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    Center Explores AI/ML Potential in Development of Chemical Biological Defense Solutions [Image 1 of 3]

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    A man in a plaid shirt is working at a desk, taking handwritten notes on a notepad while reviewing what appears to be a data spreadsheet or statistical output on a black laptop. A chalkboard to his left displays a diagram with the words 'IN' and 'MODEL' along with a drawn figure, suggesting a machine learning or data modeling context. Additional handwritten papers are visible in the background.

    ABERDEEN PROVING GROUND, MARYLAND, UNITED STATES

    12.21.2020

    Photo by Jack Bunja 

    U.S. Army Combat Capabilities Development Command Chemical Biological Center

    Patrick Riley, who is leading the Chemical Biological Center's artificial intelligence and machine learning initiative known as STEWARD, coding a software program which uses AI/ML to advance his research.

    IMAGE INFO

    Date Taken: 12.21.2020
    Date Posted: 12.21.2020 12:29
    Photo ID: 6464228
    VIRIN: 201221-A-GY757-749
    Resolution: 1080x720
    Size: 529.03 KB
    Location: ABERDEEN PROVING GROUND, MARYLAND, US
    Web Views: 71
    Downloads: 15

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    This work, Center Explores AI/ML Potential in Development of Chemical Biological Defense Solutions [Image 3 of 3], by Jack Bunja, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.

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    A man in a plaid shirt is working at a desk, taking handwritten notes on a notepad while reviewing what appears to be a data spreadsheet or statistical output on a black laptop. A chalkboard to his left displays a diagram with the words 'IN' and 'MODEL' along with a drawn figure, suggesting a machine learning or data modeling context. Additional handwritten papers are visible in the background.
    The image depicts a Random Forest classification diagram showing four decision trees (Tree #1 through Tree #4) each receiving input from an 'Engineered Data Set'. Each tree produces an individual class prediction (Class B, Class D, Class C, and Class B respectively), which are then combined through a Majority Voting mechanism to produce a 'Final Class' output.
    The image presents a flowchart titled 'CANE Machine-reading evaluation' comparing two methodological approaches: an AI method and a Traditional Method for estimating warfighter performance. Both methods follow parallel workflows ending in a 'Results Comparison' step using bidirectional green arrows. The stated goal is to compare estimates of MOPP level effects on warfighter performance derived via AI versus traditional methods.

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    U.S. Army Lab Explores AI/ML Potential in Development of Chemical Biological Defense Solutions

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    TAGS

    Artificial Intelligence
    Machine Learning
    DEVOM
    Army

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