Risky Space Business: NASA AI Risk Prediction Challenge
Status: Completed
Start Date: 2021-08-09
End Date: 2022-04-06
Description: Develop a Risk Digital Assistant using Machine Learning and/or Artificial Intelligence tools to help GCD program management with efficient, predictive, and informed decision making - Improve risk identification and mitigation efforts (with associated resource planning, ex. threats) for the program?s technology development portfolio - Provide additional capabilities regarding identifying potential risks that may be threats to schedule or financial resources - Support program level risk tools and processes to be used to ensure the proper insight and oversight of projects and facilitate technical and programmatic decisions
Predict project risks using NASA's lessons from the past. Design a project management tool that extracts past project risk information and uses AI/ML to predict risks on future projects. If you're an AI/ML specialist or someone who is great at extracting data from past project reports, this challenge is for you! The Risky Space Business Challenge is looking for White Papers and algorithms to help NASA predict risks on future projects. If you're up to the challenge, you could win a share of the $50,000 purse. To participate in this challenge, you must describe or show how you would: 1. Extract risk data from past project reports; 2. Create a template for future project reporting; 3. Create a taxonomy of risks for future use; and 4. Use AI/ML to identify potential risks in future projects using past project reports. Winning solutions will be used to help NASA, and potentially anyone managing a project, improve their ability to identify risks before they become real issues. Develop a Risk Digital Assistant using Machine Learning and/or Artificial Intelligence tools to help GCD program management with efficient, predictive, and informed decision making - Improve risk identification and mitigation efforts (with associated resource planning, ex. threats) for the program's technology development portfolio - Provide additional capabilities regarding identifying potential risks that may be threats to schedule or financial resources - Support program level risk tools and processes to be used to ensure the proper insight and oversight of projects and facilitate technical and programmatic decisions.
Predict project risks using NASA's lessons from the past. Design a project management tool that extracts past project risk information and uses AI/ML to predict risks on future projects. If you're an AI/ML specialist or someone who is great at extracting data from past project reports, this challenge is for you! The Risky Space Business Challenge is looking for White Papers and algorithms to help NASA predict risks on future projects. If you're up to the challenge, you could win a share of the $50,000 purse. To participate in this challenge, you must describe or show how you would: 1. Extract risk data from past project reports; 2. Create a template for future project reporting; 3. Create a taxonomy of risks for future use; and 4. Use AI/ML to identify potential risks in future projects using past project reports. Winning solutions will be used to help NASA, and potentially anyone managing a project, improve their ability to identify risks before they become real issues. Develop a Risk Digital Assistant using Machine Learning and/or Artificial Intelligence tools to help GCD program management with efficient, predictive, and informed decision making - Improve risk identification and mitigation efforts (with associated resource planning, ex. threats) for the program's technology development portfolio - Provide additional capabilities regarding identifying potential risks that may be threats to schedule or financial resources - Support program level risk tools and processes to be used to ensure the proper insight and oversight of projects and facilitate technical and programmatic decisions.
Benefits:
The challenge seeked solutions in White Papers and algorithms to help NASA predict risks on future projects. Participants had to extract risk data from past project reports, create a template for future project reporting, create a taxonomy of risks for future use, and use AI/ML to identify potential risks in future projects using past project reports. A summary of the submissions is included in the Additional Challenge Asset titled “NOIS2-038 - Additional Challenge Asset 1- Summary of Winning Submissions”.
Significantly Advanced Towards a Solution
Planned for future implementation
Software/App
Lead Organization: Langley Research Center