Machine Natural Language Understanding

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24 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Machine Natural Language Understanding











































Jed Leslie

Rensselaer Polytechnic Institute



Abstract


Natural language processing has
been a problem for artificial intelligence
researchers since the conception of the
fie
ld in the mid 1900's. AI's most famous
test of an agent's intelligence, the Turing
Test, is fundamentally bound to the idea
of natural language processing. Much
work has been done in this sub field, and
not without sufficient progress. But
failure to ad
dress the most basic
principals of natural language has
hindered any significant breakthroughs.
As I will disclose in this paper, our
current methods are wrong, and will
continue to fail unless they are changed.
The grail of modern AI, building a
machine

which can pass the Turing Test,
will remain unreachable unless we can
accept AI NLP as a non logical field.


I. Introduction


A. Natural Language Defined


As you are reading this paper, you are
experiencing the phenomenon that we
call
natural language
.
This is not an
easy term to define, as it encompasses
many realms of human intelligence and
cognitive theory. Natural language (NL)
can best be described as the conveying
of ideas or states through a set of signs.
These signs are generally communicated
t
hrough the air (auditory) or as some sort
of symbolic representation on something
physical, as in the case of this paper or
the characters on my computer screen.

Natural language as we know it is a
rather complex byproduct of the human
desire to communic
ate knowledge about
the world to each other. Although some
advanced species show signs of a small
set of language symbols (such as the
dolphin and chimpanzee), most other
species appear limited to the use of
conventional signs
, such as facial
expressions,

physical interactions with
one another and bounded verbal signs (a
dog's bark) [1]. Humans are the only
species to have developed an unbounded
vocabulary of language symbols which
can be interpreted by other humans in an
apparent infinite number of diffe
rent
contexts. English, Chinese and Spanish
are all examples of natural language.
Human natural language is so engrained
into our lives that it is very difficult to
appreciate the magnitude of its
significance in the development of our
lives as individua
ls and as a collective.
But natural language isn't something that
we are born with or something that is
installed, it is something that is
learned
.
Natural language is the dynamic product
of human intelligence and our species'
ever growing understanding
of the
universe we live in.


B. Human Natural Language


Studies have shown that humans have
evolved
something

which enables us to
learn, understand and communicate
using natural language [2]. Noam
Chomsky has done much work in the
field of linguistics an
d the way it relates


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to human intelligence over the past half
century. He was the first person to
propose a theory of universal grammar
(UG). The concept of UG is found in
the very depths of the human brain and
allows the assimilation and
understanding

of all human languages.


Chomsky's theory of universal
grammar is the topic of much debate
within the field of linguistics, but the
significance of his suggestions should
not be ignored in relation to our topic of
AI and natural language. If Chomsky's
theories are correct, getting to the root of
UG should become a priority for NL
researchers as it may provide the
algorithms needed for machine natural
language processing.


C. The Need for Machine NL


The potential benefits of a machine
that can interact

with humans using
natural language are absolutely mind
boggling. The need for advanced
human
-
machine communication
interfaces, such as the mouse and
keyboard, would be rendered obsolete.
The computing learning curve would
almost completely disintegrate
as
machines would be able to instantly
interpret human desires such as "open
my payroll file," or "could you find me
information on travel locations in New
Zealand?" The entire design of the
modern PC would change, from
operating system to I/O. But perso
nal
computing is not the only field that
could benefit from machine natural
language. Science fiction has addressed
the potentials very extensively, from the
"Computer" in Star Trek to the speaking
robot visions of Isaac Asimov as a tool
for the advanceme
nt of human
civilization [3]. Machine natural
language could help us tutor and teach
our youth, get help in times of
emergency, have our coffee ready by
morning and ultimately gain a greater
understanding of the way our
intelligence and consciousness work
.


D. Historic Machine NL and the Turing
Test


Since the 1950's, machine natural
language processing has posed one of
the greater challenges for artificial
intelligence researchers. The Turing
Test is Alan Turing's famous test of
machine operational inte
lligence.
Turing defined intelligence as the ability
to achieve human
-
level performance in
all cognitive tasks, sufficient to fool an
interrogator [1]. In order to pass his
test, a human interrogator would have to
be unable to decipher if the test taker

was a machine or a human. Despite 50
years of work and positive initial
speculation, AI has failed (miserably) to
produce an intelligent machine capable
of passing the Turing Test. This is
partly because the work needed to
produce the four necessary cap
abilities
that a machine would need to pass the
test was drastically underestimated, and
partly because of the ambiguity between
actual intelligence and the operational
intelligence that the test dictates.


The four necessary capabilities are
knowledge r
epresentation, automated
reasoning, machine learning, and
natural
language processing
. Knowledge
representation is the machine's ability to
store information about the universe.
Automated reasoning is the machine's
ability to interpret the information an
d
respond to different events based on that
interpretation. Machine learning is the
machine's ability to adapt it's knowledge
representation and reasoning based on a
changing environment. The forth and
most important necessary capability in
relation to t
his paper is natural language


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processing, or the machine's ability to
communicate in some human language
in such a manner as to sufficiently fool
the interrogator.


Each necessary capability is in itself a
whole field of artificial intelligence.
While sca
ttered progress has been made
in each of these areas, a system which
can truly bring together these fields has
yet to emerge. I believe that the key to
building a machine that can successfully
pass the Turing Test lies in extensive
work within the machine

learning field.
Knowledge representation, automated
reasoning and natural language
processing will all be derived from
significant progress in machine learning.


E. Modern Machine NLP Methods


Today's efforts in machine natural
language processing (NLP)

are very
mathematical. They involve extensive
research into communicate theory,
where an agent (or human) first has an
intention to generate a speech phrase. It
is then up to another agent (or human) to
correctly perceive the speech phrase.
The way hum
ans perceive speech
phrases and the way machines have been
designed to perceive speech phrases
appear drastically different.


Machines first must analyze the
speech phrase and break up their
analysis into different sentence parts.
This process is called s
entence parsing,
where nouns, verbs, articles and
adjectives are separated. The parsed
elements are then interpreted using
logical methods where semantic
ambiguities are removed, a logical
model is built and the machine's
knowledge base is updated with th
e new
information. For example, a machine
model might interpret the phrase "Mary
is sad" as NOT Happy(Mary,
Now
).
Once updated, the machine's knowledge
base will contain the information that
Mary's current state is not happy. This
may appear to be a goo
d method for
simple machine NLP tasks, but there is
still no underlying
understanding

as to
what "happy" is. The machine just can
recognize that "happy" is the same as
"not sad" and vice versa.




II. A Conceptual Failure


A. NL as a Human Construct


Nat
ural language, unlike some other
fields of artificial intelligence, must be
addressed in a human like manner as it
functions symbiotically with human
intelligence. Humans do not parse
language into it's associative word types
(verb, noun, adjective, etc…)

and
attempt to build probabilistic models of
meaning based on sentence structure.
The paragraph is as important in
deciphering the meaning of a particular
sentence as the sentence itself. The
physical setting or the state of the
universe while the parag
raph is being
uttered is absolutely essential in
correctly interpreting the meaning of the
paragraph. Human natural language
appears to be a
dynamic symbolic
representation of an individual's
universe model
. By this I suggest that
before problems of mach
ine natural
language can be properly solved,
machines will have to possess a universe
model similar to that of a humans. This
is not a new concept as it has been
widely accepted since the beginning of
AI that machines will need a form of
knowledge repres
entation in order to
become operationally intelligent. It is
within the realm of natural language
processing that the idea of a human like
universe model and the classic machine
knowledge representation diverge.


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Being that natural language is a human
con
struct, which evolved from the social
need to convey ideas about the universe
based on human perception, machines
can only acquire
natural language
understanding

through the harmonious
and simultaneous acquisition of a
universe model through human like
per
ception and social influence.


B. The Universe Model Necessity


The symbolic translation of natural
language is available to humans because
of our extensive universe models.
Natural language should not be viewed
as a layer above human intelligence, but
m
ore as in integrated component. A
universe model is made up of the way
we perceive the world around us, the
forces of the universe, the physical
constraints of our own bodies, and the
social interaction with others.


Our senses of sight, smell, taste,
sou
nd, touch, balance and spatial
relations (such as the ability to touch
your nose with your eyes closed) are
constantly providing our brains with raw
information about the universe. These
senses have been shown to be intricately
bound to one another, enabl
ing a dense
set of relational sensory information

[7]. The combination of these senses
and the brain's ability to filter out
unnecessary information (allowing focus
on a particular event or object in the
universe) build the first perceptual ideas
of a u
niverse model. Understanding the
way something will feel in relation to the
way it looks, or the way something will
taste in relation to the way it smells are
all bits of knowledge acquired through
sensory input. It is important to note
that not all info
rmation from the senses
is believed to be stored in the brain.
Much of the information remains in the
universe as humans only model the
information that is immediately relevant
[8]. A machine that can not model the
universe using human like senses will
n
ot have any understanding of
descriptive phrases like "it looks like…"
or "it smells like…"


The forces of the universe along with
the evolved physical constraints of our
own bodies play a huge role in the
development of our universe models and
ultimately
our intelligence. This could
be one of the most overlooked aspects of
the origins of human intelligence in AI.
Much of what we learn about movement
and control over our bodies stems from
what we can physically do within the
universe
. From the infant sta
ges,
gravity, friction and kinetic forces help
build our universe models as we learn
how to move our muscles and joints to
either utilize, remain neutral or
contradict these forces. A machine who
can not physically interact with the
universe, and does not

share similar
physical constraints (degrees of
freedom), will not be able to understand
even the most basic elements of human
natural language.


Above all, social interaction plays the
most important part in helping the
developing human build an
understa
nding of natural language. Just
as the sense of sight can associate red
with a fire truck, natural language
understanding allows the infant to
associate the way the word "red" sounds
with the way a fire truck is perceived.
This association is dependent

on the
interaction of the developing human and
its caregiver. Social settings also help
build universe models though what is
called learning through scaffolding [7].
Scaffolding may include reducing
distractions (gaining focus), marking the
task's criti
cal elements, or physical
assistance (when a mother helps a baby


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use a spoon by holding the baby's hand
and guiding it to the baby's mouth).


I believe that most of the universe
model in relation to natural language is
built through social interaction.
When a
mother says to a child "look at the
beautiful flower" the child's universe
model is altered to understand what a
flower looks like, smells like and that
those looks and smells are
beautiful
.


A universe model is not something
that can be installed.

The internal
representations of a universe model are
unimportant, as they will differ
significantly from person to person
(perhaps this is the definition of
somebody's taste or preference). It's the
acquisition of information about the
universe through a
ll the shared human
elements
simultaneously
that make us
able to use natural language so
extensively.


Above all, the building of a universe
model is an incremental process. We
can not learn to walk until we learn to
crawl. We can not learn to crawl un
til
we learn to sit up. We can not read
Romeo and Juliet

until we read
See spot
run
.


C. The Illogical Machine Method


So with all this evidence from
cognitive psychology and neuroscience
about the development of human
intelligence, why do we continue to

use
logical methods in our quest to build
machines which can communicate with
us using natural language?


First, I would like to suggest that the
idea of natural language as an evolved
element of human intelligence has not
been properly addressed. Evolut
ion
shows us that as species approach higher
intelligence states, as in the case of
dolphins, chimpanzees and humans, they
appear to develop natural language as a
tool for survival.


Second, diving deeper into the depths
of cognitive theory, I would like
to
suggest that high intelligence is
enabled

by the ability to convey information
about the universe to others through
natural language, not the opposite. As
the old saying goes, two heads are better
then one. Sharing information has
allowed the human to
climb to the very
top of the evolutionary chain. We have
evolved with
something

in our brains
that suggests that language is an inherent
property of modern human existence.
The actual specifics of the language,
such as the semantics of English or
Chinese
are irrelevant, as young children
will learn any language
that their
environment exposes them to

with equal
fluidity [12]. A striking example of this
is the fact that many twins develop a
personal language between themselves at
a very young age. This la
nguage sounds
like gibberish to all but each other. Most
twins spend nearly all their young lives
together constantly learning from one
another. The twin example highlights
much of the social interaction theory as
the key to the development of
intelligen
ce in a young human.


Current methods in the development
of machine natural language will
continue to fail as the fundamental
principal of natural language being a
fully integrated part of a universe model
remains unrecognized.


III. Understanding NL thro
ugh
Universe Exposure


A. The Human Infant


At birth, the body of the human infant
is brought into the world 100%
dependant on the care of others. If not
properly nourished, the baby will die.


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The brain of the human infant is equally
dependent on nouris
hment for it's
development. Even the most basic
perceptual senses are not developed until
after birth [7]. As time progresses, the
infant learns about it's environment
guided by a number of key elements.
Through physical constraints, such as
those pose
d by its own body's range of
movement and universal forces such as
gravity and pressure, the infant begins to
build a universe model. But the
physicality of the universe can only go
so far in the progression of the infant's
growing intelligence. Social i
nteraction
is the basis for the development of an
infant's mind. Interaction with it's
caregiver provides fundamental ideas of
social expectations and roles. As the
baby learns about its universe and its
ability to interact with it, the social
interactio
n develops the mind. It is
during the extremely critical first few
years of existence that the infant
develops the ability to communicate its
desires through speech [7]. Human
infants have an evolutionary ability to
understand natural language, but that
ability is nothing more then a potential.
Speech comes first, the ability to read
natural language in it's printed form
comes second. How does a child learn
to read? By having somebody read to
them. This builds the association
between auditory symbols
and the
printed symbol. It is through the child's
universe model that either can evoke
particular meaning.


The point being is that natural
language evolves in complete
conjunction with the infant's physical,
social and intelligence development.
Expectin
g a machine to possess human
like natural language understanding is
completely impossible without proper
and similar evolution of physical and
social components.


B. The Machine Infant and the COG
Project


Much of what I suggest as a guide for
machine nat
ural language understanding
has already been attempted, but with a
more general purpose, by Rodney
Brooks and his team at MIT. Brooks
was one of the first people in AI to truly
look into neuroscience evidence as a tool
for building machines with human
-
li
ke
intelligence [11]. The COG project
marks significant progress within this
newly forming area of AI [7]. Brooks'
team built a robot modeled after a
human torso (two arms, one head and
similar degrees of freedom). The robot
has cameras for eyes, microp
hones for
ears and a set of gyroscopes to give it a
sense of balance. The robot was not
programmed to do any particular task,
but rather was given the
potential

to
learn how to perform
any

task. Much
like a human infant, the robot knew
nearly nothing ab
out how to control it's
"body" physically in the universe. The
robot was put through several
unsupervised reaching exercises, as an
example of it's learning capabilities. The
robot was instructed to reach towards a
target in front of it with no implicit
instructions on how to move it's arm
(besides the ones defined by the universe
and the physical constraints of it's motor
abilities). The first few reach attempts
where horribly off, but after a few hours
of attempts, the robot was able to
accurately reac
h towards the target [7].


This is the type of adaptive
methodology that will push machine
intelligence into the future areas of AI.
Human
-
like intelligence can only be
acquired through human
-
like interaction
with the universe. While this may not be


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the
goal for all areas of AI, it is certainly
extremely relevant to natural language
understanding.


IV. Machine Natural Language
Understanding


A. Defining Initial Conditions


Building a machine that can interact
with the universe as I have described
will n
ot be an easy task. There are
clearly many elements that need to be
solidly thought out before any work
within this field can be attempted.
However, I believe a project even of this
magnitude will be far simpler then any
logical efforts to define what kn
owledge,
the universe and languge is in a machine.
I believe that the key lies in allowing the
machine to define its own universe
model. Higher levels of intelligence will
follow much like the human infant.


It is important to note that human
intelligenc
e has grown out of survival
needs. Inherent properties, such as the
need for food, rest and reproduction are
not be ignored in the quest to build a
machine with human
-
like intelligence.
These properties define how we act in
many situations. They can be
considered a set of human
desires
.


Our intelligent machine must share
these desires if it is to understand human
natural language. I recognize that
defining these initial conditions may be
the most difficult part of the project.
Theory of mind ideas l
ike these have
been discussed by Brooks' team, but
more work will be needed in order truly
understand how to implement these
desires in a machine [9].



The intelligent machine will also
need to have certain reactions which we
commonly call
instincts
. Loo
king
towards a loud noise, or feeling the need
to protect the physical structure of one's
body (feeling pain) are both examples of
necessary instincts.


With proper initial conditions, based
on the built
-
in survival needs of human
beings, the first steps t
owards machine
natural language understanding will be
in place.


B. Simple Beginnings


Much like the human infant, the
intelligent machine will need to enter the
world in a state of near helplessness.
Through its senses, the physicality of the
universe
and most importantly, the social
interaction with humans, it will begin to
build a universe model which will enable
it's future understanding of natural
language.


The intelligent machine will also
need to spend most of it's time
"listening" to the interac
tions of people.
Like a human infant, it would be
expected to only understand and say
simple things at first. As it's universe
model grows will it be able to form
natural language on the level of human
beings.


Starting simple should involve
bootstrappin
g techniques to allow the
machine to build on the knowledge it
already has about the universe. For
example, have a human "show" the
machine an object, describe the object
(or even just name the object), and the
test the machine's ability to recognize
the
object. Techniques like this exploit
what we already know about the way
humans learn through repetition and trail
and error.


C. Technology


Theory aside, it is important to
address
how

these ideas may be
accomplished. There has been great
progress withi
n the computational
intelligence field of AI. This field


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focuses on three practical methods of
implementing machines that can "learn"
from their environment: artificial neural
networks, genetic algorithms and fuzzy
logic. Of these three fields, I belie
ve
the correct implementation of an
artificial neural network is the most
promising.


The basic building blocks for
artificial neural networks (ANNs), called
neurons
, are modeled after the way the
cells in the human brain are believed to
store and process
information. Studies
have shown that information is stored in
a distributed manner throughout the
brain [5]. Learning is achieved through
constant trials and feedback. First
introduced by Warren McCulloch and
Walter Pitts in 1943, the artificial
neuron,
shown in figure 4.2 bears strong
resemblance to the biological neuron
shown in figure 4.1. Both act on the
principal of weighted inputs from other
neurons which are then transformed
using a dynamic function into outputs.



Figure 4.1



Figure 4.2




Even the fastest modern computer
lacks the ability to perform pattern
recogni
tion tasks at even the most basic
level. Logic and mathematics have been
the framework for the software that runs
on these machines. But where
conventional methods fail, ANNs have
been able to (at least partially) pick up
the slack. ANNs are pros at pat
tern
recognition and retrieval. From alarm
clocks that take voice commands to cars
that adapt to your driving habits, the
possibilities for ANN applications seem
endless.


The diverse fields and applications of
language processing and character
recogniti
on have the most to gain from
the use of ANNs. Tools such as text
-
to
-
speech conversion, auditory input for
machines, automatic language
translation, secure voice keyed locks,
automatic transcription, aids for the deaf
and for the physically disabled which

respond to voice commands, and (my
favorite) natural language processing,
recognition and response are all flogged
with ANN potential. The future will
only bring better and more accurate uses
of these technologies.


An ANN's ability to recognize
dynamic
and changing patterns will
bring life to ideas previously only heard
about in science fiction. Automobiles


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that drive themselves based on changing
scenery and what the road looks like are
quickly becoming a reality. Pattern
recognition systems implemente
d with
ANNs can figure out if there is a bomb
in somebody's airplane luggage and may
be able to pick out fugitive terrorists
using facial analysis.


Lastly, ANNs can play a enormous
roll in the progression of human health
and wellness. The pattern recogni
tion
and analysis abilities of ANNs make it a
valuable tool for the interpretation and
decoding of the human genome. When
the genome is understood, and all genes
are identified, genetic medicines can be
costumed tailored for each patient's
personal needs.

Near perfect medicine is
an obvious benefit to all of humankind
and all of our futures.


Most of today's ANNs capable of the
rather advanced computational tasks that
I have described use only two or three
layers of about thirty or less neurons.
These ne
tworks are usually implemented
in software in a two dimensional
manner. Contrary to these "simple"
ANNs, the human brain is a three
dimensional network of nearly
100
billion

neurons [1]. The computational
power of the human brain is clearly
superior to e
ven the most advanced
ANN. Modern artificial neural networks
will not be able to perform the advanced
modeling techniques required for the
implementation of machines with natural
language understanding, but the ideas of
ANNs are fundamentally sound and
ha
ve proven their ability to learn from a
changing environment.


I believe that future advances in
ANNs and other learning technologies
will enable this vision of a machine with
natural language understanding.
Through the careful coupling of
perception, p
hysical forces and
constraints, universe modeling and
social interaction, the ANNs of the
future could provide human
-
like world
interaction to a machine.


D. Potentially Interesting Experiments


The success of this project could lead
to the development of

many interesting
experiments within the realms of AI,
cognitive psychology and neuroscience.
There are several scenarios which
immediately come to mind.


First, design an intelligent machine
which possess no initial conditions, or
desires which are dra
stically different
from those defined by human survival
needs. The interesting element of this
experiment would be whether or not the
machine becomes intelligent
at all
.
Without similar survival drives, the
machine may decide that it is content in
its "d
umb" state. The machine may also
evolve into an intelligence with a
universe model drastically different then
anything human like. This could
provide us with invaluable insight into
our own minds.


A second experiment may flow along
the lines of the twin

example. Design
two machines to interact with each other,
along with a human caretaker. Perhaps a
similar development of a gibberish
language will take place.


Giving a machine the ability to
perceive the universe in ways vastly
superior to humans migh
t also yield an
interesting experiment. Why limit it's
vision to that of natural light when we
can design instruments to pick up
frequencies all across the spectrum?
Why limit it's auditory sensitivity range
to between twenty and twenty thousand
hertz?
Providing a machine with "super
senses" may hinder it's ability to view
the world in a human like fashion, but


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would give us dramatic insight into the
minds of other creatures.


V. Intelligence vs. Consciousness


A. Necessary Separation


Throughout this

paper I have spoken
a lot about "intelligence." As all AI
researchers know, this is an extremely
hard term to define. I must point out that
it is extremely important to separate
intelligence, as I have referred to it in
this paper, from consciousness.
Although I do not rule out the
possibility, it is hard to convince myself
that, even if successful at
communicating with humans through
fluid natural language, our intelligent
machine would be a
conscious

machine.
This also is highly dependent on how
one
chooses to define consciousness.


A being is generally thought to be
conscious if it recognizes that it itself (or
some part of it)
exists

in some way. I
don't think I could have produced a more
vague definition, or for that matter, a
better one. The p
oint being is that
consciousness is an extremely relative
term.


B. Consciousness as a Product of
Increasing Intelligence


I believe that consciousness as we
know it is something which evolves out
of growing intelligence. None of us can
remember being bo
rn, only some vague
point in time after. I would like to
suggest that as the human infant learns
about its environment, the physics of the
universe, the constraints of it's own
body, the social interactions with others
and the methods of which to convey
i
nformation to others through natural
language, it
comes into consciousness
.
We all must accept that there was some
point in each of our lives where we were
interacting on an intelligent level with
others, but were not aware of it. The
universe model pres
ents self analyzation,
which builds almost a recursive model of
thought. Humans are constantly
thinking to themselves
in natural
language
. They are constantly running
over mental scenarios to help them
choose the correct way to interact with
the universe
. This is a type of thought
that comes from vast experience, and
ultimately the superior hardware of the
human brain. This recursive model of
thought could be the very element that
defines consciousness.


This is certainly an abstract topic, but
it is by

no means invalid in assessing the
potential of a successfully intelligent
machine. If consciousness is derived
from a heightened awareness of one's
environment, a strong universe model,
and the ability to self analyze, there is no
reason to doubt that ou
r intelligent
machine will gain some form of
consciousness given enough time.
Whether or not it corresponds to the
human
-
like consciousness we all feel
inside of us will be as interesting as the
quest to build this machine itself.


VI. The Unexplained


A. Humor


In as much liberty I have taken in
describing my theories on human
intelligence, natural language and
consciousness, there are at least two
elements of humanity which I can only
question.


As Commander Data from Star Trek
knows, humor is not
an easy concept to
understand. Why do humans find things
funny? What makes something funny to
one person and not to another? The only
suggestion I have is that humor
somehow evokes a discrepancy in an


11

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individual's universe model, or exposes
some previou
sly unknown element of
the universe model of another. Lots of
situational comedy is funny because it
exposes ideas about the universe in
which we don't normally take as valid.
But how do humans come up with these
ideas? Through the concept we call
imagi
nation
.




B. Dream States


Dreams are another element of
humanity which are extremely hard to
describe. The concepts and events that
happen in human dreams often could
never physically happen in the universe,
and are usually very reflective of the
feeli
ngs, ideas and emotions that the
dreaming person has been experiencing
in recent memory.


Perhaps a dream is what an active
human brain does when the majority of
its sensory input is shut off or limited by
the biological need for sleep. Dreams
could be th
e recursive analysis of one's
own thoughts with less of the physical
constraints of the universe coming into
play.


References


[1]
Artificial Intelligence A Modern
Approach

Stuart Russell, Peter Norvig

Copyright 1995 Prentice
-
Hall Inc.


[2]

Universal Gram
mar and Linguistics

Michael Albert

http://www.zmag.org/ZMag/grammar.ht
m


[3]
Robot Dreams

Isaac Asimov

Copyright 1950 Isaac Asimov


[4]
The Self Organizing Map

Teuvo Kohonon

1990 IEEE Invited Paper


[5]

Neuro
-
Fuzzy and Soft Computing

J.
-
S. R. Jang, C.
-
T. Sun, E. Mizutani

Copyright 1997 Prentice
-
Hall Inc.


[6]

Recurrent Neural Networks for
Prediction

Danilo P. Mandic, Jonathon A.
Chambers

Copyright 2001 John Wiley & Sons, Ltd


[7]

The COG Project: Buildin
g a
Humanoid Robot

Rodney A. Brooks, Cynthia Breazeal,
Matthew Marjanovic, Brian Scassellati,
Matthew M. Williamson


[8]
Memory Representations in natural
tasks

D. Ballard, M. Hayhoe, J. Pelz

1995 Journal of Cognitive Neuroscience


[9]

Humanoid Robots: A
New Kind of
Tool

Rodney A. Brooks, Cynthia Breazeal,
Bryan Adams, Brian Scassellati


[10]

The Relationship Between Matter
and Life

Rodney A. Brooks

Copyright 2001 McMillan Magazines
Ltd


[11]
Intelligence Without Representation

Rodney A. Brooks

MIT Artific
ial Intelligence Lab 1987


[12]
Assessing Language Development
in Bilingual Preschool Children

Barry McLaughlin, Antoinette Gesi
Blanchard, Yuka Osanai

http://www.ncbe.gwu.edu/ncbepubs/pigs
/pi
g22.htm