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WTPF 26

Project Resilience

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​Project Resilience was initiated under the Global Initiative on AI and Data Commons to build a public AI utility where a global community of innovators and thought leaders could enhance and utilize a collection of data and AI approaches to help with better preparedness, intervention, and response to environmental, health, information, or economic threats to our communities, and contribute the general efforts towards meeting the Sustainable Development Goals (SDGs)​.
 
Project Resilience begun with predictive methods to help with health interventions to help contain COVID-19 threats, and extended this to a framework for building an AI utility to address other areas. ​The initial framework focused on climate and energy-related SDG targets, such as Target 13.2 “integrate climate change measures into national policies, strategies and planning” (under Goal ​13, Climate Action​), Target 7.2  “increase substantially the share of renewable energy in the global energy mix” (under Goal 7, Affordable and Clean Energy​). 

Project Resilience’s AI utility is a collaborative platform that empowers a global community of innovators and thought leaders to apply AI and data for real-world impact. Built under the Global Initiative on AI and Data Commons, it supports real-world decision-making efforts that advance the UN Sustainable Development Goals:
  • Decision makers: Use insights from data and models to inform impactful decisions
  • Technical builders: Gather data, train models, build UIs, and deploy apps that tackle challenges
  • General public: Explore projects and proposals, suggest policies, and share feedback
  • Moderators: Review and approve submitted proposals and porjects and organize the community

Working Groups

​1. Minimum​ Viable Product (MVP) Working Group

This Working Group was composed of subject matter experts in machine learning modeling, UX development, Architecture and DevOps who created an MVP for the machine learning ensemble model and supporting architecture for one of the SDG topics.

The working group developed a first application on land-use optimization, read more ​from the arXiv paper "Discovering effective policies for land-use planning​"​.​ The paper won the "Best Pathway to Impact" Award at the NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning; see the pre-recorded talk, slides, poster, and a short version of the paper at the workshop site​, and try out the interactive demo​ of the system.​
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MVP Demo​​​  Land Use O​ptimization demo, AI for Good Global Summit Workshop, 5 July 2023​

​​MVP Working Group Lead​​​  Baba​k Hodjat​​, Cognizant, ​United States

​2. Data Working Group

This Working Group ​was composed of subject matter experts in data science, data sharing, and data standardization who provided guidelines for data contributions towards solutions that project resilience will create, and develop specifications and conducted case studies for data sharing in a standardized way with interoperable interfaces.

Data Working Group Lead​​​  Gyu Myoung Lee, Liverpool John Moores University, United Kingdom 

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​Get started with a short tutorial that shows how to navigate the platform, browse projects, contribute models, and collaborate with the community. 
Research behind the platform:
Bi-weekly meetings were also held to advance the work

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