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SUMMARY:CIS Seminar: "Building Planetary-Scale Collaborative Intelligence"
DESCRIPTION:Today\, access to high-quality data has become the key bottleneck to deploying Machine Learning (ML). Often\, data that is most valuable is locked away in inaccessible silos due to unfavorable incentives and ethical-legal restrictions. This is starkly evident in healthcare\, where such barriers have led to highly biased and underperforming tools. \nIn my talk\, we will dive into my collaborations with public health organizations facing such issues\, and see how collaborative systems (such as federated learning) prove a natural solution. Collaborative learning can remove barriers to data sharing by respecting the privacy and interests of the data providers. Yet\, for these systems to truly succeed\, we must confront three fundamental challenges. These systems need to i) be efficient and scale to large networks\, ii) provide reliable and trustworthy training and predictions\, and iii) manage the divergent goals and interests of the participants. We discuss how tools from optimization\, statistics\, and economics can be leveraged to address these challenges.
URL:https://seasevents.nmsdev7.com/event/10961/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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