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Advancing AI Research to Help Policymakers Affordably Improve Life’s Starts and Finishes

Fei-Fei Li, Arnold Milstein 2016 - 2017

Advancing AI Research to Help Policymakers Affordably Improve Life’s Starts and Finishes

Understanding grows about childhood experiences occurring primarily in lower and middle class homes that limit fulfillment of children’s’ developmental potential. Simultaneously nations and US state governors face rising demand for costly institutional care that many seniors’ dread. In the United States, the cost of long-term care would more than double from 1.3% of US GDP in 2010 to 3% of US GDP in 2050 if the rate of functional limitations among those age 65 and older remains constant (Congressional Budget Office, 2013). These two trends confront policy-makers with painful fiscal trade-offs. The prior watershed decade was the first in which Medicaid funding demands fueled by institutional spending for seniors’ care exceeded state funds available to fund children’s’ education. Rapid advances in the capability and affordability of in-home AI systems may enable policymakers to more affordably and effectively serve these two vulnerable populations during life’s starts and finishes. Effective, scalable uses of prior generations of cyber systems have improved value-for-money in other service sectors such as airlines and banking. However, use of modern AI capabilities to improve the value of more intimate interpersonal human services is fraught with hope and fear for seniors, families, health professionals, educators and policymakers seeking to serve them cost-effectively. Both emotions are well-founded. Stanford faculty, fellows, and students from its schools of engineering and medicine seek to formulate and test psychologically nuanced applications of AI in order to increase policy-makers' and industry's understanding of how modern AI systems can more affordably and effectively (1) enable care planners to select in-home care plans that will generate the largest gains in seniors’ self-care capabilities; and (2) boost non- affluent parents’ contribution to the physical, mental and social development of children.

Publications:
Li, Fei-Fei; Milstein, A., et al. Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance. Machine Learning in Health Care Conference (MLHC 2017). https://arxiv.org/abs/1708.00163

Researchers

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Fei-Fei Li

Associate Professor, Computer Science
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Fei-Fei Li

Associate Professor, Computer Science
Director, Stanford Artificial Intelligence Lab and Director, Stanford Vision Lab
I am an Associate Professor at the Computer Science Department at Stanford University. I received my Ph.D. degree from California Institute of Technology, and a B.S. in Physics from Princeton University. I am currently the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab, where I work with the most brilliant students and colleagues worldwide to build smart algorithms that enable computers and robots to see and think, as well as to conduct cognitive and neuroimaging experiments to discover how brains see and think.
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Arnold Milstein

Professor, Medicine
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Arnold Milstein

Professor, Medicine
Director, Stanford's Clinical Excellence Research Center
Dr. Milstein is a Professor of Medicine and directs Stanford's Clinical Excellence Research Center. The Center designs and demonstrates in diverse locations scalable health care delivery innovations that provide more with less. Before joining Stanford's faculty, he created a national healthcare performance improvement firm and co-founded three nationally influential public benefit initiatives, served as a Congressional MedPAC Commissioner, and was elected to the Institute of Medicine (IOM).