medicine. He ob tained his PhD from the University of Pennsylvania in 2009.
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