Artificial Intelligence in the Military

1. Artificial Intelligence in the Military Presented by Carson English, Jason Lukis, Nathan Morse and Nathan Swanson

2. Overview • History • Neural Networks • Automated Target Discrimination • Tomahawk Missile Navigation • Ethical issues

3. History • 1918 – first tests on guided missiles • 1945 – Germany makes first ballistic missile • 1950 – AIM-7 Sparrow • “fire-and-forget

5. 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

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

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

8. Structure of a neuron in a neural net

9. Neural net with three neuron layers

10. Three Main Neural Net Types • Perceptron • Multi-Layer-Perceptron • Backpropagation Net

11. Perceptron

12. Multi-Layer-Perceptron

13. Backpropagation Net

14. 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

15. 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

16. 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

17. 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

18. Picture of SAR rendering

19. Two Constant False Alarm method for determining targets

20. Quadratic Gamma discrimination

21. Non Linear QGD

22. Example

23. 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.

24. 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

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

26. 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”

27. 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