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-23302@www.clsp.jhu.edu DTSTAMP:20240329T131248Z 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-23304@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nTransformers are essential to pretraining. As we approach 5 years of BERT\, the connection between a ttention as architecture and transfer learning remains key to this central thread in NLP. Other architectures such as CNNs and RNNs have been used t o replicate pretraining results\, but these either fail to reach the same accuracy or require supplemental attention layers. This work revisits the semanal BERT result and considers pretraining without attention. We consid er replacing self-attention layers with recently developed approach for lo ng-range sequence modeling and transformer architecture variants. Specific ally\, inspired by recent papers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM ) and a bidirectional model architecture based on multiplicative gating. W e discuss the results of the proposed Bidirectional Gated SSM (BiGS) and p resent a range of analysis into its properties. Results show that architec ture does seem to have a notable impact on downstream performance and a di fferent inductive bias that is worth exploring further.
\nBi ography
\nAbstract
\nWhile large language model s have advanced the state-of-the-art in natural language processing\, thes e models are trained on large-scale datasets\, which may include harmful i nformation. Studies have shown that as a result\, the models exhibit socia l biases and generate misinformation after training. In this talk\, I will discuss my work on analyzing and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I wil l first describe my research in the detection of dialect bias between Afri can American English (AAE) vs. Standard American English (SAE). The second part investigates the trustworthiness of models through the memorization and subsequent generation of conspiracy theories. I will end my talk with recent work in AI safety regarding text that may lead to physical harm.
\nBiography
\nSharon is a 5th-year Ph.D. candid ate at the University of California\, Santa Barbara\, where she is advised by Professor William Wang. Her research interests lie in natural language processing\, with a focus on Responsible AI. Sharon’s research spans the subareas of fairness\, trustworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LREC. She has spent summers interning at AWS\, Me ta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipi ent of the Amazon Alexa AI Fellowship for Responsible AI.
DTSTART;TZID=America/New_York:20230206T120000 DTEND;TZID=America/New_York:20230206T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sharon Levy (University of California\, Santa Barbara) “Responsible AI via Responsible Large Language Models” URL:https://www.clsp.jhu.edu/events/sharon-levy-university-of-california-sa nta-barbara-responsible-ai-via-responsible-large-language-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Levy END:VEVENT BEGIN:VEVENT UID:ai1ec-23308@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nBiases in datasets\, or un intentionally introduced spurious cues\, are a common source of misspecifi cation in machine learning. Performant models trained on such data can gen der stereotype or be brittle under distribution shift. In this talk\, we present several results in multimodal and question answering applications studying sources of dataset bias\, and several mitigation methods. We pro pose approaches where known dimensions of dataset bias are explicitly fact ored out of a model during learning\, without needing to modify data. Fina lly\, we ask whether dataset biases can be attributable to annotator behav ior during annotation. Drawing inspiration from work in psychology on cogn itive biases\, we show certain behavioral patterns are highly indicative o f the creation of problematic (but valid) data instances in question answe ring. We give evidence that many existing observations around how dataset bias propagates to models can be attributed to data samples created by ann otators we identify.
\nBiography
\nMark Ya tskar is an Assistant Professor at University of Pennsylvania in th e department of Computer and Information Science. He did his PhD at Univer sity of Washington co-advised by Luke Zettlemoyer and Ali Farhadi. He was a Young Investigator at the Allen Institute for Artificial Intelligence fo r several years working with their computer vision team\, Prior. His work spans Natural Language Processing\, Computer Vision\, and Fairness in Mach ine Learning. He received a Best Paper Award at EMNLP for work on gender b ias amplification\, and his work has been featured in Wired and the New Yo rk Times.
\nDTSTART;TZID=America/New_York:20230210T120000 DTEND;TZID=America/New_York:20230210T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Yatskar (University of Pennsylvania) “Understanding Dataset Bi ases: Behavioral Indicators During Annotation and Contrastive Mitigations” URL:https://www.clsp.jhu.edu/events/mark-yatskar-university-of-pennsylvania / X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Yatskar END:VEVENT BEGIN:VEVENT UID:ai1ec-23314@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nWhile GPT models have shown impressive performance on summa rization and open-ended text generation\, it’s important to assess their a bilities on more constrained text generation tasks that require significan t and diverse rewritings. In this talk\, I will discuss the challenges of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) paraphrase generation and (ii) text simplification . To address these challenges\, we introduce an interactive Rank-and-Rate evaluation framework. Our results show that GPT-3.5 has made a major step up from fine-tuned T5 in paraphrase generation\, but still lacks the diver sity and creativity of humans who spontaneously produce large quantities o f paraphrases.
\nAdditionally\, we demonstrate that GPT-3.5 performs similarly to a sin gle human in text simplification\, which makes it difficult for existing a utomatic evaluation metrics to distinguish between the two. To overcome th is shortcoming\, we propose LENS\, a learnable evaluation metric that outp erforms SARI\, BERTScore\, and other existing methods in both automatic ev aluation and minimal risk decoding for text generation.
\nBiography
\nWei Xu is an assistant professor in the School of Interactive Com puting at the Georgia Institute of Technology\, where she is also affiliat ed with the new NSF AI CARING Institute and Machine Learning Center. She r eceived her Ph.D. in Computer Science from New York University and her B.S . and M.S. from Tsinghua University. Xu’s research interests are in natura l language processing\, machine learning\, and social media\, with a focus on text generation\, stylistics\, robustness and controllability of machi ne learning models\, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award\, CrowdFlower AI for Everyone Award\, Criteo Faculty Research Award\, and Best Paper Award at COLING’18. She has also received funds from DARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/ NLP conferences.
DTSTART;TZID=America/New_York:20230224T120000 DTEND;TZID=America/New_York:20230224T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei Xu (Georgia Tech) “GPT-3 vs Humans: Rethinking Evaluation of Na tural Language Generation” URL:https://www.clsp.jhu.edu/events/wei-xu-georgia-tech/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Xu END:VEVENT BEGIN:VEVENT UID:ai1ec-23316@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nUnderstanding the implicat ions underlying a text is critical to assessing its impact\, in particular the social dynamics that may result from a reading of the text. This requ ires endowing artificial intelligence (AI) systems with pragmatic reasonin g\, for example to correctly conclude that the statement “Epidemics and ca ses of disease in the 21st century are “staged”” relates to unfounded cons piracy theories. In this talk\, I discuss how shortcomings in the ability of current AI systems to reason about pragmatics present challenges to equ itable detection of false or harmful language. I demonstrate how these sho rtcomings can be addressed by imposing human-interpretable structure on de ep learning architectures using insights from linguistics.
\n< p> In the first part of the talk\, I descri be how adversarial text generation algorithms can be used to improve robus tness of content moderation systems. I then introduce a pragmatic formalis m for reasoning about harmful implications conveyed by social media text. I show how this pragmatic approach can be combined with generative neural language models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents. \nBiography
\nSaadia Gabriel is a PhD candidate in the Paul G. Al len School of Computer Science & Engineering at the University of Washingt on\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research revolves around natural language processing and m achine learning\, with a particular focus on building systems for understa nding how social commonsense manifests in text (i.e. how do people typical ly behave in social scenarios)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also rece ived a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best pap er nomination and won a best paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Hol yoke College in Computer Science and Mathematics.
\nDTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT BEGIN:VEVENT UID:ai1ec-23320@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nSpeech communications repr esents a core domain for education\, team problem solving\, social engagem ent\, and business interactions. The ability for Speech Technology to extr act layers of knowledge and assess engagement content represents the next generation of advanced speech solutions. Today\, the emergence of BIG DATA \, Machine Learning\, as well as voice enabled speech systems have require d the need for effective voice capture and automatic speech/speaker recogn ition. The ability to employ speech and language technology to assess huma n-to-human interactions offers new research paradigms having profound impa ct on assessing human interaction. In this talk\, we will focus on big dat a naturalistic audio processing relating to (i) child learning spaces\, an d (ii) the NASA APOLLO lunar missions. ML based technology advancements in clude automatic audio diarization\, speech recognition\, and speaker recog nition. Child-Teacher based assessment of conversational interactions are explored\, including keyword and “WH-word” (e.g.\, who\, what\, etc.). Dia rization processing solutions are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 corpus\, resulting in a massive multi-track audio processing challenge to make available 150\,000hrs of Apollo mission data to be shared with science communities: (i) speech/language technology\, (i i) STEM/science and team-based researchers\, and (iii) education/historica l/archiving specialists. Our goals here are to provide resources which all ow to better understand how people work/learn collaboratively together. Fo r Apollo\, to accomplish one of mankind’s greatest scientific/technologica l challenges in the last century.
\nBiography
\nJohn H.L. Hansen\, received Ph.D. & M.S. degrees from Georgia Institute of Technology\, and B.S.E.E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 2005\, where he currently serves as Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair in Telecom. Engin eering\, and directs Center for Robust Speech Systems (CRSS). He is an ISC A Fellow\, IEEE Fellow\, and has served as Member and TC-Chair of IEEE Sig nal Proc. Society\, Speech & Language Proc. Tech. Comm.(SLTC)\, and Techni cal Advisor to U.S. Delegate for NATO (IST/TG-01). He served as ISCA Presi dent (2017-21)\, continues to serve on ISCA Board (2015-23) as Treasurer\, has supervised 99 PhD/MS thesis candidates (EE\,CE\,BME\,TE\,CS\,Ling.\,C og.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author of 865 journal/c onference papers including 14 textbooks in the field of speech/language/he aring processing & technology including coauthor of textbook Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and lead author of t he report “The Impact of Speech Under ‘Stress’ on Military Speech Technolo gy\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Chair/Co-Chair/Tec h.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISC A INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIOUS SERVICE Award .
\nDTSTART;TZID=America/New_York:20230303T120000 DTEND;TZID=America/New_York:20230303T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:John Hansen (University of Texas at Dallas) “Challenges and Advance ments in Speaker Diarization & Recognition for Naturalistic Data Streams” URL:https://www.clsp.jhu.edu/events/john-hansen-university-of-texas-at-dall as/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Hansen\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23439@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nAs data-based technologies proliferate\, it is increasingly important for researchers to be aware of their work’s wider impact. Concerns like navigating the IRB and figuring out copyright and licensing issues are still key\, but the current focus s hift to matters like inclusivity\, fairness\, and transparency and their i mpact on the research/development life cycle have added complexity to the research task. In this talk\, we will take a broad look at the various way s ethics intersects with natural language processing\, machine learning\, and artificial intelligence research and discuss strategies and resources for managing these concerns within the broader research framework.
\nBiography
\nDenise is responsible for the overall operation of LDC’s External Relations group which includes intellectual pr operty management\, licensing\, regulatory matters\, publications\, member ship and communications. Before joining LDC\, she practiced law for over 2 0 years in the areas of international trade\, intellectual property and co mmercial litigation. She has an A.B. in Political Science from Bryn Mawr C ollege and a Juris Doctor degree from the University of Miami School of La w.
DTSTART;TZID=America/New_York:20230310T120000 DTEND;TZID=America/New_York:20230310T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street SEQUENCE:0 SUMMARY:Denise DiPersio (Linguistic Data Consortium\, University of Pennsyl vania) “Data and Ethics: Where Does the Twain Meet?” URL:https://www.clsp.jhu.edu/events/denise-dipersio-linguistic-data-consort ium-university-of-pennsylvania-data-and-ethics-where-does-the-twain-meet/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,DiPersio\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23312@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAdvanced neural language m odels have grown ever larger and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretabi lity. The black-box nature of deep neural networks blocks humans from unde rstanding them\, as well as trusting and using them in real-world applicat ions. This talk will introduce interpretation techniques that bridge the g ap between humans and models for developing trustworthy natural language p rocessing
\n (NLP). I will first show how to explain black-box models and evaluate their explanations for understanding their p rediction behavior. Then I will introduce how to improve the interpretabil ity of neural language models by making their decision-making transparent and rationalized. Finally\, I will discuss how to diagnose and improve mod els (e.g.\, robustness) through the lens of explanations. I will conclude with future research directions that are centered around model interpretab ility and committed to facilitating communications and interactions betwee n intelligent machines\, system developers\, and end users for long-term t rustworthy AI.Biography
\nHanjie Chen is a Ph.D. candidate in Computer Science at the University of Virginia\, advis ed by Prof. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Na tural Language Processing (NLP)\, and
DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-23505@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: Interpretabl e Machine Learning. She develops interpretation techniques to explain neur al language models and make their prediction behavior transparent and reli able. She is a recipient of the Carlos and Esther Farrar Fellowship and th e Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selec ted by the National Center for Women & Information Technology (NCWIT) Coll egiate Award Finalist 2021. She (as the primary instructor) co-designed an d taught the course\, Interpretable Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teaching Award and University-wide Graduate T eaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mxAbstract
\nRecent advances in large pretrained language models have unlocked new exciting a pplications for Natural Language Generation for creative tasks\, such as l yrics or humour generation. In this talk we will discuss recent works by o ur team at Alexa AI and discuss current challenges: (1) Pun understanding and generation: We release new datasets for pun understanding and the nove l task of context-situated pun generation\, and demonstrate the value of o ur annotations for pun classification and generation tasks. (2) Song lyric generation: we design a hierarchical lyric generation framework that enab les us to generate pleasantly-singable lyrics without training on melody-l yric aligned data\, and show that our approach is competitive with strong baselines supervised on parallel data. (3) Create with Alexa: a multimodal story creation experience recently launched on Alexa devices\, which leve rages story text generation models in tandem with story visualization and background music generation models to produce multimodal stories for kids.
\nBiography
\nAlessandra Cervone is an Appli ed Scientist in the Natural Understanding team at Amazon Alexa AI. Alessan dra holds an MSc in Speech and Language Processing from University of Edin burgh and a PhD in CS from University of Trento (Italy). During her PhD\, Alessandra worked on computational models of coherence in open-domain dial ogue advised by Giuseppe Riccardi. In the first year of the PhD\, she was the team leader of one of the teams selected to compete in the first editi on of the Alexa Prize. More recently\, her research interests have been fo cused on natural language generation and its evaluation\, in particular in the context of creative AI applications.
\nDTSTART;TZID=America/New_York:20230317T120000 DTEND;TZID=America/New_York:20230317T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Alessandra Cervone (Amazon) “Controllable Text Generation for Creat ive Applications URL:https://www.clsp.jhu.edu/events/alexxandra-cervone-amazon/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Cervone\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23555@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230327T120000 DTEND;TZID=America/New_York:20230327T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Desh Raj URL:https://www.clsp.jhu.edu/events/student-seminar-desh-raj-2/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,March\,Raj END:VEVENT BEGIN:VEVENT UID:ai1ec-23513@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nDespite many recent advanc es in automatic speech recognition (ASR)\, linguists and language communit ies engaged in language documentation projects continue to face the obstac le of the “transcription bottleneck”. Researchers in NLP typically do not distinguish between widely spoken languages that currently happen to have few training resources and endangered languages that will never have abund ant data. As a result\, we often fail to thoroughly explore when ASR is he lpful for language documentation\, what architectures work best for the so rts of languages that are in need of documentation\, and how data can be c ollected and organized to produce optimal results. In this talk I describe several projects that attempt to bridge the gap between the promise of AS R for language documentation and the reality of using this technology in r eal-world settings.
\nBiography
\nAbstract
\nHow important are different temporal s peech modulations for speech recognition? We answer this question from two complementary perspectives. Firstly\, we quantify the amount of phonetic information in the modulation spectrum of speech by computing the mutual i nformation between temporal modulations with frame-wise phoneme labels. Lo oking from another perspective\, we ask – which speech modulations an Auto matic Speech Recognition (ASR) system prefers for its operation. Data-driv en weights are learned over the modulation spectrum and optimized for an e nd-to-end ASR task. Both methods unanimously agree that speech information is mostly contained in slow modulation. Maximum mutual information occurs around 3-6 Hz which also happens to be the range of modulations most pref erred by the ASR. In addition\, we show that the incorporation of this kno wledge into ASRs significantly reduces their dependency on the amount of t raining data.
\n\n
Learning How to Play With The Machines: Taking Stock of Wher e the Collaboration Between Computational and Social Science Stands
\n< p> \nSpeakers: Jeff Gill\, Ernesto Calvo\, Hale Sirin and Antonios Anastasopoulos
DTSTART;TZID=America/New_York:20230407T120000 DTEND;TZID=America/New_York:20230407T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street SEQUENCE:0 SUMMARY:JHU CLSP APSA Roundtable on Learning How to Play with the Machines URL:https://www.clsp.jhu.edu/events/jhu-clsp-apsa-roundtable-on-learning-ho w-to-play-with-the-machines/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,APSA Roundtable END:VEVENT BEGIN:VEVENT UID:ai1ec-23586@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23588@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAdvanc es in open domain Large Language Models (LLMs) starting with BERT and more recently with GPT-4\, PaLM\, and LLaMA have facilitated dramatic improvem ents in conversational systems. These improvements include an unprecedente d breadth of conversational interactions between humans and machines while maintaining and sometimes surpassing the accuracy of systems trained spec ifically for known\, closed domains. However\, many applications still req uire higher levels of accuracy than pre-trained LLMs can provide. There ar e many studies underway to accomplish this. Broadly speaking\, the methods assume the pre-trained models are fixed (due to cost/time)\, and instead look to various augmentation methods including prompting strategies and mo del adaptation/fine-tuning.
\nOne augmentation s trategy leverages the context of the conversation. For example\, who are t he participants and what is known about these individuals (personal contex t)\, what was just said (dialogue context)\, where is the conversation tak ing place (geo context)\, what time of day and season is it (time context) \, etc. A powerful form of context is the shared visual setting of the co nversation between the human(s) and machine. The shared visual scene may b e from a device (phone\, smart glasses) or represented on a screen (browse r\, maps\, etc.) The elements in the visual context can be exploited by gr ounding the natural language conversational interaction\, thereby changing the priors of certain concepts and increasing the accuracy of the system. In this talk\, I will present some of my historical work in this area as well as my recent work in the AI Virtual Assistant (AVA) Lab at Georgia Te ch.
\nBio
\nDr. Larry Hec k is a Professor with a joint appointment in the School of Electrical and Computer Engineering and the School of Interactive Computing at the Georgi a Institute of Technology. He holds the Rhesa S. Farmer Distinguished Chai r of Advanced Computing Concepts and is a Georgia Research Alliance Eminen t Scholar. His received the BSEE from Texas Tech University (1986)\, and M SEE and PhD EE from the Georgia Institute of Technology (1989\,1991). He i s a Fellow of the IEEE\, inducted into the Academy of Distinguished Engine ering Alumni at Georgia Tech and received the Distinguished Engineer Award from the Texas Tech University Whitacre College of Engineering. He was a Senior Research Engineer with SRI (1992-98)\, Vice President of R&D at Nua nce (1998-2005)\, Vice President of Search and Advertising Sciences at Yah oo! (2005-2009)\, Chief Scientist of the Microsoft Speech products and Dis tinguished Engineer in Microsoft Research (2009-2014)\, Principal Scientis t with Google Research (2014-2017)\, and CEO of Viv Labs and SVP at Samsun g (2017-2021).
\n\n
Abstract
\nOur models achieve state-of-the-art performance and lay im portant groundwork towards realizing a universal translation system. At th e same time\, we keep making open-source contributions for everyone to kee p advancing the research for the languages they care about.
\nPaco is Research Scientist Manager supporting trans lation teams in Meta AI (FAIR). He works in the field of machine translati on with a focus on low-resource translation (e.g. NLLB\, FLORES) and the a im to break language barriers. He joined Meta in 2016. His research has be en published in top-tier NLP venues like ACL\, EMNLP. He was the co-chair of the Research director at AMTA (2020-2022). He has ave organized several research competitions focused on low-resource translation and data filter ing. Paco obtained his PhD from the ITESM in Mexico\, was a visiting schol ar at the LTI-CMU from 2008-2009\, and participated in DARPA’s GALE evalua tion program. Paco was a post-doc and scientist at Qatar Computing Researc h Institute in Qatar in 2012-2016
DTSTART;TZID=America/New_York:20230417T120000 DTEND;TZID=America/New_York:20230417T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Paco Guzman (Meta AI) “Building a Universal Translation System to B reak Down Language Barriers” URL:https://www.clsp.jhu.edu/events/paco-guzman-meta-ai/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Guzman END:VEVENT BEGIN:VEVENT UID:ai1ec-23592@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nLarge language models (LLM s) have ushered in exciting capabilities in language understanding and tex t generation\, with systems like ChatGPT holding fluent dialogs with users and being almost indistinguishable from humans. While this has obviously raised conversational systems and chatbots to a new level\, it also presen ts exciting new opportunities for building artificial agents with improved decision making capabilities. Specifically\, the ability to reason with l anguage can allow us to build agents that can 1) execute complex action se quences to effect change in the world\, 2) learn new skills by ‘reading’ i n addition to ‘doing’\, and 3) allow for easier personalization and contro l over their behavior. In this talk\, I will demonstrate how we can build such language-enabled agents that exhibit the above traits across various use cases such as multi-hop question answering\, web interaction\, and rob otic tool manipulation. In the end\, I will also discuss some dangers of u sing these LLM-based systems and some challenges that lie ahead in ensurin g their safe use.
\nBiography
\nKarthi k Narasimhan is an assistant professor in the Computer Science department at Princeton University and a co-Director of the Princeton NLP group. His research spans the areas of natural language processing and reinforcement learning\, with the goal of building intelligent agents that learn to oper ate in the world through both their own experience (”doing things”) and le veraging existing human knowledge (”reading about things”). Karthik receiv ed his PhD from MIT in 2017\, and spent a year as a visiting research scie ntist at OpenAI contributing to the GPT language model\, prior to joining Princeton in 2018. His research has been recognized by the NSF CAREER\, a Google Research Scholar Award\, an Amazon research award (2019)\, Bell Lab s runner-up prize and outstanding paper awards at EMNLP (2015\, 2016) and NeurIPS (2022).
DTSTART;TZID=America/New_York:20230421T120000 DTEND;TZID=America/New_York:20230421T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Karthik Narasimhan (Princeton University) ” Towards General-Purpose Language-Enabled Agents: Machines that can Read\, Think and Act” URL:https://www.clsp.jhu.edu/events/karthik-narasimhan-princeton-university / X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Narasimhan END:VEVENT BEGIN:VEVENT UID:ai1ec-23606@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230424T120000 DTEND;TZID=America/New_York:20230424T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Brian Lu URL:https://www.clsp.jhu.edu/events/student-seminar-brian-lu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Lu END:VEVENT BEGIN:VEVENT UID:ai1ec-23608@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAutomated analysis of stud ent writing has the potential to provide alternatives to selected-response questions such as multiple choice\, and to enable teachers and instructor s to assess students’ reasoning skills based on their long-form writing. F urther\, automated support to assess both short answers and long passages could provide students with a smoother trajectory towards mastery of writt en communication. Our methods focus on the specific ideas students expres s to support formative assessment through different kinds of feedback\, wh ich aims to scaffold their abilities to reason and communicate. In this ta lk I review our work in the PSU NLP lab on methods for automated assessmen t of different forms of student writing\, from younger and older students. I will briefly illustrate highly curated datasets created in collaborati on with researchers in STEM education\, results from deployment of an olde r content analysis tool on middle school physics essays\, and very prelimi nary results on assessment of college students’ physics lab reports. I wi ll also present our current work on short answer assessment using a novel recurrent relation network that incorporates contrastive learning.
\nBio
\nBecky Passonneau has been a Professor in the Department of Computer Science and Engineering at Penn State University s ince 2016\, when she joined as the first NLP researcher. Since that time t he NLP faculty has grown to include Rui Zhang and Wenpeng Yin. Becky’s res earch in natural language processing addresses computational pragmatics\, meaning the investigation of language as a system of interactive behavior that serves a wide range of purposes. She received her PhD in Linguistics from the University of Chicago in 1985\, and worked at several academic an d industry research labs before joining Penn State. Her work is reported i n over 140 publications in journals and refereed conference proceedings\, and has been funded through 27 sponsored projects from 16 sources\, inclu ding government agencies\, corporate sponsors\, corporate gifts\, and foun dations..
DTSTART;TZID=America/New_York:20230428T120000 DTEND;TZID=America/New_York:20230428T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Becky Passonneau (Penn State University) ” Automated Support to Sca ffold Students’ Short- and Long-form STEM Writing” URL:https://www.clsp.jhu.edu/events/becky-passonneau-penn-state-university- automated-support-to-scaffold-students-short-and-long-form-stem-writing/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Passonneau END:VEVENT BEGIN:VEVENT UID:ai1ec-23880@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230828T120000 DTEND;TZID=America/New_York:20230828T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street SEQUENCE:0 SUMMARY:CLSP Town Hall – Welcome New Students\, Introductions and CLSP Over view URL:https://www.clsp.jhu.edu/events/clsp-town-hall-welcome-new-students-int roductions-and-clsp-overview/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,August\,Town Hall END:VEVENT BEGIN:VEVENT UID:ai1ec-23882@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nLarge language models (LLM s) have demonstrated incredible power\, but they also possess vulnerabilit ies that can lead to misuse and potential attacks. In this presentation\, we will address two fundamental questions regarding the responsible utiliz ation of LLMs: (1) How can we accurately identify AI-generated text? (2) W hat measures can safeguard the intellectual property of LLMs? We will intr oduce two recent watermarking techniques designed for text and models\, re spectively. Our discussion will encompass the theoretical underpinnings th at ensure the correctness of watermark detection\, along with robustness a gainst evasion attacks. Furthermore\, we will showcase empirical evidence validating their effectiveness. These findings establish a solid technical groundwork for policymakers\, legal professionals\, and generative AI pra ctitioners alike.
\nBiography
\nLei Li is an Assistant Professor in Language Technology Institute at Carnegie Mellon Un iversity. He received Ph.D. from Carnegie Mellon University School of Comp uter Science. He is a recipient of ACL 2021 Best Paper Award\, CCF Young E lite Award in 2019\, CCF distinguished speaker in 2017\, Wu Wen-tsün AI pr ize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a facul ty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI La b in 2016 and led its research in NLP\, ML\, Robotics\, and Drug Discovery . He launched ByteDance’s machine translation system VolcTrans and AI writ ing system Xiaomingbot\, serving one billion users.
DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23886@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe arms race to build inc reasingly larger\, powerful language models (LMs) in the past year has bee n remarkable. Yet incorporating LMs effectively into practical application s that facilitate manual workflows remains challenging. I will discuss LMs ’ limiting factors and our efforts to overcome them. I will start with cha llenges surrounding efficient and robust LM alignment. I will share insigh ts from our recent paper “Sel f-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for ali gning itself\, an approach that has yielded some success. Then\, I will mo ve on to the challenge of tracing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our rec ent approach of ‘according-to’ prom pting\, which steers LMs to quote directly from sources observed in it s pre-training. If time permits\, I will discuss our ongoing project to ad apt LMs to interact with web pages. Throughout the presentation\, I will h ighlight our progress\, and end with questions about our future progress.< /p>\n
Biography
\nDaniel Khashabi is an assistant professor in computer science at Johns Hopkins University and the Center for Language and Speech Processing (CLSP) member. He is interested in bui lding reasoning-driven modular NLP systems that are robust\, transparent\, and communicative\, particularly those that use natural language as the c ommunication medium. Khashabi has published over 40 papers on natural lang uage processing and AI in top-tier venues. His work touches upon developin g. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Best Paper Award\, research gifts from the Allen Institute for AI\, and an Amazon Research Award 2023. Before joining Hopkins\, he was a postdoctora l fellow at the Allen Institute for AI (2019-2022) and obtained a Ph.D. fr om the University of Pennsylvania in 2019.
DTSTART;TZID=America/New_York:20230908T120000 DTEND;TZID=America/New_York:20230908T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Johns Hopkins University) “Building More Helpful L anguage Models” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-johns-hopkins-universit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Khashabi\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23888@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nEmbedding text sequences is a widespread requirement in modern lan guage understanding. Existing approaches focus largely on constant-size re presentations. This is problematic\, as the amount of information containe d in text often varies with the length of the input. We propose a solution called Nugget\, which encodes language into a representation based on a d ynamically selected subset of input tokens. These nuggets are learned thro ugh tasks like autoencoding and machine translation\, and intuitively segm ent language into meaningful units. We demonstrate Nugget outperforms rela ted approaches in tasks involving semantic comparison. Finally\, we illust rate these compact units allow for expanding the contextual window of a la nguage model (LM)\, suggesting new future LMs that can condition on signif icantly larger amounts of content.
DTSTART;TZID=America/New_York:20230911T120000 DTEND;TZID=America/New_York:20230911T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Guanghui Qin “Nugget: Neural Agglomerative Embedd ings of Text (ICML 2023)” URL:https://www.clsp.jhu.edu/events/student-seminar-guanghui-qin/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Qin\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe growing power in compu ting and AI promises a near-term future of human-machine teamwork. In this talk\, I will present my research group’s efforts in understanding the co mplex dynamics of human-machine interaction and designing intelligent mach ines aimed to assist and collaborate with people. I will focus on 1) tools for onboarding machine teammates and authoring machine assistance\, 2) me thods for detecting\, and broadly managing\, errors in collaboration\, and 3) building blocks of knowledge needed to enable ad hoc human-machine tea mwork. I will also highlight our recent work on designing assistive\, coll aborative machines to support older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assista nt Professor in the Department of Computer Science at the Johns Hopkins Un iversity. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier venues in HRI\, HCI \, and robotics including Science Robotics\, HRI\, CHI\, and CSCW. His res earch has received media coverage from MIT Technology Review\, Tech Inside r\, and Science Nation. Huang completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www.cs.jhu.edu/~cmhuang/
DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23894@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe use of NLP in the real m of financial technology is broad and complex\, with applications ranging from sentiment analysis and named entity recognition to question answerin g. Large Language Models (LLMs) have been shown to be effective on a varie ty of tasks\; however\, no LLM specialized for the financial domain has be en reported in the literature. In this work\, we present BloombergGPT\, a 50 billion parameter language model that is trained on a wide range of fin ancial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, perhaps the largest domain-specific dataset yet\ , augmented with 345 billion tokens from general-purpose datasets. We val idate BloombergGPT on standard LLM benchmarks\, open financial benchmarks\ , and a suite of internal benchmarks that most accurately reflect our inte nded usage. Our mixed dataset training leads to a model that outperforms e xisting models on financial tasks by significant margins without sacrifici ng performance on general LLM benchmarks. Additionally\, we explain our mo deling choices\, training process\, and evaluation methodology.
\nBiography
Mark Dredze is the John C Malone Professo r of Computer Science at Johns Hopkins University and the Director of Rese arch (Foundations of AI) for the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores app lications to public health and medicine.
\nProf. Dredze is affiliate d with the Malone Center for Engineering in Healthcare\, the Center for La nguage and Speech Processing\, among others. He holds a joint appointment in the Biomedical Informatics & Data Science Section (BIDS)\, under the Depart ment of Medicine (DOM)\, Division of General Internal Medicine (GIM) in th e School of Medicine. He obtained his PhD from the University of Pennsylva nia 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-TAGS;LANGUAGE=en-US:2023\,Dredze\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23983@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nVisually rich documents (scanned or digital) remain important for man y consumer and business use cases. During this talk we will share recent work from our team in the Document In telligence Lab of Adobe Research to understand\, create\, and interact wit h these documents. First\, we’ll share a series of work on building model s to decompose and understand the structure of documents to support use ca ses around document analysis and accessibility. Next\, we’ll explore docum ent semantic understanding for a project where we convert natural language contract clauses to code to support business automation. Finally\, we’ll discuss DocEdit\, a model and dataset that enables editing structured docu ments from natural language.
\nBIOS:
\n< p>Rajiv Jain is a Senior R esearch Scientist in the Document Intelligence Lab in Adobe Research\, whe re his research focuses on understanding the layout\, content\, and intera ction with documents. Prior to joining Adobe\, Rajiv was a consultant at D ARPA\, where he worked on the Media Forensics Program to secure digital im agery. He previously served for 10 years as a researcher for the Departmen t of Defense where he worked on projects around large scale systems\, comp uter vision\, and network security. He received his PhD in computer scienc e from the University of Maryland\, College Park working in the field of d ocument image analysis and retrieval.\nChris Tensmeyer primarily focuses on multi-moda l document layout and content understanding as a Research Scientist in the Document Intelligence Lab of Adobe Research. Since joining Adobe 5 years ago\, his work has directly impacted popular Adobe features such as mobil e Acrobat Liquid Mode\, PDF table extraction\, handwriting recognition\, a nd scanned document detection. Other research interests include general C omputer Vision and Deep Learning. He received his PhD in Computer Science from Brigham Young University on the topic of Deep Learning for Document Image Analysis.
DTSTART;TZID=America/New_York:20230922T120000 DTEND;TZID=America/New_York:20230922T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rajiv Jain and Chris Tensmeyer (Adobe) “Document Intelligence at Ad obe Research” URL:https://www.clsp.jhu.edu/events/rajiv-jain-and-chris-tensmeyer-adobe-do cument-intelligence-at-adobe-research/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Jain and Tensmeyer\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23896@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe field of NLP is in the midst of a disruptive shift\, fueled m ost recently by the advent of large language models (LLMs)\, with impacts on our methodologies\, funding and public perception. While the core techn ologies and scope of real-world impact of our field may be changing (every thing is different!)\, many of the same key challenges faced since the inc eption of our field remain (nothing has changed). In this talk I’ll descri be recent work characterizing and tackling some of these challenges\, nota bly: data-efficient domain adaptation and lifelong learning. I will also a nchor discussion of cycles and shifts in the field by describing findings from a qualitative study of factors shaping the community over time\, incl uding culture\, incentives\, and infrastructure. Through these complementa ry lenses into the past\, present and future\, I aim to inspire shared hop e\, excitement and discussion.
\nBio
\n< p class='x_x_x_MsoNormal'>Emma Strubell is the Raj Reddy Assistant Professor in the Language Technologies Institu te in the School of Computer Science at Carnegie Mellon University\, and a Visiting Scientist at the Allen Institute for Artificial Intelligence. Pr eviously she held research scientist roles at Google and FAIR after earnin g her doctoral degree in 2019 from the University of Massachusetts Amherst . Her research lies at the intersection of natural language processing and machine learning\, with a focus on providing pragmatic solutions to pract itioners who wish to gain insights from natural language text via computat ion- and data-efficient AI. Her work has been recognized with a Madrona AI Impact Award\, best paper awards at ACL and EMNLP\, and cited in news out lets including the New York Times and Wall Street Journal. DTSTART;TZID=America/New_York:20230925T120000 DTEND;TZID=America/New_York:20230925T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Emma Strubell (Carnegie Mellon University) “Large Language Models: Everything’s Different and Nothing Has Changed” URL:https://www.clsp.jhu.edu/events/emma-strubell-carnegie-mellon-universit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,September\,Strubell END:VEVENT BEGIN:VEVENT UID:ai1ec-23898@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nAny valuable NLP dataset has traditionally been shipped with crow dsourced categorical labels. Instructions for collecting these labels are easy to communicate and the labels themselves are easy to annotate. Howeve r\, as self-supervision based methods are getting better at basically ever ything\, human annotations may need to provide more nuanced supervision or enable more detailed evaluation in order to be worth further collecting. One natural extension to existing categorical annotation schemes is to obt ain uncertainty information beyond a single hard label. In this talk\, I w ill discuss my recent efforts on introducing scalar labels in place of cat egorical labels as a form of uncertainty annotation. We demonstrate that\, compared to other more obvious annotation schemes for eliciting uncertain ty information\, scalar labels are significantly more cost-effective to an notate\, provide reliable evaluation\, and have a theoretical connection t o existing predictive uncertainty metrics. In particular\, they motivate u sing other losses as surrogates for calibration evaluation.
DTSTART;TZID=America/New_York:20230929T120000 DTEND;TZID=America/New_York:20230929T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Zhengping Jiang “Scalar Labels for Capturing Human Uncertainty” URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-zhengping-jiang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Jiang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-23900@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231002T120000 DTEND;TZID=America/New_York:20231002T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Anna Favaro URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-anna-favaro/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Favaro\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24115@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nOur goal is to use AI to a utomatically find tax minimization strategies\, an approach which we call “Shelter Check.” It would come in two variants. Existing-Authority Shelter Check would aim to find whether existing tax law authorities can be combi ned to create tax minimization strategies\, so the IRS or Congress can shu t them down. New-Authority Shelter Check would automate checking whether a new tax law authority – like proposed legislation or a draft court decisi on – would combine with existing authorities to create a new tax minimizat ion strategy. We had initially had high hopes for GPT-* large language mod els for implementing Shelter Check\, but our tests have showed that they d o very poorly at basic legal reasoning and handling legal text. So we are now creating a benchmark and training data for LLM’s handling legal text\, hoping to spur improvements.
DTSTART;TZID=America/New_York:20231006T120000 DTEND;TZID=America/New_York:20231006T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:CLSP Student Seminar – Andrew Blair-Stanek “Shelter Check and GPT-4 ’s Bad Legal Performance” URL:https://www.clsp.jhu.edu/events/clsp-student-seminar-andrew-blair-stane k-shelter-check-and-gpt-4s-bad-legal-performance/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Blair-Stanek\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24005@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nLarge-scale generative models such as GPT and DALL-E have r evolutionized natural language processing and computer vision research. Th ese models not only generate high fidelity text or image outputs\, but als o demonstrate impressive domain and task generalization capabilities. In c ontrast\, audio generative models are relatively primitive in scale and ge neralization.
\nIn this talk\, I will start with a brief introduction on conventional n eural speech generative models and discuss why they are unfit for scaling to Internet-scale data. Next\, by reviewing the latest large-scale generat ive models for text and image\, I will outline a few lines of promising ap proaches to build scalable speech models. Last\, I will present Voicebox\, our latest work to advance this area. Voicebox is the most versatile gene rative model for speech. It is trained with a simple task — text condition ed speech infilling — on over 50K hours of multilingual speech with a powe rful flow-matching objective. Through in-context learning\, Voicebox can p erform monolingual/cross-lingual zero-shot TTS\, holistic style conversion \, transient noise removal\, content editing\, and diverse sample generati on. Moreover\, Voicebox achieves state-of-the-art performance and excellen t run-time efficiency.
\nBiography
\nWei-Ning Hsu is a resear ch scientist at Meta Foundational AI Research (FAIR) and currently the lea d of the audio generation team. His research focuses on self-supervised le arning and generative models for speech and audio. His pioneering work inc ludes HuBERT\, AV-HuBERT\, TextlessNLP\, data2vec\, wav2vec-U\, textless s peech translation\, and Voicebox.
\nPrior to joining Meta\, Wei-Ning worked at MERL an d Google Brain as a research intern. He received his Ph.D. and S.M. degree s in Electrical Engineering and Computer Science from Massachusetts Instit ute of Technology in 2020 and 2018\, under the supervision of Dr. James Gl ass. He received his B.S. degree in Electrical Engineering from National T aiwan University in 2014\, under the supervision of Prof. Lin-shan Lee and Prof. Hsuan-Tien Lin.
DTSTART;TZID=America/New_York:20231009T120000 DTEND;TZID=America/New_York:20231009T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei-Ning Hsu (Meta Foundational AI Research) “Large Scale Universal Speech Generative Models” URL:https://www.clsp.jhu.edu/events/wei-ning-hsu-meta-foundational-ai-resea rch-large-scale-universal-speech-generative-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Hsu\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23902@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAbstract
\nRecent advances in speech technology make heavy use of pre-trained models that learn from large quan tities of raw (untranscribed) speech\, using “self-supervised” (ie unsuper vised) learning. These models learn to transform the acoustic input into a different representational format that makes supervised learning (for tas ks such as transcription or even translation) much easier. However\, *what * and *how* speech-relevant information is encoded in these representation s is not well understood. I will talk about some work at various stages of completion in which my group is analyzing the structure of these represen tations\, to gain a more systematic understanding of how word-level\, phon etic\, and speaker information is encoded.
\nBiography
\nSharon Goldwater is a Professor in the Institute for Language\ , Cognition and Computation at the University of Edinburgh’s School of Inf ormatics. She received her PhD in 2007 from Brown University and spent two years as a postdoctoral researcher at Stanford University before moving t o Edinburgh. Her research interests include unsupervised and minimally-sup ervised learning for speech and language processing\, computer modelling o f language acquisition in children\, and computational studies of language use. Her main focus within linguistics has been on the lower levels of s tructure including phonetics\, phonology\, and morphology.
DTSTART;TZID=America/New_York:20231027T120000 DTEND;TZID=America/New_York:20231027T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sharon Goldwater (University of Edinburgh) “Analyzing Representatio ns of Self-Supervised Speech Models” URL:https://www.clsp.jhu.edu/events/sharon-goldwater-university-of-edinburg h/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Goldwater\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23910@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: Prof. Goldwater has received awards incl uding the 2016 Roger Needham Award from the British Computer Society for “ distinguished research contribution in computer science by a UK-based rese archer who has completed up to 10 years of post-doctoral research.” She ha s served on the editorial boards of several journals\, including Computati onal Linguistics\, Transactions of the Association for Computational Lingu istics\, and the inaugural board of OPEN MIND: Advances in Cognitive Scien ce. She was a program chair for the EACL 2014 Conference and chaired the E ACL governing board from 2019-2020.Abstract
\nAbstract
\nMultil ingual machine translation has proven immensely useful for both parameter efficiency and overall performance for many language pairs via complete pa rameter sharing. However\, some language pairs in multilingual models can see worse performance than in bilingual models\, especially in the one-to- many translation setting. Motivated by their empirical differences\, we ex amine the geometric differences in representations from bilingual models v ersus those from one-to-many multilingual models. Specifically\, we measur e the isotropy of these representations using intrinsic dimensionality and IsoScore\, in order to measure how these representations utilize the dime nsions in their underlying vector space. We find that for a given language pair\, its multilingual model decoder representations are consistently le ss isotropic than comparable bilingual model decoder representations. Addi tionally\, we show that much of this anisotropy in multilingual decoder re presentations can be attributed to modeling language-specific information\ , therefore limiting remaining representational capacity.
DTSTART;TZID=America/New_York:20231106T120000 DTEND;TZID=America/New_York:20231106T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Neha Verma “Exploring Geometric Representational Disparities Between Multilingual and Bilingual Translation Models” URL:https://www.clsp.jhu.edu/events/student-seminar-neha-verma-exploring-ge ometric-representational-disparities-between-multilingual-and-bilingual-tr anslation-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,November\,Verma END:VEVENT BEGIN:VEVENT UID:ai1ec-24157@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nIn this talk\, I will pres ent a simple extension of image-based Masked Autoencoders (MAE) to self-su pervised representation learning from audio spectrograms. Following the Tr ansformer encoder-decoder design in MAE\, our Audio-MAE first encodes audi o spectrogram patches with a high masking ratio\, feeding only the non-mas ked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens\, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window atten tion in the decoder\, as audio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower mask ing ratio on target datasets. Empirically\, Audio-MAE sets new state-of-th e-art performance on six audio and speech classification tasks\, outperfor ming other recent models that use external supervised pre-training.
\n< p>Bio\nFlorian Metze is a Research Scientist Manag er at Meta AI in New York\, supporting a team of researchers and engineers working on multi-modal (image\, video\, audio\, text) content understandi ng for Meta’s Family of Apps (Instagram\, Threads\, Facebook\, WhatsApp). He used to be an Associate Research Professor at Carnegie Mellon Universit y\, in the School of Computer Science’s Language Technologies Institute\, where he still is an Adjunct Professor. He is also a co-founder of Abridge \, a company working on extracting information from doctor patient convers ations. His work covers many areas of speech recognition and multi-media a nalysis with a focus on end-to-end deep learning. Currently\, he focuses o n multi-modal processing of videos\, and using that information to recomme nd unconnected content. In the past\, he has worked on low resource and mu lti-lingual speech processing\, speech recognition with articulatory featu res\, large-scale multi-media retrieval and summarization\, information ex traction from medical interviews\, and recognition of personality or simil ar meta-data from speech.
\nFor more information\, please see http://www.cs.cmu.edu/directory /fmetze
\nDTSTART;TZID=America/New_York:20231110T120000 DTEND;TZID=America/New_York:20231110T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Florian Metze (CMU) “Masked Autoencoders that Listen” URL:https://www.clsp.jhu.edu/events/florian-metze-cmu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Metze\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24159@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231113T120000 DTEND;TZID=America/New_York:20231113T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Kate Sanders URL:https://www.clsp.jhu.edu/events/student-seminar-kate-sanders/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,November\,Sanders END:VEVENT BEGIN:VEVENT UID:ai1ec-24163@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nThe almost un limited multimedia content available on video-sharing websites has opened new challenges and opportunities for building robust multimodal solutions. This seminar will describe our novel multimodal architectures that (1) ar e robust to missing modalities\, (2) can identify noisy or less discrimina tive features\, and (3) can leverage unlabeled data. First\, we present a strategy that effectively combines auxiliary networks\, a transformer arch itecture\, and an optimized training mechanism for handling missing featur es. This problem is relevant since it is expected that during inference th e multimodal system will face cases with missing features due to noise or occlusion. We implement this approach for audiovisual emotion recognition achieving state-of-the-art performance. Second\, we present a multimodal f ramework for dealing with scenarios characterized by noisy or less discrim inative features. This situation is commonly observed in audiovisual autom atic speech recognition (AV-ASR) with clean speech\, where the performance often drops compared to a speech-only solution due to the variability of visual features. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual f eatures\, keeping only useful information. The approach improves\, or at l east\, maintains performance when visual features are used. Third\, we dis cuss alternative strategies to leverage unlabeled multimodal data. A promi sing approach is to use multimodal pretext tasks that are carefully design ed to learn better representations for predicting a given task\, leveragin g the relationship between acoustic and facial features. Another approach is using multimodal ladder networks where intermediate representations are predicted across modalities using lateral connections. These models offer principled solutions to increase the generalization and robustness of com mon speech-processing tasks when using multimodal architectures. p>\n
Bio
\nCarlos Busso is a Profess or at the University of Texas at Dallas’s Electrical and Computer Engineer ing Department\, where he is also the director of the Multimodal Signal Pr ocessing (MSP) Laboratory. His research interest is in human-centered mult imodal machine intelligence and application\, with a focus on the broad ar eas of affective computing\, multimodal human-machine interfaces\, in-vehi cle active safety systems\, and machine learning methods for multimodal pr ocessing. He has worked on audio-visual emotion recognition\, analysis of emotional modulation in gestures and speech\, designing realistic human-li ke virtual characters\, and detection of driver distractions. He is a reci pient of an NSF CAREER Award. In 2014\, he received the ICMI Ten-Year Tech nical Impact Award. In 2015\, his student received the third prize IEEE IT SS Best Dissertation Award (N. Li). He also received the Hewlett Packard B est Paper Award at the IEEE ICME 2011 (with J. Jain)\, and the Best Paper Award at the AAAC ACII 2017 (with Yannakakis and Cowie). He received the B est of IEEE Transactions on Affective Computing Paper Collection in 2021 ( with R. Lotfian) and the Best Paper Award from IEEE Transactions on Affect ive Computing in 2022 (with Yannakakis and Cowie). He received the ACM ICM I Community Service Award in 2023. In 2023\, he received the Distinguished Alumni Award in the Mid-Career/Academia category by the Signal and Image Processing Institute (SIPI) at the University of Southern California. He i s currently serving as an associate editor of the IEEE Transactions on Aff ective Computing. He is an IEEE Fellow. He is a member of the ISCA\, and A AAC and a senior member of ACM.
DTSTART;TZID=America/New_York:20231117T120000 DTEND;TZID=America/New_York:20231117T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Carlos Busso (University of Texas at Dallas) “Multimodal Machine Le arning for Human-Centric Tasks” URL:https://www.clsp.jhu.edu/events/carl-busso-university-of-texas-at-dalla s-multimodal-machine-learning-for-human-centric-tasks/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Busso\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24165@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20231127T120000 DTEND;TZID=America/New_York:20231127T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Aleem Khan URL:https://www.clsp.jhu.edu/events/student-seminar-aleem-khan/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Khan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24167@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nPre-trained speech represe ntation models have become ubiquitous in speech processing over the past f ew years. They have both improved the state of the art and made it feasib le to learn task-specific models with very little labeled data. However\, it is not well understood what linguistic information is encoded in pre-t rained models and how best to apply them to downstream tasks. In this talk I will describe recent work that begins to build an understanding of the layer-wise information learned by pre-trained speech models. We consider a number of popular pre-trained models and investigate the extent to which their layers encode spectral\, phonetic\, and word-level information. Th e results of these analyses also suggest some ways to improve or simplify the application of pre-trained models for downstream tasks. Finally\, I w ill describe our efforts to benchmark model performance on a variety of sp oken language understanding tasks\, in order to broaden our understanding of the capabilities of state-of-the-art models.
\nThis talk is based in part on work presented in
\nA. Pasad et al.\, “Comparative layer-wise analysis of self-supervis ed speech models\,”ICASSP 2023.
\nA. Pasad et al.\, “What do self-supervised speech models know about words?\,” arXiv:2307.00162\, 2023.
\nS. Shon et al.\, “SLUE Phase-2: A Ben chmark Suite of Diverse Spoken Language Understanding Tasks\,” ACL 202 3.
\nBio
\nKaren Livescu is a Professor at TT I-Chicago. She completed her PhD at MIT in 2005. She is an ISCA Fellow and a recent IEEE Distinguished Lecturer. She has served as a program chair/ co-chair for ICLR\, Interspeech\, and ASRU\, and is an Associate Editor fo r TACL and IEEE T-PAMI. Her group’s work spans a variety of topics in spo ken\, written\, and signed language processing.
DTSTART;TZID=America/New_York:20231201T120000 DTEND;TZID=America/New_York:20231201T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Karen Livescu (Toyota Technological Institute at Chicago) “What Do Pre-Trained Speech Representation Models Know? Layer-Wise Analysis and Ben chmarking” URL:https://www.clsp.jhu.edu/events/karen-livescu-toyota-technological-inst itute-at-chicago/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,December\,Livescu END:VEVENT BEGIN:VEVENT UID:ai1ec-24169@www.clsp.jhu.edu DTSTAMP:20240329T131248Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nFoundation models\, includ ing Chat-GPT and its many variants\, have come into prominence in the natu ral language processing (NLP) community thanks the ubiquity of text data r eadily available on the internet and the design of modern transformer arch itectures that can effectively learn from such data. However\, the develop ment of a foundation model for sequential decision-making (e.g.\, reinforc ement learning\, planning) is faced with additional challenges not present in NLP. In this talk\, we discuss some of these challenges with the hope of informing future investments that funding agencies and the academic com munity should engage in. The problem of transfer learning in the context o f sequential decision-making is also discussed and constitutes one of the challenges that foundation models must address.
\nBio
\nAlvaro Velasquez a program manager at the D efense Advanced Research Projects Agency (DARPA)\, where he currently lead s programs on neuro-symbolic AI. Before that\, Alvaro oversaw the machine intelligence portfolio for the Information Directorate of the Air Force Re search Laboratory (AFRL). Alvaro is a recipient of the distinguished paper award from AAAI and best paper and patent awards from AFRL\, the National Science Foundation Graduate Research Fellowship. He has authored over 70 papers and two patents and serves as Associate Editor of the IEEE Transact ions on Artificial Intelligence.
DTSTART;TZID=America/New_York:20231204T120000 DTEND;TZID=America/New_York:20231204T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Alvaro Velasquez (DARPA) “Foundation Models and the Transfer of Emb odied Autonomy” URL:https://www.clsp.jhu.edu/events/alvaro-velasquez/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,December\,Velasquez END:VEVENT END:VCALENDAR