Artificial Intelligence/Machine Learning, AFC, Phase I
Multi-Modal Synthetic Data Corpus to Support Machine Intelligence Development
Objective
1. Synthetically create a multi-modal data corpus that can be used to train Artificial Intelligence/Machine Learning (AI/ML) Algorithms to support multi-Intelligence (multi-INT) data fusion and machine intelligence.
2. Develop a scenario-based tool that enables the Army to create an environment that can develop and test future multi-modal AI/ML capabilities
Description
Multi-Modal data includes text, images, sounds, etc. Having a corpus of synthetic multi-modal data allows the Army to fuse this data together and rapidly generate higher preforming AI/ML algorithms.
Creating an Army owned environment that can develop and test future AI/ML capabilities with a focus on multi-INT data fusion and machine intelligence. This environment should be able use the synthetic data to simulate different scenarios for AL/ML training and validation. Some scenarios may include situations where we need to distribute AI to edge deceives.
Phase I
Conduct research and complete the initial design of the scenario-based tool for testing and developing AI/ML capabilities with a baseline dataset for the multi-modal synthetic data corpus.
Phase II
Creation of the scenario-based prototype tool for testing and developing AI/ML capabilities along with the multi-modal synthetic data corpus that can train high fidelity AI/ML algorithms.
Phase III
Maturing the prototype into a planned operational system which can be demonstrated in the operational environment.
Submission Information
Submit in accordance with DoD SBIR BAA 23.2
References:
- Microsoft https://www.microsoft.com/en-us/ai/ai-lab-synthetic-data-showcase
- TensorFlow https://www.tensorflow.org/tensorboard/
- Penn State University SYNCOIN Data set https://www.eng.buffalo.edu/~nagi/MURI/MURI/PDF%20Presentations/2%20-%20Jake%20-%20Problem%20Domain-SYNCOIN%20Data%20Set.pdf
- https://www.anylogic.com/features/artificial-intelligence/
Objective
1. Synthetically create a multi-modal data corpus that can be used to train Artificial Intelligence/Machine Learning (AI/ML) Algorithms to support multi-Intelligence (multi-INT) data fusion and machine intelligence.
2. Develop a scenario-based tool that enables the Army to create an environment that can develop and test future multi-modal AI/ML capabilities
Description
Multi-Modal data includes text, images, sounds, etc. Having a corpus of synthetic multi-modal data allows the Army to fuse this data together and rapidly generate higher preforming AI/ML algorithms.
Creating an Army owned environment that can develop and test future AI/ML capabilities with a focus on multi-INT data fusion and machine intelligence. This environment should be able use the synthetic data to simulate different scenarios for AL/ML training and validation. Some scenarios may include situations where we need to distribute AI to edge deceives.
Phase I
Conduct research and complete the initial design of the scenario-based tool for testing and developing AI/ML capabilities with a baseline dataset for the multi-modal synthetic data corpus.
Phase II
Creation of the scenario-based prototype tool for testing and developing AI/ML capabilities along with the multi-modal synthetic data corpus that can train high fidelity AI/ML algorithms.
Phase III
Maturing the prototype into a planned operational system which can be demonstrated in the operational environment.
Submission Information
Submit in accordance with DoD SBIR BAA 23.2
References:
- Microsoft https://www.microsoft.com/en-us/ai/ai-lab-synthetic-data-showcase
- TensorFlow https://www.tensorflow.org/tensorboard/
- Penn State University SYNCOIN Data set https://www.eng.buffalo.edu/~nagi/MURI/MURI/PDF%20Presentations/2%20-%20Jake%20-%20Problem%20Domain-SYNCOIN%20Data%20Set.pdf
- https://www.anylogic.com/features/artificial-intelligence/