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-22395@www.clsp.jhu.edu DTSTAMP:20240329T160304Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google\, and Amazon\, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial board of Transactions of the ACL. DTSTART;TZID=America/New_York:20221017T120000 DTEND;TZID=America/New_York:20221017T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nZipf’s law is commonly glossed by the aphorism “infre quent words are frequent\,” but in practice\, it has often meant that ther e are three types of words: frequent\, infrequent\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (wi th dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV words was not solved until sequ ence-to-sequence neural nets de-reified the concept of a word. Many other social phenomena follow power-law distributions. The number of native sp eakers of the N’th most spoken language\, for example\, is 1.44 billion ov er N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingual pre-training. In less-f requent languages\, multilingual knowledge transfer can significantly redu ce phone error rates. In languages with no training data\, unsupervised A SR methods can be proven to converge\, as long as the eigenvalues of the l anguage model are sufficiently well separated to be measurable. Other syst ems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech patterns that were never seen in the training database\, but not all disabilities need do so. The inabi lity of speech technology to work for people with even common disabilities is probably caused by a lack of data\, and can probably be solved by find ing better modes of interaction between technology researchers and the com munities served by technology.
\nBiography
\nMark Hasegawa-Johnson is a William L. Everitt Faculty Fellow of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaig n. He has published research in speech production and perception\, source separation\, voice conversion\, and low-resource automatic speech recogni tion.
\n X-TAGS;LANGUAGE=en-US:2022\,December\,Hasegawa-Johnson END:VEVENT BEGIN:VEVENT UID:ai1ec-24507@www.clsp.jhu.edu DTSTAMP:20240329T160304Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nHistory repeats itself\, sometimes in a bad way. Prev enting natural or man-made disasters requires being aware of these pattern s and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the COVID pandemic and the Ukrai ne Crisis\, require a time-sensitive comprehensive understanding of the si tuation to allow for appropriate decision-making and effective action resp onse. Automated generation of situation reports can significantly reduce t he time\, effort\, and cost for domain experts when preparing their offici al human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have yet been conducted to automat e such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information retrieval techniques are insuf ficient to identify\, locate\, and summarize important information\, and l ack detailed\, structured\, and strategic awareness. In this talk I will p resent SmartBook\, a novel framework that cannot be solved by large langua ge models alone\, to consume large volumes of multimodal multilingual news data and produce a structured situation report with multiple hypotheses ( claims) summarized and grounded with rich links to factual evidence throug h multimodal knowledge extraction\, claim detection\, fact checking\, misi nformation detection and factual error correction. Furthermore\, SmartBook can also serve as a novel news event simulator\, or an intelligent prophe tess. Given “What-if” conditions and dimensions elicited from a domain ex pert user concerning a disaster scenario\, SmartBook will induce schemas f rom historical events\, and automatically generate a complex event graph a long with a timeline of news articles that describe new simulated events a nd character-centric stories based on a new Λ-shaped attention mask that c an generate text with infinite length. By effectively simulating disaster scenarios in both event graph and natural language format\, we expect Smar tBook will greatly assist humanitarian workers and policymakers to exercis e reality checks\, and thus better prevent and respond to future disasters .\nBio\nHeng Ji is a professor at Computer Science Department\, and an aff iliated faculty member at Electrical and Computer Engineering Department a nd Coordinated Science Laboratory of University of Illinois Urbana-Champai gn. She is an Amazon Scholar. She is the Founding Director of Amazon-Illin ois Center on AI for Interactive Conversational Experiences (AICE). She re ceived her B.A. and M. A. in Computational Linguistics from Tsinghua Unive rsity\, and her M.S. and Ph.D. in Computer Science from New York Universit y. Her research interests focus on Natural Language Processing\, especiall y on Multimedia Multilingual Information Extraction\, Knowledge-enhanced L arge Language Models\, Knowledge-driven Generation and Conversational AI. She was selected as a Young Scientist to attend the 6th World Laureates As sociation Forum\, and selected to participate in DARPA AI Forward in 2023. She was selected as “Young Scientist” and a member of the Global Future C ouncil on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI ( Class of 2023) by Project Voice\, “AI’s 10 to Watch” Award by IEEE Intelli gent Systems in 2013\, NSF CAREER award in 2009\, PACLIC2012 Best paper ru nner-up\, “Best of ICDM2013” paper award\, “Best of SDM2013” paper award\, ACL2018 Best Demo paper nomination\, ACL2020 Best Demo Paper Award\, NAAC L2021 Best Demo Paper Award\, Google Research Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-20 18. She was invited to testify to the U.S. House Cybersecurity\, Data Anal ytics\, & IT Committee as an AI expert in 2023. She was invited by the Sec retary of the U.S. Air Force and AFRL to join Air Force Data Analytics Exp ert Panel to inform the Air Force Strategy 2030\, and invited to speak at the Federal Information Integrity R&D Interagency Working Group (IIRD IWG) briefing in 2023. She is the lead of many multi-institution projects and tasks\, including the U.S. ARL projects on information fusion and knowledg e networks construction\, DARPA ECOLE MIRACLE team\, DARPA KAIROS RESIN te am and DARPA DEFT Tinker Bell team. She has coordinated the NIST TAC Knowl edge Base Population task 2010-2022. She was the associate editor for IEEE /ACM Transaction on Audio\, Speech\, and Language Processing\, and served as the Program Committee Co-Chair of many conferences including NAACL-HLT2 018 and AACL-IJCNLP2022. She is elected as the North American Chapter of t he Association for Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DA RPA\, NSF\, DoE\, ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebook\, Bosch\, IBM\, Disney). DTSTART;TZID=America/New_York:20240405T120000 DTEND;TZID=America/New_York:20240405T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, Maryland 21218 SEQUENCE:0 SUMMARY:Heng Ji (University of Illinois Urbana-Champaign) “SmartBook: an AI Prophetess for Disaster Reporting and Forecasting” URL:https://www.clsp.jhu.edu/events/heng-ji-university-of-illinois-urbana-c hampaign-smartbook-an-ai-prophetess-for-disaster-reporting-and-forecasting / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nHistory repeats itself\, sometimes in a bad way. Prev enting natural or man-made disasters requires being aware of these pattern s and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the COVID pandemic and the Ukrai ne Crisis\, require a time-sensitive comprehensive understanding of the si tuation to allow for appropriate decision-making and effective action resp onse. Automated generation of situation reports can significantly reduce t he time\, effort\, and cost for domain experts when preparing their offici al human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have yet been conducted to automat e such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information retrieval techniques are insuf ficient to identify\, locate\, and summarize important information\, and l ack detailed\, structured\, and strategic awareness. In this talk I will p resent SmartBook\, a novel framework that cannot be solved by large langua ge models alone\, to consume large volumes of multimodal multilingual news data and produce a structured situation report with multiple hypotheses ( claims) summarized and grounded with rich links to factual evidence throug h multimodal knowledge extraction\, claim detection\, fact checking\, misi nformation detection and factual error correction. Furthermore\, SmartBook can also serve as a novel news event simulator\, or an intelligent prophe tess. Given “What-if” conditions and dimensions elicited from a domain ex pert user concerning a disaster scenario\, SmartBook will induce schemas f rom historical events\, and automatically generate a complex event graph a long with a timeline of news articles that describe new simulated events a nd character-centric stories based on a new Λ-shaped attention mask that c an generate text with infinite length. By effectively simulating disaster scenarios in both event graph and natural language format\, we expect Smar tBook will greatly assist humanitarian workers and policymakers to exercis e reality checks\, and thus better prevent and respond to future disasters .
\nBio
\nHeng Ji is a professor at Computer Science Department\, and an affiliated faculty member at Electrical and Co mputer Engineering Department and Coordinated Science Laboratory of Univer sity of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Fo unding Director of Amazon-Illinois Center on AI for Interactive Conversati onal Experiences (AICE). She received her B.A. and M. A. in Computational Linguistics from Tsinghua University\, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing\, especially on Multimedia Multilingual Information Ex traction\, Knowledge-enhanced Large Language Models\, Knowledge-driven Gen eration and Conversational AI. She was selected as a Young Scientist to at tend the 6th World Laureates Association Forum\, and selected to participa te in DARPA AI Forward in 2023. She was selected as “Young Scientist” and a member of the Global Future Council on the Future of Computing by the Wo rld Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI (Class of 2023) by Project Voice\, “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013\, NSF CAREER award in 2009\, PACLIC2012 Best paper runner-up\, “Best of ICDM2013” paper award\, “Best of SDM2013” paper award\, ACL2018 Best Demo paper nomination\, ACL20 20 Best Demo Paper Award\, NAACL2021 Best Demo Paper Award\, Google Resear ch Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invited to testify to the U.S. House Cybersecurity\, Data Analytics\, & IT Committee as an AI expert in 2 023. She was invited by the Secretary of the U.S. Air Force and AFRL to jo in Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030\, and invited to speak at the Federal Information Integrity R&D Inter agency Working Group (IIRD IWG) briefing in 2023. She is the lead of many multi-institution projects and tasks\, including the U.S. ARL projects on information fusion and knowledge networks construction\, DARPA ECOLE MIRAC LE team\, DARPA KAIROS RESIN team and DARPA DEFT Tinker Bell team. She has coordinated the NIST TAC Knowledge Base Population task 2010-2022. She wa s the associate editor for IEEE/ACM Transaction on Audio\, Speech\, and La nguage Processing\, and served as the Program Committee Co-Chair of many c onferences including NAACL-HLT2018 and AACL-IJCNLP2022. She is elected as the North American Chapter of the Association for Computational Linguistic s (NAACL) secretary 2020-2023. Her research has been widely supported by t he U.S. government agencies (DARPA\, NSF\, DoE\, ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebook\, Bosch\, IBM\, Disney).
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