Anton Kulaga - Machine learning against biological aging | Øredev 2023
Session description:
In this 40-minute session, participants will learn about the role of AI in biological aging research. The discussion begins with an examination of 'aging clocks', computational models that predict chronological and biological aging, offering insights into perspectives of radical healthspan extension. The session will then detail the application of Explainable AI (XAI) in understanding aging mechanisms. XAI allows for interpreting complex machine learning models, providing a clearer understanding of their predictions. The final part of the session addresses the use of Large Language Models (LLMs) in aging research. LLMs are employed to analyze scientific literature, generate hypotheses, and draft research articles. This session is recommended for individuals interested in the intersection of AI and aging research, providing an overview of how AI tools enhance our understanding of biological aging.
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