KNOWLEDGE SCIENCE is the study of those systems with a capacity to learn from past experience, to preserve that legacy, and employ it successfully to adapt their future behaviors toward ever-higher goals of survival and achievement.
It has emerged as a vigorous field of systems engineering over the past 30 years as machine technologies have given human civilization the unparalleled capacity to acquire, store, and use with lightning speed everything knowable. Still in these founding moments of a completely new industry, the central understanding of this science and its most significant, past application experience is closely held by only a handful of individual scientist/engineers. This is the first course anywhere in the world to take pioneering students to this furthest leading edge.
The course aims to teach scientists and engineers the foundational theory and practice of this new science as it has been used in business and engineering projects. Dr. Richard Ballard -- the leading developer of this new field, will teach it. His research and development projects have spanned the full 30 years -- from early National Science Foundation science education initiatives at UC Berkeley and UC Irvine through advanced engineering work on over 50 projects of national importance for NASA, DoD, and Office of the President.
This is a career-making course for those who want to take hands-on leadership roles in knowledge engineering. Originally designed as a 10 week, 3 hour, one night a week extension course. It draws every student into modeling state-of-the-art tool and applications designs. Participants are required to form into engineering teams around any shared subject interest, using their-own resources to amass suitable source knowledge reports, books, and other reference materials. They will use these to develop individual model work-products to be shared and discussed weekly. This teamwork focuses on the roles and job titles of project manager, tool designer, knowledge modeler, content outliner, knowledge editor, and subject-specialist / validator. All roles, tasks, and work-products, are practiced and examined in detail.
The three-hour sessions are divided between hour long (1) lecture, (2) closed team discussions, and (3) open meeting work-product reviews and challenge Q&A. Teams will be asked to assemble their work products into a final development process presentation designed to "explain and sell" their capabilities to prospective project sponsors.
OUTLINE FOR A 10 WEEK COURSE:
Introduction To Knowledge Science And Engineering (1 week)
Creating a Common Semantics for Humans and Machines (3 weeks)
.....1.1.1 Conceptualism, Semantics, & Ontological Primitives
.....1.1.2 Knowledge Theory, Relationship, and Constraints
.....1.1.3 Mediating Structures -- Ubiquitous Patterns of Thought
Organization and Economics of Knowledge Engineering (3 weeks)
.....1.1.4 Knowledge Commerce and Production Models
.....1.1.5 Knowledge Engineering -- Modelers and Content Outliners
.....1.1.6 Knowledge Integration -- Editors and Content Validation
Notional Architectures For Systems that Learn (3 weeks)
.....1.1.7 Passive Reference Layers -- "KnowBots"
.....1.1.8 Dawn of Machine Experience and Self-Awareness
.....1.1.9 Machine Relationship Testing and Theory Formation
Presentation of Project Concepts and Work Models (final)
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INTRODUCTION TO KNOWLEDGE SCIENCE & ENGINEERING
LECTURE: Profile of Knowledge Age Goals and Science
Knowledge Science challenges civilization to make the intelligence attributed to humankind a capacity shared and innate within all systems. Each might continually learn, adapt, and transform its own functions and behavior, focusing everything known on achieving and advancing their missions with ever-greater success. The assumptions of science hold that such success rests upon our collective understanding and shared use of all the "natural laws" to be discovered within every causative and influential pursuit -- physical, economic, ethical, social, esthetic, political, ….
Within this ever striving enterprise -- knowledge science aspires to be a quantitative, empirical, "hard science" that underlies and integrates all sciences. Its singular role is to perfect a common, "evolutionary semantic form" suited to the encoding and sharing of all knowledge. For the next four weeks, we trace the progress of this new science and examine is underlying and emergent "Theory of Knowledge".
In this lecture, we look first at the silent contradictions that stymied Information Age thought and the need to confront these with decisive leaps and changes in view. The transition from Information to Knowledge Age bids us to abandon unchanging machine behaviors and pursue the robotic requirement that systems adopt human-like theory-based reasoning. This symbiotic convergence on rational form impacts profoundly the scope of human progress and the time spans over which civilization can plan and persevere. It dramatically alters too our future goals and expectations in conceptualizing a new and quite revolutionary Knowledge Age technology base.
The middle three weeks of this course focus exclusively on the early role of human industry formations and knowledge engineering job skills in priming the pump of this symbiotic, human-machine partnering. For that we center on the early training and widespread use of existing office workforces in translating human-kind's legacy reference sources into permanent, economic assets expressed within a new, coded, "universal, knowledge representation" -- sharable by humans and machines.
In the last three weeks, we turn from the economics of legacy knowledge capture to brainstorm totally new tool and machine design goals. These put aside the notion that human involvement is a necessity in machine learning and adaptation. So while our initial focus began with human-centered design of commercial, knowledge asset architectures, it shifts quickly to study how machines become self-aware of their own successes and failures. Our game then is to imagine just how these new machines use such self-awareness to start learning and creating theories of their own. In this final imaginative play -- your work is transformed from learning knowledge science -- to inventing it.
WEEK ONE: CONTENT OUTLINE
1. Natural History of Earth and Human Civilization
2. Animal Evolution and Origin of Brains
3. Brainless Computing to Machine Awareness of Theory
4. Philosophy -- Defining a Language for Knowledge Types
5. Reality vs. Imagination, Physics vs. Metaphysics, Information vs. Theory
6. Fundamental Differences Between "a' posteriori" and "a' priori" knowledge
WEEK ONE: GROUP EXERCISES
1. Description of Knowledge Engineering Study Group Projects
2. Calendar of Group Organization and Deliverables
3. Deeply Structured Sources -- Libraries of World Expertise
4. Developmental Technology Resources
4. Class Biographies and Statements of Interest & Resources
6. Areas of Shared Commitment
WEEK ONE: CANDIDATE SPONSORS & PROJECT TYPES
1. Project Types -- Markets & Engineering Phase
2. Project Sponsor Candidates & Budget Objectives
3. Project Source Assets -- Focus Groups -- Assessment
4. Project Deliverables -- Tools & Applications
5. Development Business Models
WEEK ONE: ASSIGNMENTS
1. Knowledge Definitions
2. Deepest Reference Source Samples
3. Ranking of Project Interests
4. Ranking of Subject Expertise
5. List of Job Responsibility Selections
6. Questions before Committing
ABOUT THE INSTRUCTOR:
Dr. Richard L. Ballard, Ph.D., is a physicist by education and innovative technology pioneer by vocation. For the past 30 years Dr. Ballard has been a leader in knowledge science and semantic technology development. While at UC Irvine, Ballard was cited for the first application of artificial intelligence to conceptual learning (Expert Systems in Business). A founder of the Apple Education Foundation with Steve Jobs in 1978, he began work on semantic web applications in support of educational publishing and game development. His research lead him into Star Wars missile defense programs, which emerged in the early 1990's, into the development of semantic web and decision management tools that have been deployed at the highest levels of governement. Ballard's revolutionary knowledge science and declarative semantic technologies are now moving the world from the Information Age into the Knowledge Age. Ballard has received 128 software citations, developed 21 Educational Software Workshops, 3 Management Software Workshops and his current 10 Week Professional Knowledge Engineering course. Ballard has been published in 35 publications and technical reports. Ph.D. UC Berkeley; Fellow NASA Institute for Space Physics, Columbia University.