2011 inns iesnn 1 michigan state university a computational introduction to the brain-mind juyang...
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2011 INNS IESNN 1Michigan State University
A Computational Introduction to the Brain-Mind
Juyang (John) Weng
Michigan State University
East Lansing, MI 49924 USA
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Human Physical and Mental Development
Studies on the adult brain
Studies on how the brain develops
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Machine Mental Development
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Totipotency
Stem cells and somatic cells Genomic equivalence:
All cells are totipotent: whose genome is sufficient to guide the development from a single cell to the entire adult body
Consequence: the developmental program is cell-centered
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Genomic Equivalence
Each somatic cell carries the complete genome in its nucleus
Evidence: cloning (e.g., sheep Dolly) Consequences:
Genome is cell centered, directing individual cell to develop in cell’s environment
No genome is dedicated to more than one cell Cell learning is “in place”: Each neuron does not
have an extra-celluer learner: cell learning must be fully accomplished by each cell itself while it interacts with its cell’s environment
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How to Measure Problems in AI
Time and space complexity? High or low “level”? Tasks that look intelligent when a machine
does it? Rational or irrational? Handling uncertainty? …
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Task Muddiness
Independent of problem domain Independent of technology level Independent of the performer: machines or animals Can be quantified Help us to understand why AI is difficult Help us to see essence of intelligence Can be used to evaluate intelligent machines Help to appreciate human intelligence
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Task Muddiness
Agent independent Categories only Each category can be extended Categories adopted to model task muddiness:
Environment Input Output Internal state Goal
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Environmental Muddiness
Measure Clean Muddy Awareness Known Unknown Complexity Simple Complex Controllability Controlled Uncontrolled Naturalness Artificial Natural Variation Fixed Changing Foreseeability Foreseeable Nonforeseeable
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Task Executor
Human agent:the human is the sole executor
Machine agent:Dual task executor A task is given to a
human The human programs
an machine agent The agent executes
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A Partial List of Input Muddiness
Measure Clean MuddyRawness Symbolic Real sensorSize Small LargeBackground None ComplexVariation Simple ComplexOcclusion None SevereActiveness Passive ActiveModality Simple ComplexMultimodality Single Multiple
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A Partial List of Other Muddiness
Category Measure Clean Muddy Size Small Large Representation Given Not given Observability Observable Unobservable Imposability Imposable Nonimposable
State
Time coverage Simple Complex Terminalness Low High Size Small Large Modality Simple Complex
Output
Multimodality Single Multiple Richness Low High Variability Fixed Variable Availability Given Unknown
Goal
Conveying mode Simple Complex
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2-D Muddiness Frame
Size ofinput
Rawnessof input
Languagetranslation
Computerchess
Visualrecognition
Sonar-basednavigation
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Composite Muddiness
m = m1 m2 m3 … mn
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Autonomous Mental Development (AMD)
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Traditional Manual Development
A = H(Ec , T)A: agentH: humanEc: Ecological conditionT: Task
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New Autonomous Development
A = H(Ec )Autonomous inside the skullA: agentH: humanEc: Ecological condition
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Mode of Development: AA-Learning
AA-learning: Automated animal-like learning
Unbiased Sensors
biased Sensors
Effectors
Closed brain
World
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Existing Machine Learning Types
Supervised learningClass labels (or actions) are given in training
Unsupervised learningClass labels (or actions) are not given in training
Reinforcement learningClass labels (or actions) are not given in training but reinforcement (score) is given
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New Classification for Machine Learning
Need for considering state imposability after the task is given
3-tuple (s, e, b):symbolic internal representation, effector, biased sensor State: state imposable after the task is given Biased sensor: whether the biased sensor is used Effector: whether the effector is imposed
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8 Types of Machine LearningLearning type 0-7 is based on 3-tuple (s, e, b):
Symbolic internal (s=1), effector-imposed (e=1), biased sensors used (b=1)
Type Internal Effector Biased 0 (000) emergent autonomous Communicative 1 (001) emergent autonomous Reinforcement 2 (010) emergent imposed Communicative 3 (011) emergent imposed Reinforcement 4 (100) symbolic autonomous Communicative 5 (101) symbolic autonomous Reinforcement 6 (110) symbolic imposed Communicative 7 (111) symbolic imposed Reinforcement
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The Developmental Approach
Enable a machine to perform autonomous mental development (AMD)
Impractical to faithfully duplicate biological AMD Hardware: Embodiment (a robot) Software: A developmental program
Task nonspecific AA-learning mode, from the “birth” time through the
“life” span
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Comparison of Approaches
Approaches SpeciesArchitecture
World Knowledge System behavior Task-specific
Knowledge-based Programming Manual modeling Manual modeling Yes
Behavior-based Programming Avoid modeling Manual modeling Yes
Learning-based Programming Models withparameters
Models withparameters
Yes
Evolutionary Genetic search Models withparameters
Models withparameters
Yes
Developmental Programming Avoid modeling Avoid modeling No
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Developmental Program vs Traditional Learning
Properties of program Traditional programs
Developmental programs
Sensor-specific and Effector-specific
Yes Yes
Program is task-non-specific No Yes Tasks are unknown at programming time
No Yes
Generate representation automatically [1]
No Yes
Animal-like online learning No Yes Open-ended learning for more new tasks
No Yes
[1] For tasks unknown at the programming time.
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Motives of Research for Development
Developmental mechanisms are easier to program:lower level, more systematic, task-independent, clearly understandable
Relieve humans from intractable programming tasks: vision, speech, language, complex behaviors, consciousness
User-friendly machines and robots:humans issue high-level commands to machines
Highly adaptive manufacturing systems (e.g., self-trainable, reconfigurable machining systems)
Help to understand human intelligence
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Task Nonspecificity
A program is not task specific means: Open to muddy environment Tasks are unknown at programming time “The brain” is closed after the birth Learn an open number of muddy tasks after birth
Avoid trivial cases: A thermostat A robot that does task A when temperature is high and
does task B when temperature is low A robot that does simple reinforcement learning
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8 Requirements for Practical AMD
Eight necessary operational requirements:1. Environmental openness: muddy environments2. High dimensional sensing3. Completeness in internal representation for each age group4. Online5. Real time speed6. Incremental:
for each fraction of second (e.g., 10-30Hz)7. Perform while learning8. Scale up to large memory
Existing works (other than SAIL) aimed at some, but not all. SAIL deals with the 8 requirements altogether
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Definition of AA-Learning
A machine M conducts AA-learning if the operation mode is as follows:
For t = t0, t1, t2, ... , the brain program f recursively updates the brain B, sensory input-ouput x and effector input-output z
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The Central Nervous System
The forebrain The midbrain
and hindbrain The spinal cord
Kandel, Schwartz and Jessell 2000
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Brodmann Areas (1909)
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Kandel, Schwartz and Jessell 2000
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Sensory and Motor Pathways
Adapted from Kandel, Schwartz and Jessell 2000
My hypothesis:Brain has complex networksthat emerge largely shapedby signal statistics (Weng IJCNN 2010)
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Multimodal Integration
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Weng IJCNN 2010
The brain has only two exposed endsto interact with the environment:
Brain’s Vision System
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Triple Loops
Weng IJCNN 2010
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Solving the Feature Binding Problem
Weng IJCNN 2010
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Area as A Building Block
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Weng IJCNN 2010
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Neurons as Feature Detectors: The Lobe Component Model
Biologically motivated: Hebbian learning lateral inhibition
Partition the input space into c regions X = R1 U R2 U ... U Rc
Lobe component i: the principal component of the region Ri
Weng et al. WCCI 2006
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Different Normalizations
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Dual Optimality of CCI LCA Spatial optimality leads to the best target:
Given the number of neurons (limited resource), the target of the synaptic weight vectors minimizes the representation error based on “observation” x:
Temporal optimality leads to the best runner to the target: Given limited experience up to time t, find the best direction and step size for each t based on “observation” u = r x
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Weng & Luciw TAMD vol. 1, no. 1, 2009
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CCI LCA Algorithm (1)
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CCI LCA Algorithm (2)
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Plasticity Schedule
t1 t2
t
2
(t)
r = 10000
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Natural Images
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IC from Natural Images
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Temporal Architectures
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Based on FA Ideas
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From FA to ED network
FA: sn = f(sl,am) s: state; a: symbol input ED:
The internal area learns:yi = fy (sl, am)
The motor area learns: sn = fz (yi)
s: a numeric pattern of z, a sample of Z spacea: a numeric pattern of x, a sample of X spacey: a numeric pattern of y, a sample of Y space
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Training and Tests
Luciw & Weng IJCNN 2010
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Performance
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Three Types of Information Flow
Different directions for different intents
Mixed modes are possible
There is no “if-then-else” type of switches
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For any FA there is an ED network
ED: Epigenetic Developer
FS: Finite Automaton
Relation: An ED network can learn any FA
Marvin Minsky at MIT criticized ANNs
Weng IJCNN 2010
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Almost Perfect Disjoint TestUsing Temporal Context
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Luciw, Weng & Zeng ICDL 2008
More Views, Better Confidence
Externally sensed Internally generated context
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For any FA there is an ED network
ED: Epigenetic Developer
FS: Finite Automaton
Relation: An ED network can learn any FA
Marvin Minsky at MIT criticized ANNs
Weng IJCNN 2010
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From FA to ED network
FA: sn = f(sl,am) s: state; a: symbol input ED:
The internal area learns:yi = fy (sl, am)
The motor area learns: sn = fz (yi)
s: a numeric pattern of z, a sample of Z spacea: a numeric pattern of x, a sample of X spacey: a numeric pattern of y, a sample of Y space
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Complex text processing
New sentence problem Recognize new sentences from synonyms
Word sense disambiguation problem Temporal context
Part of speech tagging problem Label words according to part of speech
Chunking problem Grouping sequences of words and classify them by syntactic labels
Weng, Zhang, Chi & Xue ICDL 2009
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Recent Events on AMD
ICDL series: http://cogsci.ucsd.edu/~triesch/icdl/ Workshop on Development and Learning (WDL) 2000, MSU, MI USA 2nd International Conf. on Development and Learning (ICDL’02): MIT, MA USA 3rd ICDL (2004): San Diego, CA USA 4th ICDL (2005): Osaka, Japan 5th ICDL (2006): Bloomington IN, USA 6th ICDL (2007): London, UK 7th ICDL (2008): Monterey, CA, USA 8th ICDL (2009): Shanghai, China 9th ICDL (2010): An Arbor, Michigan USA 10th ICDL (2011), Frankfurt, Germany
EpiRob workshop series, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10 AMD Technical Committee of IEEE Computational Intelligence Society
http://www.ieee-cis.org/AMD/ AMD Newsletters
http:///www.cse.msu.edu/amdtc/amdnl/ IEEE Transactions on Autonomous Mental Development
http://www.ieee-cis.org/pubs/tamd/
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Now and Future Now (not many people agree):
Humans start to know roughly how the brain-mind works Future (not too far):
Systematic breakthroughs in artificial intelligence along all fronts: Vision Speech Natural language Robotics Creative intelligence
A new industry: New type of software industry Cloud computing for brain-scale applications Service robots and smart toys entering homes Robots widely used in public environments