16-10-2023: Auckland, New Zealand. Blog post by #davidlimnz @davidlim
How Does the Military Use AI and Machine Learning?
AI empowers defense organizations with capabilities to support the warfighter at the tactical edge, simplifying data collection and analysis while transforming logistics. Here are a few examples.
Edge-ready model deployment
Forces require data access to enable their AI workflows despite closed or disconnected networks. Edge nodes need the ability to ingest data and send pared-down outputs to environments with more compute resources to improve models. To meet these challenges, Booz Allen rapidly deploys tactical AI solutions using leading technologies to address new and evolving mission sets.
Synthetic data generation
Intelligence teams face major collection obstacles in anti-access/area-denial environments. In the digital battlespace of the future, synthetic data-generation techniques will help paint the picture of unknown operating environments, generate data that trains algorithmic models to analyze the operational picture, and enable improved machine learning models.
Predictive maintenance and joint logistics
Many organizations currently rely on time-consuming, error-prone legacy processes for analyzing and forecasting near- and long-term supply chain needs. AI can help ensure equipment availability and readiness with automated data acquisition, preparation, and analysis that free resources to focus on strategic actions while improving accuracy, cost-effectiveness, and decision making.
See the full article here:
https://www.boozallen.com/markets/defense/ai-for-military.html
How Does the Military Use AI and Machine Learning?
AI empowers defense organizations with capabilities to support the warfighter at the tactical edge, simplifying data collection and analysis while transforming logistics. Here are a few examples.
Performance at the tactical edge
To deploy AI in a hub-and-spoke network, models need to integrate existing hardware and support low size, weight, and power scenarios. By training algorithms on small-form factors that mimic the existing sensor fleet, organizations can reduce inference latency in tactical environments. Booz Allen leverages partnerships with graphics processing unit manufacturers such as NVIDIA to optimize platforms while pushing models forward through our Machine Learning operations (MLOps) framework.
Edge-ready model deployment
Forces require data access to enable their AI workflows despite closed or disconnected networks. Edge nodes need the ability to ingest data and send pared-down outputs to environments with more compute resources to improve models. To meet these challenges, Booz Allen rapidly deploys tactical AI solutions using leading technologies to address new and evolving mission sets.
Synthetic data generation
Intelligence teams face major collection obstacles in anti-access/area-denial environments. In the digital battlespace of the future, synthetic data-generation techniques will help paint the picture of unknown operating environments, generate data that trains algorithmic models to analyze the operational picture, and enable improved machine learning models.
Predictive maintenance and joint logistics
Many organizations currently rely on time-consuming, error-prone legacy processes for analyzing and forecasting near- and long-term supply chain needs. AI can help ensure equipment availability and readiness with automated data acquisition, preparation, and analysis that free resources to focus on strategic actions while improving accuracy, cost-effectiveness, and decision making.
See the full article here:
https://www.boozallen.com/markets/defense/ai-for-military.html
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