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Artificial Intelligence in the Military. Presented by Carson English, Jason Lukis, Nathan Morse and Nathan Swanson. Overview. History Neural Networks Automated Target Discrimination Tomahawk Missile Navigation Ethical issues. History. 1918 – first tests on guided missiles

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Artificial intelligence in the military l.jpg

Artificial Intelligence in the Military

Presented by

Carson English, Jason Lukis,

Nathan Morse and Nathan Swanson


Overview l.jpg
Overview

  • History

  • Neural Networks

  • Automated Target Discrimination

  • Tomahawk Missile Navigation

  • Ethical issues


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History

  • 1918 – first tests on guided missiles

  • 1945 – Germany makes first ballistic missile

  • 1950 – AIM-7 Sparrow

    • “fire-and-forget


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History

  • 1973 – remotely piloted vehicles (RPVs)

    • Used to confuse enemy air defenses

  • 1983 – tomahawk missile first used by navy

    • Uses terrain contour matching system

  • 1983 – Reagan make his famous star wars speech

  • 1988 – U.S.S. Vincennes mistakenly destroys Iranian airbus due to autonomous friend/foe radar system


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History

  • 1991 – Smart bombs used in Gulf War to selectively destroy enemy targets

    • Praised for its precision and effectiveness


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Neural Networks

  • Inspired by studies of the brain

  • Massively parallel

  • Highly connected

  • Many simple units




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Three Main Neural Net Types

  • Perceptron

  • Multi-Layer-Perceptron

  • Backpropagation Net





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Areas where neural nets are useful

·   pattern association

·   pattern classification

·   regularity detection

·   image processing

·   speech analysis

·   optimization problems

·   robot steering

·   processing of inaccurate or incomplete inputs

·   quality assurance

·   simulation


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Limits to Neural Networks

  • the operational problem encountered when attempting to simulate the parallelism of neural networks

  • inability to explain any results that they obtain


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Automated Target Discrimination

As researched by the Computational

NeuroEngineering Laboratory in Gainsville, FL

  • SAR (Synthetic Aperture Radar)

  • CFAR (Constant False Alarm Rate)

  • QGD (Quadratic Gamma discriminator)

  • NL-QGD (multi-layer perceptron)

  • Example

  • Results


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Synthetic Aperture Radar

  • Data collection for ATD

  • Self-illuminating imaging radar

  • Creates a height map of a surface

  • Maintains spatial resolution regardless of distance from target

  • Can be used day and night regardless of cloud cover







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Results

  • After training, all three discriminators were run on a data set representing 7km2 of terrain. Target detection threshold was set to 100%.

  • CAFR resulted in 4,455 false alarms.

  • QGD resulted in 385 false alrams.

  • NL-QGD resulted in 232 false alarms.


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Tomahawk Missile Navigation

  • Missile contains a map of terrain

  • Figures out its current position from percepts (radar & altimeter)

  • Uses a modified Gaussian least square differential correction algorithm, a step size limitation filter, and a radial basis function


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Weight matrix

Radial Basis Function

Gaussian Least Square Correction

Necessary Condition

Sufficient Condition

Step size limitation filter

Tolerence error = 10^-8


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Ethics

  • Accountability

    • Legal

    • Political

    • Example: Aegis defense system shoots down an Iranian Airbus jetliner in 1988

  • Use of AI in warfare

  • Ethics of Research and Development

    • Potential uses

    • Military Funding of AI

    • Passing of the blame “just doing my job”


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Sources

  • “Target Discrimination in Synthetic Aperture Radar (SAR) using Artificial Neural Networks” Jose C. Principe, Munchurl Kim, John W. Fisher III. Computational NeuroEngineering Laboratory. EB-486 Electrical and Computer Engineering Department. University of Florida.

  • Sandia National Laboratories. http://www.sandia.gov/radar/sar.html

  • Jet Propulsion Laboratory: California Institute of Technology. http://southport.jpl.nasa.gov/desc/imagingradarv3.html

  • Wageningen University, The Netherlands. http://www.gis.wau.nl/sar/sig/sar_intr.htm


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