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-21259@www.clsp.jhu.edu DTSTAMP:20240329T143401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNatural language processin g has been revolutionized by neural networks\, which perform impressively well in applications such as machine translation and question answering. D espite their success\, neural networks still have some substantial shortco mings: Their internal workings are poorly understood\, and they are notori ously brittle\, failing on example types that are rare in their training d ata. In this talk\, I will use the unifying thread of hierarchical syntact ic structure to discuss approaches for addressing these shortcomings. Firs t\, I will argue for a new evaluation paradigm based on targeted\, hypothe sis-driven tests that better illuminate what models have learned\; using t his paradigm\, I will show that even state-of-the-art models sometimes fai l to recognize the hierarchical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\ , I will show how these behavioral failings can be explained through analy sis of models’ inductive biases and internal representations\, focusing on the puzzle of how neural networks represent discrete symbolic structure i n continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning\, structured architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Science at Johns Hopkins University. As an undergr aduate\, he studied computational linguistics at Yale. His research combin es natural language processing\, cognitive science\, and machine learning to study how we can achieve robust generalization in models of language\, as this remains one of the main areas where current AI systems fall short. In particular\, he focuses on inductive biases and representations of lin guistic structure\, since these are two of the major components that deter mine how learners generalize to novel types of input.
DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-21489@www.clsp.jhu.edu DTSTAMP:20240329T143401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nSince it is increasingly h arder to opt out from interacting with AI technology\, people demand that AI is capable of maintaining contracts such that it supports agency and ov ersight of people who are required to use it or who are affected by it. To help those people create a mental model about how to interact with AI sys tems\, I extend the underlying models to self-explain—predict the label/an swer and explain this prediction. In this talk\, I will present how to gen erate (1) free-text explanations given in plain English that immediately t ell users the gist of the reasoning\, and (2) contrastive explanations tha t help users understand how they could change the text to get another labe l.
\nBiography
\nAna Marasović is a postdocto ral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen S chool of Computer Science & Engineering at University of Washington. Her r esearch interests broadly lie in the fields of natural language processing \, explainable AI\, and vision-and-language learning. Her projects are mot ivated by a unified goal: improve interaction and control of the NLP syste ms to help people make these systems do what they want with the confidence that they’re getting exactly what they need. Prior to joining AI2\, Ana o btained her PhD from Heidelberg University.
\nHow to pronounce my name: the first name is Ana like in Spanish\, i.e.\, with a long “a” like in “water”\; regarding the last name: “mara” as in actress mara wilso n + “so” + “veetch”.
DTSTART;TZID=America/New_York:20220228T120000 DTEND;TZID=America/New_York:20220228T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ana Marasović (Allen Institute for AI & University of Washington) “ Self-Explaining for Intuitive Interaction with AI” URL:https://www.clsp.jhu.edu/events/ana-marasovic-allen-institute-for-ai-un iversity-of-washington-self-explaining-for-intuitive-interaction-with-ai/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Marasovic END:VEVENT BEGIN:VEVENT UID:ai1ec-22374@www.clsp.jhu.edu DTSTAMP:20240329T143401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nIn recent years\, the fiel d of Natural Language Processing has seen a profusion of tasks\, datasets\ , and systems that facilitate reasoning about real-world situations throug h language (e.g.\, RTE\, MNLI\, COMET). Such systems might\, for example\, be trained to consider a situation where “somebody dropped a glass on the floor\,” and conclude it is likely that “the glass shattered” as a result . In this talk\, I will discuss three pieces of work that revisit assumpti ons made by or about these systems. In the first work\, I develop a Defeas ible Inference task\, which enables a system to recognize when a prior ass umption it has made may no longer be true in light of new evidence it rece ives. The second work I will discuss revisits partial-input baselines\, wh ich have highlighted issues of spurious correlations in natural language r easoning datasets and led to unfavorable assumptions about models’ reasoni ng abilities. In particular\, I will discuss experiments that show models may still learn to reason in the presence of spurious dataset artifacts. F inally\, I will touch on work analyzing harmful assumptions made by reason ing models in the form of social stereotypes\, particularly in the case of free-form generative reasoning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Co mputer Science at the University of Maryland\, College Park. She holds joi nt appointments in the Department of Linguistics and the Institute for Adv anced Computer Studies (UMIACS). In 2019\, Rachel completed her Ph.D. in C omputer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Investigator at the A llen Institute for AI in Seattle\, and a visiting researcher at the Univer sity of Washington. Her research interests include computational semantics \, common-sense reasoning\, and issues of social bias and fairness in NLP.
DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240329T143401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230130T120000 DTEND;TZID=America/New_York:20230130T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Fried (CMU) URL:https://www.clsp.jhu.edu/events/daniel-fried-cmu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Fried\,January END:VEVENT BEGIN:VEVENT UID:ai1ec-24239@www.clsp.jhu.edu DTSTAMP:20240329T143401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nNon-in vasive neural interfaces have the potential to transform human-computer in teraction by providing users with low friction\, information rich\, always available inputs. Reality Labs at Meta is developing such an interface fo r the control of augmented reality devices based on electromyographic (EMG ) signals captured at the wrist. Speech and audio technologies turn out to be especially well suited to unlocking the full potential of these signal s and interactions and this talk will present several specific problems an d the speech and audio approaches that have advanced us towards this ultim ate goal of effortless and joyful interfaces. We will provide the necessar y neuroscientific background to understand these signals\, describe automa tic speech recognition-inspired interfaces generating text and beamforming -inspired interfaces for identifying individual neurons\, and then explain how they connect with egocentric machine intelligence tasks that might re side on these devices.
\nBiography
\nMichael I Mandel is a Research Scientist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn College and the CUNY Graduate Center working at the intersection of machi ne learning\, signal processing\, and psychoacoustics. He earned his BSc i n Computer Science from the Massachusetts Institute of Technology and his MS and PhD with distinction in Electrical Engineering from Columbia Univer sity as a Fu Foundation Presidential Scholar. He was an FQRNT Postdoctoral Research Fellow in the Machine Learning laboratory (LISA/MILA) at the Uni versité de Montréal\, an Algorithm Developer at Audience Inc\, and a Resea rch Scientist in Computer Science and Engineering at the Ohio State Univer sity. His work has been supported by the National Science Foundation\, inc luding via a CAREER award\, the Alfred P. Sloan Foundation\, and Google\, Inc.
DTSTART;TZID=America/New_York:20240129T120000 DTEND;TZID=America/New_York:20240129T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Michael I Mandel (Meta) “Speech and Audio Processing in Non-Invasiv e Brain-Computer Interfaces at Meta” URL:https://www.clsp.jhu.edu/events/michael-i-mandel-cuny/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,January\,Mandel END:VEVENT END:VCALENDAR