Data, Predictions, and Decisions in Support of People and Society

author: Eric Horvitz, Microsoft Research
published: Oct. 7, 2014,   recorded: August 2014,   views: 701
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Description

Deep societal benefits will spring from advances in data availability and in computational procedures for mining insights and inferences from large data sets. I will describe efforts to harness data for making predictions and guiding decisions, touching on work in transportation, healthcare, online services, and interactive systems. I will start with efforts to learn and field predictive models that forecast flows of traffic in greater city regions. Moving from the ground to the air, I will discuss fusing data from aircraft to make inferences about atmospheric conditions and using these results to enhance air transport. I will then focus on experiences with building and fielding predictive models in clinical medicine. I will show how inferences about outcomes and interventions can provide insights and guide decision making. Moving beyond data captured by hospitals, I will discuss the promise of transforming anonymized behavioral data drawn from web services into large-scale sensor networks for public health, including efforts to identify adverse effects of medications and to understand illness in populations. I will conclude by describing how we can use machine learning to leverage the complementarity of human and machine intellect to solve challenging problems in science and society.

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Download slides icon Download slides: kdd2014_horvitz_people_society_01.pdf (9.9┬áMB)


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