Archive for the ‘Smart Systems’ Category

A Brief Ontology Of Time

May 23, 2018


The melting of digital fences between enterprises and business environments is putting a new light on the way time has to be taken into account.


Time is what happens between events (Josef Koudelka)

The shift can be illustrated by the EU GDPR : by introducing legal constraints on the notifications of changes in personal data, regulators put systems’ internal events on the same standing as external ones and make all time-scales equal whatever their nature.

Ontological Limit of WC3 Time Recommendation

The W3C recommendation for OWL time description is built on the well accepted understanding of temporal entity, duration, and position:


While there isn’t much to argue with what is suggested, the puzzle comes from what is missing, namely the modalities of time: the recommendation makes use of calendars and time-stamps but ignores what is behind, i.e time ontological dimensions.

Out of the Box

As already expounded (Ontologies & Enterprise Architecture) ontologies are at their best when a distinction can be maintained between representation and semantics. That point can be illustrated here by adding an ontological dimension to the W3C description of time:

  1. Ontological modalities are introduced by identifying (#) temporal positions with regard to a time-frame.
  2. Time-frames are open-ended temporal entities identified (#) by events.

How to add ontological modalities to time

It must be noted that initial truth-preserving properties still apply across ontological modalities.

Conclusion: OWL Descriptions Should Not Be Confused With Ontologies

Languages are meant to combine two primary purposes: communication and symbolic representation, some (e.g natural, programming) being focused on the former, other (e.g formal, specific) on the latter.

The distinction is somewhat blurred with languages like OWL (Web Ontology Language) due to the versatility and plasticity of semantic networks.


Ontologies and profiles are meant to target domains, profiles and domains are modeled with languages, including OWL.

That apparent proficiency may induce some confusion between languages and ontologies, the former dealing with the encoding of time representations, the latter with time modalities.

Further Readings

External Links


Collaborative Systems Engineering: From Models to Ontologies

April 9, 2018

Given the digitization of enterprises environments, engineering processes have to be entwined with business ones while kept in sync with enterprise architectures. That calls for new threads of collaboration taking into account the integration of business and engineering processes as well as the extension to business environments.


Collaboration can be personal and direct, or collective and mediated (Wang Qingsong)

Whereas models are meant to support communication, traditional approaches are already straining when used beyond software generation, that is collaboration between humans and CASE tools. Ontologies, which can be seen as a higher form of models, could enable a qualitative leap for systems collaborative engineering at enterprise level.

Systems Engineering: Contexts & Concerns

To begin with contents, collaborations should be defined along three axes:

  1. Requirements: business objectives, enterprise organization, and processes, with regard to systems functionalities.
  2. Feasibility: business requirements with regard to architectures capabilities.
  3. Architectures: supporting functionalities with regard to architecture capabilities.

Engineering Collaborations at Enterprise Level

Since these axes are usually governed by different organizational structures and set along different time-frames, collaborations must be supported by documentation, especially models.

Shared Models

In order to support collaborations across organizational units and time-frames, models have to bring together perspectives which are by nature orthogonal:

  • Contexts, concerns, and languages: business vs engineering.
  • Time-frames and life-cycle: business opportunities vs architecture stability.

Harnessing MBSE to EA

That could be achieved if engineering models could be harnessed to enterprise ones for contexts and concerns. That is to be achieved through the integration of processes.

 Processes Integration

As already noted, the integration of business and engineering processes is becoming a key success factor.

For that purpose collaborations would have to take into account the different time-frames governing changes in business processes (driven by business value) and engineering ones (governed by assets life-cycles):

  • Business requirements engineering is synchronic: changes must be kept in line with architectures capabilities (full line).
  • Software engineering is diachronic: developments can be carried out along their own time-frame (dashed line).

Synchronic (full) vs diachronic (dashed) processes.

Application-driven projects usually focus on users’ value and just-in-time delivery; that can be best achieved with personal collaboration within teams. Architecture-driven projects usually affect assets and non-functional features and therefore collaboration between organizational units.

Collaboration: Direct or Mediated

Collaboration can be achieved directly or through some mediation, the former being a default option for applications, the latter a necessary one for architectures.


Both can be defined according to basic cognitive and organizational mechanisms and supported by a mix of physical and virtual spaces to be dynamically redefined depending on activities, projects, locations, and organisation.

Direct collaborations are carried out between individuals with or without documentation:

  • Immediate and personal: direct collaboration between 5 to 15 participants with shared objectives and responsibilities. That would correspond to agile project teams (a).
  • Delayed and personal: direct collaboration across teams with shared knowledge but with different objectives and responsibilities. That would tally with social networks circles (c).


Mediated collaborations are carried out between organizational units through unspecified individual members, hence the need of documentation, models or otherwise:

  • Direct and Code generation from platform or domain specific models (b).
  • Model transformation across architecture layers and business domains (d)

Depending on scope and mediation, three basic types of collaboration can be defined for applications, architecture, and business intelligence projects.


Projects & Collaborations

As it happens, collaboration archetypes can be associated with these profiles.

Collaboration Mechanisms

Agile development model (under various guises) is the option of choice whenever shared ownership and continuous delivery are possible. Application projects can so be carried out autonomously, with collaborations circumscribed to team members and relying on the backlog mechanism.

The OODA (Observation, Orientation, Decision, Action) loop (and avatars) can epitomize projects combining operations, data analytics, and decision-making.


Collaboration archetypes

Projects set across enterprise architectures cannot be carried out without taking into account phasing constraints. While ill-fated Waterfall methods have demonstrated the pitfalls of procedural solutions, phasing constraints can be dealt with a roundabout mechanism combining iterative and declarative schemes.

Engineering vs Business Driven Collaborations

With collaborative engineering upgraded at enterprise level, the main challenge is to iron out frictions between application and architecture projects and ensure the continuity, consistency and effectiveness of enterprise activities. That can be achieved with roundabouts used as a collaboration mechanism between projects, whatever their nature:

  • Shared models are managed at roundabout level.
  • Phasing dependencies are set in terms of assertions on shared models.
  • Depending on constraints projects are carried out directly (1,3) or enter roundabouts (2), with exits conditioned by the availability of models.

Engineering driven collaboration: roundabout and backlogs

Moreover, with engineering embedded in business processes, collaborations must also bring together operational analytics, decision-making, and business intelligence. Here again, shared models are to play a critical role:

  • Enterprise descriptive and prescriptive models for information maps and objectives
  • Environment predictive models for data and business understanding.

Business driven collaboration: operations and business intelligence

Whereas both engineering and business driven collaborations depend on sharing information  and knowledge, the latter have to deal with open and heterogeneous semantics. As a consequence, collaborations must be supported by shared representations and proficient communication languages.

Ontologies & Representations

Ontologies are best understood as models’ backbones, to be fleshed out or detailed according to context and objectives, e.g:

  • Thesaurus, with a focus on terms and documents.
  • Systems modeling,  with a focus on integration, e.g Zachman Framework.
  • Classifications, with a focus on range, e.g Dewey Decimal System.
  • Meta-models, with a focus on model based engineering, e.g models transformation.
  • Conceptual models, with a focus on understanding, e.g legislation.
  • Knowledge management, with a focus on reasoning, e.g semantic web.

As such they can provide the pillars supporting the representation of the whole range of enterprise concerns:


Taking a leaf from Zachman’s matrix, ontologies can also be used to differentiate concerns with regard to architecture layers: enterprise, systems, platforms.

Last but not least, ontologies can be profiled with regard to the nature of external contexts, e.g:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to established procedures.
  • Corporate: Defined by enterprises, changes subject to internal decision-making.
  • Social: Defined by usage, volatile, continuous and informal changes.
  • Personal: Customary, defined by named individuals (e.g research paper).

Cross profiles: capabilities, enterprise architectures, and contexts.

Ontologies & Communication

If collaborations have to cover engineering as well as business descriptions, communication channels and interfaces will have to combine the homogeneous and well-defined syntax and semantics of the former with the heterogeneous and ambiguous ones of the latter.

With ontologies represented as RDF (Resource Description Framework) graphs, the first step would be to sort out truth-preserving syntax (applied independently of domains) from domain specific semantics.


RDF graphs (top) support formal (bottom left) and domain specific (bottom right) semantics.

On that basis it would be possible to separate representation syntax from contents semantics, and to design communication channels and interfaces accordingly.

That would greatly facilitate collaborations across externally defined ontologies as well as their mapping to enterprise architecture models.


To summarize, the benefits of ontological frames for collaborative engineering can be articulated around four points:

  1. A clear-cut distinction between representation semantics and truth-preserving syntax.
  2. A common functional architecture for all users interfaces, humans or otherwise.
  3. Modular functionalities for specific semantics on one hand, generic truth-preserving and cognitive operations on the other hand.
  4. Profiled ontologies according to concerns and contexts.

Clear-cut distinction (1), unified interfaces architecture (2), functional alignment (3), crossed profiles (4).

A critical fifth benefit could be added with regard to business intelligence: combined with deep learning capabilities, ontologies would extend the scope of collaboration to explicit as well as implicit knowledge, the former already framed by languages, the latter still open to interpretation and discovery.

Further Reading


Business Intelligence & Semantic Galaxies

March 26, 2018

Given the number and verbosity of alternative definitions pertaining to enterprise and systems architectures, common sense would suggest circumspection if not agnosticism. Instead, fierce wars are endlessly waged for semantic positions built on sand hills bound to crumble under whoever tries to stand defending them.

Nature & Nurture (Wang Xingwei)

Such doomed attempts appear to be driven by a delusion seeing concepts as frozen celestial bodies; fortunately, simple-minded catalogs of unyielding definitions are progressively pushed aside by the need to understand (and milk) the new complexity of business environments.

Business Intelligence: Mapping Semantics to Circumstances

As long as information systems could be kept behind Chinese walls semantic autarky was of limited consequences. But with enterprises’ gates crumbling under digital flows, competitive edges increasingly depend on open and creative business intelligence (BI), in particular:

  • Data understanding: giving form and semantics to massive and continuous inflows of raw observations.
  • Business understanding: aligning data understanding with business objectives and processes.
  • Modeling: consolidating data and business understandings into descriptive, predictive, or operational models.
  • Evaluation: assessing and improving accuracy and effectiveness of understandings with regard to business and decision-making processes.

BI: Mapping Semantics to Circumstances

Since BI has to take into account the continuity of enterprise’s objectives and assets, the challenge is to dynamically adjust the semantics of external (business environments) and internal (objects and processes) descriptions. That could be explained in terms of gravitational semantics.

Semantic Galaxies

Assuming concepts are understood as stars wheeling across unbounded and expanding galaxies, semantics could be defined by gravitational forces and proximity between:

  • Intensional concepts (stars) bearing necessary meaning set independently of context or purpose.
  • Extensional concepts (planets) orbiting intensional ones. While their semantics is aligned with a single intensional concept, they bear enough of their gravity to create a semantic environment.

On that account semantic domains would be associated to stars and their planets, with galaxies regrouping stars (concepts) and systems (domains) bound by gravitational forces (semantics).


Conceptual Stars & Planets


Semantic Dimensions & Concepts Metamorphosis

While systems don’t leave much, if any, room for semantic wanderings, human languages are as good as they can be pliant, plastic, and versatile. Hence the need for business intelligence to span the stretch between open and fuzzy human semantics and systems straight-jacketed modeling languages.

That can be done by framing concepts metamorphosis along Zachman’s architecture description: intensional concepts being detached of specific contexts and concerns are best understood as semantic roots able to breed multi-faceted extensions, to be eventually coerced into system specifications.


Framing concepts metamorphosis along Zachman’s architecture dimensions

The Alignment of Planets

As stars, concepts can be apprehended through a mix of reason and perception:

  • Figured out from a conceptual void waiting to be filled.
  • Fortuitously discovered in the course of an argument.

The benefit in both cases would be to delay verbal definitions and so to avoid preempted or biased understandings: as for the Schrödinger’s cat, trying to lock up meanings with bare words often breaks their semantic integrity, shattering scraps in every direction.

In contrast, making room for semantic alignments would help to consolidate overlapping definitions within conceptual galaxies, as illustrated by the examples below.

Example: Data

Wikipedia: Any sequence of one or more symbols given meaning by specific act(s) of interpretation; requires interpretation to become information.

Merriam-Webster: Factual information such as measurements or statistics; information in digital form that can be transmitted or processed; information and noise from a sensing device or organ that must be processed to be meaningful.

Cambridge Dictionary: Information, especially facts or numbers; information in an electronic form that can be stored and used by a computer.

Collins: Information that can be stored and used by a computer program.

TOGAF: Basic unit of information having a meaning and that may have subcategories (data items) of distinct units and values.


Example: System

Wikipedia: A regularly interacting or interdependent group of items forming a unified whole; Every system is delineated by its spatial and temporal boundaries, surrounded and influenced by its environment, described by its structure and purpose and expressed in its functioning.

Merriam-Webster: A regularly interacting or interdependent group of items forming a unified whole

Business Dictionary: A set of detailed methods, procedures and routines created to carry out a specific activity, perform a duty, or solve a problem; organized, purposeful structure that consists of interrelated and interdependent elements.

Cambridge Dictionary: A set of connected things or devices that operate together

Collins Dictionary: A way of working, organizing, or doing something which follows a fixed plan or set of rules; a set of things / rules.

TOGAF: A collection of components organized to accomplish a specific function or set of functions (from ISO/IEC 42010:2007).

Further Reading

Ontologies as Productive Assets

January 22, 2018


An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.

Ontological Questioning (The Thinker Monkey, Breviary of Mary of Savoy)

Yet that interest has often been led astray by misguided perspectives, in particular:

  • Universality: one-fits-all approaches are pointless if not self-defeating considering that ontologies are meant to target specific domains of concerns.
  • Implementation: the focus is usually put on representation schemes (commonly known as Resource Description Frameworks, or RDFs), instead of the nature of targeted knowledge and the associated cognitive capabilities.

Those misconceptions, often combined, may explain the limited practical inroads of ontologies. Conversely, they also point to ontologies’ wherewithal for enterprises immersed into boundless and fluctuating knowledge-driven business environments.

Ontologies as Assets

Whatever the name of the matter (data, information or knowledge), there isn’t much argument about its primacy for business competitiveness; insofar as enterprises are concerned knowledge is recognized as a key asset, as valuable if not more than financial ones, and should be managed accordingly. Pushing the comparison still further, data would be likened to liquidity, information to fixed income investment, and knowledge to capital ventures. To summarize, assets whatever their nature lose value when left asleep and bear fruits when kept awake; that’s doubly the case for data and information:

  • Digitized business flows accelerates data obsolescence and makes it continuous.
  • Shifting and porous enterprises boundaries and markets segments call for constant updates and adjustments of enterprise information models.

But assessing the business value of knowledge has always been a matter of intuition rather than accounting, even when it can be patented; and most of knowledge shapes up well beyond regulatory reach. Nonetheless, knowledge is not manna from heaven but the outcome of information processing, so assessing the capabilities of such processes could help.

Admittedly, traditional modeling methods are too stringent for that purpose, and looser schemes are needed to accommodate the open range of business contexts and concerns; as already expounded, that’s precisely what ontologies are meant to do, e.g:

  • Systems modeling,  with a focus on integration, e.g Zachman Framework.
  • Classifications, with a focus on range, e.g Dewey Decimal System.
  • Conceptual models, with a focus on understanding, e.g legislation.
  • Knowledge management, with a focus on reasoning, e.g semantic web.

And ontologies can do more than bringing under a single roof the whole of enterprise knowledge representations: they can also be used to nurture and crossbreed symbolic assets and develop innovative ones.

Ontologies Benefits

Knowledge is best understood as information put to use; accounting rules may be disputed but there is no argument about the benefits of a canny combination of information, circumstances, and purpose. Nonetheless, assessing knowledge returns is hampered by the lack of traceability: if a part of knowledge is explicit and subject to symbolic representation, another is implicit and manifests itself only through actual behaviors. At philosophical level it’s the line drawn by Wittgenstein: “The limits of my language mean the limits of my world”;  at technical level it’s AI’s two-lanes approach: symbolic rule-based engines vs non symbolic neural networks; at corporate level implicit knowledge is seen as some unaccounted for aspect of intangible assets when not simply blended into corporate culture. With knowledge becoming a primary success factor, a more reasoned approach of its processing is clearly needed.

To begin with, symbolic knowledge can be plied by logic, which, quoting Wittgenstein again, “takes care of itself; all we have to do is to look and see how it does it.” That would be true on two conditions:

  • Domains are to be well circumscribed. 
  • A water-tight partition must be secured between the logic of representations and the semantics of domains.

That could be achieved with modular and specific ontologies built on a clear distinction between common representation syntax and specific domains semantics.

As for non-symbolic knowledge, its processing has for long been overshadowed by the preeminence of symbolic rule-based schemes, that is until neural networks got the edge and deep learning overturned the playground. In a few years’ time practically unlimited access to raw data and the exponential growth in computing power have opened the door to massive sources of unexplored knowledge which is paradoxically both directly relevant yet devoid of immediate meaning:

  • Relevance: mined raw data is supposed to reflect the geology and dynamics of targeted markets.
  • Meaning: the main value of that knowledge rests on its implicit nature; applying existing semantics would add little to existing knowledge.

Assuming that deep learning can transmute raw base metals into knowledge gold, enterprises would need to understand, assess, and improve the refining machinery. That could be done with ontological frames.

A Proof of Concept

Compared to tangible assets knowledge may appear as very elusive, yet, and contrary to intangible ones, knowledge is best understood as the outcome of processes that can be properly designed, assessed, and improved. And that can be achieved with profiled ontologies.

As a Proof of Concept, an ontological kernel has been developed along two principles:

  • A clear-cut distinction between truth-preserving representation and domain specific semantics.
  • Profiled ontologies designed according to the nature of contents (concepts, documents, or artifacts), layers (environment, enterprise, systems, platforms), and contexts (institutional, professional, corporate, social.

That provides for a seamless integration of information processing, from data mining to knowledge management and decision making:

  • Data is first captured through aspects.
  • Categories are used to process data into information on one hand, design production systems on the other hand.
  • Concepts serve as bridges to knowledgeable information.


A beta version is available for comments on the Stanford/Protégé portal with the link: Caminao Ontological Kernel (CaKe).

Further Reading

External Links

Open Ontologies: From Silos to Architectures

January 1, 2018

To be of any use for enterprises, ontologies have to embrace a wide range of contexts and concerns, often ill-defined for environments, rather well expounded for systems.

Circumscribed Contexts & Crossed Concerns (Robert Goben)

And now that enterprises have to compete in open, digitized, and networked environments, business and systems ontologies have to be combined into modular knowledge architectures.

Ontologies & Contexts

If open-ended business contexts and concerns are to be taken into account, the first step should be to characterize ontologies with regard to their source, justification, and the stability of their categories, e.g:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accords.
  • Corporate: Defined by enterprises, changes subject to internal decision-making.
  • Social: Defined by usage, volatile, continuous and informal changes.
  • Personal: Customary, defined by named individuals (e.g research paper).

Assuming such an external taxonomy, the next step would be to see what kind of internal (i.e enterprise architecture) ontologies can be fitted into, as it’s the case for the Zachman framework.

The Zachman’s taxonomy is built on well established concepts (Who,What,How, Where, When) applied across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies). These layers can be generalized and applied uniformly across external contexts, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

That “divide to conquer” strategy is to serve two purposes:

  • By bridging the gap between internal and external taxonomies it significantly enhances the transparency of governance and decision-making.
  • By applying the same motif (Who,What, How, Where, When) across the semantics of contexts, it opens the door to a seamless integration of all kinds of knowledge: enterprise, professional, institutional, scientific, etc.

As can be illustrated using Zachman concepts, the benefits are straightforward at enterprise architecture level (e.g procurement), due to the clarity of supporting ontologies; not so for external ones, which are by nature open and overlapping and often come with blurred semantics.

Ontologies & Concerns

A broad survey of RDF-based ontologies demonstrates how semantic overlaps and folds can be sort out using built-in differentiation between domains’ semantics on one hand, structure and processing of symbolic representations on the other hand. But such schemes are proprietary, and evidence shows their lines seldom tally, with dire consequences for interoperability: even without taking into account relationships and integrity constraints, weaving together ontologies from different sources is to be cumbersome, the costs substantial, and the outcome often reduced to a muddy maze of ambiguous semantics.

The challenge would be to generalize the principles as to set a basis for open ontologies.

Assuming that a clear line can be drawn between representation and contents semantics, with standard constructs (e.g predicate logic) used for the former, the objective would be to classify ontologies with regard to their purpose, independently of their representation.

The governance-driven taxonomy introduced above deals with contexts and consequently with coarse-grained modularity. It should be complemented by a fine-grained one to be driven by concerns, more precisely by the epistemic nature of the individual instances to be denoted. As it happens, that could also tally with the Zachman’s taxonomy:

  • Thesaurus: ontologies covering terms and concepts.
  • Documents: ontologies covering documents with regard to topics.
  • Business: ontologies of relevant enterprise organization and business objects and activities.
  • Engineering: symbolic representation of organization and business objects and activities.

Ontologies: Purposes & Targets

Enterprises could then pick and combine templates according to domains of concern and governance. Taking an on-line insurance business for example, enterprise knowledge architecture would have to include:

  • Medical thesaurus and consolidated regulations (Knowledge).
  • Principles and resources associated to the web-platform (Engineering).
  • Description of products (e.g vehicles) and services (e.g insurance plans) from partners (Business).

Such designs of ontologies according to the governance of contexts and the nature of concerns would significantly reduce blanket overlaps and improve the modularity and transparency of ontologies.

On a broader perspective, that policy will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).

Open Ontologies’ Benefits

Benefits from open and formatted ontologies built along an explicit distinction between the semantics of representation (aka ontology syntax) and the semantics of context can be directly identified for:

Modularity: the knowledge basis of enterprise architectures could be continuously tailored to changes in markets and corporate structures without impairing enterprise performances.

Integration: the design of ontologies with regard to the nature of targets and stability of categories could enable built-in alignment mechanisms between knowledge architectures and contexts.

Interoperability: limited overlaps and finer granularity are to greatly reduce frictions when ontologies bearing out business processes are to be combined or extended.

Reliability: formatted ontologies can be compared to typed programming languages with regard to transparency, internal consistency, and external validity.

Last but not least, such reasoned design of ontologies may open new perspectives for the collaboration between cognitive humans and pretending ones.

Further Reading

External Links

2018: Clones vs Octopuses

December 4, 2017

In the footsteps of robots replacing workmen, deep learning bots look to boot out knowledge workers overwhelmed by muddy data.

Cloning Knowledge (Tadeusz Cantor, from “The Dead Class”)

Faced with that , should humans try to learn deeper and faster than clones, or should they learn from octopuses and their smart hands.

Machine Learning & The Economics of Clones

As illustrated by scan-reading AI machines, the spreading of learning AI technology in every nook and cranny introduces something like an exponential multiplier: compared to the power-loom of the Industrial Revolution which substituted machines for workers, deep learning is substituting replicators for machines; and contrary to power looms, there is no physical limitation on the number of smart clones that can be deployed. So, however fast and deep humans can learn, clones are much too prolific: it’s a no-win situation. To get out of that conundrum humans have to put their hand on a competitive edge, e.g some kind of knowledge that cannot be cloned.

Knowledge & Competition

Appraising humans learning sway over machines, one can take from Spinoza’s categories of knowledge with regard to sources:

  1. Senses (views, sounds, smells, touches) or beliefs (as nurtured by the supposed common “sense”). Artificial sensors can compete with human ones, and smart machines are much better if prejudiced beliefs are put into the equation.
  2. Reasoning, i.e the mental processing of symbolic representations. As demonstrated by AlphaGo, machines are bound to fast extend their competitive edge.
  3. Philosophy which is by essence meant to bring together perceptions, intuitions, and symbolic representations. That’s where human intelligence could beat its artificial cousin which is clueless when purposes are needed.

That assessment is bore out by evolution: the absolute dominance established by humans over other animal species comes from their use of knowledge, which can be summarized as:

  1. Use of symbolic representations.
  2. Ability to formulate and exchange representations of contexts, concerns, and policies.
  3. Ability to agree on stakes and cooperate on policies.

On that basis, the third dimension, i.e the use of symbolic knowledge to cooperate on non-zero-sum endeavors, can be used to draw the demarcation line between human and artificial intelligence:

  • Paths and paces of pursuits as part and parcel of the knowledge itself. The fact that both are mostly obviated by search engines gives humans some edge.
  • Operational knowledge is best understood as information put to use, and must include concerns and decision-making. But smart bots’ ubiquity and capabilities often sap information traceability and decisions transparency, which makes room for humans to prevail.

So humans can find a clear competitive edge in this knowledge dimension because it relies on a combination of experience and thinking and is therefore hard to clone. Organizations should make sure that’s where smart systems take back and humans take up.

Organization & Innovation

Innovation being at the root of competitive edge, understanding the role played by smart systems is a key success factor; that is to be defined by organization.

As epitomized by Henry Ford, industrial-era thinking associated innovation with top-down management and the specialization of execution:

  • At execution level manual tasks were to be fragmented and specialized.
  • At management level analysis and decision-making were to be centralized and abstracted.

That organizational paradigm puts a double restraint on innovation:

  • On execution side the fragmentation of manual tasks prevents workers from effectively assessing and improving their performances.
  • On management side knowledge is kept in conceptual boxes and bereft of feedback from actual uses.

That railing between smart brains and dumb hands may have worked well enough for manufacturing processes limited to material flows and subject to circumscribed and predictable technological changes. It didn’t last.

First, as such hierarchies necessarily grow with processes complexity, overheads and rigidity force repeated pruning. Then, flat hierarchies are of limited use when information flows are to be combined with material ones, so enterprises have to start with matrix organization. Finally, with the seamless integration of digital and material flows, perpetuating the traditional line between management and execution is bound to hamstring innovation:

  • Smart tools may be able to perform a wide range of physical tasks without human supervision, but the core of innovation core as well as its front lines are where human and machines collaborate in processing a mix of material and information flows, both learning from the experience.
  • Hierarchies and centralized decision-making are being cut out from feeders when set in networked business environments colonized by smart bots on both sides of corporate boundaries.

Not surprisingly, these innovation trends seem to tally with the social dimension of knowledge.

Learning from the Octopus

The AI revolution has already broken all historical records of footprint (everything is affected) and speed (a matter of years). Given the length of human education cycles, appraising the consequences comes with some urgency, beginning with the disposal of two entrenched beliefs:

At individual level the new paradigm could be compared to the nervous system of octopuses: each arm gets its brain and neurons, and so its own touch of knowledge and taste of decision-making.

On a broader (i.e enterprise) perspective, knowledge should be supported by two organizational layers, one direct and innovation-driven between trusted co-workers, the other networked and knowledge-driven between remote workers, trusted or otherwise.

Further Reading

External Links

Transcription & Deep Learning

September 17, 2017

Humans looking for reassurance against the encroachment of artificial brains should try YouTube subtitles: whatever Google’s track record in natural language processing, the way its automated scribe writes down what is said in the movies is essentially useless.

A blank sheet of paper was copied on a Xerox machine.
This copy was used to make a second copy.
The second to make a third one, and so on…
Each copy as it came out of the machine was re-used to make the next.
This was continued for one hundred times, producing a book of one hundred pages. (Ian Burn)

Experience directly points to the probable cause of failure: the usefulness of real-time transcriptions is not a linear function of accuracy because every slip can be fatal, without backup or second chance. It’s like walking a line: for all practical purposes a single misunderstanding can throw away the thread of understanding, without a chance of retrieve or reprieve.

Contrary to Turing machines, listeners have no finite states; and contrary to the sequence of symbols on tapes, tales are told by weaving together semantic threads. It ensues that stories are work in progress: readers can pause to review and consolidate meanings, but listeners have no other choice than punting on what comes to they mind, hopping that the fabric of the story will carry them out.

So, whereas automated scribes can deep learn from written texts and recorded conversations, there is no way to do the same from what listeners understand. That’s the beauty of story telling: words may be written but meanings are renewed each time the words are heard.

Further Reading

Why Virtual Reality (VR) is Late

July 25, 2017


Whereas virtual reality (VR) has been expected to be the next breakthrough for IT human interfaces, the future seems to be late.

Detached Reality (N.Ghesquiere, G.Coddington)

Together with the cost of ownership, a primary cause mentioned for the lukewarm embrace is the nausea associated with the technology. Insofar as the nausea is provoked by a delay in perceptions, the consensus is that both obstacles should be overcame by continuous advances in computing power. But that optimistic assessment rests on the assumption that the nausea effect is to be uniformly decreasing.

Virtual vs Augmented

The recent extension of a traditional roller-coaster at SeaWorld Orlando illustrates the difference between virtual and augmented reality. Despite being marketed as virtual reality, the combination of actual physical experience (roller-coaster) with virtual perceptions (3D video) clearly belongs to the augmented breed, and its success may put some new light on the nausea effect.

Consciousness Cannot Wait

Awareness is what anchors living organisms to their environment. So, lest a confusion is introduced between individuals experience and their biological clock, perceptions are to be immediate; and since that confusion is not cognitive but physical, it will cause nausea. True to form, engineers initial answer has been to cut down elapsed time through additional computing power; that indeed brought a decline in the nausea effect, as well as an increase in the cost of ownership. Unfortunately, benefits and costs don’t tally: however small is the remaining latency, nausea effects are disproportionate.

Aesop’s Lesson

The way virtual and augmented reality deal with latency may help to understand the limitations of a minimizing strategy:

  • With virtual reality latency occurs between users voluntary actions (e.g moving their heads) and devices (e.g headset) generated responses.
  • With augmented reality latency occurs between actual perceptions and software generated responses.

That’s basically the situation of Aesop’s “The Tortoise and the Hare” fable: in the physical realm the hare (aka computer) is either behind or ahead of the tortoise (the user), which means that some latency (positive or negative) is unavoidable.

That lesson applies to virtual reality because both terms are set in actuality, which means that nausea can be minimized but not wholly eliminated. But that’s not the case for augmented reality because the second term is a floating variable that can be logically adjusted.

The SeaWorld roller-coaster takes full advantage of this point by directly tying up augmented stimuli to actual ones: augmented reality scripts are aligned with roller-coaster episodes and their execution synchronized through special sensors. Whatever the remaining latency, it is to be of a different nature: instead of having to synchronize their (conscious) actions with the environment feedback, users only have to consolidate external stimuli, a more mundane task which doesn’t involve consciousness.

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Deep Blind Testing

March 21, 2017


Tests are meant to ensure that nothing will go amiss. Assuming that expected hazards can be duly dealt with beforehand, the challenge is to guard against unexpected ones.

Unexpected Outcome (Ariel Schlesinger)

That would require the scripting of every possible outcomes in an unlimited range of unknown circumstances, and that’s where Deep Learning may help.

What to Look For

As Donald Rumsfeld once famously said, there are things that we know we don’t know, and things we don’t know we don’t know; hence the need of setting things apart depending on what can be known and how, and build the scripts accordingly:

  • Business requirements: tests can be designed with respect to explicit specifications; yet some room should also be left for changes in business circumstances.
  • Functional requirements: assuming business requirements are satisfied, the part played by supporting systems can be comprehensively tested with respect to well-defined boundaries and operations.
  • Quality of service: assuming business and functional requirements are satisfied, tests will have to check how human interfaces and resources are to cope with users behaviors and expectations which, by nature, cannot be fully anticipated.
  • Technical requirements: assuming business and functional requirements are satisfied as well as users’ expectations for service, deployment, maintenance, and operations are to be tested with regard to feasibility and costs.

Automated testing has to take into account these differences between scope and nature, from bounded and defined specifications to boundless, fuzzy and changing circumstances.

Automated Software Testing

Automated software testing encompasses two basic components: first the design of test cases (events, operations, and circumstances), then their scripted execution. Leading frameworks already integrate most of the latter together with the parts of the former targeting technical aspects like graphical user interfaces or system APIs. Artificial intelligence (AI) and machine learning (ML) have also been tried for automated test generation, yet with a scope limited by dependency on explicit knowledge, and consequently by the need of some “manual” teaching. That hurdle may be overcame by the deep learning ability to get direct (aka automated) access to implicit knowledge.

Reconnaissance: Known Knowns

Systems are designed artifacts, with the corollary that their components are fully defined and their behavior predictable. The design of technical test cases can therefore be derived from what is known of software and systems architectures, the former for test units, the latter for integration and acceptance tests. Deep learning could then mine recorded log-files in order to identify critical cases’ events and circumstances.

Exploration: Known Unknowns

Assuming that applications must be tested for use during their expected shelf life, some uncertainty has to be factored in for future business circumstances. Yet, assuming applications are designed to meet specific business objectives, such hypothetical circumstances should remain within known boundaries. In that context deep learning could be applied to exploration as well as policies:

  • Compared to technical test cases that can rely on the content of systems log-files, business and functional ones have to look outside and mine raw data from business environments.
  • In return, the relevancy of observations can be assessed with regard to business objectives, improved, and feed the policy module in charge of defining test cases.

Blind Errands: Unknown Unknowns

Even with functional and technical capabilities well-tested and secured, quality of service may remain contingent on human quirks: instinctive or erratic behaviors that could thwart the best designed handrails. On one hand, and due to their very nature, such hazards are not to be easily forestalled by reasoned test cases; but on the other hand they don’t take place in a void but within known functional circumstances. Given that porosity of functional and cognitive layers, the validity of functional test cases may be compromised by unfathomable cognitive associations, and that could open the door to unmanageable regression. Enter deep learning and its ability to extract knowledge from insignificance.

Compared to business and functional test cases, hazards are not directly related to business activities. As a consequence, the learning process cannot be guided by business and functional test cases but has to chart unpredictable human behaviors. As it happens, that kind of learning combining random simulation with automated reinforcement is what makes the specificity of deep learning.

From Non-regression to Self-improvement

As a conclusion, if non-regression is to be the cornerstone of quality management, test cases are to be set along clear swim-lanes: business logic (independently of systems), supporting systems functionalities (for shared applications), users interfaces (for non shared interactions). Then, since test cases are also run across swim-lanes, it opens the door to feedback, e.g unit test cases reassessed directly from business rules independently of systems functionalities, or functional test cases reassessed from users’ behaviors.

Considering that well-defined objectives, sound feedback mechanisms, and the availability of massive data from systems logs (internal) and business environment (external) are the main pillars of deep learning technologies, their combination in integrated frameworks could result in a qualitative leap toward self-improving automated test cases.

Further Reading


Alternative Facts & Augmented Reality

February 5, 2017


Coming alongside the White House creative use of facts, the upcoming Snap’s IPO is to bring another perspective on reality with its Snapchat star product integrating augmented reality (AR) with media.


Layers of Reality (Marcel Duchamp)

Whatever the purpose, the “alternative facts” favored by the White House communication detail may bring to the fore two related issues of present-day relevancy: virtual and augmented reality on one hand, the actuality of George Orwell’s Newspeak on the other hand.

Facts and Fiction

To begin with, facts are not given but observed, and that can only be achieved through a mix of conceptual and technical apparatus, the former to design fact-finding vessels, the latter to fill them with actual observations. Based on that understanding, alternatives are less about the facts themselves than about the apparatuses used to collect them, which may be trustworthy, faulty, or deceitful. Setting flaws aside, trust is also what distinguishes augmented and virtual reality:

  • Augmented reality (AR) technologies operate on apparatuses that combine observation and analysis before adding layers of information.
  • Virtual reality (VR) technologies simply overlook the whole issue of reality and observation, and are only concerned with the design of trompe l’oeuils.

The contrast between facts (AR) and fiction (VR) may account for the respective applications and commercial advances: whereas augmented reality is making rapid inroads in business applications, its virtual cousin is still testing the water in games. More significantly perhaps, the comparison points to a somewhat unexpected difference in the role of language: necessary for the establishment of facts, accessory for the creation of fictions.

Speaking of Alternative Facts

As illustrated (pun intended) by virtual reality, fiction can do without words, which is not the case for facts. As a matter of fact (intended again), even facts can be fictional, as epitomized by Orwell’s Newspeak, the language used by the totalitarian state in his 1949 novel Nineteen Eighty-Four. Figuratively speaking, that language may be likened to a linguistic counterpart of virtual reality as its purpose is to bypass the issue of trusty discourse about reality by introducing narratives wholly detached from actual observations. And that’s when fiction catches up with reality: no much stretch of imagination is needed to recognize a similar scheme in current White House’s comments.

Language Matter

As far as humans are concerned, reality comes with semantic and social dimensions that can only be carried out through language. In other words truth is all about the use of language with regard to purpose: communication, information, or knowledge. Taking Trump’s inauguration crowd for example:


Data come from observations, Information is Data put in form, Knowledge is Information put to use.

  • Communication: language is used to exchange observations associated to immediate circumstances (the place and the occasion).
  • Information: language is used to map observations to mental representations and operations (estimates for the size of the audience).
  • Knowledge: language is use to associate information to purposes through categories and concepts detached of the original circumstances (comparison of audiences for similar events and political conclusions).

Augmented Reality devices on that occasion could be used to tally people on viewed portions of the audience (fact), figure out estimates for the whole audience (information), or decide on the best itineraries back home (knowledge). By contrast, Virtual Reality (aka “alternative facts”) could only be used at communication level to deceive the public.

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