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Mizuho India Japan Study Centre to host its next ‘Meijin-Samvad’ webinar on 11th August

The session is titled ‘Don’t We Have ChatGPT? Problems and Challenges in Machine Learning and Robotics’ 

7 August, 2023, Bengaluru: The Mizuho India Japan Study Centre (MIJSC) at IIM Bangalore will host the next webinar of its panel presentation series: ‘Meijin-Samvad’, that is, ‘Expert-Conversation’, on 11th August 2023, from 3.30 pm to 5.00 pm. The webinar is titled, ‘Don’t We Have ChatGPT? Problems and Challenges in Machine Learning and Robotics’. The session will be held via Zoom.

The panel discussion unravelling the data maze challenges in Machine Learning and Robotics will feature Dr. Chintan Amrit, Associate Professor, Department of Business Analytics, University of Amsterdam, and Dr. Floris Erich, Researcher, National Institute of Advanced Industrial Science and Technology (AIST), Japan. The moderator of the discussion would be Saideep Rathnam, Chief Operating Officer, Mizuho India Japan Study Centre, IIMB.

The registration link for the webinar is: bit.ly/43WCXdB

The webinar will be live streamed on IIMB's YouTube page. The Live YouTube Streaming Link is: https://youtube.com/live/nnr810kYG9Q?feature=share

The ‘Meijin-Samvad’ discussion series involves a select group of panelists from India and Japan, who share different perspectives on a particular topic, with a larger audience.

Abstract

Most data-driven machine learning models used by corporate businesses to solve their practical problems are not based on large data. The available data is often small, sparse, noisy and incomplete. This poses a challenge to developers of predictive (machine learning) models who would like to train such models to perform effectively.

In this talk, the panelists will discuss the differences between the most practical Machine Learning (ML) models (where the data is small, sparse and noisy) and models that can utilize deep neural networks (where larger datasets are available). The panelists will also explain how one can gain a deeper understanding of the nature of the 'problems and perils' of data. They will also explain possible ways of handling data problems in ML. One of the approaches that could be used to address this problem is learning from one domain and applying the insights in a different but similar domain. Further, cross-domain learning is essential for evaluating and enhancing the performance of a predictive model in a particular domain. The panelists will also discuss robotics and explain how Generative Adversarial Networks (GANs) can be used to generate appropriate data. 

For more information, please contact: mijsc@iimb.ac.in

About the speakers

Prof. Chintan Amrit completed his PhD from the University of Twente in the area of Coordination in Software Development. He holds a Master’s degree in Computer Science from the Indian Institute of Science, Bangalore. His research interests are in the areas of business intelligence (using machine learning), open-source development and mining software repositories and applying analytics in projects that focus on the UN’s sustainable development goals. He has published over 70 research articles and serves as a department editor of IEEE Transactions on Engineering Management, coordinating editor of Information Systems Frontiers, an associate editor of PeerJ Computer Science, and is a regular track chair at the European Conference on Information Systems. 

Dr. Floris Erich is a permanent researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. His research is focused around bridging the gap between the virtual world and the physical world, by developing tools and techniques that enable people to easily model real-world conditions and use these to verify the correct behavior of robot systems. Floris has contributed as a researcher to various projects sponsored by the New Energy and Industrial Technology Development Organization (NEDO) and Japan Science and Technology Agency (JST). He got his PhD in Human Informatics from the University of Tsukuba.

 

Mizuho India Japan Study Centre to host its next ‘Meijin-Samvad’ webinar on 11th August

The session is titled ‘Don’t We Have ChatGPT? Problems and Challenges in Machine Learning and Robotics’ 

7 August, 2023, Bengaluru: The Mizuho India Japan Study Centre (MIJSC) at IIM Bangalore will host the next webinar of its panel presentation series: ‘Meijin-Samvad’, that is, ‘Expert-Conversation’, on 11th August 2023, from 3.30 pm to 5.00 pm. The webinar is titled, ‘Don’t We Have ChatGPT? Problems and Challenges in Machine Learning and Robotics’. The session will be held via Zoom.

The panel discussion unravelling the data maze challenges in Machine Learning and Robotics will feature Dr. Chintan Amrit, Associate Professor, Department of Business Analytics, University of Amsterdam, and Dr. Floris Erich, Researcher, National Institute of Advanced Industrial Science and Technology (AIST), Japan. The moderator of the discussion would be Saideep Rathnam, Chief Operating Officer, Mizuho India Japan Study Centre, IIMB.

The registration link for the webinar is: bit.ly/43WCXdB

The webinar will be live streamed on IIMB's YouTube page. The Live YouTube Streaming Link is: https://youtube.com/live/nnr810kYG9Q?feature=share

The ‘Meijin-Samvad’ discussion series involves a select group of panelists from India and Japan, who share different perspectives on a particular topic, with a larger audience.

Abstract

Most data-driven machine learning models used by corporate businesses to solve their practical problems are not based on large data. The available data is often small, sparse, noisy and incomplete. This poses a challenge to developers of predictive (machine learning) models who would like to train such models to perform effectively.

In this talk, the panelists will discuss the differences between the most practical Machine Learning (ML) models (where the data is small, sparse and noisy) and models that can utilize deep neural networks (where larger datasets are available). The panelists will also explain how one can gain a deeper understanding of the nature of the 'problems and perils' of data. They will also explain possible ways of handling data problems in ML. One of the approaches that could be used to address this problem is learning from one domain and applying the insights in a different but similar domain. Further, cross-domain learning is essential for evaluating and enhancing the performance of a predictive model in a particular domain. The panelists will also discuss robotics and explain how Generative Adversarial Networks (GANs) can be used to generate appropriate data. 

For more information, please contact: mijsc@iimb.ac.in

About the speakers

Prof. Chintan Amrit completed his PhD from the University of Twente in the area of Coordination in Software Development. He holds a Master’s degree in Computer Science from the Indian Institute of Science, Bangalore. His research interests are in the areas of business intelligence (using machine learning), open-source development and mining software repositories and applying analytics in projects that focus on the UN’s sustainable development goals. He has published over 70 research articles and serves as a department editor of IEEE Transactions on Engineering Management, coordinating editor of Information Systems Frontiers, an associate editor of PeerJ Computer Science, and is a regular track chair at the European Conference on Information Systems. 

Dr. Floris Erich is a permanent researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. His research is focused around bridging the gap between the virtual world and the physical world, by developing tools and techniques that enable people to easily model real-world conditions and use these to verify the correct behavior of robot systems. Floris has contributed as a researcher to various projects sponsored by the New Energy and Industrial Technology Development Organization (NEDO) and Japan Science and Technology Agency (JST). He got his PhD in Human Informatics from the University of Tsukuba.