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

1. How does one design a mind?(In 4 billion years or less) Troy Kelley U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen, MD USA

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. Brain needs • 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”

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 Computing power of a mouse! Source: Dr. Ray Kurzweil, Kurzweil Technologies Approach – Neurological approach

7. Supercomputer and the Human Brain How does Blue Gene, today’s most powerful supercomputer, compare with the human brain? 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 Human brain is 100 times more powerful *Data provided by Lawrence Livermore National Laboratory

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. AI answer • "In from three to eight years,we'll have a machine with thegeneral intelligence of anaverage human being... a machine that will be able toread Shakespeare [or] greasea car." • Marvin Minsky, Life magazine, 1970 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 Humans do play chess, and perhaps we want to build robots that can play chess Approach – Reactive Architecture

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. X + Y = Z Architectures for Modeling Cognition Symbolic Complex cognition = Serial in nature Localized representation Cognitive Architectures Subsymbolic Simple cognition = Parallel in nature Distributed representation Neural Networks

27. 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. Intellectual continuumwithin the human anatomy Frontal Lobes The actions of the Frontal Lobes are similar to complex Symbolic processing architectures Reflexes The actions of reflexes are similar to a simple feed-forward Neural Network

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. Goals Production System “Attention” is the highest level goal Symbolic Production system operates on memories Results go to memory Semantic network Parallel processing all of the inputs simultaneously Subsymbolic Subsymbolic processing Camera inputs Laser inputs Sound inputs SS-RICS Stimuli

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

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