Artificial Intelligence in the Military

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Artificial Intelligence in the Military

• Presented by
• Carson English, Jason Lukis,
• Nathan Morse and Nathan Swanson

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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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Structure of a neuron in a neural net
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Neural net with three neuron layers
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Three Main Neural Net Types

• Perceptron
• Multi-Layer-Perceptron
• Backpropagation Net

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Perceptron
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Multi-Layer-Perceptron
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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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Picture of SAR rendering
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Two Constant False Alarm method for determining
targets
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Quadratic Gamma discrimination
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Non Linear QGD
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Example
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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.g
  ov/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