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 1945 – Germany makes first ballistic missile 1950 – AIM-7 Sparrow –“fire-and-forget

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

History 1991 – Smart bombs used in Gulf War to selectively destroy enemy targets –Praised for its precision and effectiveness

Neural Networks Inspired by studies of the brain Massively parallel Highly connected Many simple units

Structure of a neuron in a neural net

Neural net with three neuron layers

Three Main Neural Net Types Perceptron Multi-Layer-Perceptron Backpropagation Net

Perceptron

Multi-Layer-Perceptron

Backpropagation Net

· pattern association · pattern classification · regularity detection · image processing · speech analysis · optimization problems · robot steering · processing of inaccurate or incomplete inputs · quality assurance · simulation Areas where neural nets are useful

the operational problem encountered when attempting to simulate the parallelism of neural networks inability to explain any results that they obtain Limits to Neural Networks

Automated Target Discrimination SAR (Synthetic Aperture Radar) CFAR (Constant False Alarm Rate) QGD (Quadratic Gamma discriminator) NL-QGD (multi-layer perceptron) Example Results As researched by the Computational NeuroEngineering Laboratory in Gainsville, FL

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

Picture of SAR rendering

Two Constant False Alarm method for determining targets

Quadratic Gamma discrimination

Non Linear QGD

Example

Results After training, all three discriminators were run on a data set representing 7km 2 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.

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

Radial Basis Function Gaussian Least Square Correction Necessary Condition Sufficient Condition Step size limitation filter Weight matrix Tolerence error = 10^-8

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”

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. Jet Propulsion Laboratory: California Institute of Technology. Wageningen University, The Netherlands.