How does one design a mind? (In 4 billion years or less)
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Transcript of How does one design a mind? (In 4 billion years or less)
Human Research and Engineering Directorate
Troy KelleyU.S. Army Research Laboratory
Human Research and Engineering DirectorateAberdeen, MD
USA
How does one design a mind?(In 4 billion years or less)
Human Research and Engineering Directorate
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
Human Research and Engineering Directorate
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?
Human Research and Engineering Directorate
• 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
Human Research and Engineering Directorate
Approaches to needs• Neurological Systems
– Simulate every neuron• Symbolic Systems
– Traditional AI systems • Complete sub-symbolic systems
– Reactive architecture• Cognitive Architectures
– ACT-R, Soar
Human Research and Engineering Directorate
Simulating Every Neuron
Source: Dr. Ray Kurzweil, Kurzweil Technologies
Approach – Neurological approach
Human Research and Engineering Directorate
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 cyclesper second
Human Brain
4 lbs
0.06 cubic ft
?????
10 quadrillion cyclesper second
Human Research and Engineering Directorate
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
Human Research and Engineering Directorate
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?
Human Research and Engineering Directorate
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!
Human Research and Engineering Directorate
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!
Human Research and Engineering Directorate
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?
Human Research and Engineering Directorate
Sensor problem?
• The creature with the best sensor wins?
Human Research and Engineering Directorate
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][Bazan lab, ICB, UCSB]
Signal amplification mediated by elements that fit together by precise lock-and-key molecular recognition
Human Research and Engineering Directorate
Approaches
• Neurological Systems– Simulate every neuron
• Symbolic Systems– Traditional AI systems
Human Research and Engineering Directorate
AI Approach
• Computationally intensive
• Task specific
• Not necessarily biologically based
• Suffers from brittleness and lack of robust behaviour in dynamic environments
Human Research and Engineering Directorate
• "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
Human Research and Engineering Directorate
Approaches
• Neurological Systems– Simulate every neuron
• Symbolic Systems– Traditional AI systems
• Complete sub-symbolic systems– Reactive architecture
Human Research and Engineering Directorate
Reactive Architecture
• Anti-symbolic
• Tight pairing between sensing and reaction
• Current system for the military (4DRCS)
• No representation of the environment
Human Research and Engineering Directorate
“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
Human Research and Engineering Directorate
Approaches
• Neurological Systems– Simulate every neuron
• Symbolic Systems– Traditional AI systems
• Complete sub-symbolic systems– Reactive architecture
• Cognitive Architectures– ACT-R, Soar
Human Research and Engineering Directorate
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
Human Research and Engineering Directorate
ComplexityA 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?
Human Research and Engineering Directorate
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?
Human Research and Engineering Directorate
Architectures for Modeling Cognition
X + Y = ZX + Y = Z
SymbolicComplex cognition
= Serial in natureLocalized representationCognitive Architectures
SubsymbolicSimple cognition
= Parallel in natureDistributed representationNeural Networks
Human Research and Engineering Directorate
Intellectual continuumwithin 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 complexSymbolic 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.
Human Research and Engineering Directorate
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.”
Human Research and Engineering Directorate
Stimuli
Subsymbolic processing
Production System
Goals
Camera inputsLaser inputsSound inputs
Parallel processingall of the inputssimultaneously
Results go to memory
Production systemoperates on memories
“Attention” is the highestlevel goal
Semantic network
Human Research and Engineering Directorate
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