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-20120@www.clsp.jhu.edu DTSTAMP:20240328T132235Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRobotics@Google’s mission is to make robots useful in the real world through machine learning. We are excited about a new model for robotics\, designed for generalization across diverse environments an d instructions. This model is focused on scalable data-driven learning\, w hich is task-agnostic\, leverages simulation\, learns from past experience \, and can be quickly adapted to work in the real-world through limited in teractions. In this talk\, we’ll share some of our recent work in this dir ection in both manipulation and locomotion applications.\nBiography\nCarol ina Parada is a Senior Engineering Manager at Google Robotics. She leads t he robot-mobility group\, which focuses on improving robot motion planning \, navigation\, and locomotion\, using reinforcement learning. Prior to th at\, she led the camera perception team for self-driving cars at Nvidia fo r 2 years. She was also a lead with Speech @ Google for 7 years\, where sh e drove multiple research and engineering efforts that enabled Ok Google\, the Google Assistant\, and Voice-Search. Carolina grew up in Venezuela an d moved to the US to pursue a B.S. and M.S. degree in Electrical Engineeri ng at University of Washington and her Phd at Johns Hopkins University at the Center for Language and Speech Processing (CLSP). DTSTART;TZID=America/New_York:20210423T120000 DTEND;TZID=America/New_York:20210423T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Carolina Parada (Google AI) “State of Robotics @ Google” URL:https://www.clsp.jhu.edu/events/carolina-parada-google-ai/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nRobotics@Google’s mission is to make robots useful i n the real world through machine learning. We are excited about a new mode l for robotics\, designed for generalization across diverse environments a nd instructions. This model is focused on scalable data-driven learning\, which is task-agnostic\, leverages simulation\, learns from past experienc e\, and can be quickly adapted to work in the real-world through limited i nteractions. In this talk\, we’ll share some of our recent work in this di rection in both manipulation and locomotion applications.
\n< strong>Biography
\nCarolina Parad a is a Senior Engineering Manager at Google Robotics. She leads the robot-mobility group\, which focuses on improving robot motion planning\, navigation\, and locomotion\, using reinforcement learning. Prior to that \, she led the camera perception team for self-driving cars at Nvidia for 2 years. She was also a lead with Speech @ Google for 7 years\, where she drove multiple research and engineering efforts that enabled Ok Google\, t he Google Assistant\, and Voice-Search. Carolina< /span> grew up in Venezuela and moved to the US to pursue a B.S. and M.S. degree in Electrical Engineering at University of Washington and her Phd a t Johns Hopkins University at the Center for Language and Speech Processin g (CLSP).
\n X-TAGS;LANGUAGE=en-US:2021\,April\,Parada END:VEVENT BEGIN:VEVENT UID:ai1ec-23306@www.clsp.jhu.edu DTSTAMP:20240328T132235Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile large language models have advanced the state-o f-the-art in natural language processing\, these models are trained on lar ge-scale datasets\, which may include harmful information. Studies have sh own that as a result\, the models exhibit social biases and generate misin formation after training. In this talk\, I will discuss my work on analyzi ng and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I will first describe my researc h in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part investigates the trus tworthiness of models through the memorization and subsequent generation o f conspiracy theories. I will end my talk with recent work in AI safety re garding text that may lead to physical harm.\nBiography\nSharon is a 5th-y ear Ph.D. candidate at the University of California\, Santa Barbara\, wher e she is advised by Professor William Wang. Her research interests lie in natural language processing\, with a focus on Responsible AI. Sharon’s res earch spans the subareas of fairness\, trustworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LREC. She has spent summers inter ning at AWS\, Meta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nWhile large language models have advanced the state-o f-the-art in natural language processing\, these models are trained on lar ge-scale datasets\, which may include harmful information. Studies have sh own that as a result\, the models exhibit social biases and generate misin formation after training. In this talk\, I will discuss my work on analyzi ng and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I will first describe my researc h in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part investigates the trus tworthiness of models through the memorization and subsequent generation o f conspiracy theories. I will end my talk with recent work in AI safety re garding text that may lead to physical harm.
\nBiography
\nSharon is a 5th-year Ph.D. candidate at the University of Ca lifornia\, 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\, trus tworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LR EC. She has spent summers interning at AWS\, Meta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI.
\n X-TAGS;LANGUAGE=en-US:2023\,February\,Levy END:VEVENT END:VCALENDAR