Yingxu Wang Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors
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Transcript of Yingxu Wang Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors
12th Int’l Conference on Computer Science and Its Applications (ICCSA 2012)
Towards the Next Generation of Cognitive Computers:
Knowledge vs Data ProcessorsKnowledge vs. Data ProcessorsYingxu Wang, PhD, Prof., PEng, FWIF, FICIC, SMIEEE, SMACM
President, International Institute of Cognitive Informatics & Cognitive Computing (ICIC)
Director Lab for Cognitive Informatics & Cognitive ComputingDirector, Lab for Cognitive Informatics & Cognitive Computing University of Calgary, Canada
Email: [email protected]://www.enel.ucalgary.ca/People/wangyx/http://www.enel.ucalgary.ca/People/wangyx/
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 1
1.1. IntroductionIntroduction
► 1. Introduction2. Cognitive informatics (CI) g ( )3. Denotational mathematics (DM)4. Cognitive computers (cCs) Cog t e co pute s (cCs)5. Conclusions
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 2
The Need for Computational Intelligence in Intelligent Computers The Need for Computational Intelligence in Intelligent Computers
• In celebrating the 100th anniversary of Turing and hispioneer work, curiosity may lead to a fundamentalquestion:q
- If more intelligent computers that think, reason, andlearn may be developed?learn may be developed?
- They are known as Cognitive Computers (cCs)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 3
Computing Power: Speed vs. Intelligence
I vc
C omput ing spee d
No rma l hu ma n
intellige nce
3 ye ar o ld kit’s
t / /
1940s 1950s 1980s 2010s
o ld kit s inte llige nc e AI/C I
Computational intelligence is not merely a speed issue!
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 4
Abstract Intelligence (ααII)
• Intelligence is a human or system ability thatautonomously transfers a pieceautonomously transfers a pieceof information into a behavior:
:f I BI
• Abstract intelligence (I)
:f I BI
g ( )- A theory of intelligence sciencethat studies abstract, natural, and artificial intelligence across the neural, cognitive, functional, and mathematical levels from the bottom up.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 5
Roles of Intelligence in Cognitive Computing
The abstract world (AW)
I
The natural world (NW)
I
EM EM
The physical world (PW)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 6
Constraints of Classic Computers Constraints of Classic Computers
• The Turing and von Neumann machines are generic data• The Turing and von Neumann machines are generic dataprocessors created on a basic assumption that objectsand behavior of any computing problem can be reduced
t th bit l lonto the bit level.
• However, there is an entire range of complex problems inthe real world that may impossibly, or at least, inefficientlybe reduced onto bits.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 7
Data Processors vs. Knowledge Processors Data Processors vs. Knowledge Processors
• Is it possible to advance the classic computing theories Is it possible to advance the classic computing theoriesand technologies closer to those of human brains as anatural knowledge processor that does not reason in ?
• Instead of reducing every computing problem andsolution onto as in conventional data computers, the
t ti f k l d t knext generation of knowledge computers known ascognitive computers need to be able to directly processhuman knowledge in .
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 8
2. Cognitive Informatics (CI)2. Cognitive Informatics (CI)
1. Introduction► 2. Cognitive informatics (CI) g ( )
3. Denotational mathematics (DM)4. Cognitive computers (cCs) Cog t e co pute s (cCs)5. Conclusions
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 9
Cognitive Informatics Cognitive Informatics
• Cognitive informatics (CI) is a transdisciplinary enquiry Cognitive informatics (CI) is a transdisciplinary enquiryof computer science, information science, cognitivescience, and intelligence science, which studies:
- The internal information processing mechanisms andprocesses of natural intelligence;
- The theoretical framework and denotationalmathematics of abstract intelligence; Their engineering applications by cognitive computing- Their engineering applications by cognitive computing.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 10
Advances of Human Brain of Natural Intelligence
• What make human beingsas human?- Walk- Walk- Making tools- Work- Languagesg g- Abstract thinking/inference capability of the brain
• The quantitative advantage of human brain states that the magnitudeof the memory capacity of the brain is tremendously larger thanof the memory capacity of the brain is tremendously larger thanthat of the closest species.
• The qualitative advantage of human brain states that the possessionThe qualitative advantage of human brain states that the possessionof the abstract layer of memory and the abstract reasoning capacitymakes human brain profoundly powerful on the basis of thequantitative advantage.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 11
Abstract Intelligence (ααI)I)
• Abstract intelligence, I, is the universal mathematicalform of intelligence that transfers information intok l d d b h i
No. Form of intelligence Embodying means
knowledge and behaviors.
g y g1 Natural intelligence (NI) Naturally grown biological and
physiological organisms
2 A tifi i l i t lli (AI) C iti l i i d tifi i l d l2 Artificial intelligence (AI) Cognitively-inspired artificial models and man-made systems
3 Machinable intelligence (MI) Complex machine and wired systems4 Computational intelligence
(CoI)Computational methodologies and software systems
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 12
Theoretical Framework of Theoretical Framework of ααII
Logical model
Dimension of paradigms
Dimension of embodying
meansFunctional model means
Abstract Intelligence
(I)
Machinable Intelligence
Artificial Intelligence
Natural Intelligence
Computational Intelligence
Cognitive model
Neural model
Cognitive model
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 13
The Generic Abstract Intelligence Model (GAIM) The Generic Abstract Intelligence Model (GAIM)
K LTM
Stimuli I
I
Stimuli
I
D SBM
B ABM
Enquiries
BehaviorsIr
Ic
I
Ii ISTM
Ip
p
c
: (Perceptive)
|| : (Cognitive)
I D I
I K
I
Ic
i
r
|| ( g ) || : (Instructive) || : (Reflective)
I BD B
I
I
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 14
r|| : ( e ect ve)I
The Layered Reference Model of the Brain (LRMBThe Layered Reference Model of the Brain (LRMB))
LRMB: Configuration of Processes L if e b eh a v io r s a n d c om p le x a ct ion s
C o m p reh en s io n L e arn in g Pr o b le m D ecis io n C re at io n P la n n in g Pa t te rn s o lv in g m ak in g re co g n i t io n
L a ye r 7: T h e h ig h e r c o g n itiv e p ro c es se s
L a ye r 6: M et a in fe ren c e p ro c e ss es
O b je ct A b st r a- C on cep t C ateg o r i - Co m p a- M em or i - Q u al i fi - Q u an t i fi - Sele ct io n S ear ch M o d e l Im a g ery Id i f i b l i h i i i i i b l i h
D ed u c tio n In d u ct ion A b d u ct io n A n alo g y A n a lys is Sy n th es is
y p
L a ye r 5 : M et a co g n itive p ro ce ss es
W ir ed ac tio n s C on t in g e n t a ct ion s ( Sk i l l s) (T em p or ar y b eh av io rs )
Id en ti fy c t io n e sta b l is h . z at ion r i so n z at io n c at io n ca tio n es tab l i sh .
L a ye r 4: A c tio n p ro ce ss es
S b ff Sh t t L t A t i b ff
S el f- A t ten t ion M o t iv at ion an d E m o t ion s A tt i tu d es Se n s e o f Sen se o f C o n scio u s n e ss g o a l -s et t in g s p at ial i ty m ot io n
L a ye r 3: P e rce p tio n p ro c es se s
L a ye r 2: M em o ry p ro ce ss es
S en s o ry bu ffe r Sh o r t -term L o n g - te rm A ct ion b u ff er M em o ry M em o r y M e m or y M em o ry
V isio n A u d it io n Sm el l T ac ti l i ty T as te
L a ye r 1: S e n sa tio n a l p ro ce ss es
T h e p h ys io lo g ica l /n eu ro lo g ica l Br ain
The Abstract Intelligence Model of the Brainb n
Sensories
LTM (Visual)
[Occipital lobe]
STM (Working)
[Frontal lobe]
LTM (Knowledge)
[Temporal lobe]
LTM (Experience/episode)
[Parietal lobe]
O i i l
[Cerebrum]
Temporal lobe
EyesMUX
(attention
Vision
Audition FaceArms
BehaviorsSensories
Perception Engine
[Thalamus]
B-CPU
ABM
Action drive
Muscle servos
Occipital lobe
[Visual area]
lobe [Auditory
area] switch)
[Hippo-
campus]
[Pons]
Smell
Taste
Arms
Others…Conscious Engine
[Hypothalamus]
[Primary motor
cortex]
[Pons/ medulla]
[motor neurons]
Parietal lobe
[Somat. area]
Legs
[Pons]
Touch
SBM
CSM
[Cerebellum] Survival behaviors
[spinal cord]
area]
Reflect ive actions
Body stimuli
[Medulla] Stimuli
SBM
The Logical Model of the Brain (LMOB) - Wang, 2012
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 17
The The OAR Model OAR Model of Memory of Memory and Knowledgeand Knowledge
OAR = (O, A, R)O – objectA ibA – attributeR – relation
LTM: A hierarchical and partially connected neural clusters
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 18
3. Denotational Mathematics (DM)3. Denotational Mathematics (DM)
1. Introduction2. Cognitive informatics (CI) g ( )
► 3. Denotational mathematics (DM)4. Cognitive computers (cCs) Cog t e co pute s (cCs)5. Conclusions
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 19
ααI is Mainly a I is Mainly a Mathematical Mathematical Entity Entity
• The lasting vigor of automata theory, Turing machines,and formal inference methodologies reveals thatand formal inference methodologies reveals thatsuitable mathematical means such as set, relations,tuples, processes, and symbolic logics are theessences of abstract and computational intelligenceessences of abstract and computational intelligence.
• Although these profound mathematical structuresunderlie the modeling of natural and machineunderlie the modeling of natural and machineintelligence, the level of their mathematical entities istoo low to be able to process concepts, knowledge, and
i f b h i lseries of behavioral processes.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 20
Mathematical Foundations of Cognitive Computers Mathematical Foundations of Cognitive Computers
• The problem- The computing needs for complex real-world problems mayimpossibly, or at least, inefficiently be reduced onto bits ().p y, , y ( )
- Most of the complex entities in the real world cannot be abstractedand represented by pure numbers in or (real numbers).
• The finding- The computing problems are a Hyper Structure () beyond and .
E F l k l d b b h i l- E.g.: Formal knowledge, abstract concepts, behavioral processes, semantics, causations, inferences, abstract systems
• The need The need- Denotational mathematics (DM) - Those beyond Boolean algebra and predicate logic
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 21
New Problems Need New Forms of Mathematics
• The domain of problems in CI and ααI are Hyper Structures beyondthat of pure real numbers or bits .
• The maturity of a discipline is characterized by the maturity of itsmathematical means.
• The requirements for reduction of complex knowledge onto the low-level data objects in conventional computing technologies andtheir associated analytic mathematical means have greatly
t i d th i f d ti bilit t d thconstrained the inference and computing ability toward thedevelopment of intelligent knowledge processors known ascognitive computers.
• This has triggered the current transdisciplinary investigation intonew mathematical structures for I in the category of denotationalmathematics.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 22
Categories of Mathematics in Science & Engineering Categories of Mathematics in Science & Engineering
A l ti th ti d t i i ti f ti Analytic mathematics – deterministic functions on Analytic mathematics deals with mathematical entities with accuraterelations and functions.
Numerical mathematics – recursive and approx. functions on Numerical mathematics deals with mathematical entities with discreteand recursively approximate relations and functions.
Denotational mathematics – Series of dynamic functions on [HyperStructures] Denotational mathematics deals with high-level mathematical entitiesbeyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, inferences, decisions, intelligence, and systems.
Given a certain mathematical structure, when both its functions and I/O areadaptive in a series, it belongs to the category of denotational mathematics;otherwise, it falls into the category of analytic mathematics or numericalmathematicsmathematics.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 23
What is What is DMDM??
D t ti l th ti (DM) i t f• Denotational mathematics (DM) is a category of complex mathematical structures that deals with high-level mathematical entities in beyond numbersand sets, such as abstract objects, complex relations,perceptual information, abstract concepts, knowledge,intelligent behaviors, behavioral processes, formalg , p ,semantics, and systems.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 24
Denotational Denotational Mathematics Mathematics Function Category Mathematical Means
Conven- Denotationaltional
Identify objects & attributes
To be (|=) Logic Concept algebra Semantic algebra Visual semantic algebra (VSA)
Describe relations& i
To have (|) Set theory System algebra& possession
Describe status and To do (|>) Functions Behavioral process algebrabehaviors (BPA)
Inference algebra
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 25
DM: A Formal Means for Solving Problems in DM: A Formal Means for Solving Problems in CCCC
• The requirements for reduction of complex knowledgeonto the low-level data objects in conventionalonto the low level data objects in conventionalcomputing technologies and their associated analyticmathematical means have greatly constrained theinference and computing ability toward the developmentinference and computing ability toward the developmentof intelligent knowledge processors known as cognitivecomputers.
• This has triggered the current transdisciplinaryinvestigation into new mathematical structures for Ii th t f d t ti l th tiin the category of denotational mathematics.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 26
Paradigms of DM
Concept Algebra
Bank
bo = br = bs =
bank(organization) bank(river) bank(storage)
Words (ambiguity) vs. Concepts (unique)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 28
The Generic Model of an Abstract Concept
An abstract concept c is a 5-tuple, i.e.:
A
ORi RoOther Cs Other Cs
c
where
Rc
O R R Other Cs Other Cs
),,,,( oic RRRAOc where
O is a nonempty set of objects of the concept, O = {o1, o2, …, om} Þ, where Þ denotes a power set of abstract objects in the universaldiscourse U.
A is a nonempty set of attributes, A = {a1, a2, …, an} Þ, where Þdenotes a power set of attributes in U.
Rc = O A is a set of internal relations. Ri C c is a set of input relations, where C is a set of external
concepts in U. Ro c C is a set of output relations.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 29
bo = bank(organization)
b o S T = (A , O , R c, R i , R o ) = ( b o S T .A = { o r g a n i z a t io n , c o m p a n y , f in a n c ia l b u si n e s s , m o n e y , d e p o s it , w it h d r a w , in v e s t , e x c h a n g e } ,
b S T O = { in t e r n a t io n a l b a n k n a t io n a l b a n k b o S T .O = { in t e r n a t io n a l _ b a n k , n a t io n a l _ b a n k , lo c a l_ b a n k , in v e s tm e n t_ b a n k , A T M } b o S T .R c = O A , b o S T .R i = K b o S T , b o S T .R o = b oS T K )
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 30
br = bank(river)
b r S T = (A , O , R c , R i, R o ) = ( b rS T .A = {s id e s o f a r iv e r , ra is ed g ro u n d , a p i le o f ea r th , lo c a ti o n } , b r S T .O = { r iv e r_ b a n k , lak e _ b an k , ca n a l_ b a n k } b r S T .R c = O A , b r S T .R i = K b r S T ,
S T S T b r S T .R o = b r S T K )
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 31
bs = bank(storage)
b sS T = (A , O , R c , R i, R o) = ( b sS T .A = { s to rag e , co n ta i n er , p l ace , o rga n iz ati o n }, b sS T .O = { in fo rm ati o n b an k , res o u rce b an k ,{ _ , _ , b lo o d _ b a n k } b sS T .R c = O A , b sS T .R i = K b sS T ,
S T S T b sS T .R o = b sS T K )
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 32
Knowledge Representation in Concept Algebra
c1 c2
c3 Knowledge level (K) stationery
pen printer
O O
o11 o13 o12 o22 o21 Object level (U)
O1 O2
ballpoint fountain
b h I k j t
laser
AA2
brush Ink-jet
a4 a3 …A5 A6 A7 Attribute level (M)
A1
a1 a2
a writing using having with an ink a printing using with a toner tool ink a nib container tool papers cartridge
Concept Concept AlgebraAlgebra( )CA C OP Concept Algebra
(Wang, 2006) r p c
( , , )= ({ , , }, { , , }, )c i o
CA = C OPO A R , R , R
Concept Algebra
Operation Operator C1HS, C2HS Related RelatedBL C1HS, C2HS Independent IndependentBL
Relational Operations
( r )
C1HS, C2HS Superconcept SuperconceptBL
C1HS, C2HS Subconcept SubconceptBL
C1HS, C2HS Equivalent = EquivalentBL C1HS, C2HS Consistent ConsistentBL C1HS C2HS Comparison ~ DegreeOfSimilarityBLC1HS, C2HS Comparison ~ DegreeOfSimilarityBL
C1HS, C2HS Definition DefinedBL
Compositional Operations
C1HS Inheritance C2HS C1HS Tailoring C2HS
C1HS Extension + C2HS
HS HS( p )
Compositional Operations
( )
C1HS Substitute C2HS
c1HS Instantiation o1HS
C1HS, C2HS, …, CnHS Composition CHS CHS Decomposition C1HS, C2HS, …, CnHS
C1HS, C2HS, …, CnHS Aggregation Chs ( c )
cHS Specification C1HS, C2HS, …, CnHS
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 34
E.g. Equivalence and Comparison Operations
1 2 1 2 1 2c c A A O O ( ) ( )ˆ| |1 2
1 21 2
| A A |~| A A |
c c
1 2
1 2
0 ,1 =
c c, c c
1 2| |
2
1
| A , c c=| A
1 2| |
1
2
| A , c c| A
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 35
E.g. Concept Composition
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 36
The Mathematical Model of Memory/Knowledge The Mathematical Model of Memory/Knowledge Th b t t bj t k l d K i th b i i• The abstract object, knowledge K, in the brain is aperceptive representation of information by a function rkthat maps a given concept C0 into all related concepts,i.e.:
: ( ), Xn
k 0 i kK r C C r R
• The entire knowledge K is represented by a conceptt k hi h i hi hi l t k f t
= 1Xi
network, which is a hierarchical network of conceptsinterlinked by the set of nine associations defined inconcept algebra, i.e.:
: X Xi ji=1 j=1
= C C Kn n
i=1 j=1
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 37
4. 4. Cognitive Computers (Cognitive Computers (cCscCs))
1. Introduction2. Cognitive informatics (CI) g ( )3. Denotational mathematics (DM)
► 4. Cognitive computers (cCs) ► g p ( )5. Conclusions
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 38
Cognitive Computing: Cognitive Computing: Toward Machines that Learn and ThinkToward Machines that Learn and Think
• Cognitive Computing (CC) is an emerging paradigm of intelligent computing methodologies and systemsthat implements computational intelligence bythat implements computational intelligence byautonomous inferences and perceptions mimicking the mechanisms of the brain.
• CC is developed based on the trans-disciplinaryresearch in cognitive informatics and abstractintelligence.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 39
Cognitive Computers (Cognitive Computers (cCscCs) )
• Cognitive ComputersA cognitive computer (cC) is a category of intelligentcomputers that think, perceive, learn, and reason.
• cCs are designed for knowledge processing as that of a conventional von Neumann computer for dataprocessingprocessing.
• cCs are able to embody machinable intelligencefsuch as computational inferences, causal analyses,
knowledge manipulation, learning, and problem solving.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 40
CI Foundations for CI Foundations for cCscCs
• The theoretical framework of cognitive informatics [Wang 2002/07]• Information-Matter-Energy-Intelligence (IME-I) model [Wang 2002/06]• The Layered Reference Model of the Brain (LRMB) [Wang et al. 2006] • The Object-Attribute-Relation (OAR) model of knowledge
representation in the brain [Wang 2003/07]representation in the brain [Wang 2003/07]• The cognitive informatics model of the brain [Wang, 2003]• The computational intelligence model of the brain [Wang, 2003]• Abstract Intelligence (I) [Wang 2007]• Neuroinformatics [Wang 2003]
Th l i l/f ti l d l f th b i (LMOB/FMOB) [W 2012]• The logical/functional models of the brain (LMOB/FMOB) [Wang 2012]• The Cognitive Reference Model of Autonomous Agent Systems
[Wang 2008]
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 41
Denotational Mathematical Foundations of Denotational Mathematical Foundations of cCscCs
• Because the basic unit of knowledge is an abstract concept in , the mathematical model of knowledge is a Cartesian product of power sets of formal conceptspower sets of formal concepts.
: X Xi j= C C Kn n
• The mathematical foundations of classic data computers are Booleanalgebra and its logical counterparts in
X X ji=1 j=1
algebra and its logical counterparts in .
• The mathematical foundations of cognitive computers are based oncontemporary denotational mathematics (DMs) such as concept co te po a y de otat o a at e at cs ( s) suc as co ceptalgebra, inference algebra, semantic algebra and process algebra in for rigorously modeling and manipulating knowledge, perception,leaning and inferences.
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 42
Abstract Intelligence (ααI) I) Foundations of cCsB n
LTMSTM LTM LTM[Cerebrum]
V isio n Behaviors
Sensories
B-CPU
LTM(Visual)
[Occipital lobe]
STM (Working)
[Frontal lobe]
LTM (K no wledge)
[T emporal lobe]
LTM(Experience/ep isode)
[Parietal lobe]
Occipital lobe
[Visual
[Cerebrum]
Temporal lobe
[Auditory area]
E yesM UX
(a ttentio n sw itch)
[Hippo-
Auditio n FaceArms
…
Perception Engine
[Thalamus]
Conscious Engine
ABM [Primary
motor cortex]
Action drive
[Pons/
medulla]
M uscle servos
[motor neurons]
P i t l
[Visual area]
Legs[ pp
campus]
[Pons]
To uch
Smell
Taste
Others[Hypothalamus]
CSM
CSurvival behaviors
cortex]Parieta l lobe
[Somat. area]
Body stimu li
[Med lla ]Stimuli
SBM
[Cerebellum] [spinal cord]Reflective action s
[Medulla ] Stimuli
The Architectural Model of Cognitive Computers
• A cognitive computer (cC) is a category of intelligentcomputers that think, perceive, learn, and reason.p , p , ,- cCs: knowledge processors- von Neumann computers: data processors
• The architectural model of cCs
cC = AIE || CLE || SPE || FKB (CN)cC AIE || CLE || SPE || FKB (CN)- AIE: autonomous inference engine - CLE: cognitive learning engine
SPE: sensory perception engine- SPE: sensory perception engine - FKB: formal knowledge base- CN: concept network
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 44
The CPU of Cognitive ComputersFacet Conventional
Computers (DC)Cognitive computers
(CC)Objects Bits Concepts (Formal knowledge) Objects Bits
DataConcepts (Formal knowledge)CausationsSemantics
Basic Logic Concept identification
Basicoperations
LogicArithmeticFunctional
Concept identificationSemantic analysesBehavioral processes
Advancedoperations
AlgorithmsProcessesPrograms
Concept formulationKnowledge representationComprehensionL iLearningInferencesCausal reasoning
The Cognitive
CPUC U
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 45
The Behavioral Spaces of Cognitive Computers
CognitiveCSMachine
behaviorsHuman
behaviorsAutonomic
CS
behaviors behaviors
Imperative CS
BI = {Be, Bt, Bint} BA = {Bg, Bd} BI
B C={Bp, Bin f} BI BA
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 46
The Layered Reference Model of the Brain (The Layered Reference Model of the Brain (LRMBLRMB)) -- Wang etWang et al.,al., 20200606
The Cognitive Learning Engine (CLE)The Cognitive Learning Engine (CLE) Internal knowledge representation
Knowledge analyzer
Knowledge capturer
Knowledge integrator
Language knowledgebase
(WordNet) Knowledge presenter Information
input Knowledge
output
g p
Conceptual knowledge
representation(sOAR)
(Concept)
Logical knowledge
representation(OAR)
Physical knowledgebase
OAR/DCN visualization
input output
(DCN)
Concept formulator
Relational knowledge
manipulator
Memory manager
(CN updating)
Concept formulator
Compositional knowledge
manipulator The kernel of CLE
Knowledge retriever
(Queries)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 48
Cognitive Computing Based on Concept Algebra (1/3)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 49
Cognitive Computing Based on Concept Algebra (2/3)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 50
Cognitive Computing Based on Concept Algebra (3/3)
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 51
Final Result of Leaning by cCs
C gC ’ST = C gC ST
IC ST
K PS T = ( C C ’ST A = { C C ST A IC S T A K PS T A } ( C gC ST.A { C gC ST. A IC S T.A K PS T.A },
C gC ’ST .O = C gC ST .O IC ST .O K PS T.O , C gC ’ST .R c = O A ,
C C ’ST R i = OA R C C ’ C gC ST .R = OA R C gC , C gC ’ST .R o = C gC ’ O AR )
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 52
Advantages of Advantages of CLE in CLE in cCscCs
• Learn common or professional knowledge faster thanhuman does
• Learn and process knowledge continually beyond thenatural memory creation constraints of humansy
• They may never forget a piece of learned knowledge once that has been cognized and memorizedonce that has been cognized and memorized
• Most excitingly, they can directly transfer learned knowledge to peers without requiring re learningknowledge to peers without requiring re-learning because they use the same knowledge representationmodel and manipulation mechanisms
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5. Conclusions5. Conclusions
1. Introduction2. Cognitive informatics (CI) g ( )3. Denotational mathematics (DM)4. Cognitive computers (cCs) Cog t e co pute s (cCs)
► 5. Conclusions
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 54
ConclusionsConclusions
C f (C )• Cognitive informatics (CI)- Abstract intelligence (αI)- The Generic Abstract Intelligence Mode (GAIM) - The Layered Reference Model of the Brain (LRMB)The Layered Reference Model of the Brain (LRMB)- The Logical Model of the Brain (LMOB)
• Denotational mathematics (DM) - Extension of the computing domain from to - Extension of the computing domain from to - Concept algebra - System algebra- Behavioral process algebra (BPA)- Inference algebra- Visual semantic algebra (VSA)
• Cognitive computers (cCs) g p ( )- The CI foundations of cCs- The DM foundations of cCs- The αI foundations of cCs- cCs: architecture CPU behaviors and CLEcCs: architecture, CPU, behaviors, and CLE
ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 55
Application Areas of Application Areas of cCscCs
• A wide range of applications of cC & CI have beeng ppidentified such as:
- eBraineBrain- Cognitive networks for collective computational intelligence- Cognitive robots- Autonomous agent networksg- Cognitive learning engines- Distributed cognitive sensor networks- Cognitive inference engine- Cognitive Internet and WWW+
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Cognitive Cognitive Robots Robots -- IEEE Robotics & Automation IEEE Robotics & Automation
Wang, 2011
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ICIC
The International eBrain Consortium
T h e eB ra inC o ns o r ti u m
R e s ea r c h e r s ( 9 )
C a n a d i a nU n iv e r s it i es ( 8 )
I n d u st r ia lP a r t n er s ( 6 )
I nt er na t i on a lU n iv e rs i ti e s (2 )
K e y r es e ar ch er s ( 9 )
G r a d u at e s t u d e n t s /
U C B e r k e le yI B M C a n a d a
T R L b
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U . o f C a l ga r y
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S ta n fo r d U n i v .O r a cl e ( S u n ) C a n a d a
s t u d e n t s / P D Fs ( 4 0 ) T R L a b s
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E n g in e e rs of i n d u s tr i al p ar t n e r s ( 1 0 )
E M R G
A A I
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IEEE ICCI*CC 2012