IIT Gandhinagar, IIT Kharagpur hold 3rd edition of Indian symposium on machine learning

NEW DELHI: Indian Institute of Technology Gandhinagar (IITGN) in collaboration with IIT Kharagpur concluded the third edition of Indian symposium on machine learning (IndoML) 2022 on December 17, 2022. IndoML saw 300 participants from 49 institutions across India.

The event brings researchers and practitioners from diverse domains to share their knowledge and experience on the significance of the intersection of Artificial Intelligence (AI) and Machine Learning (ML) with other fields of research.

The symposium included 17 talks, tutorials, and poster presentations by eminent speakers, researchers, and industry personnel from across the globe, including Georgia Tech, Rice University, Northeastern University, UMass Amherst, University of Michigan, the University of Texas at Dallas, NIST, IISc Bangalore, IIT Kharagpur, IIT Bombay, IIT Jodhpur, Google, Amazon, MerlynMind, Accenture, Hewlett Packard, among many others.

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The three day symposium from December 15 to 17 covered various topics challenges, and promising solutions from the field of AI and ML, such as using ML techniques for predicting chemical properties of crystalline materials, modelling climate change, robotics, the intersection of ML and economics, sustainable solutions to the growing problem of AI carbon emissions, etc.

In addition to this, IndoML 2022 hosted a Datathon for students as well as early career professionals. The event received over600 submissions from 107 teams. The top selected teams were invited to IndoML 2022 to present their work before the expert panel of ML researchers and practitioners.

Speaking about “Human-Centered Artificial Intelligence – A Crowd Computing Perspective”, Delft University of Technology, Netherlands, assistant professor, Ujjal Gadiraju, said: “Crowd Computing offers a promising means to overcome fundamental challenges in computation and interaction. It can herald a new generation of Human-centered AI systems”.

Another expert speaker, Prof Anshumali Shrivastava, Rice University, associate professor, ThirdAI, founder stressed on the promise of Dynamic Sparsity algorithm and said: “The hurdle in Universal AI is the scale of models. Simple architectures are fine, but we cannot ignore model size, as large models perform better. Dynamic Sparsity is the idea of using different subsets of parameters in the whole network for different inputs. This can allow training of billion parameter models on CPUs significantly faster.”