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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.
UNIVERSE IS A COMPLEX NETWORK




         © 2002 Imagination Engines, Inc.
NEURAL NETS MODEL UNIVERSE
 Connection weights are tantamount to expansion coefficients within a curve fit.
                                     input pattern = x
can be any
basis function                                                                 because of some
                                                                                initiating event.




                                                                             because something
                                                                               else happened

MLPs capture
complex causal
chains.                                                                     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.
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.
CREATIVITY MACHINE PARADIGM
                                 hopping synaptic
“CM”                               perturbations                        inputs clamped for context


                                                                             Imagination
 Stream of                                                                     Engine
   Ideas
                                                                                 (IE)
     feedback
                                                                                 Alert
Evaluation                                                                    Associative
 of Ideas                                                                      Center
                                                                                (AAC)
                                           outputs

        US05659666, 08/19/199, Device for the Autonomous Generation of Useful Information



                           © 2002 Imagination Engines, Inc.
AUTONOMOUS WARHEAD ADAPTATION




         © 2002 Imagination Engines, Inc.
AUTONOMOUS MOTION PLANNING
Creativity Machine                      30 degree of freedom mastered in
implemented cockroach                   30 seconds. A 1,000+ degree of
simulation…                             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’ to
User drags roach while                  proceed in a certain direction,
simulation invents the                  toward a particular goal, as
required legwork!                       represented by the cursor drag.

                         © 2002 Imagination Engines, Inc.
SELF-TRAINING ANN OBJECT
                                               synaptic
“STANNO”                                     adjustments                        input patterns




 Synaptic                                                                              Untrained
Corrections                                                                          Neural Network

    feedback
                                                                                      ANN-Based
Evaluation of                                                                        Neural Network
   Error                                                                                Trainer
                                                   outputs

    US05845271, 12/01/1998, Non-Algorithmically implemented artificial neural networks and components thereof



                                   © 2002 Imagination Engines, Inc.
STANNO-BASED ZDATA GENERATOR




         © 2002 Imagination Engines, Inc.
MEMBERSHIP / ANOMALY DETECTION
Intact feed forward passage through auto-associative
net, 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, δ ≡ P – P’ = (x1, x2, x3, …, xN)
                          US05852816, 12/22/1998, Neural network based database scanning system



                                        © 2002 Imagination Engines, Inc.
BATTLE DAMAGE ASSESSMENT




       © 2002 Imagination Engines, Inc.
BATTLE DAMAGE ASSESSMENT




       © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGET RECOGNITION




         © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING

            y
quiescent
    xy                        (x, y)




   xy

     (0,0)                                         x


                © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING


perturbed
    xy                      (x, y)
                                (x’, y’)

                       (x’’, y’’)



   x’y’

      (0,0)                                      x


              © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING

            y
perturbed            Membership
    xy                      (x, y)
                       filter
                                  (x’, y’)

                δ        (x’’, y’’)



   x’y’

      (0,0)                                        x


                © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING

               y
perturbed                        Membership
    xy                                  (x, y)
                                   filter
                                                     (x’, y’)

                       δ               (x’’, y’’)



   x’y’

      (0,0)                                                                  x
          US05852816, 12/22/1998, Neural network based database scanning system



                        © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING




     © 2002 Imagination Engines, Inc.
AUTONOMOUS TARGETING
Objective: Autonomously find the toy F4…

attention window                      robot held camera




               © 2002 Imagination Engines, Inc.
AUTONOMOUS, BRILLIANT UAVS


Creativity Machines         +        STANNOs          +      Group/Anomaly Filters

Aircraft / 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-diagnosis…………………………….. Let them tell us when / if they’re ready to go.
Flight Control / Dogfight / Egress………. Let them fly themselves.
Massively Parallel Sensor Integration…. Let them fuse inputs and resolve ambiguities.
Mission / Sortie Planning…………………Let them plan based on loosely posed objectives.
Strategy / Tactics………………………… Let them improvise as battlefield evolves.
Battle Damage Recovery……………….. Let them repair themselves in flight.
Low Observables Adaptation…………… Let them reconfigure themselves to evade.
Autonomous Targeting………………….. Let them lock on without slow human judgment.
Battle Damage Assessment…………….. Let them evaluate their effects on target and react.
Legal Repercussions…………………….. Let them be their own, instantaneous cyber-lawyer.
Political/Philosophical Perception………. Let them have similar motivating “feelings.”

                                © 2002 Imagination Engines, Inc.

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Artificially intelligent autopilots/airplanes/drones/UAVs

  • 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 = x can be any basis function because of some initiating event. because something else happened MLPs capture complex causal chains. 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 synaptic “CM” perturbations inputs clamped for context Imagination Stream of Engine Ideas (IE) feedback Alert Evaluation 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 PLANNING Creativity Machine 30 degree of freedom mastered in implemented cockroach 30 seconds. A 1,000+ degree of simulation… 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’ to User drags roach while proceed in a certain direction, simulation invents the toward a particular goal, as required legwork! represented by the cursor drag. © 2002 Imagination Engines, Inc.
  • 8. SELF-TRAINING ANN OBJECT synaptic “STANNO” adjustments input patterns Synaptic Untrained Corrections Neural Network feedback ANN-Based Evaluation 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 DETECTION Intact feed forward passage through auto-associative net, 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, δ ≡ 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 y quiescent xy (x, y) xy (0,0) x © 2002 Imagination Engines, Inc.
  • 15. AUTONOMOUS TARGETING perturbed xy (x, y) (x’, y’) (x’’, y’’) x’y’ (0,0) x © 2002 Imagination Engines, Inc.
  • 16. AUTONOMOUS TARGETING y perturbed Membership xy (x, y) filter (x’, y’) δ (x’’, y’’) x’y’ (0,0) x © 2002 Imagination Engines, Inc.
  • 17. AUTONOMOUS TARGETING y perturbed Membership xy (x, y) filter (x’, y’) δ (x’’, y’’) x’y’ (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 TARGETING Objective: Autonomously find the toy F4… attention window robot held camera © 2002 Imagination Engines, Inc.
  • 20. AUTONOMOUS, BRILLIANT UAVS Creativity Machines + STANNOs + Group/Anomaly Filters Aircraft / 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-diagnosis…………………………….. Let them tell us when / if they’re ready to go. Flight Control / Dogfight / Egress………. Let them fly themselves. Massively Parallel Sensor Integration…. Let them fuse inputs and resolve ambiguities. Mission / Sortie Planning…………………Let them plan based on loosely posed objectives. Strategy / Tactics………………………… Let them improvise as battlefield evolves. Battle Damage Recovery……………….. Let them repair themselves in flight. Low Observables Adaptation…………… Let them reconfigure themselves to evade. Autonomous Targeting………………….. Let them lock on without slow human judgment. Battle Damage Assessment…………….. Let them evaluate their effects on target and react. Legal Repercussions…………………….. Let them be their own, instantaneous cyber-lawyer. Political/Philosophical Perception………. Let them have similar motivating “feelings.” © 2002 Imagination Engines, Inc.