Archives for concept computing

Transformational change demands architecture that is different.

Transformation demands different architecture.

Transformation demands different architecture.

Here is a little story to illustrate the point:

A scientist discovered a way to grow the size of a flea by several orders of magnitude. She was terribly excited. After all, a flea can jump vertically more than 30 times its body size. She reasoned that a flea this big would be able leap over a tall building. Perhaps, there could be a Nobel Prize in this.

When the day came to show the world, she pushed the button and sure enough out came this giant flea, over two meters high. But, rather than leaping a tall building, it took one look around and promptly fell over dead. Turns out it couldn’t breath. No lungs. Passive air holes that worked fine for oxygen exchange in a tiny flea were useless for a creature so big.

Seven examples of the business value of ontologies

Each year at the National Institutes of Standards and Technology, the Ontolog Forum brings together a community of  researchers, educators, and practitioners to discuss the role of ontologies in next generation solutions.  This presentation highlights seven case examples showing how ontologies deliver business value.


Understanding our digital universe

Expanding Universe

Transformation is and always has been a pervasive property of our universe.

How is the Digital Universe expanding?

Dr. Michael Brodie has an interesting take on trends we are struggling with this decade.

Check out this tutorial:
It articulates key themes of our rapidly expanding “Digital Universe.” The talk summary reads as follows:

“This is a remarkable time in human history. Our real world is rapidly becoming digital and our digital worlds are rapidly becoming real. Ubiquitous digital worlds such as online shopping and auctions, stock and equity trading systems, electronic banking, social networks, and the e-‘s (e-government, e-health, e-commerce, e-business) contribute to our rapidly expanding Digital Universe that is as fascinating in the 21st Century as the physical universe was in the 20th Century. Digital worlds have an enormous, and far from understood, impact on our real world and vice versa. The growth, adoption, and power of these digital worlds and the amazing opportunities and threats that they offer suggest a forthcoming Digital Industrial Revolution. The Digital Industrial Revolution, accelerated by the Web, will have far reaching effect, as did the Industrial Revolution, accelerated by printing press. Both revolutions unleashed natural social, economic, and political forces and both flattened the world through transparency and openness. But the Digital Industrial revolution, because of the phenomena surrounding the Web and its interactions with society, is occurring at lightning speed with profound impacts on society, the economy, politics, and more.

Our Digital Universe is leading to fundamental changes in human endeavors – how people interact, how science and business is conducted, and how governments operate, leading in turn to planned and unforeseen consequences such as universal and instantaneous access to information and other resources, globalization of enterprises and industries, as well as economic and social crises, and threats to security and civil liberties. No longer do computer systems provide back-office, administrative support; they are emerging as platforms for digital ecosystems of automated and human agents that operate real business, social, and government processes; thus creating digital worlds that are an integral part of our real world. Yet we build them with little understanding of these digital worlds or their impacts on our real world. Stated simply, the Web is unleashing natural social, economic, political, and other forces – for good and for ill.

This talk explores our expanding Digital Universe that has been emerging slowly for half a century but has reached a tipping point due to the convergence of technical and world trends such as the Web and its continuously astounding adoption. We investigate key contributors to this remarkable time of change and transformation. Digital worlds are being used to transform social, business, scientific, and government activities creating the potential that we can redefine our world. But how do we redefine our world? Where do we start? We look at the need for fundamentally new methods to understand our digital worlds and their actual and potential interactions with and impacts on our world; and for the conception, design, development, and use of digital worlds (previously called “applications” presumable of computing) and the real and digital worlds with which they interact. Since the problems being addressed are real, so is the problem solving. No more “boffins in the back room”. The related problem solving methods must be holistic, multi-disciplinary, and collaborative and that facilitate problem solving across technical, social, and other domains to develop secure, realistic, and robust digital worlds. The need for such methods is illustrated with a healthcare information system failure costing £12.4 billion and a corresponding success due largely to its multi-disciplinary life cycle. We examine examples of these methods by applying Jarvis’s Google Rules to failing real worlds and their growing digital counterparts. The emergence of our Digital Universe and its impact on and potential for our world raises the challenge to aspire to the principles of Web Science to work collaboratively across relevant disciplines to create digital worlds that contribute to improving our world.”


The power and limitations of relational database technology in the age of information ecosystems

Dr. Michael Brody

Dr. Michael Brody is concerned with the Big Picture, including business, economic, social, application, and technical aspects of information ecosystems, core technologies, and integration. He has served as Chief Scientist of a Fortune 20 company, an Advisory Board member of leading national and international research organizations, and an invited speaker and lecturer. Dr. Brodie researches and analyzes challenges and opportunities in advanced technology, architecture, and methodologies for Information Technology strategies. The following is a link to a presentation in which Dr.  Brody examines trends in data management, integration at scale, and information ecosystems.

Industrial giants placing big bets on smart technologies and concept computing

GE’s vision of the industrial internet

GE’s vision of the industrial internet

In the fall of 2012 General Electric came out with a study predicting huge economic growth resulting from the Industrial Internet.  The two authors are GE’s top strategist and chief economist. It’s a serious report.

Here is the thesis. Mechanization of work over the past 200 years has resulted in a 50X worker productivity increase.  The next stage is the integration of machines with computing and the Internet. The result, they predict will be tens of trillions of dollars in economic expansion and improved quality of life worldwide.

Industrial internet fuels global economic expansion.

Industrial internet fuels global economic expansion.

Here are two slides from the GE report.

The one on the left identifies three key elements of the industrial internet.  The implication is that patterns of work will change and that industrial products and processes will gain a cradle to sunset life history.

The diagram to the right projects the value of the industrial internet in the form of potential performance gains across five economic sectors. This is a minimal projection, the power of 1 percent, but we’re still talking $ billions. GE’s overall projection for industrial internet fueled economic expansion to 2030 is closer to $40 trillion.

Agent Smith (from the Matrix) helping GE promote smart technologies.

Agent Smith (from the Matrix) helping GE promote smart technologies.

During 2013 GE began taking its industrial internet thesis to the street. Their recent TV commercials bring back Agent Smith from the Matrix. This scene is about the interconnection and intelligent interaction of machines, software, and healthcare professionals to deliver improved outcomes for patients — a waiting room becomes, just a room.

One version of the ad ends with agent Smith offering a child a choice of lollypops — a red one or a blue one.

Search to knowing

The spectrum of knowledge representation and reasoning

More expressive knowledge representation enables more powerful reasoning

More expressive knowledge representation powers greater reasoning capability.

Not all knowledge representation is the same. This figure shows a spectrum of executable knowledge representation and reasoning capabilities. As the rigor and expressive power of the semantics and knowledge representation increases, so does the value of the reasoning capacity it enables.

From bottom-to-top, the amount, kinds, and complexity, and expressive power knowledge
representation increases.From left-to-right, reasoning capabilities advance from:
(a) Information recovery based on linguistic and statistical methods, to
(b) Discovery of unexpected relevant information and associations through mining, to
(c) Intelligence
based on correlation of data sources, connecting the dots, and putting information into context, to
(d) Question answering ranging from simple factoids to complex decision-support, to
(e) Smart behaviors including robust adaptiveand autonomous action.

Moving from lower left to upper right, the diagram depicts a spectrum of progressively more capable forms of knowledge representation together with standards and formalisms used to express metadata, associations, models, contexts, and modes of reasoning. More expressive forms of metadata and semantic modeling encompass the simpler forms, and extend their capabilities. In the following topics, we discuss different forms of knowledge representation,then the types of reasoning capabilities they enable.

What is knowledge representation?

Knowledge representation is the application of theory, values, logic, and ontology to the task of constructing computable patterns of some domain.

The future is n-ary concept encoding.

The future of knowledge representation is n-ary concept encoding.

Knowledge is “captured and preserved”, when it is transformed into a perceptible and manipulable system of representation.

Systems of knowledge representation differ in their fidelity, intuitiveness, complexity, and rigor. The computational theory of knowledge predicts that ultimate economies and efficiencies can be achieved through variable-length n-ary concept coding and pattern reasoning resulting in designs that are linear and proportional to knowledge measure.

“Semantic networks” (entity-relationship) are the most powerful and general form for knowledge representation. They model knowledge as a nodal mesh of mental concepts and physical entities (boxes, circles, etc.) tied by constraining relationships (arrows, directed lines). Relationships describe “constraints” on concepts including: (a) logical constraints — prepositions of direction or proximity, action verbs connecting subject to object, etc., and (b) reality constraints — linking concepts to their time, image, attributes, or perceptible measures.

Physical knowledge is Information, or the a posteriori constraints of spatial-temporal reality. It includes sense data / measurements, observed or recorded independently — often dependent on time, place or conditions observed. Information representations include: numbers and units, tables of measurement, statistics, data bases, language, drawings, photographic images.

Metaphysical knowledge is rational structure, or the a priori constraint of mental concepts & perceived relationships, dictated by axiology, accepted theory, logic, and conditioned expectation — expressed as truth, correctness, and self-consistency — usually independent of time, place, or a particular reality. Representations include computer programs, rules, E-R diagrams, language, symbols, formula, algorithms, recipes, ontologies.

Axiology trumps logic

What is value?

The measure of the worth or desirability of something.
The foundation of meaning.

Value is the foundation of meaning.
It is the measure of the worth or desirability (positive or negative) of something, and of how well something conforms to its concept or intension. Value formation and value-based reasoning are fundamental to all areas of human endeavor. Theories embody values. The axiom of value is based on “concept fulfillment.”

Most areas of human reasoning require application of multiple theories; resolution of conflicts, uncertainties, competing values, and analysis of trade-offs. For example, questions of guilt or innocence require judgment of far more than logical truth or falsity.

Axiology is the branch of philosophy that studies value and value theory.
Things like honesty, truthfulness, objectiveness, novelty, originality, “progress,” people satisfaction, etc. The word ‘axiology’, derived from two Greek roots ‘axios’ (worth or value) and ‘logos’ (logic or theory), means the theory of value, and concerns the process of understanding values and valuation.

What is theory?

Any conditional or unconditional assertion, axiom or constraint used for reasoning about the world.

A theory is any conjecture, opinion, or speculation. In this usage, a theory is not necessarily based on facts and may or may not be consistent with verifiable descriptions of reality.

We use theories to reason about the world. In this sense, theory is a set of interrelated constructs — formulas and inference rules and a relational model (a set of constants and a set of relations defined on the set of constants).

“The ontology of a theory consists in the objects theory assumes there to be.”
— Quine — Word and Object, 1960

Theories are accepted or rejected as a whole. If we choose to accept and use a theory for reasoning, then we must commit to all the ideas and relationships the theory needs to establish its existence.

In science, theory is a proposed rational description, explanation, or model of the manner of interaction of a set of natural phenomena.

Scientific theory should be capable of predicting future occurrences or observations of the same kind, and capable of being tested through experiment or otherwise falsified through empirical observation.

Values for theory construction include that theory should:

  • Add to our understanding of observed phenomena by explaining them in the simplest form possible (parsimony)
  • Fit cleanly with observed facts and with established principles
  • Be inherently testable and verifiable, and
  • Imply further investigations and predict new discoveries.
Theory is as theory does.

Theory is as theory does.

Physical theory of knowledge

Expect rapid progress towards a universal knowledge technology that provides a full spectrum of information, metadata, semantic modeling, and advanced reasoning capabilities for any who want it.

Computational theory of knowledge -- after Shannon.

Computational theory of knowledge — after Shannon.

Why? Large knowledgebases, complex forms of situation assessment, sophisticated reasoning with uncertainty and values, and autonomic and autonomous system behavior exceed the capabilities and performance capacity of current description logic-based approaches to concept computing.

Universal knowledge technology will be based on a physical theory of knowledge that holds that knowledge is anything that decreases uncertainty. The formula is:
Knowledge = Theory + Information.

Theories are the conditional constraints that give meaning to concepts, ideas and thought patterns. Theory asserts answers to “how”, “why” and “what if” questions. For humans, theory is learned through enculturation, education, and life experience.

Information, or data, provides situation awareness — who, what, when, where and how-much facts of situations and circumstances. Information requires theory to define its meaning and purpose.

Theory persists and always represents the lion’s share of knowledge content — say 85%. Information represents a much smaller portion of knowledge — perhaps only 15%

What will distinguish universal knowledge technology is enabling both machines and humans to understand, combine, and reason with any form of knowledge, of any degree of complexity, at any scale.

Answer to a query is a rational path.

Answer to a query is a rational path.