Artificial Cognition Systems

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23 Φεβ 2014 (πριν από 3 χρόνια και 6 μήνες)

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Artificial Cognition Systems

General Cognition Engine Module; January 2013

Marcelo Funes
-
Gallanzi, Ph.D.

The Goodwill Company, Ltd.

Guildford, England.

E
-
mail: mfg@thegoodwillcompany.co.uk


DISTRIBUTION A:

Approved for public release; distribution unlimited.


Executive Summary


In

short,

the

system

can

be

described

as

a

first
-
generation

general

cognition

engine
.

It

is

able

to

understand

natural

language,

that

is

successfully

acquire,

store

and

retrieve,

associate

and/or

process

concepts,

even

if

provided

by

unstructured

sources,

regardless

of

the

grammar

or

wording

used,

is

also

able

to

combine

contextual

information

and

user
-
supplied

information,

thanks

to

the

fact

that

the

position

of

information

in

its

knowledge

base

is

given

by

meaning,

regardless

of

the

source,

method

or

order

of

data

or

knowledge

input
.

It

is

also

able

to

improve

upon

itself

and

do

some

basic

“reasoning”

using

the

knowledge

available

in

its

knowledge

base

to

yield

suitable

replies,

statements

or

actionable

conclusions
.



The

scheme

involves

acquiring,

storing,

retrieving

and

processing

an

input

and

combining

said

input

with

knowledge

from

a

basic
-
level

categorization

representation

of

knowledge

realized

by

representing

knowledge

as

ideograms

in

a

multidimensional

space,

conditioned

by

abstract

relations

between

essential

words
;

as

in

those

of

Basic

English
.




At

the

International

Joint

Conference

for

Artificial

Intelligence

in

Pasadena,

California,

on

15

July

2009
,

the

results

of

an

analysis

by

a

panel

of

experts

were

presented

under

the

chairmanship

of

Eric

Horvitz

on

the

prospects

for

AI
.




Panel

members

agreed

that

creating

human
-
level

artificial

intelligence

is

possible

in

principle
.

Human
-
level

artificial

intelligence

or

self
-
evolving

machines

were

seen

as

long
-
term,

abstract

goals

not

yet

ready

for

serious

consideration
.





Panel

member

Tom

Dietterich

pointed

out

that

much

of

today’s

AI

research

is

not

aimed

at

building

a

general

human
-
level

AI

system,

but

rather

focuses

on

systems

which

are

effective

at

tasks

in

a

very

narrow

range

of

application,

such

as

mathematics
.




The

panel

discussed

at

length

the

idea

of

a

runaway

chain

reaction

of

machines

capable

of

building

ever
-
better

machines

at

length
.

Most

members

were

skeptical

that

such

an

AI

explosion

would

occur

in

the

foreseeable

future,

given

the

lack

of

projects

today

that

could

lead

to

systems

capable

of

improving

upon

themselves

which

exist

today
.




Our

research

programme

is

precisely

directed

at

building

a

human
-
level

artificial

cognition

system

capable

of

improving

upon

itself
.

Background information




In

order

to

achieve

the

goal

of

developing

a

viable

artificial

cognition

system,

we

first

of

all

need

to

develop

a

general

cognition

engine

(GCE),

i
.
e
.

a

self
-
evolving

brain

analogue

that

is

able

to

acquire

unstructured

knowledge

(e
.
g
.

books,

speech,

patents,

etc
.
),

store

and

retrieve

knowledge,

and

constantly

improve

upon

itself,

irrespective

of

the

field

of

knowledge
.

This

module

is

commonly

referred

to

as

the

knowledge

database

in

currrent

systems,

which

are

normally

trained

with

information,

deal

with

structured

or

semi
-
structured

knowledge

and

cannot

self
-
evolve
.




Possibly

the

most

fundamental

aspect

of

cognition

is

memory,

which

is

itself

broken

down

into

3

distinct

tasks
:

acquisition,

storage

and

retrieval
;

bearing

in

mind

that

it

is

well
-
established

that

order

helps

memory
.



Knowledge

and

experience

are

most

easily

transferred

through

language

and

at

the

root

of

the

concept

of

language

lies

the

very

definition

of

a

word
.


Wittgenstein

(
1953
)

suggested

that

there

exists

a

"family

resemblance"

which

allows

us

to

identify

a

particular

instance

as

a

member

of

a

group,

and

following

the

work

of

Rosch

(
1973
)

we

can

conceive

of

these

family

resemblances

as

based

on

the

fact

that

the

human

brain

really

possesses

prototypes,

in

order

to

represent

the

meaning

of

a

particular

word

in

relation

to

these
.


She

found

that

there

are

in

fact

about

400

core

concepts

in

western

children,

which

are

intensively

used

in

growing

up

to

interpret

meaning
.


Moreover,

Rosch

argued

that

there

is

a

"natural"

level

of

categorization

that

we

tend

to

use

to

communicate
.


This

level

is

known

as

"basic
-
level

categorization"
.



Using

the

idea

of

meaning

being

defined

in

terms

of

a

word's

relationship

to

others

is

attractive

but

involves

deriving

a

matrix

of

word

relations

that

implies

many

more

entries

than

the

average

number

of

neurons

in

a

human

brain,

if

a

standard

vocabulary

is

used
.

This

fact

leads

us

to

two

conclusions
:

first,

that

it

is

likely

that

basic
-
level

categorization

is

in

fact

used

by

the

brain,

and

second

that

it

would

be

useful

to

find

a

way

to

represent

a

full

vocabulary

in

terms

of

a

reduced

vocabulary,

that

can

act

as

a

proxy

for

this

set

of

basic
-
level

categorizations
.

Background information




Simplish

is

a

tool

that

converts

standard

English

into

a

reduced
-
vocabulary

version

of

1
,
000

words,

850

basic

words,

50

international

words

and

100

specialized

words,

and

we

propose

to

use

this

as

a

proxy

for

a

set

of

basic
-
level

categorizations,

there

being

other

potential

candidates
.

This

representation

therefore

yields

an

effective

means

of

knowledge

acquisition
.

A

reduced
-
vocabulary

also

has

the

advantage

of

reducing

ambiguity

as

a

by
-
product

of

the

translation

process
.



Using

a

reduced
-
vocabulary

representation

enables

the

mapping

of

language,

through

the

use

of

standard

multivariate

techniques,

to

a

low
-
dimensionality

space

where

a

multidimensional

ideogram

(a

graphic

symbol

that

represents

an

idea

or

concept)

can

be

produced

as

an

illustrative

point

in

this

subspace,

as

might

be

represented

in

the

brain

(using

variables

such

as

coordinates

x,

y,

z,

potential,

neurotransmitter,

frequency,

phase,

etc
.
)
.

The

low
-
dimensionality

ideogram

is

the

storage

medium
.

These

ideograms

will

be

similar

even

if

different

words

or

grammar

are

used,

because

their

form

and

position

is

given

by

the

relationship

of

a

concept/word

to

all

other

members

of

the

basic
-
level

categorization
.




The

result

of

this

strategy

will

be

to

establish

a

means

to

map

semantic

similarity

into

spatial

proximity,

i
.
e
.

the

distance

between

two

points

(concepts)

is

a

measure

of

how

similar

their

meanings

are
.



Spatial

proximity

can

be

used

to

yield

a

means

for

information

retrieval

for

a

given

query,

via

the

association

of

ideas

as

a

human

does,

implemented

here

through

the

application

of

nearest
-
neighbour

search

algorithms,

a

method

that

is

well
-
known

in

the

art
.



This

strategy

enables

concepts

to

be

mapped

either

as

part

of

a

data
-
driven

step

or

concept
-
driven

contextual

information

needed

for

problem
-
solving
.

We

can

plot

and

step

along

an

evolving

path

and

come

across

both

types

of

information

if

relevant
.



Thus,

we

can

move

from

keywords

and

pattern
-
matching

to

concept
-
matching
,

for

instance

in

a

web

search

engine!

Background information




STANDARD

ENGLISH

BASIC
ENGLISH

(Annotated)

100,000

+
Words

incl. 30,000 scientific

1,000
+
Words

www.simplish.org


A standard English to Basic English tool



Some

words

such

as

“name”

have

arguments

(e
.
g
.

Jesus)

while

others

do

not

(e
.
g
.

flat)
.




Idiomatic

phrases,

names

and

many

places

are

also

considered



Personal

dictionary

allows

adapting

the

translation

to

the

user's

vocabulary



File

standards
:

.
doc,

.
txt,

.
pdf

&

.
html



Aimed

also

at

Orientals

(
5
m

S&T

graduates

last

year!
...
)

&

non
-
scientists



Pending

improvements
:

apostrophes,

images,

spaces,

hyphens,

etc
.

This approach enables:

UNIQUE INFINITE EXPRESSIVENESS



The

use

of

basic
-
level

categorization

in

order

to

represent

the

meaning

of

a

particular

word,

in

relation

to

all

other

words,

results

in

a

matrix

where

all

words

can

be

related

to

each

other
.


This

matrix

kernel

is

what

confers

order

to

the

memory

process,

each

such

matrix

being

equivalent

to

describing

one

mind's

perception,

so

no

confusion

(deriving

from

many

authors'

use

of

language)

as

in

a

corpus

of

data

so

ambiguity

is

reduced
.

Thus,

the

shape

and

position

of

an

ideogram

depends

on

this

unique

broad

abstract

“association

criteria”,

with

no

need

for

sp
ecific

training

or

ontologies

(descriptions

of

the

concepts

and

relationships

that

can

exist

for

an

agent

or

a

community

of

agents)
.




Entries

are

rated

between

opposite

(minimum)

up

to

highly

related

(maximum)

with

columns

being

ordered

by

syntax

and

rows

by

a

rough

semantic

classification

of

50

semantic

tags,

so

word

order

is

important
;

unlike

in

say

Latent

Semantic

Analysis
.




In

fact,

various

models

already

exist

that

provide

automatic

means

of

determining

similarity

of

meaning

by

analysis

of

large

text

corpora,

without

any

understanding
.


Order in Memory...






The

matrix

of

basic

broad

abstract

relations

between

words

as

proposed

by

Wittgenstein

can

be

converted

into

a

low
-
dimensionality

representation

using

standard

singular

value

decomposition

methods,

such

as

Principal

Component

Analysis
.

This

subspace,

conditioned

by

word

relations,

is

used

to

display

scientific

words

and

all

user

data,

with

a

position

given

by

the

meaning

of

user
-
defined

data

streams
.





We

can

then

display

the

semantic

relations,

converted

into

spatial

distances,

between

words

in

a

low
-
dimensionality

space

as

shown

(and

equivalently

for

the

case

of

syntax)
:

We

can

also

display

more

complex

concepts

being

explained

in

terms

of

Basic

English

by

assigning

a

high

value

to

the

words

being

used

in

an

illustrative

labelled

extra

row

displayed

in

the

GCE

space

such

as
:

Humerus

-

The bone of the top part of the arm in man


In

some

cases,

a

mapping

module

converts

words

into

a

valid

form

(e
.
g
.

“worked”



“past

work”

&

“unsafe”

-

not

safe)
.

It

also

deals

with

compound

words

(e
.
g
.

“outline”)
.



More

complex

concepts

can

be

displayed

that

use

previously

defined

simpler

concepts
:

Elbow

-

JOINT in the arm between the HUMERUS and the ULNA.

The

problem

here

is

that

in

order

to

map

the

word

“elbow”

we

also

need

to

use

points

defined

by

“joint”

&

“ulna”

as

well

as

humerus
:

Joint

-

part

or

structure

where

two

bones

or

parts

of

an

animal's

body

are

so

joined

that

they

have

the

power

of

motion

in

relation

to

one

another
.


Ulna

-

The

back

one

of

the

two

bones

of

the

lower

front

leg

in

animals

with

4

legs

or

the

arm

in

man
.


So,

the

solution

is

that

where

we

need

to

use

previously

defined

points

to

display

a

new

more

complex

concept,

we

can

simply

join

them

together

and

build

trajectories

(i
.
e
.

ideograms)

between

a

number

of

points

in

the

GCE
.

This

trajectory

definition

is

done

by

the

mapping

module,

which

segments

phrases

as

required
.

Of

course,

we

can

build

trajectories

with

many

sentences,

even

multiple

sources

multiplexed

all

updating

the

GCE

in

a

parallelized

scheme
,

and

in

that

process

extrapolate

and

come

across

relevant

facts,

thereby

resolving

incorporating

contextual

data

to

a

data
-
driven

process
.


Displaying knowledge in a preconditioned space...



Trajectories
-

Ideograms

A segmentation module helps to determine fragments
that can be assigned to a point and those that must be
broken down and displayed as a trajectory.

Elbow
-

JOINT in the arm between the HUMERUS
and the ULNA...

this is really just a machine
-
generated
multidimensional ideogram!

For any segment/point in a trajectory there is a
definition:

[JOINT ; in the arm between the ; HUMERUS]

[HUMERUS ; and the ; ULNA]


Compare with Chinese symbols for example:




man” +



tree” =



to rest” or...




sun” +



moon” =





clear, bright”.

or even the Aztec symbol
for Mazatlan:

Mazatl: deer

tlantle: teeth



In

order

to

check

if

similar
-
meaning

sentences

in

fact

are

displayed

near

one

another,

and

the

computer

is

actually

able

to

understand

language,

we

can

try

displaying

four

similar

sentences,

taken

from

the

New

Testament
:

LU

-

LUKE

23
:
38

And

these

words

were

put

in

writing

over

him,

THIS

IS

THE

KING

OF

THE

JEWS
.

MT

-

MATHEW

27
:
37

-

And

they

put

up

over

his

head

the

statement

of

his

crime

in

writing,

THIS

IS

JESUS

THE

KING

OF

THE

JEWS
.

MC

-

MARK

15
:
26

-

And

the

statement

of

his

crime

was

put

in

writing

on

the

cross,

THE

KING

OF

THE

JEWS
.

JH

-

JOHN

19
:
19

And

Pilate

put

on

the

cross

a

statement

in

writing
.

The

writing

was
:

JESUS

THE

NAZARENE,

THE

KING

OF

THE

JEWS
.


Notes
:


1
)

where

words

have

arguments,

multiple

points

in

the

same

position

are

generated

(Jesus/Pilate)
.


2
)

There

is

no

training,

just

the

sentence

being

displayed

in

the

GCE

module

memory

space

according

to

meaning!

Demonstration of

computer

understanding





In

the

previous

graphical

display

we

can

see

that

this

method

does

indeed

convert

semantic

similarity

into

spatial

proximity!

Thus,

if

two

phrases

have

the

same

meaning

they

will

be

mapped

close

to

each

other,

even

if

different

words

or

grammar

is

used
.




In

the

display

we

can

see

that

Mathew

and

Luke

are

closest,

with

Mark

who

mentions

the

cross

some

distance

away,

and

the

sentence

that

is

most

dissimilar

of

John

lies

furthest

away
.





It

is

possible

to

do

some

conventional

ascending

hierarchical

clustering

and

show

these

relationships

as

below
:














Grouping

into

clusters

has

the

effect,

more

generally,

of

agglomerating

information

according

to

knowledge

domain
.

Thus,

information

about

anatomy,

the

New

Testament,

etc
.

will

agglomerate

into

distinct

clusters
.






REPRESENTATION OF THE HIERARCHICAL CLASSIFICATION




+
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+
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+
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+
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+
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+
---------
+
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+




lu
---------------
*
----
*
----------------------------------------------------
*
------------------------
*
-


| | | |


mt
----------------

| | |


| | |


mc
---------------------

| |


| |


jh
--------------------------------------------------------------------------

|


|


prueba
---------------------------------------------------------------------------------------------------






One

can

obviously

also

do

the

reverse

and

create

a

dummy

ideogram

for

a

concept

for

which

a

certain

response

is

required
.

This

can

include

the

firing

of

contextual

information,

updating

or

other

actions

related

to

a

given

concept
.

Note

that

the

reply

to

a

given

type

of

concept,

however

stated,

does

not

have

to

be

a

simple

answer
.

It

could

have

contextual

data

attached

to

the

dummy

ideogram,

routines

that

have

to

be

performed,

updating,

monitoring,

calculations,

etc
.

in

order

to

give

an

output

such

as

a

command,

action,

answer

or

a

simple

statement

as

a

response
.


For

illustration

purposes,

the

diagnosis

of

diabetes

could

be

undertaken

by

uploading

(although

the

system

could

have

acquired

such

knowledge

itself

by

“reading”

a

book

for

instance

and

mapping

this

knowledge

to

the

correct

position

and

form)

onto

the

knowledge

base

5

common

symptoms

and

if

they

are

found

to

be

true,

then

output

the

diagnosis

of

suspected

diabetes
.

In

this

specific

case

some

more

complicated

vocabulary

is

also

required

in

order

to

display

the

relevant

ideograms
:


Dummy

ideograms

to

serve

as

contextual

knowledge
:

[Med
.
]

An

increase

in

thirst

or

urination

in

a

child

is

a

sign

of

diabetes
.
[Med
.
]

Lethargy

in

a

child

is

a

sign

of

diabetes
.
[Med
.
]

Increased

desire

for

food

with

sudden

or

unexplained


weight

loss

in

a

child

is

a

sign

of

diabetes
.
[Med
.
]

Vision

changes

in

a

child

is

a

sign

of


diabetes
.
[Med
.
]

Odor

of

fruit

to

the

breath

in

a

child

is

sign

of

diabetes
.

Diagnosis

[answer] Diabetes in a child has five common signs that have to be confirmed.


Responding to

a
given

concept
-

I



Responding to

a
given

concept
-

Diagnosis

In

this

example,

if

the

user

enters

a

sentence

that

is

semantically

close

to

one

of

the

symptoms

(“My

child

is

thirsty

and

goes

to

urinate

all

day”),

whatever

the

specific

wording

or

grammar,

the

mapping

process,

contextual

knowledge

and

association

modules

enable

the

system

to

identify

the

suspected

symptom

and,

if

all

other

symptoms

are

confirmed

in

the

patient,

the

system

is

able

to

confirm

the

diagnosis

as

diabetes
:







In

order

to

test

the

viability

of

this

approach

for

scientific

material

we

took

the

example

of

some

knowledge

about

anatomy

(
260

concepts)
.

If

we

look

at

the

following

three

phrases
:


1)
Joint

in

the

arm

between

the

humerus

and

the

ulna
.


2)
Outgrowth

of

bone

at

the

top

end

of

the

ulna,

forming

the

point

of

the

elbow,

to

which

the

muscle

pulling

the

lower

arm

straight

is

fixed
.


3)
A

rounded

expansion

at

the

end

of

a

bone

which

goes

into

the

hollow

end

of

another

bone

forming

a

joint

with

limited

power

of

motion
.



The

3

concepts

lie

very

close

to

each

other
.

Thus,

unstructured

knowledge

can

be

acquired,

stored

in

a

form

and

position

related

to

its

meaning

and

retrieved,

with

similar

semantic

units

being

stored

in

a

similar

shape

and

position,

regardless

of

the

specific

wording

or

grammar

used,

without

any

kind

of

further

training

or

association

of

ideas

as

in

an

ontology

for

example,

i
.
e
.

the

system

is

able

to

“understand”

the

meaning

of

language

and

logically

stores

knowledge

accordingly

(definition

of

elbow,

olecranon

&

condyle

respectively

above)
.



On

the

other

hand,

this

GCE

is

able

to

correctly

acquire,

store

and

retrieve

these

three

concepts

successfully

and

identify

that

they

are

closely

related
.

If

more

information

is

input,

it

can

be

displayed

in

the

correct

semantic

position

and

where

many

equivalent

sentences

are

input,

the

GCE

can

fuse

together

trajectories

as

equivalent,

if

ideograms

are

similarly

shaped

and

close
.




Specialist knowledge & vocabulary



Specialist knowledge & vocabulary

Comparison of information (I)

As

an

example

of

the

capability

to

compare

incoming

information,

4

paragraphs

were

compared

and,

by

clustering

analysis

in

the

GCE

space,

those

whose

information

was

corroborated

by

one

or

more

other

sources

were

highlighted
.

The

random

example

chosen

was

of

the

descriptions

of

the

arrival

of

Christ

to

Jerusalem

in

the

New

Testament

(
Mathew

21

(
1
-
11
),

Mark

11

(
1
-
11
),

Luke

19

(
28
-
40
)

&

John

12

(
12
-
19
))

with

no

other

text

used

neither

to

compare

to

or

train

the

engine
:


Comparison of information (II)

The

Gospel

of

Mathew

is

the

most

corroborated,

except

for

one

fragment

of

text
.

Mark

and

Luke

increasingly

differ,

while

only

one

segment

is

corroborated

of

what

John

reports
.

Based

on

the

corroboration

of

specific

concepts

by

other

Gospels,

we

can

conclude

that

the

most

reliable

is

Mathew,

followed

by

Mark,

Luke

and

lastly

John,

whose

account

is

the

least

reliable

one
;

a

view

consistent

with

the

order

in

which

the

Gospels

are

generally

believed

to

have

been

written
.


Luke,

19

(
28
-
40
)

And

when

he

had

said

this,

he

went

on

in

front

of

them,

going

up

to

Jerusalem
.

And

it

came

about

that

when

he

got

near

Beth
-
phage

and

Bethany

by

the

mountain

which

is

named

the

Mountain

of

Olives,

he

sent

two

of

the

disciples,

Saying,

Go

into

the

little

town

in

front

of

you,

and

on

going

in

you

will

see

a

young

ass

fixed

with

a

cord,

on

which

no

man

has

ever

been

seated
;

let

him

loose

and

take

him
.

And

if

anyone

says

to

you,

Why

are

you

taking

him?

say,

The

Lord

has

need

of

him
.

And

those

whom

he

sent

went

away,

and

it

was

as

he

said
.

And

when

they

were

getting

the

young

ass,

the

owners

of

it

said

to

them,

Why

are

you

taking

the

young

ass?

And

they

said,

The

Lord

has

need

of

him
.

And

they

took

him

to

Jesus,

and

they

put

their

clothing

on

the

ass,

and

Jesus

got

on

to

him
.

And

while

he

went

on

his

way

they

put

their

clothing

down

on

the

road

in

front

of

him
.

And

when

he

came

near

the

foot

of

the

Mountain

of

Olives,

all

the

disciples

with

loud

voices

gave

praise

to

God

with

joy,

because

of

all

the

great

works

which

they

had

seen
;

Saying,

A

blessing

on

the

King

who

comes

in

the

name

of

the

Lord
;

peace

in

heaven

and

glory

in

the

highest
.

And

some

of

the

Pharisees

among

the

people

said

to

him,

Master,

make

your

disciples

be

quiet
.

And

he

said

in

answer,

I

say

to

you,

if

these

men

keep

quiet,

the

very

stones

will

be

crying

out
.


John,

12

(
12
-
19
)

The

day

after,

a

great

number

of

people

who

were

there

for

the

feast,

when

they

had

the

news

that

Jesus

was

coming

to

Jerusalem,

Took

branches

of

palm
-
trees

and

went

out

to

him,

crying,

A

blessing

on

him

who

comes

in

the

name

of

the

Lord,

the

King

of

Israel!

And

Jesus

saw

a

young

ass

and

took

his

seat

on

it
;

as

the

Writings

say,

Have

no

fear,

daughter

of

Zion
:

see

your

King

is

coming,

seated

on

a

young

ass
.

(These

things

were

not

clear

to

his

disciples

at

first
:

but

when

Jesus

had

been

lifted

up

into

his

glory,

then

it

came

to

their

minds

that

these

things

in

the

Writings

were

about

him

and

that

they

had

been

done

to

him
.
)

Now

the

people

who

were

with

him

when

his

voice

came

to

Lazarus

in

the

place

of

the

dead,

and

gave

him

life

again,

had

been

talking

about

it
.

And

that

was

the

reason

the

people

went

out

to

him,

because

it

had

come

to

their

ears

that

he

had

done

this

sign
.

Then

the

Pharisees

said

one

to

another,

You

see,

you

are

unable

to

do

anything
:

the

world

has

gone

after

him
.

Mathew

21

(
1
-
11
)

And

when

they

were

near

Jerusalem,

and

had

come

to

Beth
-
phage,

to

the

Mountain

of

Olives,

Jesus

sent

two

disciples,

Saying

to

them,

Go

into

the

little

town

in

front

of

you,

and

straight

away

you

will

see

an

ass

with

a

cord

round

her

neck,

and

a

young

one

with

her
;

let

them

loose

and

come

with

them

to

me
.

And

if

anyone

says

anything

to

you,

you

will

say,

The

Lord

has

need

of

them
;

and

straight

away

he

will

send

them
.

Now

this

took

place

so

that

these

words

of

the

prophet

might

come

true,

Say

to

the

daughter

of

Zion,

See,

your

King

comes

to

you,

gentle

and

seated

on

an

ass,

and

on

a

young

ass
.

And

the

disciples

went

and

did

as

Jesus

had

given

them

orders,

And

got

the

ass

and

the

young

one,

and

put

their

clothing

on

them,

and

he

took

his

seat

on

it
.

And

all

the

people

put

their

clothing

down

in

the

way
;

and

others

got

branches

from

the

trees,

and

put

them

down

in

the

way
.

And

those

who

went

before

him,

and

those

who

came

after,

gave

loud

cries,

saying,

Glory

to

the

Son

of

David
:

A

blessing

on

him

who

comes

in

the

name

of

the

Lord
:

Glory

in

the

highest
.

And

when

he

came

into

Jerusalem,

all

the

town

was

moved,

saying,

Who

is

this?

And

the

people

said,

This

is

the

prophet

Jesus,

from

Nazareth

of

Galilee
.



Mark

11

(
1
-
11
)

And

when

they

came

near

to

Jerusalem,

to

Beth
-
phage

and

Bethany,

at

the

Mountain

of

Olives,

he

sent

two

of

his

disciples,

And

said

to

them,

Go

into

the

little

town

opposite
:

and

when

you

come

to

it,

you

will

see

a

young

ass

with

a

cord

round

his

neck,

on

which

no

man

has

ever

been

seated
;

let

him

loose,

and

come

back

with

him
.

And

if

anyone

says

to

you,

Why

are

you

doing

this?

say,

The

Lord

has

need

of

him

and

will

send

him

back

straight

away
.

And

they

went

away

and

saw

a

young

ass

by

the

door

out
-
side

in

the

open

street
;

and

they

were

getting

him

loose
.

And

some

of

those

who

were

there

said

to

them,

What

are

you

doing,

taking

the

ass?

And

they

said

to

them

the

words

which

Jesus

had

said
;

and

they

let

them

go
.

And

they

took

the

young

ass

to

Jesus,

and

put

their

clothing

on

him,

and

he

got

on

his

back
.

And

a

great

number

put

down

their

clothing

in

the

way
;

and

others

put

down

branches

which

they

had

taken

from

the

fields
.

And

those

who

went

in

front,

and

those

who

came

after,

were

crying,

Glory
:

A

blessing

on

him

who

comes

in

the

name

of

the

Lord
:

A

blessing

on

the

coming

kingdom

of

our

father

David
:

Glory

in

the

highest
.

And

he

went

into

Jerusalem

into

the

Temple
;

and

after

looking

round

about

on

all

things,

it

being

now

evening,

he

went

out

to

Bethany

with

the

twelve
.




We

are

also

able

to

upload

in

free
-
text

form

general

knowledge

and

memory

as

a

human

brain

would

possess

so

that

the

system

can

respond

adequately

and

pass

the

Turing

test

for

example
.

We

uploaded

a

general

knowledge

and

basic

memory

file

into

the

GCE

of

our

conversational

agent

Rachael

Repp

(
www
.
rachaelrepp
.
org
)

where

the

source

file

can

be

found
.

It

contains

a

little

about

history,

about

Rachael's

house

and

also

her

family
.

Question/imperative/statement

forms

as

well

as

anaphora

&

cataphora

resolution

are

implemented

in

the

conversational

agent

and

in

the

process

of

being

ported

to

the

GCE
.






Memory and general knowledge

Of

course,

the

nearest

concept

to

a

user

enquiry/comment

in

fact

could

be

quite

far
...

hence

the

need

for

a

substantial

memory/knowledge

base

(vocabulary,

books,

memory)

so

that

responses

are

closely

related

to

the

input
.

Many

simultaneous

sources

can

be

used

to

update

the

knowledge

base,

being

displayed

in

a

position

dependent

on

their

meaning

so

that

contextually

important

information

will

immediately

be

displayed

to

a

user

looking

at

a

specific

subject

(i
.
e
.

a

sphere

of

interest

in

space)
.

We

can

also

create

dummy

ideograms

to

give

a

reply

or

command

to

a

certain

concept,

which

can

be

embodied

in

a

point,

trajectory

or

event

a

group

of

interconnected

ideograms,

that

could

have

arguments,

such

as

speed,

bearing,

etc
..



Logic capabilities: word problems

We

have

implemented

a

logic

function

for

equivalence

based

on

a

minimum

distance

between

concepts

below

which

we

define

two

concepts

as

being

equivalent,

and

for

the

case

of

similarity

we

again

define

a

slightly

larger

range

of

distances

over

which

we

consider

two

concepts

as

being

similar
.

There

is

another

form

of

logic

that

is

easy

to

implement

using

the

well
-
known

STUDENT

algorithm
:

word

problems
.

To

test

this

capability,

we

stated


In

my

university,

the

number

of

professors

is

not

enough

for

the

number

of

students


and

found

using

a

standard

nearest
-
neighbour

algorithm

(means

of

retrieval)

that

the

nearest

sentence

in

the

memory

space

was


the

number

of

students

is

50

times

the

number

of

professors



Now,

by

extracting

all

intersecting/interacting

trajectories

(i
.
e
.

a

contextual

information

search
),

it

is

possible

to

derive

other

information

such

as


the

number

of

professors

plus

the

number

of

students

is

2040


and

other

contextual

information,

which

allows

the

system

to

respond

to

the

question


what

is

the

number

of

professors

in

your

university?


with

the

correct

answer,

that

is

40

professors
.



Of

course,

closely

related

knowledge

could

be

close

but

not

actually

intersect

a

set

of

trajectories

already

identified

as

being

of

interest,

so

it

is

possible

to

identify

by

a

nearest
-
neighbour

search

the

closest

piece

of

knowledge

and

then

the

intersecting

trajectories

to

that

concept,

in

an

iterative

fashion

in

order

to

widen

the

relevant

knowledge

base

and

enable

the

resolution

of

a

given

query

or

other

input
.

It

is

this

methodology

of

identifying

relevant

knowledge

that

is

a

key

novelty
.

Once

all

relevant

knowledge

is

available,

there

exist

many

methods

to

resolve

queries

in

the

previous

art
.







Logic capabilities: word problems

Apart

from

implementing

the

many

already

known

methods,

current

work

centres

on

developing

more

advanced

and

versatile

expert

system

architectures

that

are

able

to

use

the

contextual

information

and

nearest
-
neighbor

search

functions

to

be

able

to

answer

more

sophisticated

questions
.





GCE Overview



As

initially

configured,

the

GCE

is

a

general

system

that

only

has

a

large

vocabulary

(i
.
e
.

basic

abstract

concepts)

and

can

be

then

uploaded

with

information

on

any

subject,

as

well

as

a

more

specialized

vocabulary,

i
.
e
.

a

memory

that

can

self
-
improve

as

it

is

fed

with

unstructured

knowledge
.




The

GCE

currently

uses

simplish

to

acquire

knowledge,

has

uploaded

a

scientific

dictionary

of

30
,
000

common

scientific

concepts,

as

well

as

a

few

books

(Tolstoy,

Kafka,

Machiavelli,

etc
.
)

and

some

general

knowledge,

chosen

to

demonstrate

the

technology

in

a

conversational

agent
.





The

uploaded

data

in

the

engine

can

be

fused

together

where

equivalent

concepts

are

found

and

used

to

recalculate

the

original

ordering

kernel,

thus

altering

its

memory

structure

and

making

it

better

able

to

deal

with

the

specific

material

being

uploaded,

i
.
e
.

a

self
-
improving

system
.





The

GCE

is

able

to

successfully

associate

concepts

regardless

of

the

grammar

or

wording

used,

and

it

is

also

able

to

combine

contextual

information

and

user
-
supplied

data,

thanks

to

the

fact

that

the

position

of

information

is

given

by

its

meaning

regardless

of

the

source,

method

or

order

of

data

input
.




Currently,

the

GCE

is

able

to

identify

equivalence

between

concepts,

similarity,

do

clustering,

nearest
-
neighbour

search,

find

contextual

information,

and

to

carry

out

simple

word

algebra
.





Many

improvements

remain

to

be

implemented

but

in

its

current

state,

it

is

certainly

the

most

advanced

realization

of

a

general

cognition

engine

to

date,

to

our

knowledge
.






A

first

step

towards

an

artificial

cognition

system

is

a

system

capable

of

intelligent

human
-
machine

interaction
.


Rachel

is

a

conversational

agent

that

can

be

contacted

at

www
.
rachaelrepp
.
org



She

has

memory

as

previously

described,

including

some

recollection

of

her

personal

details,

history,

science

and

knowledge

of

a

few

books
.



She

can

also

work

out

algebra

and

logic

problems

(STUDENT),

clustering

and

syntactic

analysis
.




She

can

understand

standard

English

of

100
,
000

words

using

simplish
.



Rachael

can

also

do

some

simple

semantic

associations
.



She’ll

be

competing

for

the

Loebner


Prize

in

2013
.



She

has

a

multimedia

interface

in

Blender

A practical application: Rachael Repp bot


Current Work




Endow

Rachael

with

more

extensive

memory

and

knowledge
.




Extend

her

logic

capability
.



Improve

general

cognition

engine
.




Improvements

to

the

Simplish

tool
.



Expansion

of

Rachael's

vocabulary
.



Improvements

to

cognition

capabilities

to

go

beyond

memory
.

Potential short term applications




All

sources

intelligence

analysis

(can

accommodate

contextual

data

and

multiple

source

trajectories)

so

we

can

arrive

to

actionable

conclusions
.



Human
-
machine

interaction
.



Internet

large

data

volumes

analysis
.




Semantic

search

engines




Data

mining



Databases



Games



Bio
-
informatics



Expert

systems



Virtual

assistants


Initial application chosen for licensing




Databases

has

been

chosen

as

the

first

commercial

application

area

in

which

we

shall

seek

to

license

our

technology
.



Our

technology

is

protected

by

a

UK

patent

application

No
.

GB
1301143
.
2

filed

on

the

22
nd

January,

2013
.



The

main

players

in

this

field

are
:

SAP,

Oracle,

SAS,

Microsoft

and

IBM
.



We

are

approaching

these

companies

and

choose

the

most

attractive

economic

proposal,

subject

to

our

strategic

limitations

(licensing

only,

preferably

non
-
exclusive,

product

and

niche
-
specific)
.




Our

initial

internal

valuation

of

this

license

is

based

on

an

estimate

of

the

impact

of

our

technology

on

sales

of

the

above

companies

in

a

market

we

estimate

at

24

billion

dollars

with

a

penetration

potential

for

this

technology

of

the

order

of

20
%
,

factoring

in

a

10
%

royalty,

a

5
%

interest

rate

and

a

contract

duration

of

10

years
.



The

process

of

auction

will

be

that

of

“best

and

final

offer”

between

interested

parties
.



Who we are




The

Goodwill

Company

Ltd
.

was

established

as

a

private

limited

company

on

the

12
th

of

September

2000

in

Guildford,

England

where

it

is

registered

at

Companies

House

with

No
.

4070363
.

The

company

is

owned

by

75
%

British

capital

and

25
%

Mexican

capital,

with

offices

both

in

the

UK

and

in

Guadalajara,

Mexico
.



Our

principal

line

of

business

is

doing

research

and

development

both

for

our

own

projects

and

for

third

parties,

mainly

in

the

aerospace,

electronics,

IT

and

defense

industries
.



Past

customers

include

Technicolor

Inc
.
,

Hitachi

GST,

Alstom

Power

PLC,

BAE

Systems

PLC,

Rolls
-
Royce

PLC,

Mexico

City's

International

Airport

Authority

(ASA)

and

more

recently,

for

an

artificial

intelligence

project,

the

Information

and

Intelligence

Warfare

Directorate

of

the

U
.
S
.

Army

(I
2
WD
-
CERDEC)
.



Current

R&D

programmes

include

an

entirely

autonomous

solar
-
powered

micro
-
helicopter,

hybrid

fuel

cells,

an

Android

basic

English

language

learning

app

using

advanced

AI

techniques,

an

online

automatic

English

simplifying

tool,

geographical

information

systems,

an

artificial

cognition

systems

programme,

a

night
-
vision

advanced

camera

for

defense

applications

and

3
D

reconstruction

from

video,

among

others
.

Thank You!!



www.thegoodwillcompany.co.uk

References



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S

et

al
.
,

“Indexing

by

latent

semantic

analysis”,

J
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Am
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Inf
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Sci
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41

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391
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1990
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T
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K
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259
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K
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Burgess,

C
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28
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203
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Hofmann,

T
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99
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Stokholm,

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D
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M
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Mach
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L
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L
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“Word

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Applications,

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in

Honour

of

L
.

Bourne,

W
.

Kintsch

and

T
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Landauer,

ed
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A
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Healy
.



Landauer,

T
.
K
.

&

Dumais,

S
.
T
.
,

“A

solution

of

Plato’s

problem
:

the

latent

semantic

analysis

theory

of

the

acquisition,

induction,

and

representation

of

knowledge,

Psychol
.

Rev
.
,

vol
.

104
,

pp
.

211
,

1997
.


References



Landauer,

T
.
K
.
,

“On

the

computational

basis

of

learning

an

cognition
:

arguments

from

LSA”,

Psychology

of

Learning

and

Motivation,

vol
.

41
,

ed
.

B
.
H
.

Ross

(New

York
:

Academic)

pp
.

43
,

2002
.



Landauer,

T
.
K
.

et
.

al
.
,

“Learning

human
-
like

knowledge

by

singular

value

decomposition
:

a

progress

report”,

Advances

in

Neural

Information

processing

Systems,

vol
.

10

(Cambridge,

MA
:

MIT

Press),

pp
.

45
,

1998
.




Davies,

J
.
,

Studer,

R
.
,

Warren,

P
.
,

(eds
.
),

“Semantic

Web

Technologies”,

Wiley,

2006
.



Jurafsky,

D
.
,

Martin,

J
.
H
.
,

“Speech

and

Language

Processing”,

Prentice

Hall,

2000
.



Katona,

G
.
,

“Organizing

and

Memorizing”,

New

York
:

Columbia

University

Press,

1940
.



Moorhouse,

A
.
C
.
,

“The

Triumph

of

the

Alphabet”,

Henry

Schuman,

New

York,

1953
.



Reisberg,

D
.
,

“Cognition”,

Third

Edition,

W
.
W
.

Norton

&

Co
.
,

2006
.



Rosch,

Eleonor,

“Principles

of

categorization”,

in

E
.

Rosch

&

B
.
B
.

Lloyd

(eds
.
)

Cognition

and

Categorization,

pp
.

27
-
48
,

Hillsdale,

NJ
:

Erlbaum,

1978
.




Wittgenstein,

L
.
,

“Philosophical

Investigations”,

(G
.
E
.
M
.

Anscombe,

trans
.
),

Oxford,

England,

Blackwell,

1953
.