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Artificially intelligent autopilots/airplanes/drones/UAVs

Nov 18, 2014

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The Brain of the Truly Autonomous UAV.

  • 1. NEURAL NETWORKS THE BRAIN OF THE TRULY AUTONOMOUS UAV Stephen L. Thaler, Ph.D. President & CEO, Imagination Engines, Inc.___________________________________________________________ 40th Annual NDIA Air Targets, UAVs Range Operations Symposium 2002 Imagination Engines, Inc.
  • 2. UNIVERSE IS A COMPLEX NETWORK 2002 Imagination Engines, Inc.
  • 3. NEURAL NETS MODEL UNIVERSE Connection weights are tantamount to expansion coefficients within a curve fit. input pattern = xcan be anybasis function because of some initiating event. because something else happenedMLPs capturecomplex causalchains. something happened output pattern = FN(FN-1(F1(x))) in contrast to the usual F(x) = a 0F0(x) + a2F1(x) + + aNFN(x) 2002 Imagination Engines, Inc.
  • 4. MLPS* CAPTURE ENTITIES & CONNECTS * MLP = Multi-Layer Perceptron, the workhorse of artificial neural networks. bitmaps of faces and other objects = x Eye, nose, mouth, classification ear, etc. eye detectors nose detectors detecting layer colonies form. If eyes, nose,constraint layer(s) mouth, and ears are detected then face is present. BOOL isaFace(x) output patterns BOOL isaFoot(x) 2002 Imagination Engines, Inc.
  • 5. CREATIVITY MACHINE PARADIGM hopping synapticCM perturbations inputs clamped for context Imagination Stream of Engine Ideas (IE) feedback AlertEvaluation Associative of Ideas Center (AAC) outputs US05659666, 08/19/199, Device for the Autonomous Generation of Useful Information 2002 Imagination Engines, Inc.
  • 6. AUTONOMOUS WARHEAD ADAPTATION 2002 Imagination Engines, Inc.
  • 7. AUTONOMOUS MOTION PLANNINGCreativity Machine 30 degree of freedom mastered inimplemented cockroach 30 seconds. A 1,000+ degree ofsimulation freedom system represented in flight controls do not pose a problem. GPS coordinates, chemical or biological gradients, etc. All that need be supplied is a will toUser drags roach while proceed in a certain direction,simulation invents the toward a particular goal, asrequired legwork! represented by the cursor drag. 2002 Imagination Engines, Inc.
  • 8. SELF-TRAINING ANN OBJECT synapticSTANNO adjustments input patterns Synaptic UntrainedCorrections Neural Network feedback ANN-BasedEvaluation of Neural Network Error Trainer outputs US05845271, 12/01/1998, Non-Algorithmically implemented artificial neural networks and components thereof 2002 Imagination Engines, Inc.
  • 9. STANNO-BASED ZDATA GENERATOR 2002 Imagination Engines, Inc.
  • 10. MEMBERSHIP / ANOMALY DETECTIONIntact feed forward passage through auto-associativenet, trained upon some interrelated group of patterns,signifies membership within that group of patterns.Features key to Relationships group G between membership sensed identified in features tested hidden layers. against those of group G in G = {P1, P2, PN} output layer(s). P = P P G P G P P P P G novelty vector, P P = (x1, x2, x3, , xN) US05852816, 12/22/1998, Neural network based database scanning system 2002 Imagination Engines, Inc.
  • 11. BATTLE DAMAGE ASSESSMENT 2002 Imagination Engines, Inc.
  • 12. BATTLE DAMAGE ASSESSMENT 2002 Imagination Engines, Inc.
  • 13. AUTONOMOUS TARGET RECOGNITION 2002 Imagination Engines, Inc.
  • 14. AUTONOMOUS TARGETING yquiescent xy (x, y) xy (0,0) x 2002 Imagination Engines, Inc.
  • 15. AUTONOMOUS TARGETINGperturbed xy (x, y) (x, y) (x, y) xy (0,0) x 2002 Imagination Engines, Inc.
  • 16. AUTONOMOUS TARGETING yperturbed Membership xy (x, y) filter (x, y) (x, y) xy (0,0) x 2002 Imagination Engines, Inc.
  • 17. AUTONOMOUS TARGETING yperturbed Membership xy (x, y) filter (x, y) (x, y) xy (0,0) x US05852816, 12/22/1998, Neural network based database scanning system 2002 Imagination Engines, Inc.
  • 18. AUTONOMOUS TARGETING 2002 Imagination Engines, Inc.
  • 19. AUTONOMOUS TARGETINGObjective: Autonomously find the toy F4attention window robot held camera 2002 Imagination Engines, Inc.
  • 20. AUTONOMOUS, BRILLIANT UAVSCreativity Machines + STANNOs + Group/Anomaly FiltersAircraft / UAV Design. Let them design themselves.Web-based Logistical Support Let them search for their own components.Field Kit for Tailored Assembly. Let them recommend their own field configurations.Self