AI Accelerator projects are developing and prototyping challenges that engage the public to advance AI.
A challenge is a means to engage academia, industry, and the public towards solving a common problem. In the context of Machine Learning, challenges have commonly involved open competitions organized around a publicly released data set. The team or individual developing the best Machine Learning model to learn from or analyze that data set wins! The world-wide collaboration resulting from such challenges has historically driven fundamental advances in many fields. In some communities, these challenges are also called “Hackathons.” Challenges such as the Datathon (DoD challenge) and the MagNav (public challenge) at JuliaCon play an important role in advancing diverse fields of AI research. A major goal of each AI Accelerator project is to develop and prototype challenges. AI Accelerator challenges are coordinated by Dr. Vijay Gadepally.
List of AI Accelerator Challenges
Pizza Simultaneous Localization and Mapping Challenge (NEW)
- Project: Transferring Multi-Robot Learning through Virtual & Augmented Reality for Rapid Disaster Response
- Learn more about this challenge here
MultiEarth 2022 Challenge (NEW)
- Project: Multimodal Vision for Synthetic Aperture Radar
- Learn more about this challenge on The Amazon Rainforest Challenge and Linkedin
Magnetic Navigation Challenge
- Project: Robust Neural Differential Models for Navigation and Beyond
- Learn more about this challenge on MAGNAV.MIT.EDU
SEVIR: Weather Challenge
Maneuver ID Challenge
CogPilot Data Challenge
- Project: Objective Performance Prediction & Optimization Using Physiological and Cognitive Metrics
- Learn more about this challenge on https://pilotperformance.mit.edu/
MORE ON NEW CHALLENGES COMING SOON!
- Please CONTACT US for more information
- Please review the supplemental resource page for additional information on AI challenges and their development.