It is sometimes hard to see how AI technology benefits society, but applications like drug discovery really bring the power home. Sriram Chandrasekaran, Assistant Professor of Biochemical Engineering at the University of Michigan, is using machine learning to assess the properties of drug candidates to fight antibiotic-resistant bacteria. Presented with millions of different potential drugs, machine learning can identify the few most useful to be tested clinically. Because it tries everything and anything without preconceived biases, ML can uncover novel combinations that researchers might never notice. We also discuss specifics of the AI environment, including the preference for random forests to deep learning, privacy concerns, bias in datasets, and the interplay between domain expertise and data science.
Three Questions
- From Stephen Foskett: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- From Chris Grundemann: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- From Zach DeMeyer, Gestalt IT: What’s the most innovative use of AI you’ve seen in the real world?
Guest
Sriram Chandrasekaran, Assistant Professor of Biomedical Engineering at University of Michigan. Connect with Sriram on LinkedIn or on Twitter at @sriram_lab . You can also email Sriram at [email protected].
Hosts
- Chris Grundemann, Gigaom Analyst and Managing Director of Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com and on Twitter at @ChrisGrundemann.
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.
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