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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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2011 INNS IESNN 2Michigan State University
Human Physical and Mental Development
Studies on the adult brain
Studies on how the brain develops
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2011 INNS IESNN 3Michigan State University
Machine Mental Development
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2011 INNS IESNN 4Michigan State University
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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2011 INNS IESNN 5Michigan State University
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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2011 INNS IESNN 6Michigan State University
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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2011 INNS IESNN 7Michigan State University
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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2011 INNS IESNN 8Michigan State University
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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2011 INNS IESNN 9Michigan State University
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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2011 INNS IESNN 10Michigan State University
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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2011 INNS IESNN 11Michigan State University
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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2011 INNS IESNN 12Michigan State University
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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2011 INNS IESNN 13Michigan State University
2-D Muddiness Frame
Size ofinput
Rawnessof input
Languagetranslation
Computerchess
Visualrecognition
Sonar-basednavigation
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2011 INNS IESNN 14Michigan State University
Composite Muddiness
m = m1 m2 m3 … mn
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2011 INNS IESNN 15Michigan State University
Autonomous Mental Development (AMD)
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2011 INNS IESNN 16Michigan State University
Traditional Manual Development
A = H(Ec , T)A: agentH: humanEc: Ecological conditionT: Task
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2011 INNS IESNN 17Michigan State University
New Autonomous Development
A = H(Ec )Autonomous inside the skullA: agentH: humanEc: Ecological condition
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2011 INNS IESNN 18Michigan State University
Mode of Development: AA-Learning
AA-learning: Automated animal-like learning
Unbiased Sensors
biased Sensors
Effectors
Closed brain
World
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2011 INNS IESNN 19Michigan State University
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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2011 INNS IESNN 20Michigan State University
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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2011 INNS IESNN 21Michigan State University
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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2011 INNS IESNN 22Michigan State University
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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2011 INNS IESNN 23Michigan State University
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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2011 INNS IESNN 24Michigan State University
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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2011 INNS IESNN 25Michigan State University
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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2011 INNS IESNN 26Michigan State University
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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2011 INNS IESNN 27Michigan State University
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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2011 INNS IESNN 28Michigan State University
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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2011 INNS IESNN 29Michigan State University
The Central Nervous System
The forebrain The midbrain
and hindbrain The spinal cord
Kandel, Schwartz and Jessell 2000
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2011 INNS IESNN 30Michigan State University
Brodmann Areas (1909)
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Kandel, Schwartz and Jessell 2000
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2011 INNS IESNN 31Michigan State University
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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2011 INNS IESNN 32Michigan State University
Multimodal Integration
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2011 INNS IESNN 33Michigan State University
Weng IJCNN 2010
The brain has only two exposed endsto interact with the environment:
Brain’s Vision System
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2011 INNS IESNN 34Michigan State University
Triple Loops
Weng IJCNN 2010
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2011 INNS IESNN 35Michigan State University
Solving the Feature Binding Problem
Weng IJCNN 2010
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2011 INNS IESNN 36Michigan State University
Area as A Building Block
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Weng IJCNN 2010
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2011 INNS IESNN 37Michigan State University
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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2011 INNS IESNN 38Michigan State University
Different Normalizations
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2011 INNS IESNN 39Michigan State University
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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2011 INNS IESNN 40Michigan State University
CCI LCA Algorithm (1)
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2011 INNS IESNN 41Michigan State University
CCI LCA Algorithm (2)
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2011 INNS IESNN 42Michigan State University
Plasticity Schedule
t1 t2
t
2
(t)
r = 10000
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2011 INNS IESNN 43Michigan State University
Natural Images
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2011 INNS IESNN 44Michigan State University
IC from Natural Images
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2011 INNS IESNN 45Michigan State University
Temporal Architectures
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2011 INNS IESNN 46Michigan State University
Based on FA Ideas
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2011 INNS IESNN 47Michigan State University
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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2011 INNS IESNN 48Michigan State University
Training and Tests
Luciw & Weng IJCNN 2010
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2011 INNS IESNN 49Michigan State University
Performance
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2011 INNS IESNN 50Michigan State University
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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2011 INNS IESNN 51Michigan State University
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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2011 INNS IESNN 52Michigan State University
Almost Perfect Disjoint TestUsing Temporal Context
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Luciw, Weng & Zeng ICDL 2008
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More Views, Better Confidence
Externally sensed Internally generated context
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2011 INNS IESNN 54Michigan State University
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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2011 INNS IESNN 55Michigan State University
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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2011 INNS IESNN 56Michigan State University
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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2011 INNS IESNN 57Michigan State University
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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2011 INNS IESNN 58Michigan State University
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