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


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)


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!


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.


Roles of Intelligence in Cognitive Computing

The abstract world (AW)

I

The natural world (NW)

I

EM EM

The physical world (PW)


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.


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 .


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


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.


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.


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


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


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


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


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


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


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.


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


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.


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.


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.


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


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.


Paradigms of DM

Concept Algebra

Bank

bo = br = bs =

bank(organization) bank(river) bank(storage)

Words (ambiguity) vs. Concepts (unique)


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.


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 )


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 )


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 )


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


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


E.g. Concept Composition


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


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


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.


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.


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]


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.


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


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


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


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)


Cognitive Computing Based on Concept Algebra (1/3)


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 )


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


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


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


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+


Cognitive Cognitive Robots Robots -- IEEE Robotics & Automation IEEE Robotics & Automation

Wang, 2011


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

U . o f A lb e r t a

U . o f C a l ga r y

U . o f T or o n to

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

U . o f M a n i t ob a

R U

I n d u s A u to m at i on In c. U . o f R eg in aU n d e r g r a d .

S tu d e n t s ( 5 y ea rs , 10 0) A A I

U . o f N ew B r u n s w ic k

U . o f W a t e r lo o

R y er so n U .

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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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