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How does one design a mind? (In 4 billion years or less)

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  • 1. Troy Kelley U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen, MD USA How does one design a mind? (In 4 billion years or less)
  • 2. What is cognition?
    • Cognition is a collection of pre-programmed algorithms developed during evolution
      • This is both high level
        • Language
        • Searching
      • And low level
        • Reflexes
        • Movement toward light
  • 3. What is cognition? (con’t)
    • Cognition is also changes in neurological connections based on experience
      • Learning at the low levels (reflexes)
      • And the high level as well (language)
    • If I know what cognition is, does that mean I can recreate a cognitive system?
  • 4.
    • At the very least a cognitive system needs:
    • Perceptual System
      • Visual
      • Auditory
      • Tactile
      • SICK, IR, LADAR
    • Memory System
      • LTM, STM, Working memory, visual spatial memory, auditory memory (memory for each sensor?)
    • Hierarchical organization
      • Some kind of hierarchical organization process
    • Can’t really create a “black box”
    Brain needs
  • 5. Approaches to needs
    • Neurological Systems
      • Simulate every neuron
    • Symbolic Systems
      • Traditional AI systems
    • Complete sub-symbolic systems
      • Reactive architecture
    • Cognitive Architectures
      • ACT-R, Soar
  • 6. Simulating Every Neuron Source: Dr. Ray Kurzweil, Kurzweil Technologies Computing power of a mouse! Approach – Neurological approach
  • 7.
    • How does Blue Gene, today’s most powerful supercomputer, compare with the human brain?
    *Data provided by Lawrence Livermore National Laboratory Supercomputer and the Human Brain Human brain is 100 times more powerful Supercomputer 100,000 lbs 5,000 cubic ft 2,000,000 watts 100 trillion cycles per second Human Brain 4 lbs 0.06 cubic ft ????? 10 quadrillion cycles per second
  • 8. Approaches
    • Neurological Systems
      • Simulate every neuron?
        • How do we program all of those neurons?
          • Are they all basically the same or are they different?
          • We know from biological systems that different cells have different functions even within the neurological system
          • So we can’t use one type of “perceptron” or neural network
  • 9. Approaches
    • Neurological Systems
      • Simulate every neuron?
        • How do we program all of those neurons?
          • Are they all basically the same or are they different?
          • We know from biological systems that different cells have different functions even within the neurological system
          • So we can’t use one type of “perceptron” or neural network
        • How do we determine the fitness of our cell clusters?
  • 10. Charles Darwin Said…. “ It is not the strongest of the species that survives..…but rather the one most responsive to change.” Adaptation in Nature is essential!
  • 11. Evolutionary approach
    • How to determine fitness?
    • Organisms evolved in conjunction with the earth evolving
    • Evolving a complex organism needs to be done using a complex environment!
  • 12. Approaches
    • Neurological Systems
      • Simulate every neuron?
        • How do we program all of those neurons?
          • Are they all basically the same or are they different?
          • We know from biological systems that different cells have different functions even within the neurological system
          • So we can’t use one type of “perceptron” or neural network
        • How do we determine the fitness of our cell clusters?
        • Much of evolution has revolved around motor/sensor optimization – is that the answer for robotics?
  • 13. Sensor problem?
    • The creature with the best sensor wins?
  • 14. Moth Sense and Control System
    • Biological sensors exhibit unequaled sensitivity, specificity, speed and refresh-rate
      • The chemical sensors of the moth can detect a single molecule of the sex pheromone of the female up to a mile away
    [Bazan lab, ICB, UCSB]
    • Signal amplification mediated by elements that fit together by precise lock-and-key molecular recognition
  • 15. Approaches
    • Neurological Systems
      • Simulate every neuron
    • Symbolic Systems
      • Traditional AI systems
  • 16. AI Approach
    • Computationally intensive
    • Task specific
    • Not necessarily biologically based
    • Suffers from brittleness and lack of robust behaviour in dynamic environments
  • 17.
    • " In from three to eight years, we'll have a machine with the general intelligence of an average human being...  a machine that will be able to read Shakespeare [or] grease a car."
    • Marvin Minsky, Life magazine, 1970
    AI answer Approach – Traditional AI approach
  • 18. Approaches
    • Neurological Systems
      • Simulate every neuron
    • Symbolic Systems
      • Traditional AI systems
    • Complete sub-symbolic systems
      • Reactive architecture
  • 19. Reactive Architecture
    • Anti-symbolic
    • Tight pairing between sensing and reaction
    • Current system for the military (4DRCS)
    • No representation of the environment
  • 20. “ Elephants Don’t Play Chess” – Rodney Brooks Approach – Reactive Architecture Humans do play chess, and perhaps we want to build robots that can play chess
  • 21. Approaches
    • Neurological Systems
      • Simulate every neuron
    • Symbolic Systems
      • Traditional AI systems
    • Complete sub-symbolic systems
      • Reactive architecture
    • Cognitive Architectures
      • ACT-R, Soar
  • 22. Cognitive Architectures
    • Cognitive architectures have ignored the “perceptual problem”
    • Cognitive architectures grew out of the symbolic tradition of AI
    • Newell and Simon’s General Problem Solver production system served as the birth of AI as well as the birth of cognitive architectures
    • Cognitive Architectures are complex
  • 23. Complexity
    • A software mind should be at least as complex as an operating system?
      • 1993 Windows NT 3.1 6 million lines of code
      • 1994 Windows NT 3.5 10 million lines of code
      • 1996 Windows NT 4.0 16 million lines of code
      • 2000 Windows 2000 29 million lines of code
      • 2002 Windows XP 40 million lines of code
    • 40 million lines of code and 9 years of development
    • Imagine this development cycle, except that, due to sensor error, you never knew exactly where the user was clicking with the mouse, or you never knew exactly what key was being selected on the keyboard. How would this affect the development cycle?
  • 24. Approaches
    • Neurological Systems
      • Simulate every neuron
    • Symbolic Systems
      • Traditional AI systems
    • Complete sub-symbolic systems
      • Reactive architecture
    • Cognitive Architectures
      • ACT-R, Soar
        • Hybrid approach
        • How do we merge a symbolic and sub-symbolic system?
  • 25. Knowledge Architectures Symbolic Sub-symbolic
  • 26. Architectures for Modeling Cognition Symbolic Complex cognition = Serial in nature Localized representation Cognitive Architectures Subsymbolic Simple cognition = Parallel in nature Distributed representation Neural Networks X + Y = Z
  • 27. Intellectual continuum within the human anatomy Reflexes The actions of reflexes are similar to a simple feed-forward Neural Network Frontal Lobes The actions of the Frontal Lobes are similar to complex Symbolic processing architectures Kelley, T. D., (2003), “Symbolic and sub-symbolic representations in computational models of human cognition: What can be learned from biology?” Theory and Psychology , TAP 13(6), December.
  • 28. Robotics Architectures
    • In a DARPA report (2001) by Singh and Thayer of the CMU Robotics Institute the authors concluded that:
      • “a mixed strategy [hybrid] provides a more reasonable method for robot coordination for a general case where there are natural constraints during operation in a complex environment.”
  • 29. Stimuli Subsymbolic processing Camera inputs Laser inputs Sound inputs Parallel processing all of the inputs simultaneously Results go to memory Production system operates on memories “Attention” is the highest level goal Semantic network Symbolic Subsymbolic SS-RICS Production System Goals
  • 30. Sub-symbolic
    • How to develop pre-programmed algorithms that look for one item?
      • Algorithms for corners, gaps, lines
      • Two programmers (graduate level) working for one year
      • Still problems with these low level algorithms
  • 31.  
  • 32. Conclusions
    • Complex behavior requires a complex approach to cognition
    • Hybrid architectures offer one solution to a complex problem
    • Combinations of symbolic and sub-symbolic architectures offer one approach