DAF-MIT AI Accelerator to commence CogPilot Data Challenge 2.0

This week, the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator will commence the CogPilot Data Challenge 2.0. The challenge invites participants to explore AI-based solutions linking a pilot’s physiology, such as heart rate, eye tracking, and muscle activity, to their behavior and performance of flying tasks varying in difficulty. The AI Accelerator welcomes all participants across the Department of Defense, academia, and industry, and aims to accelerate innovation by engaging the broader AI community in attacking tough DoD technology needs.

Since its inception in 2019, the AIA has released more than 10 challenges from its various research projects. “These challenges have turned into some of our greatest successes at the AI Accelerator,” said Maj. Kyle “Gouge” McAlpin, Performance Prediction and Optimization project liaison. “They have surfaced and fostered organic machine learning talent across the Air and Space Forces, built vibrant communities of cross-disciplinary researchers and operators pushing the state-of-the-art, invited the public to join in solving some of our hardest problems, and given back to the machine learning community by funding and releasing large, machine learning-ready public datasets. The machine learning community has found time and time again that fundamental advances in ML start with strong competition on large, unique, and public datasets.”

The CogPilot Data Challenge 2.0, hosted by AIA’s Performance Prediction and Optimization research team, consists of two tasks. First, participants are challenged to develop a model that predicts the difficulty level of an aircraft landing performed in virtual reality based only on pilot physiology. For the second task, participants predict how well the pilot performed each approach and landing task using only pilot physiology.

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