Artificial intelligence (AI) is the simulation of human intelligence in machines. Machine Learning (ML) is a subfield of AI which enables machines to learn from past data or experiences to make predictions or take some decisions. Can AI and ML be used to find solutions for public health challenges?
The KnowDis Machine Learning Day 2022, a virtual conference on October 14, will strive to answer critical questions related to the use of AI to address health problems and how AI is becoming a necessity for any business to stay relevant in the digital era. The conference is being organised by KnowDis Data Science in collaboration with the Yardi School of AI, IIT Delhi.
The conference will explore the potential of AI/ML in improving human health and life, including industrial applications.
Professor Pushpak Bhattacharya, professor at the Computer Science and Engineering Department of IIT Bombay, and a leading authority on Natural Language Processing (NLP), will talk about the ways in which NLP can be used to enhance mental health solutions. NLP combines computational linguistics with machine learning to enable computers to understand the full meaning of the human language, including intent and sentiment. Using NLP enabled devices, round the clock care and timely interventions can be provided to mental health patients, thus improving their quality of life.
Another topic of discussion will address the seriously high neonatal mortality rate in India. How can neonatal mortality rates be reduced to at least be at par with the global averages? Professor Milind Tambe of Harvard University and Director “AI for Social Good” at Google Research India, with Dr. Aparna Taneja from Google Research India will demonstrate the efficacy of AI tools in delivering maternal and child health programs.
Saurabh Singal, IIT Delhi & Carnegie Mellon alumnus, and Founder of KnowDis, will reveal the research and application potential of AI for developing highly accurate and effective antibodies. Through the use of AI, naturally occurring antibodies that fight specific diseases can be identified, and new antibodies can be designed at much lesser costs than conventional research. The aim is to develop an algorithm that will predict with high throughput, antibodies effective in treating neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). This will result in making drugs more affordable.