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.
- Stephen’s Question: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?
- Chris’s Question: How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?
- Zach DeMeyer, Gestalt IT: What’s the most innovative use of AI you’ve seen in the real world
Guests and Hosts
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 firstname.lastname@example.org .