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Partha Talukdar - Episode 52

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内容由Association for Computing Machinery (ACM)提供。所有播客内容(包括剧集、图形和播客描述)均由 Association for Computing Machinery (ACM) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this episode of ACM ByteCast, Bruke Kifle hosts Partha Talukdar, Senior Staff Research Scientist at Google Research India, where he leads a group focused on natural language processing (NLP), and an Associate Professor at the Indian Institute of Science (IISc) Bangalore. Partha was previously a postdoctoral fellow at Carnegie Mellon University’s Machine Learning Department and received his PhD in computer information science from the University of Pennsylvania. He is broadly interested in natural language processing, machine learning, and making language technologies more inclusive. Partha is a co-author of a book on graphs-based learning and the recipient of several awards, including the ACM India Early Career Researcher Award for combining deep scholarship of NLP, graphical knowledge representation, and machine learning to solve long-standing problems. He is also the founder of Kenome, an enterprise knowledge graph company with the mission to help enterprises make sense of big dark data.

Partha shares how exposure to language processing drew him to languages with limited resources and NLP. He and Bruke discuss the role of language in machine learning and whether current AI systems are merely memorizing and reproducing data or are actually capable of understanding. He also talks about his recent focus on inclusive and equitable language technology development through multilingual-multimodal Large Language Modeling, including Project Bindi. They discuss current limitations in machine learning in a world with more than 7,000 languages, as well as data scarcity and how knowledge graphs can mitigate this issue. Partha also shares his insights on balancing his time and priorities between industry and academia, recent breakthroughs that were impactful, and what he sees as key future achievements for language inclusion.

  continue reading

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Partha Talukdar - Episode 52

ACM ByteCast

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Manage episode 414185716 series 2667187
内容由Association for Computing Machinery (ACM)提供。所有播客内容(包括剧集、图形和播客描述)均由 Association for Computing Machinery (ACM) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this episode of ACM ByteCast, Bruke Kifle hosts Partha Talukdar, Senior Staff Research Scientist at Google Research India, where he leads a group focused on natural language processing (NLP), and an Associate Professor at the Indian Institute of Science (IISc) Bangalore. Partha was previously a postdoctoral fellow at Carnegie Mellon University’s Machine Learning Department and received his PhD in computer information science from the University of Pennsylvania. He is broadly interested in natural language processing, machine learning, and making language technologies more inclusive. Partha is a co-author of a book on graphs-based learning and the recipient of several awards, including the ACM India Early Career Researcher Award for combining deep scholarship of NLP, graphical knowledge representation, and machine learning to solve long-standing problems. He is also the founder of Kenome, an enterprise knowledge graph company with the mission to help enterprises make sense of big dark data.

Partha shares how exposure to language processing drew him to languages with limited resources and NLP. He and Bruke discuss the role of language in machine learning and whether current AI systems are merely memorizing and reproducing data or are actually capable of understanding. He also talks about his recent focus on inclusive and equitable language technology development through multilingual-multimodal Large Language Modeling, including Project Bindi. They discuss current limitations in machine learning in a world with more than 7,000 languages, as well as data scarcity and how knowledge graphs can mitigate this issue. Partha also shares his insights on balancing his time and priorities between industry and academia, recent breakthroughs that were impactful, and what he sees as key future achievements for language inclusion.

  continue reading

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