When you Talk about “Information Processing” What Actually
Do you have in Mind?
Emanuel Diamant
VIDIA-mant, POB 933, Kiriat Ono 55100, Israel
Keywords: Information Definition, Information Processing, Computing with Words.
Abstract: “Information Processing” is a recently launched buzzword whose meaning is vague and obscure even for
the majority of its users. The reason for this is the lack of a suitable definition for the term “information”. In
my attempt to amend this bizarre situation, I have realized that, following the insights of Kolmogorov’s
Complexity theory, information can be defined as a description of structures observable in a given data set.
Two types of structures could be easily distinguished in every data set – in this regard, two types of
information (information descriptions) should be designated: physical information and semantic
information. Kolmogorov’s theory also posits that the information descriptions should be provided as a
linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as
a kind of text processing. The idea is not new – inspired by the observation that human information
processing is deeply rooted in natural language handling customs, Lotfi Zadeh and his followers have
introduced the so-called “Computing With Words” paradigm. Despite of promotional efforts, the idea is not
taking off yet. The reason – a lack of a coherent understanding of what should be called “information”, and,
as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues
would be helpful in avoiding common traps and pitfalls.
1 INTRODUCTION
“Information processing” is a not-so-long-ago
launched buzzword that is extensively used in many
research fields and communities. Despite of its
widespread popularity, the real meaning of it is far
less acknowledged and understood. Wikipedia
(2012) and Plato – The Stanford Encyclopaedia of
Philosophy (Maroney, 2009) provide special entries
for it, but even in the lightest manner, these entries
do not confront the threatening ambiguity and
incomprehensibility of this expression. Positing that
“Information processing is the change (processing)
of information“ (Wikipedia, 2012)
in any way does
not clarify its elusive essence. The reason for that is
simple – the key component of the expression
(“information”) has never been defined and never
determined, neither in the times of ancient
philosophers nor in these glorious days, when
“information era” has become our blossoming
reality. It is worth to be mentioned – even today
“information” does not have an accepted and a
generally agreed definition. Far worse than that – it
has always been (and continues to be) a “bone of
contention” between many prominent thinkers,
scholars and scientists.
I do not intend to take part in this controversy. In
the paper’s Reference section I provide a list of
some relevant publications addressing this issue,
with only one and a definite purpose in mind – to
give the vigilant readers a fair opportunity to verify
by themselves how useful and applicable are the
concepts of information that these leading thinkers
and scholars are endorsing and promote (Floridi,
2010); (Piccinini and Scarantino, 2011); (Capurro
and Hjorland, 2003); (Cohen and Meskin, 2007);
(Reading, 2011); (Jablonka, 2002); (Sloman, 2011).
To be suitable for an act of processing,
information has to be something substantial. That
was the reason for Michael Buckland’s proposition
to see information as a thing, “Information as Thing”
(Buckland, 1991). A warm welcome despite, the
idea did not survive long after its introduction.
For the mentioned above reasons, I was forced to
try and to work out my own conception of “What is
information?” In the rest of the paper I would like to
share with you some surprising results of this my
enterprise.
238
Diamant E..
When you Talk about “Information Processing” What Actually Do you have in Mind?.
DOI: 10.5220/0004214602380243
In Proceedings of the 5th International Conference on Agents and Artificial Intelligence (ICAART-2013), pages 238-243
ISBN: 978-989-8565-39-6
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
2 AN INTIMATE TOUCH WITH
THE PROBLEM
My first encounter with information processing
(problems) can be dated back to the early eighties of
the past century, when, as a research engineer, I
have become engaged in design and development of
homeland security systems. It is well known that
such systems heavily rely on visual information
gathering and use. But – What is visual information?
– Nobody knew then, nobody knows today.
However, that has never restrained anybody from
trying again and again to put up such systems and
deploy them everywhere. On the other hand, I was
considered that there must be a better way to cope
with such a mysterious problem (as visual
information processing).
I do not intend to bore you with the history of my
attempts (to reach an acceptable understanding of
information handling peculiarities). Interested
readers are invited to visit my website
{http://www.vidia-mant.info}, where a full list of
my publications on the subject is available. For the
sake of time and space saving, I will only provide
some short excerpts from these (mostly unknown)
papers.
I have dared to publish my first definition of
“information” somewhere in the year 2005
(Diamant, 2005). At that time it has sound as
follows:
Right from the beginning, it must be accepted
that information is a description, a certain
language-based description, which Kolmogorov’s
Complexity theory regards as a program that, being
executed, trustworthy reproduces the original source
object. In an image, such source objects are visible
data structures from which an image is comprised of.
So, a set of reproducible descriptions of image
data structures is the information contained in an
image. (Because “Visual Information” has always
been my prime concern, image-related bag-of-words
is ubiquitously used in my arguments. That does not
mean that image-inspired definitions are only good
for image information content depiction. Certainly
not, certainly all definitions used for visual
information content description could be easily
generalized and extended to many other cases and
settings).
Certainly, an image is a good example of a two-
dimensional data set composed of a vast amount of
closely spaced elementary picture elements (pixels).
It is taken for granted that an image is not a random
collection of these picture elements, but, as a rule,
the pixels are naturally aggregated in specific
clusters (structures). These clusters (structures)
emerge as a result of data elements aggregation
shaped by similarity in their physical properties
(e.g., pixels’ luminosity, colour, brightness and so
on). For that reason, I have proposed to call these
structures the primary or physical data structures.
In the eyes of an external observer, the primary
data structures are further grouped into more larger
and complex aggregations, which I propose to call
secondary data structures. These secondary
structures reflect human observer’s view on the
arrangement of primary data structures, and
therefore they could be called meaningful or
semantic data structures. While formation of
primary data structures is guided by objective
(natural, physical) properties of data elements,
ensuing formation of secondary structures is a
subjective process guided by human habits and
customs, mutual agreements and conventions.
As it has been declared earlier, Description of
structures observable in a data set has to be
called “Information”. Following the given above
explanation about the nature of structures discernible
in an image (in a given data set), two types of
information must be distinguished therefore –
Physical Information and Semantic Information.
They are both language-based descriptions;
however, physical information can be described with
a variety of languages (recall that mathematics is
also a language), while semantic information can be
described only with the use of a natural human
language.
I will not explain here what the interrelations
between physical and semantic information are –
Although that is a very important topic, for the
purposes of this discussion, I will bring again only
short excerpts from my early mentioned papers:
Every information description is a top-down
evolving coarse-to-fine hierarchy of descriptions
representing various levels of description complexity
(various levels of description details). Physical
information hierarchy is located at the lowest level
of the semantic hierarchy. The process of sensor data
interpretation is reified as a process of physical
information extraction from the input data, followed
by an attempt to associate the input physical
information with physical information already
retained at the lowest level of a semantic hierarchy.
If such association is achieved, the input physical
information becomes related (via the physical
information retained in the system) with a relevant
linguistic term, with a word that places the physical
information in the context of a phrase which
provides the semantic interpretation of it. That is, the
WhenyouTalkabout"InformationProcessing"WhatActuallyDoyouhaveinMind?
239
input physical information becomes named with an
appropriate linguistic label and framed into a
suitable linguistic phrase (and further – in a story, a
tale, a narrative), which provides the desired
meaning for the input physical information. (More
about the subject can be found in Diamant (2012)).
3 FROM INFORMATION TO
INFORMATION PROCESSING
Now, equipped with a clear definition of “what is
information”, we can start to scrutinize the
peculiarities of information processing procedures.
Keeping in mind that physical information and
semantic information are two different kinds of
information, it is reasonable to investigate their
processing activities separately.
Physical information processing takes place at
the system’s input front-end. System’s input sensors
(human sensing organs) constantly supply the
system with huge amounts of sensor data, and this
data has to be immediately processed in order to
extract the physical information. (Because
information processing systems are destined to
process information, not data).
A common mistake is to see data processing
systems as aimed to extract meaningful information
from the submitted input data. Again and again –
only physical information can be extracted from the
data. Nothing else. (How exactly this can be done I
describe in my previous papers).
As it has been already explained earlier, primary
data structures are taking part in a process of further
secondary structures formation. Usually, these
secondary structures are the first semantic
(linguistic) structures encountered in the system
which constitute the lowest level of the semantic
hierarchy (the first ‘words’ in the system). The
designated words participate in the next level
structure creation (e.g., a phrase or a sentence
formation). The phrases are then structured in
paragraphs, paragraphs in chapters, chapters in
something more complex and complicated, until the
whole story is accomplished.
As it was already mentioned, the rules of
secondary (semantic) structures arrangement are not
known in advance and not predetermined. The rules
are arbitrary and subjective, established as an
agreement between members of a certain user group,
an outcome of their common practice and
conventions. Therefore, for a successful arrangement
of secondary structures the system has to be
provided with a prototype semantic hierarchy, where
a suitable structure is already exemplified.
Traditional semantic processing architectures have
also such prototyping hierarchies, but they call them
with different names – e.g., previous experience
records, prior knowledge databases. Their designers
and users pretend that such knowledge can be
derived directly from the data which is available for
processing and which is representing the domain
knowledge.
What I claim is that the prototypical semantic
information (the prototypical semantic hierarchy)
has to be provided to the system’s disposal in
advance, before the system starts to cope with a new
task of raw data processing. And semantic
information processing has to be seen as a recursive
procedure of a lower level structure placement into a
higher level structure, (thus the meaning, the
semantics of a lower level structure is defined by its
place and its use in the higher level structure).
Such search for a proper placement (of a lower
level structure into a higher level one) is not always
successful. In such cases, the higher level structure
has to be modified to allow the accommodation of a
lower level structure. That is what could be called a
‘process of a new story production’, a process of a
prototyping information hierarchy modification and
a new prototyping information hierarchy generation,
which has to be seen as a semantic information
processing procedure that is the basis for such
cognitive tasks as reasoning, decision making, action
planning, and so on.
It must be remembered that all these information
processing actions are fulfilled upon linguistic text
structures (compositions). It must also be
remembered that text reading input systems are also
processors of sensor data (visual data in text reading
imaging systems, tactile data in Braille code reading
systems, 0/1 sequences in electronic data handling
systems). In all such cases, primary data structures
are extracted first and then subjected to the lowest
level semantic information processing which results
with the lowest level secondary structures
production (character recognition stage). The
characters are then subjected to the next level
semantic processing where they are composed into
the first linguistic words. Only at this stage the main
semantic information processing is commenced:
processing of linguistic sequences, strings and pieces
of text.
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4 COMPUTING WITH WORDS
It is commonly known that semantic information
processing is somehow connected with the natural
language word processing custom. The paradigm
“Computing with Words” (CWW) was introduced
by Lotfi Zadeh in the mid-nineties of the past
century (Zadeh, 1996).
Computing with Words was
proposed as “a system of computation which offers
an important capability that traditional systems do
not have—a capability to compute with information
described in natural language”, (Zadeh, 2010).
It was inspired by an insight that humans
perform their cognitive tasks in a very specific
manner: “Computing, in its usual sense, is centered
on manipulation of numbers and symbols. In
contrast, computing with words, or CW for short, is
a methodology in which the objects of computation
are words and propositions drawn from a natural
language”, (Zadeh, 2000).
(To avoid any blames of misrepresentation of the
core CWW principles, I will keep on to exploit
extensive citations drawn from the founding fathers’
seminal papers).
“Computing with words is inspired by the
remarkable human capability to perform a wide
variety of physical and mental tasks without any
measurements and any computations. Underlying
this remarkable capability is the brain's crucial
ability to manipulate perceptions − perceptions of
distance, size, weight, colour, speed, time, direction,
force, number, truth, likelihood and other
characteristics of physical and mental objects.
Manipulation of perceptions plays a key role in
human recognition, decision and execution
processes. As a methodology, computing with words
provides a foundation for a computational theory of
perceptions… A basic difference between
perceptions and measurements is that, in general,
measurements are crisp whereas perceptions are
fuzzy...” (Zadeh, 2000).
Thus, Computing with words assumes that
“computers would be activated by words, which
would be converted into a mathematical
representation using fuzzy sets (FSs), and that these
FSs would be mapped by means of a CWW engine
into some other FS, after which the latter would be
converted back into a word “ (Mendel, 2007).
“Another basic assumption in CW is that
information is conveyed by constraining the value of
variables. Moreover, the information is assumed to
consist of a collection of propositions expressed in a
natural or synthetic language, that is, variables take
as possible values linguistic ones” (Herrera et al.,
2009).
“One objective of Computing with Words is to
enable the inclusion of human sourced information
in the formal computer based decision-making
models that are becoming more and more pervasive.
Central to CWW is a translation process. This
process involves taking linguistically expressed
information and translating into a machine
manipulative format. The types of information that
have to be translated are not restricted to the
linguistic values of variables but must also include
linguistically expressed information for processing
information”, (Mendel et al., 2010).
“Another objective of CWW is to help in the
human understanding of the results of information
acquisition and information processing. This
involves techniques of linguistic summarization and
retranslation. Retranslation involves taking the
results of the manipulation of formal objects and
converting them into linguistic terms understandable
to the human. Here we are going in the opposite way
of the previous objective. With linguistic
summarization we are trying to summarize large sets
of data, with the aid of words, in a way that a human
can get a global understanding of the content of the
data”, (Mendel et al., 2010).
“The use of the linguistic semantic model based
on type-2 fuzzy sets is a current trend in decision
making. Several recent works have developed new
decision making models in which the linguistic
information is computed and aggregated by means
of interval type-2 fuzzy sets to maintain a higher
(and more realistic) degree of uncertainty of the
linguistic information”, (Herrera et al., 2009).
Initially, CWW has been accepted with great
admiration and great expectations have been aroused
when attempts to apply CWW principles to different
aspects of human information processing customs
have been considered, e.g., perception computing
and judgment (Mendel, 2002), reasoning (Khorasani
and Rahimi, 2010),
decision making (Herrera et al.,
2009); (Martinezl et al., 2010), and text processing
(Zadrozny and Kacprzyk, 2006).
Unfortunately, these expectations have not been
satisfied. “The theory of CW, as currently
introduced, is considered raw and needs intensive
work and research before it can be applied to the
practical use. Since its introduction, quite a few
researches have been undertaken in this area but the
ultimate goal of building an actual CW
computational engine has thus far proven to be
elusive”, (Khorasani and Rahimi, 2010).
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241
“It is important not to confuse CW with natural
language processing. CW does not claim that it is
able to fully model complex natural language
propositions nor does it argue that it can perform
reasoning on such statements. But it rather offers a
system of computation that is superior to the
traditional bivalent computing systems because of its
capability to reason and compute with linguistic
words hence modelling human reasoning“,
(Khorasani and Rahimi, 2010).
To summarize, in this brief review of the CWW
literature one thing must be noted and not to be left
unattended: dealing with information representation
issues, CWW never asked itself the question: What
is information? And then, as a consequence, dealing
with undefined (linguistic) information, CWW
repeatedly associates it with a single word or with a
bundle of several “precisiated” words. This,
naturally, leads it to difficulties and troubles, some
of which have been just mentioned above.
5 CONCLUDING REMARKS
What follows from the proposed definition of
information (as a linguistic description of structures
observable in a data set) is that information
processing must be defined as a text processing
enterprise. And not as an act of processing of
separate single words or simple word compositions,
like it is commonly done in the CWW paradigm,
ontology-based world representations, key-words
data mining (practice), and so on.
How this speculative declaration can be
converted into a practical implementation? – I still
do not know (at least at the current stage of my
research). What I do know and about what I am
perfectly certain is that the term “computation” is
not applicable to the action that hypothetically takes
place when humans are busy with processing
information, that is, are busy with processing
linguistic texts.
I hope that my humble insights about the essence
and the linguistic nature of the term “information”
would be helpful in paving the way to an increased
information-related issues appreciation and
establishing the right means for an effective
information processing accomplishment.
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