Researchers now employ artificial intelligence (AI) models based on deep learning to make functional predictions about big datasets. While the concepts behind these networks are well established, their inner workings are often invisible to the user. The emerging area of explainable AI (xAI) provides model interpretation techniques that empower life science researchers to uncover the underlying basis on which AI models make such predictions.
In this month’s episode, Deanna MacNeil from The Scientist spoke with Jim Collins from Massachusetts Institute of Technology to learn how researchers are using explainable AI and artificial neural networks to gain mechanistic insights for large scale antibiotic discovery.
Artificial Neural Networks: Learning by Doing
The Scientist Speaks is a podcast produced by The Scientist’s Creative Services Team. Our podcast is by scientists and for scientists. Once a month, we bring you the stories behind news-worthy molecular biology research. This month's episode is sponsored by LabVantage.
Speaker:
Jim Collins, PhD
Termeer Professor of Medical Engineering and Science
Professor of Biological Engineering
Massachusetts Institute of Technology (MIT)
Broad Institute of MIT and Harvard
The Wyss Institute, Harvard
This podcast is brought to you by LabVantage, serving disease researchers with AI-driven scientific data management solutions that increase discovery and speed time-to-market. Learn more at LabVantage.com/analytics.
