Cognitive Computing Conference Paper on 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 Processors
vs.
Yingxu Wang, PhD, Prof., PEng, FWIF, FICIC, SMIEEE, SMACM
President, International Institute of
Cognitive Informatics & Cognitive Computing (ICIC)
Director,
Director Lab for Cognitive Informatics & Cognitive Computing
University of Calgary, Canada
Email: yingxu@ucalgary.ca
http://www.enel.ucalgary.ca/People/wangyx/

ICCSA’12, Salvador, Brazil, June 18-20, 2012 Dr. Y. Wang 1

1. Introduction

► 1. Introduction
2. Cognitive informatics (CI)
g ( )
3. Denotational mathematics (DM)
4. Cognitive co pute s (cCs)
Cog t e computers
5. Conclusions


The Need for Computational Intelligence in Intelligent Computers

• In celebrating the 100th anniversary of Turing and his
pioneer work, curiosity may lead to a fundamental
q
question:

- If more intelligent computers that think, reason, and
learn may be developed?
- They are known as Cognitive Computers (cCs)


Computing Power: Speed vs. Intelligence

I vc
N o rma l
hu ma n C omput ing
intellige nce spee d

3 ye ar
o ld kit s
kit’s
inte llige nc e A I/C I
// t
1940s 1950s 1980s 2010s

Computational intelligence is not merely a speed issue!


Abstract Intelligence (αI)
α
• Intelligence is a human
or system ability that
autonomously transfers a piece
of information into a behavior:

I  f :I  B
• Abstract intelligence (I)
g ( )
- A theory of intelligence science
that 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

M E
The physical world (PW )


Constraints of Classic Computers

• The Turing and von Neumann machines are generic data
processors created on a basic assumption that objects
and behavior of any computing problem can be reduced
onto th bit l
t the level.
l

• However, there is an entire range of complex problems in
the real world that may impossibly, or at least, inefficiently
be reduced onto bits.


Data Processors vs. Knowledge Processors

• Is it possible to advance the classic computing theories
and technologies closer to those of human brains as a
natural knowledge processor that does not reason in ?

• Instead of reducing every computing problem and
solution onto  as in conventional data computers, the
next generation of k
t ti f knowledge computers k
l d t known as
cognitive computers need to be able to directly process
human knowledge in .


2. Cognitive Informatics (CI)

1. Introduction
► 2. Cognitive informatics (CI)
g ( )
Cog t e computers
5. Conclusions


Cognitive Informatics

• Cognitive informatics (CI) is a transdisciplinary enquiry
of computer science, information science, cognitive
science, and intelligence science, which studies:

- The internal information processing mechanisms and
processes of natural intelligence;
- The theoretical framework and denotational
mathematics of abstract intelligence;
- Their engineering applications by cognitive computing.
computing


Advances of Human Brain of Natural Intelligence

• What make human beings
as human?
- Walk
- Making tools
- Work
- Languages
g g
- Abstract thinking/inference capability of the brain

• The quantitative advantage of human brain states that the magnitude
of the memory capacity of the brain is tremendously larger than
that of the closest species.

• The qualitative advantage of human brain states that the possession
of the abstract layer of memory and the abstract reasoning capacity
makes human brain profoundly powerful on the basis of the
quantitative advantage.


Abstract Intelligence (αI)
α

• Abstract intelligence, I, is the universal mathematical
form of intelligence that transfers information into
knowledge and behaviors.
k l d db h i

No. Form of intelligence
g Embodying means
y g
1 Natural intelligence (NI) Naturally grown biological and
physiological organisms
2 Artificial intelligence (AI)
A tifi i l i t lli Cognitively-inspired artificial models
C iti l i i d tifi i l d l
and man-made systems
3 Machinable intelligence (MI) Complex machine and wired systems
4 Computational intelligence Computational methodologies and
(CoI) software systems


Theoretical Framework of αI
Logical model

Dimension of Dimension of
paradigms embodying
means
Functional model

Computational Machinable Abstract Artificial Natural
Intelligence Intelligence Intelligence Intelligence Intelligence
(I)

Cognitive model

Neural model


The Generic Abstract Intelligence Model (GAIM)

K
LTM
Stimuli Ir
D B Behaviors
SBM
Ic ABM
Enquiries
Ip I Ii
STM

 I  I p : D  I (Perceptive)
|| I c : I  K ( g
(Cognitive)
)
|| I i : I  B (Instructive)
|| I r : D  B ( e ect ve)
(Reflective)


The Layered Reference Model of the Brain (LRMB)
(LRMB)

LRMB: Configuration of Processes
L if e b eh a v io r s a n d c om p l e x a ct i on s

L a ye r 7: T h e h i g h e r c o g n i ti v e p r o c es se s
C o m p reh en s i o n L e arn in g Pr o b le m D eci s i o n C re at i o n P la n n in g Pa t te rn
s o lv i n g m ak i n g re co g n i t io n

L a ye r 6: M et a i n fe r en c e p r o c e ss es
y

D ed u c ti o n In d u ct i on A b d u ct i o n A n al o g y A n a l ys i s Sy n t h es i s

L a ye r 5 : M et a co g n i ti ve p ro ce ss es
O b je ct A b st r a- C on cep t C at eg o r i - C o m p a- M em or i - Q u al i fi - Q u an t i fi - Sel e ct i o n S ear ch Mode l Im a g ery
Id en ti f
i fy c t io n e st a b l is h .
i i z at i on r i so n z at i o n c at i o n ca ti o n
i es t ab l i sh .
b h

L a ye r 4: A c ti o n p ro ce ss es
W ir ed ac ti o n s C on t in g e n t a ct i on s
( Sk i l l s) (T em p or ar y b eh av i o rs )

L a ye r 3: P e r ce p ti o n p r o c es se s
S el f- A t t en t i on M o t i v at i on an d E m o t i on s A tt i t u d es Se n s e o f Sen se o f
C o n s ci o u s n e ss g o a l -s et t in g s p at i al i t y m ot i o n

L a ye r 2: M em o r y p r o ce ss es
S en s o ry b ff r
bu ffe Sh o r t -t erm
t L o n g - t rm
te A ct i on b u ff er
t
M em o ry M em o r y M e m or y M em o ry

L a ye r 1: S e n sa ti o n a l p r o ce ss es

V i si o n A u d it i o n Sm el l T ac ti l i t y T as t e

T h e p h ys i o l o g i ca l /n eu ro l o g i ca l
B r ai n

The Abstract Intelligence Model of the Brain
b n

[Cerebrum] STM LTM LTM LTM
(Working) (Visual) (Knowledge) (Experience/episode)
[Frontal lobe] [Occipital lobe] [Temporal lobe] [Parietal lobe]

Sensories Occipital
O ii l Behaviors
Vision lobe B-CPU
[Visual
area] Eyes
Perception Engine ABM Action Muscle
MUX drive servos Face
Temporal [Thalamus]
Audition lobe (attention
Arms
[ Auditory switch) [Primary [Pons/ [motor
area] neurons] Legs
[Hippo- Conscious Engine motor medulla]
cortex] …
Smell Parietal campus] [Hypothalamus]
lobe Others
Taste [Somat.
area] [Pons]

Touch Body
stimuli CSM Survival behaviors
Stimuli [Medulla] Reflective [Cerebellum] [spinal cord]
actions
SBM

The Logical Model of the Brain (LMOB) - Wang, 2012


The OAR Model of Memory and Knowledge

OAR = (O, A, R)
O – object
A – attribute
ib
R – relation

LTM: A hierarchical and partially connected neural clusters


3. Denotational Mathematics (DM)

1. Introduction
g ( )
► 3. Denotational mathematics (DM)
Cog t e computers
5. Conclusions


αI is Mainly a Mathematical Entity

• The lasting vigor of automata theory, Turing machines,
and formal inference methodologies reveals that
suitable mathematical means such as set, relations,
tuples, processes, and symbolic logics are the
essences of abstract and computational intelligence
intelligence.

• Although these profound mathematical structures
underlie the modeling of natural and machine
intelligence, the level of their mathematical entities is
too low to be able to process concepts, knowledge, and
series of behavioral processes.
i fb h i l


Mathematical Foundations of Cognitive Computers

• The problem
- The computing needs for complex real-world problems may
impossibly, or at least, inefficiently be reduced onto bits ().
p y, , y ( )
- Most of the complex entities in the real world cannot be abstracted
and represented by pure numbers in  or  (real numbers).

• The finding
- The computing problems are a Hyper Structure () beyond  and .
-E
E.g.: F
Formal knowledge, abstract concepts, behavioral processes,
lk l d b b h i l
semantics, causations, inferences, abstract systems

• 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 αI are Hyper Structures  beyond
that of pure real numbers  or bits .

• The maturity of a discipline is characterized by the maturity of its
mathematical means.

• The requirements for reduction of complex knowledge onto the
low-level data objects in conventional computing technologies and
their associated analytic mathematical means have greatly
constrained th inference and computing ability toward the
t i d the i f d ti bilit t d th
development of intelligent knowledge processors known as
cognitive computers.

• This has triggered the current transdisciplinary investigation into
new mathematical structures for I in the category of denotational
mathematics.


Categories of Mathematics in Science & Engineering

 Analytic
A l ti mathematics – d t
th ti deterministic functions on 
i i ti f ti
Analytic mathematics deals with mathematical entities with accurate
relations and functions.
 Numerical mathematics – recursive and approx. functions on 
Numerical mathematics deals with mathematical entities with discrete
and recursively approximate relations and functions.
 Denotational mathematics –Series of dynamic functions on  [HyperStructures]
Denotational mathematics deals with high-level mathematical entities
beyond 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 are
adaptive in a series, it belongs to the category of denotational mathematics;
otherwise, it falls into the category of analytic mathematics or numerical
mathematics.
mathematics


What is DM?
DM?

•D
Denotational mathematics (DM) i a category of
t ti l th ti is t f
complex mathematical structures that deals with
high-level mathematical entities in  beyond numbers
and sets, such as abstract objects, complex relations,
perceptual information, abstract concepts, knowledge,
intelligent behaviors, behavioral processes, formal
g , p ,
semantics, and systems.


Denotational Mathematics
Function Category Mathematical Means

Conven- Denotational
tional
Identify objects & To be (|=) Logic  Concept algebra
attributes  Semantic algebra
 Visual semantic algebra (VSA)

Describe relations To have (|) Set theory  System algebra
& possessioni

Describe status and To do (|>) Functions  Behavioral process algebra
behaviors (BPA)
 Inference algebra


DM: A Formal Means for Solving Problems in CC

• The requirements for reduction of complex knowledge
onto the low level data objects in conventional
low-level
computing technologies and their associated analytic
mathematical means have greatly constrained the
inference and computing ability toward the development
of intelligent knowledge processors known as cognitive
computers.

• This has triggered the current transdisciplinary
investigation into new mathematical structures for I
in th
i the category of d
t f denotational mathematics.
t ti l th ti


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.:
c

A

Ri Ro

c  (O, A, R , R , R )
Other Cs O Other Cs
c i o c
R

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 universal
discourse U.
 A is a nonempty set of attributes, A = {a1, a2, …, an}  Þ, where Þ
denotes 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 s i 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 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 , r a is e d g r o u n d , a p i le o f
e a r th , lo c a ti o n } ,
b r S T.O = { r iv e r _ b a n k , la k e _ b a n k , c a n a l_ b a n k }
b r S T.R = O  A ,
c

b r S T.R = K  b r S T ,
i

b rS T o = b rS T  K
T.R
)


bs = bank(storage)

b s S T = ( A , O , R c , R i, R o )
= ( b s S T .A = { s to r a g e , c o n ta i n e r , p l a c e , o r ga n iz a ti o n },
b s S T .O = { in f o r m a ti o n _ b a n k , r e s o u r c e _ b a n k ,
b lo o d _ b a n k }
b s S T .R c = O  A ,
b s S T .R i = K  b s S T,
b s S T .R o = b s S T  K
)


Knowledge Representation in Concept Algebra

c3
pen printer Knowledge
c1 c2 level (K)
stationery

O1 O2
fountain
ballpoint
o11 o12 o13 o21 o22 Object level
laser (U)
brush
b h Ink-jet
I kj t

A2
A1

a1 a2 a3 a4 A5 A6 … A7 Attribute level
(M)

a writing using having with an ink a printing using with a toner
tool ink a nib container tool papers cartridge

Concept Algebra
 Concept Algebra CA= (C, OP, )
(Wang, 2006)
= ({O, A, R , R , R }, {r , p , c}, )
c i o

Concept Algebra
Operation Operator
C1HS, C2HS  Related   RelatedBL
C1HS, C2HS  Independent   IndependentBL
C1HS, C2HS  Superconcept   SuperconceptBL
Relational C1HS, C2HS  Subconcept   SubconceptBL
Operations
C1HS, C2HS  
( r )
Equivalent = EquivalentBL
C1HS, C2HS  Consistent   ConsistentBL
C1HS C2HS
HS,  Comparison ~  DegreeOfSimilarityBL
C1HS, C2HS  Definition   DefinedBL

C1HS  Inheritance   C2HS
C1HS  Tailoring   C2HS
Compositional
+
Operations C1HS  Extension   C2HS
( p ) C1HS  Substitute 
  C2HS

c1HS  Instantiation   o1HS
C1HS, C2HS, …, CnHS  Composition   CHS
Compositional CHS    C1HS, C2HS, …, CnHS
Operations Decomposition
C1HS, C2HS, …, CnHS    Chs
(c )
Aggregation
cHS  Specification   C1HS, C2HS, …, CnHS


E.g. Equivalence and Comparison Operations

c1  c2  ( A1  A2 )  (O1  O2 )
ˆ
| A1  A 2 |
c1 ~ c 2 
| A1  A 2 |
 0, c1  c 2
1 , c1 = c 2

 | A2 |
=  , c1  c 2
 | A1 |
 | A1 |
 , c1  c 2
 | A2 |


E.g. Concept Composition


The Mathematical Model of Memory/Knowledge
• Th abstract object, k
The b t t bj t knowledge K, i the brain is a
l d K in th b i i
perceptive representation of information by a function rk
that maps a given concept C0 into all related concepts,
i.e.:
n
K  rk : C0  ( XC ), r  R
i k
i =1

• The entire knowledge K is represented by a concept
network, which is a hierarchical network of concepts
t k hi h i hi hi l t k f t
interlinked by the set of nine associations  defined in
concept algebra, i.e.:
n n
K =  : XCi  XC j
i=1 j=1


4. Cognitive Computers (cCs)
(cCs)

1. Introduction
g ( )
► 4. Cognitive computers (cCs)
g p ( )
5. Conclusions


Cognitive Computing:
Toward Machines that Learn and Think

• Cognitive Computing (CC) is an emerging paradigm
of intelligent computing methodologies and systems
that implements computational intelligence by
autonomous inferences and perceptions mimicking
the mechanisms of the brain.

• CC is developed based on the trans-disciplinary
research in cognitive informatics and abstract
intelligence.


Cognitive Computers (cCs)
(cCs)

• Cognitive Computers
A cognitive computer (cC) is a category of intelligent
computers that think, perceive, learn, and reason.

• cCs are designed for knowledge processing as that of
a conventional von Neumann computer for data
processing.
processing

• cCs are able to embody machinable intelligence
such as computational inferences, causal analyses,
f
knowledge manipulation, learning, and problem solving.


CI Foundations for cCs

• 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]
• 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 models of the brain (LMOB/FMOB) [W
The logical/functional d l f th b i [Wang 2012]
• The Cognitive Reference Model of Autonomous Agent Systems
[Wang 2008]


Denotational Mathematical Foundations of cCs

• 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 concepts
concepts.
n n
K =  : XCi  XC j
i=1 j=1

• The mathematical foundations of classic data computers are Boolean
algebra and its logical counterparts in 
.

• The mathematical foundations of cognitive computers are based on
co te po a y de otat o a at e at cs (
contemporary denotational mathematics (DMs) such as concept
s) suc co cept
algebra, inference algebra, semantic algebra and process algebra in
 for rigorously modeling and manipulating knowledge, perception,
leaning and inferences.


Abstract Intelligence (αI) Foundations of cCs
α
B n

[Ce reb rum] STM LT M LTM LTM
( Wor king) ( Visua l) (K no wle dge) (Expe rienc e/ep isode )
[Fr ontal lo b e] [O ccip ital lo b e] [T emp or al l ob e] [ Pa rietal l ob e]
Se nsori es
Occ ipital Beh avi ors
V isio n lobe B- CPU
[ Vi sual
a rea] E ye s
Pe rc ep tion E ngine AB M Act ion M u sc le
M UX d rive se rvos F ac e
Temp oral
(a ttentio n [ Th ala mus]
Auditio n lobe Ar ms
[ Auditory sw itc h) [Primar y [P on s/ [ moto r
ar ea] mo to r medulla ] neurons ] Legs
[
[Hi pp o- Con sc ious En gine …
Pa i t
P rieta l corte x]
S me ll ca mp us] [ Hypoth ala mus] Othe rs
lobe
[S omat.
Ta ste are a] [Po ns]

To uch Body
stimu li CSM Su rvival b eh aviors
S timuli [ Me d lla ]
du Refle ctive [Ce re b ell um]
C [sp in al co rd]
S BM ac tion s

The Architectural Model of Cognitive Computers

• A cognitive computer (cC) is a category of intelligent
computers 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)
- AIE: autonomous inference engine
- CLE: cognitive learning engine
- SPE: sensory perception engine
- FKB: formal knowledge base
- CN: concept network


The CPU of Cognitive Computers
Facet Conventional Cognitive computers
Computers (DC) (CC)
Objects Bits  Concepts (Formal knowledge) 
Data Causations
Semantics
Basic Logic Concept identification
operations Arithmetic Semantic analyses
Functional Behavioral processes
Advanced Algorithms Concept formulation
operations Processes Knowledge representation
Programs Comprehension
Learning
L i The
Inferences
Causal reasoning
Cognitive
C U
CPU


The Behavioral Spaces of Cognitive Computers

Cognitive
Machine CS Human
behaviors behaviors
Autonomic
CS

Imperative
CS

B I = {B e , B t , B int } B A = { B g, B d}  B I

B C={B p, B in f}  B I  B A


The Layered Reference Model of the Brain (LRMB) - Wang et al., 2006
(LRMB) 2006

The Cognitive Learning Engine (CLE)
Internal knowledge representation
g p

Language
Knowledge Knowledge Knowledge Knowledge
Information knowledgebase
capturer analyzer integrator presenter Knowledge
input (WordNet)
output
Conceptual Logical
knowledge knowledge
(Concept) representation representation OAR/DCN
Physical visualization
(sOAR) (OAR) knowledgebase

(DCN)

Relational knowledge Memory manager
manipulator (CN updating)

Concept formulator

Compositional knowledge Knowledge retriever
manipulator (Queries)
The kernel of CLE


Cognitive Computing Based on Concept Algebra (1/3)


Final Result of Leaning by cCs

 
C g C ’S T = C gC S T  IC S T  K PS T
= ( C g C ’S T A = { C g C S T A  IC S T A  K PS T A }
S T.A T. T.A T.A },
C g C ’S T .O = C g C S T .O  IC S T .O  K PS T.O ,
C g C ’S T .R c = O  A ,
C g C ’S T .R i = O A R  C g C ’ ,
S R
C g C ’S T .R o = C gC ’  O A R
)


Advantages of CLE in cCs

• Learn common or professional knowledge faster than
human does

• Learn and process knowledge continually beyond the
natural memory creation constraints of humans
y

• They may never forget a piece of learned knowledge
once that has been cognized and memorized

• Most excitingly, they can directly transfer learned
knowledge to peers without requiring re-learning
re learning
because they use the same knowledge representation
model and manipulation mechanisms


5. Conclusions

1. Introduction
g ( )
Cog t e computers
► 5. Conclusions


Conclusions
•C
Cognitive informatics (C )
f (CI)
- Abstract intelligence (αI)
- The Generic Abstract Intelligence Mode (GAIM)
- 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 
- 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 CLE
architecture, CPU, behaviors,


Application Areas of cCs

• A wide range of applications of cC & CI have been
g pp
identified such as:

- eBrain
- Cognitive networks for collective computational intelligence
- Cognitive robots
- Autonomous agent networks
g
- Cognitive learning engines
- Distributed cognitive sensor networks
- Cognitive inference engine
- Cognitive Internet and WWW+


Cognitive Robots - IEEE Robotics & Automation

Wang, 2011


The International eBrain Consortium

T h e eB ra in
C o ns o r ti u m

R e s ea r c h e r s C a n ad i an I n d u st r ia l I nt er na t i on a l
( 9) U n iv e r s it i es ( 8 ) P a r t n er s ( 6 ) U n iv e rs i ti e s (2 )

K ey U . o f C a l ga r y IB M C a na da U C B e r k e le y
r es e ar ch er s
(9) U . o f A lb e r t a O r a cl e ( S u n ) S ta n fo r d U n i v .
Ca na da
G r a d u at e U . o f T or o n to
s t ude nt s / T R L a bs
P D Fs (4 0)
U . o f M a n i t ob a
In du s
U nde rgra d . A u to m at i on In c.
S tu d e n t s U . o f R eg in a
( 5 y e a rs , 10 0 ) A A I
R y er so n U .
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Cognitive Computing Conference Paper on Knowledge vs Data Processors

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