Knowledge Technologies and the Asymmetric Threat
Paul S.
Prueitt, PhD
Research Professor
Cyber
Security Policy & Research Center
George
Washington University
3/16/03
(revised on 10/12/03)
Note on
Artificial Intelligence
Positive
social and economic consequences
Note on Language
and Linguistics
Knowledge
Technologies and the Asymmetric Threat
New
methodology will soon allow the continuous measurement of world wide social
discourse. The measurement can be both
transparent and have built-in protection for individual privacy. What has been missing is an integration of
the best natural science and computer science.
Currently
a number of web spider systems provide experimental instrumentation for the real-time
measurement of social discourse. This
experimental instrumentation provides the data that could drive the development
of high fidelity knowledge acquisition technology. A non-classified archive exists (at INSCOM) dating from at least
October 2001. Natural language processing
and machine-ontology construction could provide a representation of social
discourse occurring from this point to the present.
Figure 1: Experimental system producing polling like output (November 2002) from government based web harvest
Two
different levels of organization provide built-in protection for privacy from
the ground up. At one level is a stream
of data, most of which is processed into abstractions about invariances in
linguistic variation. At the other
level is an archive of invariance types and compositional rules related to how
grammar is used and actually observed in the construction of social
meaning. Between this grammatical layer
and the real time data stream can be placed Constitutional restrictions that
require judicial review before individual data elements are investigated.
Put
simply, linguists and social scientists have developed a real time and evolving
model of how languages are used to express intention. This model is stratified to represent natural organizational
processes.
General
systems properties and social issues involved in the adoption of stratified
methodology are outlined in this paper.
Technical issues related to logic, mathematics and natural science are
touched on briefly. A full treatment
requires an extensive background in mathematics, logic, computer theory and
human factors theory. This technical
treatment is open and public and is being published by a not for profit science
association, the Behavioral Computational Neuroscience Group Inc (BCNGroup.org),
located in Chantilly Virginia.
Community
building and community transformation have always involved complex processes
that are instantiated from the interactions of humans in the form of social
discourse. Ubiquitous community transformation
must be involved in responding to asymmetric threats. Informational transparency is needed to facilitate the social
response to the causes of these threats.
The American public must examine the nature of those inhibitions to
informational transparency in greater detail.
Knowledge
management models involve components that are structured around lessons learned
and lessons encoded into long-term educational processes. A new educational curriculum, supporting the
Knowledge Sciences, must be created if the knowledge of the structure of social
discourse is to have high fidelity when viewed by the average citizen. The necessary community transformation
requires a more complete and mature understanding by the public of the issues.
The
coupling between measurement and positive action has to be public and
transparent, not simply as a matter of public policy but as a matter of public
trust. Otherwise the fidelity of information
will be subject to narrow interpretation and, more often than not, to false
sense making. Information without
knowledge can become propaganda and subject to control for narrow interests.
As
a precursor to our present circumstance, for example, the Business Process
Reengineering (BPR) methodologies provide for AS-IS models and TO-BE
frameworks. But often these
methodologies did not work well because the AS-IS model did not have high
fidelity to the nature and causes of the enterprise. Over the past several decades, additional various knowledge
management disciplines have been developed and taught. We conjecture that these knowledge
management disciplines have seen limited success due to a systemic failure in
attempts to model complex social processes.
In knowledge management
practices there are deficits in scholarship and methodology that are due to a
type of memetic (definition: “memetic” - having to do with the expression of
concepts in social systems) shallowness and to the intellectual requirements
imposed on understanding issues related to a stratification and encapsulazation
of individual and social intention. The
shallowness of the discipline of “knowledge management” might be understood as
rooted in an economic fundamentalism that will not accept that human knowledge
is on a commodity.
The New War calls, on us, to
express maturity on issues of viewpoint and truth finding and to reject of all
forms of fundamentalism, including those within our own society. This maturity is needed because our response
to terrorism’s fundamentalism has had a tendency to engage forms of ideological
fundamentalism within our own society.
American has a strong multi-cultural identity, as well as a treasured
political renewal mechanism. When
challenged by fundamentalism we rise to the challenge. In this case, we are called on to reinforce
multi-culturalism.
One
can make the argument that something is missing from those technologies that
are being acquired for intelligence vetting within the military defense system. Clearly biodefense information awareness
requires much more that what DARPA’s Total Information Awareness (TIA) programs
contemplated in 2001 and 2002The complex interior of the individual human is
largely unaccounted for in the US government’s first attempts at measuring the
thematic structure of social discourse.
But the individual is where demand for social reality has its primary
origin.
What
is missing is the available science on social expression and individual
experience. Funding is not applied, as
yet, to this science because of the narrowness of the current procurement
processes that, not surprisingly, are focused on near term issues related to
continuing funding for corporate incumbents.
There is then, a bootstrap that is needed to shift the focus from
methodology and philosophy that is not sophisticated and is not accounting for
social complexity.
Why
individual variation in response patterns, for example, is not being
accommodated by commercial information technology, e.g. commercial advertising,
is due to many factors. Some of these
are technical issues. Many of these
issues are related to the commercialization of information production and the increasing
control over information by business processes. This increasing control over information is seen, by the advertising
business, as somehow essential to economic health. This is, we feel, wrong minded.
A
very large part of the Gross National Product is expended in advertising, often
in ways that are unwanted, unnecessary and deceptive. Advertising has become a disease, with a well-developed immunology
that uses a false sense of American patriotism and religious membership to
punish those who question the degree to which advertising controls our social
construction. It is quite easy to lie
about the social reality caused by television programming and the advertising industry.
But
a deeper problem is related to the nature of formal systems. Science and mathematics is improperly used
to prop up a theory of social construction that is not tenable. This social construction has controlled
science, causing confusion and dysfunction within the Academia. There is no
clear guidance coming from the academia.
But the issues have been laid out within a scholarly literature.
Scholarship
informs us that natural language is NOT a formal system. One would suspect that even children already
know this about natural language.
Perhaps the problem is in our cultural understanding of the best traditions
in mathematics and science. These best
traditions do not advertise capabilities that are not present, and close off
debate and analysis. Rather these best
traditions remain open to correction and express a willingness to experience
first hand.
Yes,
abstraction is used in spoken language; but a reliance on gesture and other
forms of non-verbal expression helps to bring the interpretation of meaning
within a social discussion, as it occurs and is experienced. So the abstraction involved in language is
grounded in circumstances. These
circumstances are experienced as part of the process of living. The experience relies on one’s being in the
world as a living system with awareness of self. This experience is not an abstraction. Again, even children already understand this. One has to experience truth for itself and
not allow others to “sell” truth using manipulation and addictive processes.
Written
language extends a capability to point at the non-abstract, using language
signs, to what is NOT said but is experienced.
Human social interaction has evolved to support the level of
understanding that is needed for living humans, within culture, to form social
constructs. We use language signs to
point at what is NOT said. But computer
based information systems have so far failed to fully account for human tacit
knowledge, even though computer networks now support billons of individual
human communicative acts, per day, via e-mail and collaborative
environments.
So,
one observes a mismatch between human social interaction and computers.
How
is the mismatch to be understood?
We
suggest that the problem is properly understood in the light of a specific form
of complexity theory.
An
evolution of natural science is moving in the direction of a stratification of
formal systems using complexity theory.
A number of open questions face this evolution, including the re-examination
of notions of non-finite, the notion of an axiom, and the development of the
understanding of human induction.
Induction, in counter position to deduction, is seen as a means to
"step away from" the formal system so as to observe the real world
directly.
We
do have an alternative to axiomatic formalization that results in a fixed model. It is via this modification of constructs
lying within the foundations of logic and mathematics. The alternative is in
knowledgeable education that depends on an appeal to direct experience. We suggest that an extension of the field of
mathematics and computer science is in order that accounts for the complexity
of natural systems.
Note on Artificial
Intelligence
The “artificial intelligence” failure can be viewed, and often is, as
simply because humans have not yet understood how to develop the right types of
computer programs. This viewpoint is an
important viewpoint that has lead to interesting work on computer
representation of human and social knowledge.
But put quite simply, a representation of knowledge is an abstraction
and does not have the physical nature required to be an “experience” of
knowledge.
The fact that humans experience knowledge so easily may lead us to
expect that knowledge can be experienced by an abstraction. And we may even forget that the computer
program, running on hardware, is doing what it is doing based on a machine
reproduction of abstract states. These
machine states are Markovian, a mere mathematical formalism. By this, we mean that the states have no
dependency on the past or the future; except as specified in the abstractions
that the state is an instantiation of.
There is no dependency on the laws of physics either, except as encoded
into other abstractions.
This fact separates computer science and natural science.
The tri-level architecture models the relationship
between memory of the past, and awareness of the present, and the anticipation
of the future. However, once this
machine architecture is in place, we still will be working with abstraction and
not a physical realization of (human) memory or anticipation.
Stratification seems to matter, and may help on issues
of consistency and completeness, the Godel issues in formal foundations to
logic. The clean separation of memory
functions and anticipatory functions allows one to bring the experimental
neuroscience and the cognitive science into play.
The measurement of the physical world results in abstraction. The measurement of invariance produces a
finite class of categorical Abstraction (cA), which we call cA atoms. cA atoms have relationships that are
expressed together in patterns and these patterns are then expressed in correspondence
to some aspects of the measured events.
The cA atoms are the building blocks of events, or at least the abstract
class that can be developed by looking at many instances of events of various
types.
Anticipation is then regarded as expressed in event Chemistries (eC)
and these chemistries are encoded in a quite different type of abstraction
similar in nature to natural language grammar.
We
are arguing that the development of categorical abstraction and the viewing of
abstract models of social events, called “event chemistry”, are essential to
national security. We are arguing that
the current procurement process is not looking at and is not nurturing the
types of science that is needed in this case.
Response
mechanisms to these threats must start with proper and clear intelligence
about event structures expressed in the social world. Because computers cannot alone provide proper and clear
intelligence, human sharing of tacit knowledge must lie at the foundation of
these response mechanisms.
The
technology we propose is based on class:object
informational pairing to produce atoms for situational
logics.
But
transparent human knowledge sharing technology is not part of the culture
within the intelligence communities.
Compounding the cultural problems in our intelligence community, the
current funded and deployed computer science; with its artificial intelligence
and machine based first order predicate logic is confused about the nature of
natural complexity. Within this
confusion, over the nature of information, a culture of profiteering on the New
War can be clearly seen at DARPA of example, and may itself become an indirect
threat to the participatory democracy.
Current
computer science talks about a “formal complexity” because natural complexity
has a nature that is not addressable as an abstraction expressible as first
order predicate logic. Formal
complexity is just overly complicated, and it is complicated because of the
incorrectness that is imposed by the tacit assumption that the world is little more
than something captured by a confused abstraction.
Putting
artificial intelligence in context is vital if we are to push the cognitive
load back onto human specialists where both cognition and perception can guide
the sense making activity. Computer
science has made many positive contributions within the context of a myth based
on a strong form of scientific reductionism.
This myth is that the real world can be reduced, in every aspect, to the
abstraction that is the formal system that computer science is instantiating as
a computer program. Natural science is
clear in rejecting this myth.
Understanding
the difference between computer-mediated knowledge exchanges and human discourse
in the “natural” setting is critically important. One of our challenges is due to advances in warfare capabilities,
including the existence of weapons of mass destruction. Another obvious challenge is due to the
existence of the Internet and other forms of communication systems. Economic globalization and the distribution
of goods and services presents yet another set of challenges. If the world social system is to be healthy,
it is necessary that these security issues be managed.
We have no choice but to develop a transparency about the
environmental, genetic, economic and social processes. Human technology is now simply too powerful
and too intrusive to allow simple economic and social processes to exercise
fundamentalism in various and separate ways.
If
we are to know who and where we are fighting, event models related to the onset of a
terrorist behavior must be derived from the data mining of global social
discourse. But the science to do this
has NOT been developed as yet. We must
define a science that has deep roots in legal theory, category theory, logic,
and the natural sciences.
This
is how the New War is won, not by accelerating the global arms race. Accelerating the global arms race is a stated
purpose of large DoD contractors, one simply has to check the public record. They would create a world that is not the
type of world that we envision. Alternatives
exist, but the sword must be turned into a plow.
A
secrete government project like the DARPA proposed TIA (Total Information
Awareness) project is not a proper response to challenges in information science.
In
any case, the American democracy is resilient enough to conduct proper science
and to develop the knowledge technologies, required to win the New War, in the
public view. The BCNGroup.org is calling for a Manhattan-type project to
establish the academic foundation for the knowledge sciences.
The
current natural security requirements demand that this science be synthesized
quickly from the available scholarship.
Within this new science, stratified logics will compute event
abstractions, at one scale of observation and event atom abstractions at a
second scale of observation. The atom abstractions are themselves to be derived
from polling and data mining processes in order to create the
abstractions.
Again,
we stress that the science needed has not been developed. But there is a wealth of scholarship that
can be integrated quickly if only there was a small effort and Presidential
leadership.
Note on Privacy Issues
A stratification of information can be made into two layers of
analysis.
The first layer is the set of individual polling results or the
individual text placed into social discourse.
In real time, and as trended over time, categorical abstraction is
developed based on the repeated patterns within word structure. Polling methodology and machine learning
algorithms are used.
The second layer is a derived aggregation of patterns that are analyzed
to infer the behavior of social collectives and to represent the thematic
structure of opinions. Drilling down into
the specific information about, or from, specific individuals will require
government analysts to make a conscious step and thus the very act of drilling
down from the abstract layer to the specific informational layer is an enforceable
legal barrier that stands in protection of Constitutional Rights.
New
science/technology is needed to “see” events that lead to or support
terrorism. Data mining is a start, as
are the pattern recognition systems that have been developed. But we also need data synthesis into
information, and a reification process that knows the importance of
human-in-the-loop perception and feedback.
To
control the computer mining and synthesis processes, we need something that
stands in for natural language.
Linguistic theory tells us that language use is not reducible to the
algorithms expressed in computer science.
But if “computers” are to be a mediator of social discourse, must not
the type of knowledge representation be more structured than human
language? What can we do?
The
issues of knowledge evocation and encoding of knowledge representation shape
this most critical of inquiries.
According
to our viewpoint, the computer does not, and cannot, have tacit knowledge to
disambiguate natural language, in spite of several decades of effort to create
knowledge technologies that have “common sense”. Based on principled argument a community of natural scientists
has argued that the computer will not have tacit knowledge; ever.
The New War presents
daunting challenges that cannot be addressed using anything we have developed
within information technology and computer science. Asymmetric threats are organizing distributed communities to
attack the vulnerabilities of our economic and political systems.
We
propose a new foundation to information technology based on class:object
informational pairing with categoricalAbstraction (cA)
and eventChemistry (eC) processes. We
propose a new science of knowledge systems.
The new operational
technology is based on operational class:object
informational pairing .
A simple and well-defined extension to this operational technology
supports a Differential
Ontology Framework (DOF) that has an open loop
architecture showing critical dependency on human sensory and cognitive
acuity. An Appendix
to this paper discusses the DOF.
Social and economic consequences
There are many positive
social and economic consequences to knowledge technology.
A large number of social organizations
have organically developed around the economic value of one to many
communications systems. These systems
are held into position by television and media institutions that have largely
abrogated social responsibility in the name of corporate profits. How the media corporations have done this
is something that must eventually be understood and accommodated for by the
American public. We conjecture that
these corporations purposefully nurture social acceptance of addictions to shallow
exploitation involving sexual, horror and violence themes.
Developing agility and
fidelity to our information systems is the strongest defense against asymmetric
threats. The differential ontology framework may enable processes, which have
one to many structural coupling, to make a transition to a many to many
technology. The asymmetric threat is
using many to one activity, loosely organized by the hijacking of various
religions to serve the expression of private hatred and grief. The defense to this threat is the
development of many to many communication systems.
The many to many
technologies allow relief from the stealth that asymmetric threats are
depending on. The relief comes when
machine ontology is used as a means to represent, in the abstract, the social
discourse. This representation can be done
via the development and algorithmic interaction of human structured knowledge
artifacts.
The evolution of user
self-structuring of knowledge artifacts in knowledge ecosystems must be
validated by community perception of that structure. In this way the interests of communities is established through a
private to public vetting of perception.
Without protection for privacy built in the technology, and protected by
law, this validation cannot be successful and the technology will fail to have
fidelity to what is actually the true structure of social discourse.
Knowledge validation occurs
as private tacit knowledge becomes public.
The anticipated relief from
the asymmetric threat will evolve because community structure is
facilitated.
The validation of artifacts
leads to structured community knowledge production processes and these
processes differentiate into economic processes. To achieve these benefits, a careful dance over the issues of
privacy and justice is required. But
global repression of all communities that feel injustice is not consistent with
the strength of the American people.
Our strength is in our multi-culturalism and our Constitution not in our
fundamentalisms. Our strength has been
in compassion and action based on compassion.
Individual humans, small
coherent social units, and business ecosystems are all properly regarded as
complex systems embedded in other complex systems. Understanding how events unfold in this environment has not been
easy. But the New War requires that
science devote attention to standing up information production systems that are
transparency and are many to many.
Relational databases and
artificial intelligence has been a good first effort, but more is
demanded. The current IT standards
often ignores certain difficult aspects of the complex environment and attempts
to:
1)
Navigate
between models and the perceived ideal system state, or
2)
Construct
models with an anticipation of process engineering and change management
bridging the difference between the model and reality.
The new knowledge science
changes this dynamic by allowing individuals to add and subtract from a common
knowledge base expressed within a differential ontology framework.
This technology can remain
transparent.
Note on Language and Linguistics
Language and
linguistics are relevant to our work for three reasons.
First, the new knowledge technologies are an extension to natural
spoken languages. The technology
reveals itself within a community as a new form of social communication.
Second, we are achieving the establishment of knowledge ecosystems
using peer-to-peer ontology streaming.
Natural language and the ontologies serve a similar purpose. However the ontologies are specialized
around virtual communities existing within an Internet culture. Thus ontology streaming represents an
extension of the phenomenon of naturally occurring language.
Third, the terminology used in various disciplines is often not
adequate for interdisciplinary discussion.
Thus we reach into certain schools of science, into economic theory and
into business practices to find bridges between these disciplines. This work on interdisciplinary terminology
is kept in the background, as there are many difficult challenges that remain
not properly addressed. To assist in understanding this issue, general systems
theory is useful.
These issues are in a
context. Within this context, we make a
distinction between computer computation, language systems, and human knowledge
events. The distinction opens the door to certain deep theories about the nature
of human thought.
Within existing scholarly
literatures one can ground a formal notation defining data structures that
store and allow the manipulation of topical taxonomies and related resources
existing within the knowledge base.
The differential ontology
framework consists of knowledge units and auxiliary resources used in report
generation and trending analysis. The
new knowledge science specifically recognizes that the human mind binds
together topics of a knowledge unit.
The new knowledge science holds that the computer cannot do this
binding for us. The knowledge science
reflects this reality. The rules of how
cognitive binding occurs are not captured into the data structure of the knowledge
unit, as this is regarded as counter to the differential ontology framework. The human remains central to all knowledge
events, and the relationship that a human has with his or her environment is
taken into account. The individual
human matters, always.