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Collaboration, Competition, Crowdsourcing: Incentivizing open source AI for...

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About the Speaker:

Peter is a co-founder at DrivenData, whose mission is to bring the power of data science to the social sector. DrivenData builds software that uses data and artificial intelligence for non-profits, NGOs, and governments. DrivenData also engages a global community of data scientists is online competitions that leverage data for the greater good. Recently he has worked on projects in lung cancer prediction, anti-human-trafficking, crop yield modeling, and digital financial services for rural populations. He earned his master's in Computational Science and Engineering from Harvard. Previously he worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.

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We’re at a turning point where state of the art AI can begin to make a difference in people’s lives. There is so much opportunity, but also a lot of uncertainty about how to start having an impact quickly, efficiently, and safely. This webinar will take a perspective on the right approach using a case study of our initiative, Concept to Clinic.

Right now, there is an enormous gap between algorithms that produce highly accurate results on a validation dataset and practical, deployed machine learning systems. Bridging this gap requires the skills of expert data scientists, software engineers, and designers—not just a researcher with a great idea. We’ve created a new model for crowdsourced data science challenges that drives collaboration—rather than just competition—between these experts. We’ve put this model to work in a project called Concept to Clinic, which brings cutting-edge deep learning algorithms to radiologists. Given that the lung cancer survival rate for early detection is 55% instead of a mere 4%, there is an urgent need for AI that helps clinicians now. We have galvanized a community of experts through incentives and gamification to transform the algorithms that won the 2017 Data Science Bowl into clinic-ready software. This new model of crowdsourcing transforms theory into practice, resulting in open-source AI that is consumable by hospitals, research groups, and healthcare IT providers—not just highly trained data scientists.

In addition to using this project as a case study, we’ll use it as a jumping off point to explore the overarching context for the project. How can non-profits use data more effectively? What are the barriers in the sector? How should organizations that want to engage with the open source software community proceed? And, importantly, what are the emerging opportunities for social impact?

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