Welcome to the CogPilot Data Challenge 2.0!
We are part of the DAF-MIT AI Accelerator.

The CogPilot Data Challenge 2.0, hosted by the Department of the Air Force - MIT Artificial Intelligence (AI) Accelerator, strives to leverage innovative AI research from academia, government, and industry to optimize pilot training.

The CogPilot Data Challenge 2.0 seeks to explore how quantitative performance measurements and multimodal physiological data may support individualized and more accurate assessment of a student pilot’s competency than current subjective, coarse measures.

The Challenge is open to participants from the DoD, academia, and industry. We will be hosting a multi-month event during which participants will have the opportunity to explore solutions to any or all data challenge tasks. Please see the Challenge Description page for details.

Updates will be posted to this website. Please check back regularly for the latest information.

Schedule
Launch - Fall 2022
Sign Up Deadline - Registration will be open through January
Evaluation Dataset Posted - no later than Febuary 10th, 2023
Notify CogPilot Team of Intended Submission - Febuary 21st, 2023
Submission Due Date - February 28th, 2023
Team Results Posted - March 7th, 2023
Closing Ceremony - Late March 2023
 
 

Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the MIT - Unspecified under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the MIT - Unspecified.