(
Index
)
Proposal roadmap
(SAIC/OntologyStream proposal for Real
World Reasoning due September 2003)
Using the Knowledge Sharing
Core Concept
Goals:
We seek a breakthrough in measured knowledge sharing fidelity based on
differential ontology formation and real time ontology processing. The initial 12-month period will establish a
market driven method and a deployment/use ecosystem that will drive an
anticipated advent of machine interoperability and agility within the semantic
web.
Benefits:
Benefits include better intelligence findings from any large data stream,
especially in practical terms related to detecting novelty, low salience
changes, and broad emergent shifts that other methods miss. The use process of the ecosystem will self
synchronize and reinforce those processes that analysts actually use. Intuitions about how to use advanced
computational processes will be enhanced because the deep knowledge of how the
analytic system works will not be held as proprietary secrets and will be
provided in a transparent fashion via scholarship exposition from leading
university faculty [1]
.
Technical barriers:
A specific class of technical barriers stem from specific paradigms in computer
science and Information Technology, and from institutions traditionally
involved in evaluation, procurement and deployment. Both the specific paradigms and the evaluation, procurement and
deployment practices are typical of Industrial Age Organizations [2]. These barriers inhibit collaboration and
rapid innovation based on relevant natural (cognitive and social) science and
on new advanced computational theory – including control theory, number theory,
general systems theory and theory of algorithms. Many of these technical barriers come from the market rejection
of the strong form of Artificial Intelligence.
It is true, however that AI has given us some useful experience in
machine representation of human knowledge.
But our group bypasses many of the technical barriers by redefining what
is expected from algorithmic processes and pushing the cognitive load away from
the computer, where it cannot be, into the human domain [3].
Elements of approach:
The elements of approach have four independently managed components.
(1) (Algorithms and science) Algorithms from competitive
traditions are modified, selected, compared, combined, and tested rapidly, and
are organized by hypotheses derived from a specific set of natural science
theories about memetic expression, human reasoning, and human perception. Those who have originated and continue to
develop the theories advise the program and will provide internal scientific
peer review over the results.
(2) (Knowledge component) We provide distance learning
modules on elementary algorithmic processes within the ecosystem so that anyone
can learn how and what the internal processes do and do NOT do. Social and cognitive
sciences are very clear in the claim that distance-learning activities are
necessary for adoption of transformational technology. These are not traditional training programs
but a clear and transparent presentation of the underlying principles and
processes involved in the Knowledge Sharing Core.
(3) (Infrastructure) We radically reduce
dependencies on enterprise software such as .NET and J2EE and provide increased
stability and access allowing innovation to be confidentially expressed
directly into a common design language.
The radical reduction uses Open Source tools such as the Berkeley
Database, and Linux kernels to build a solid foundation in software modules
that have a commonality of functionality determined by the design language
(called Cubicon [4]).
(4) (Patents and Intellectual Property) We believe that
relevant parts of the intelligence property space can be organized to reflect
scientific consensus and fairness to true innovators. First we see the application of the Knowledge Sharing Core
technologies in the development of a map of the memetic expression of
innovators. Second, we offer a common
design language. This design language
allows (a) the easy expression of the innovation’s reduction to practice as
computer process and (b) facilitates the rapid testing of the innovation within
the Knowledge Sharing Core as one of many tools available for use and
reinforcement via metrics on tools usage.
Rationale that builds
confidence: New
algorithms, and new synergistic combinations of existing tools, are applied
rapidly to outperform methods currently applied to high value intelligence
problems. This program will rapidly build a community of practice involving
between 20 – 30 leading scientists and engineers from participating software
companies, many of which will collaborate openly in highly focused workshops
with low cost to the project.
Nature of expected
results: More
analysts are willing to use these new tools compared to other research systems,
not only because the results are better and more obvious, but because the role
of the analyst is fully recognized and enhanced rather than reduced.
Risk if work is not
done: In the absence of this alternative, elaboration
(at great expense) of the general artificial intelligence paradigm will
continue. Many scientists recognize
this paradigm as founded on outdated notions about the nature of human
reasoning. Lack of fidelity and real
time usability in processing of semantic data guarantees continuation of a
broad class of intelligence failures.
Criteria for annual
progress evaluation:
Level of adoption by intelligence analysts in pilot settings. Findings in pilot operations are
corroborated by other existing means offering less fidelity. One measure of progress is the number of
patent applications from ecosystem members.
However, we anticipate the development of a revenue model based on the
notion of micro-transaction reporting.
The Knowledge Sharing Core will be self-sustaining within five years.
Cost for each performance year
|
FY 04 |
FY 05 |
FY 06 |
FY 07 |
FY 08 |
Total |
|
1,600K |
800K |
600K |
600K |
400K |
4M |