The objective of the National Information Exchange Model (NIEM) is to provide a “dictionary of agreed-upon terms, definitions, relationships, and formats that are independent of how information is stored in individual systems.”
For that purpose NIEM’s model combines commonly agreed core elements with community-specific ones. Weighted against the benefits of simplicity, this architecture overlooks critical distinctions:
Inputs: Data vs Information
Dictionary: Lexicon and Thesaurus
Meanings: Lexical Items and Semantics
Usage: Roots and Aspects
That shallow understanding of information significantly hinders the exchange of information between business or institutional entities across overlapping domains.
Inputs: Data vs Information
Data is made of unprocessed observations, information makes sense of data, and knowledge makes use of information. Given that NIEM is meant to be an exchange between business or institutional users, it should have no concern with data mining or knowledge management.
The problem is that, as conveyed by “core of data elements that are commonly understood and defined across domains, such as person, activity, document, location”, NIEM’s model makes no explicit distinction between data and information.
As a corollary, it implies that data may not only be meaningful, but universally so, which leads to a critical trap: as substantiated by data analytics, data is not supposed to mean anything before processed into information; to keep with examples, even if the definition of persons and locations may not be specific, the semantics of associated information is nonetheless set by domains, institutional, regulatory, contractual, or otherwise.
Not surprisingly, that medley of data and information is mirrored by NIEM’s dictionary.
Dictionary: Lexicon & Thesaurus
As far as languages are concerned, words (e.g “word”, “ξ∏¥” ,”01100″) remain data items until associated to some meaning. For that reason dictionaries are built on different levels, first among them lexical and semantic ones:
Lexicons take items on their words and gives each of them a self-contained meaning.
Thesauruses position meanings within overlapping galaxies of understandings held together by the semantic equivalent of gravitational forces; the meaning of words can then be weighted by the combined semantic gravity of neighbors.
In line with its shallow understanding of information, NIEM’s dictionary only caters for a lexicon of core standalone items associated with type descriptions to be directly implemented by information systems. But due to the absence of thesaurus, the dictionary cannot tackle the semantics of overlapping domains: if lexicons alone can deal with one-to-one mappings of items to meanings (a), thesauruses are necessary for shared (b) or alternative (c) mappings.
With regard to shared mappings (b), distinct lexical items (e.g qualification) have to be mapped to the same entity (e.g person). Whereas some shared features (e.g person’s birth date) can be unequivocally understood across domains, most are set through shared (professional qualification), institutional (university diploma), or specific (enterprise course) domains .
Conversely, alternative mappings (c) arise when the same lexical items (e.g “mole”) can be interpreted differently depending on context (e.g plastic surgeon, farmer, or secret service).
Whereas lexicons may be sufficient for the use of lexical items across domains (namespaces in NIEM parlance), thesauruses are necessary if meanings (as opposed to uses) are to be set across domains. But thesauruses being just tools are not sufficient by themselves to deal with overlapping semantics. That can only be achieved through a conceptual distinction between lexical and semantic envelops.
Meanings: Lexical Items & Semantics
NIEM’s dictionary organize names depending on namespaces and relationships:
Namespaces: core (e.g Person) or specific (e.g Subject/Justice).
Relationships: types (Counselor/Person) or properties (e.g PersonBirthDate).
But since lexicons know only names, the organization is not orthogonal, with lexical items mapped indifferently to types and properties. The result being that, deprived of reasoned guidelines, lexical items are chartered arbitrarily, e.g:
Based on core PersonType, the Justice namespace uses three different schemes to define similar lexical items:
“Counselor” is described with core PersonType.
“Subject” and “Suspect” are both described with specific SubjectType, itself a sub-type of PersonType.
“Arrestee” is described with specific ArresteeType, itself a sub-type of SubjectType.
Based on core EntityType:
The Human Services namespace bypasses core’s namesake and introduces instead its own specific EmployerType.
The Biometrics namespace bypasses possibly overlapping core Measurer and BinaryCaptured and directly uses core EntityType.
Lest expanding lexical items clutter up dictionary semantics, some rules have to be introduced; yet, as noted above, these rules should be limited to information exchange and stop short of knowledge management.
Usage: Roots and Aspects
As far as information exchange is concerned, dictionaries have to deal with lexical and semantic meanings without encroaching on ontologies or knowledge representation. In practice that can be best achieved with dictionaries organized around roots and aspects:
Roots and structures (regular, black triangles) are used to anchor information units to business environments, source or destination.
Aspects (italics, white triangles) are used to describe how information units are understood and used within business environments.
As it happens that distinction can be neatly mapped to core concepts of software engineering.
P.S. Thesauruses & Ontologies
Ontologies are systematic accounts of existence for whatever is considered, in other words some explicit specification of the concepts meant to make sense of a universe of discourse. From that starting point three basic observations can be made:
Ontologies are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
Ontologies are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.
With regard to models, only the second one puts ontologies apart: contrary to models, ontologies are about understanding and are not supposed to be driven by empirical purposes.
On that basis, ontologies can be understood as thesauruses describing galaxies of concepts (stars) and features (planets) held together by semantic gravitation weighted by similarity or proximity. As such ontologies should be NIEM’s tool of choice.
As should be expected for machines capabilities, artificial intelligence has for long been fettered by technological handcuffs; so much so that expert systems were initially confined to a flat earth of knowledge to be explored through cumbersome sets of explicit rules. But exponential increase in computing power has allowed neural networks to take a bottom-up perspective, mining for implicit knowledge hidden in large amount of raw data.
Like digging tunnels from both extremities, it took some time to bring together top-down and bottom-up schemes, namely explicit (rule-based) and implicit (neural network-based) knowledge processing. But now that it comes to fruition, the alignment of perspectives puts a new light on the cognitive and social dimensions of intelligence.
Intelligence as a Cognitive Capability
Assuming that intelligence is best defined as the ability to solve problems, the first criterion to consider is the type of input (aka knowledge) to be used:
Explicit: rational processing of symbolic representations of contexts, concerns, objectives, and policies.
Implicit: intuitive processing of factual (non symbolic) observations of objects and phenomena.
That distinction is broadly consistent with the one between humans, seen as the sole symbolic species with the ability to reason about explicit knowledge, and other animal species which, despite being limited to the processing of implicit knowledge, may be far better at it than humans. Along that understanding, it would be safe to assume that systems with enough computing power will sooner or later be able to better the best of animal species, in particular in the case of imperfect inputs.
Intelligence as a Social Capability
Alongside the type of inputs, the second criterion to be considered is obviously the type of output (aka solution). And since classifications are meant to be built on purpose, a typology of AI outcomes should focus on relationships between agents, humans or otherwise:
Self-contained: problem-solving situations without opponent.
Competitive: zero-sum conflictual activities involving one or more intelligent opponents.
Collaborative: non-zero-sum activities involving one or more intelligent agents.
That classification coincides with two basic divides regarding communication and social behaviors:
To begin with, human behavior is critically different when interacting with living species (humans or animals) and machines (dumb or smart). In that case the primary factor governing intelligence is the presence, real or supposed, of beings with intentions.
Then, and only then, communication may take different forms depending on languages. In that case the primary factor governing intelligence is the ability to share symbolic representations.
A taxonomy of intelligence with regard to cognitive (reason vs intuition) and social (symbolic vs non-symbolic) capabilities may help to clarify the role of AI and the importance of deep learning.
Between Intuition and Reason
Google’s AlphaGo astonishing performances have been rightly explained by a qualitative breakthrough in learning capabilities, itself enabled by the two quantitative factors of big data and computing power. But beyond that success, DeepMind (AlphaGo’s maker) may have pioneered a new approach to intelligence by harnessing both symbolic and non symbolic knowledge to the benefit of a renewed rationality.
Perhaps surprisingly, intelligence (a capability) and reason (a tool) may turn into uneasy bedfellows when the former is meant to include intuition while the latter is identified with logic. As it happens, merging intuitive and reasoned knowledge can be seen as the nexus of AlphaGo decisive breakthrough, as it replaces abrasive interfaces with smart full-duplex neural networks.
Intelligent devices can now process knowledge seamlessly back and forth, left and right: borne by DeepMind’s smooth cognitive cogwheels, learning from factual observations can suggest or reinforce the symbolic representation of emerging structures and behaviors, and in return symbolic representations can be used to guide big data mining.
From consumers behaviors to social networks to business marketing to supporting systems, the benefits of bridging the gap between observed phenomena and explicit causalities appear to be boundless.
As already noted, the seamless integration of business processes and IT systems may bring new relevancy to the OODA (Observation, Orientation, Decision, Action) loop, a real-time decision-making paradigm originally developed by Colonel John Boyd for USAF fighter jets.
Of particular interest for today’s business operational decision-making is the orientation step, i.e the actual positioning of actors and the associated cognitive representations; the point being to use AI deep learning capabilities to surmise opponents plans and misdirect their anticipations. That new dimension and its focus on information brings back cybernetics as a tool for enterprise governance.
In the Loop: OODA & Information Processing
Whatever the topic (engineering, business, or architecture), the concept of agility cannot be understood without defining some supporting context. For OODA that would include: territories (markets) for observations (data); maps for orientation (analytics); business objectives for decisions; and supporting systems for action.
One step further, contexts may be readily matched with systems description:
Business contexts (territories) for observations.
Models of business objects (maps) for orientation.
Business logic (objectives) for decisions.
Business processes (supporting systems) for action.
That provides a unified description of the different aspects of business agility, from the OODA loop and operations to architectures and engineering.
Architectures & Business Agility
Once the contexts are identified, agility in the OODA loop will depend on architecture consistency, plasticity, and versatility.
Architecture consistency (left) is supposed to be achieved by systems engineering out of the OODA loop:
Technical architecture: alignment of actual systems and territories (red) so that actions and observations can be kept congruent.
Software architecture: alignment of symbolic maps and objectives (blue) so that orientation and decisions can be continuously adjusted.
Functional architecture (right) is to bridge the gap between technical and software architectures and provides for operational coupling.
Operational coupling depends on functional architecture and is carried on within the OODA loop. The challenge is to change tack on-the-fly with minimum frictions between actual and symbolic contexts, i.e:
Discrepancies between business objects (maps and orientation) and business contexts (territories and observation).
Departure between business logic (objectives and decisions) and business processes (systems and actions)
Taking a leaf from thermodynamics, cybernetics defines entropy as a measure of the (supposedly negative) variation in the value of the information supporting the control of viable systems.
With regard to corporate governance and operational decision-making, entropy arises from faults between environments and symbolic surrogates, either for objects (misleading orientations from actual observations) or activities (unforeseen consequences of decisions when carried out as actions).
While much has been written about how data analytics and operational decision-making can be neatly and easily fitted in the OODA paradigm, a particular attention is to be paid to orientation.
As noted before, the concept of Orientation comes with a twofold meaning, actual and symbolic:
Actual: the positioning of an agent with regard to external (e.g spacial) coordinates, possibly qualified with the agent’s abilities to observe, move, or act.
Symbolic: the positioning of an agent with regard to his own internal (e.g beliefs or aims) references, possibly mixed with the known or presumed orientation of other agents, opponents or associates.
That dual understanding underlines the importance of symbolic representations in getting competitive edges, either directly through accurate and up-to-date orientation, or indirectly by inducing opponents’ disorientation.
Agility vs Entropy
Competition in networked digital markets is carried out at enterprise gates, which puts the OODA loop at the nexus of information flows. As a corollary, what is at stake is not limited to immediate business gains but extends to corporate knowledge and enterprise governance; translated into cybernetics parlance, a competitive edge would depend on enterprise ability to export entropy, that is to decrease confusion and disorder inside, and increase it outside.
Working on that assumption, one should first characterize the flows of information to be considered:
Territories and observations: identification of business objects and events, collection and analysis of associated data.
Maps and orientations: structured and consistent description of business domains.
Objectives and decisions: structured and consistent description of business activities and rules.
Systems and actions: business processes and capabilities of supporting systems.
Then, a static assessment of information flows would start with the standing of technical and software architecture with regard to competition:
Technical architecture: how the alignment of operations and resources facilitate actions and observations.
Software architecture: how the combined descriptions of business objects and logic facilitate orientation and decision.
A dynamic assessment would be carried out within the OODA loop and deal with the role of functional architecture in support of operational coupling:
How the mapping of territories’ identities and features help observation and orientation.
How decision-making and the realization of business objectives are supported by processes’ designs.
Assuming a corporate cousin of Maxwell’s demon with deep learning capabilities standing at the gates in its OODA loop, his job would be to analyze the flows and discover ways to decrease internal complexity (i.e enterprise representations) and increase external one (i.e competitors’ representations).
That is to be achieved with the integration of operational analytics, business intelligence, and decision-making.
Contrary to security breaks and information robberies that can be kept from public eyes, crashes of business applications or internet access are painfully plain for whoever is concerned, which means everybody. And as illustrated by the last episode of massive distributed denial of service (DDoS), they often come as confirmation of hazards long calling for attention.
Things Don’t Think
To be clear, orchestrated attacks through hijacked (if unaware) computers have been a primary concern for internet security firms for quite some time, bringing about comprehensive and continuous reinforcement of software shields consolidated by systematic updates.
But while the right governing hand was struggling to make a safer net, the other hand thoughtlessly brought in connected objects to a supposedly new brand of internet. As if adding things with software brains cut to the bone could have made networks smarter.
And that’s the catch because the internet of things (IoT) is all about making room for dumb ancillary objects; unfortunately, idiots may have their use for literary puppeteers with canny agendas.
Think Again, or Not …
For old-timers with some memory of fingering through library cardboard, googling topics may have looked like dreams: knowledge at one’s fingertips, immediately and comprehensively. But that vision has never been more than a fleeting glimpse in a symbolic world; in actuality, even at its semantic best, the web was to remain a trove of information to be sifted by knowledge workers safely seated in their gated symbolic world. Crooks of course could sneak in as knowledge workers, armed with fountain pens, but without guns covered by the second amendment.
So, from its inception, the IoT has been a paradoxical endeavor: trying to merge actual and symbolic realms that would bypass thinking processes and obliterate any distinction. For sure, that conundrum was supposed to be dealt with by artificial intelligence (AI), with neural networks and deep learning weaving semantic threads between human minds and networks brains.
Not surprisingly, brainy hackers have caught sight of that new wealth of chinks in internet armour and swiftly added brute force to their paraphernalia.
But in addition to the technical aspect of internet security, the recent Dyn DDoS attack puts the light on its social perspective.
Things Behavior & Social Responsibility
As far as it remained intrinsically symbolic, the internet has been able to carry on with its utopian principles despite bumpy business environments. But things have drastically changed the situation, with tectonic frictions between symbolic and real plates wreaking havoc with any kind of smooth transition to internet.X, whatever x may be.
Yet, as the diagnose is clear, so should be the remedy.
To begin with, the internet was never meant to become the central nervous system of human societies. That it has happened in half a generation has defied imagination and, as a corollary, sapped the validity of traditional paradigms.
As things happen, the epicenter of the paradigms collision can be clearly identified: whereas the internet is built from systems, architectures taxonomies are purely technical and ignore what should be the primary factor, namely what kind of social role a system could fulfil. That may have been irrelevant for communication networks, but is obviously critical for social ones.
Just like models are meant to describe sets of actual instances, meta-models are meant to do the same for sets of modeling artifacts independently of their targets. Along that reasoning, conceptual modeling of automation systems could be achieved either with a single language covering all aspects, or with a meta-language dealing with different sets of models, e.g MDA’s computation independent, platform independent, and platform specific models.
Given a model based engineering framework (e.g MDA), meta-models are generally used to support downstream models transformation targeting designs and code. But when upstream conceptual models are concerned, the challenge is to tackle the knowledge-to-systems transition. For that purpose some shared modeling roof is required for the definition of the symbolic footprint of the targeted business in the automation system under consideration.
Given that automation systems are meant to manage symbolic objects (aka surrogates), one should expect the distinction between actual instances and their symbolic representations to be the cornerstone of corresponding modeling languages. Along that reasoning, modeling of automation systems should start with the symbolic representation of actual business footprints, namely: the sets of objects, events, and processes, the roles played by agents (aka active objects), and the description of the associated states and rules. Containers would be added for the management of collections.
Next, as illustrated by the Object/Agent hierarchy, business worlds are not flat but built from sundry structures and facets to be represented by multiple levels of descriptions. That’s where abstractions are to be introduced.
Abstraction & Variants
The purpose of abstractions is to manage variants, and as such they can be used in two ways:
For partial descriptions of actual instances depending on targeted features. That can be achieved using composition (for structural variants) and partitions (for functional ones).
As hierarchies of symbolic descriptions (aka types and sub-types) subsuming variants identified at instances level.
On that basis the challenge is to find the level of detail (targeted actual instances) and abstraction (symbolic footprint) that will best describe supporting systems functionalities. Such level will have to meet two conditions:
A minimal number of comprehensive and exclusive categories covering the structural variants of the sets of instances to be uniformly, consistently, and continuously identified by both enterprise and supporting systems.
A consistent but adjustable set of types and sub-types anchored to the core structural categories and covering the functional variants .
Climbing up and down abstraction ladders looking for right levels is arguably the critical part of conceptual modeling, but the search will greatly benefit from the distinction between models and meta-models. Assuming meta-models are meant to ignore domain specific features altogether, they introduce a qualitative gap on abstraction scales as the respective hierarchies of models and meta-models are targeting different kind of instances. The modeling of agents and roles epitomizes the benefits of that distinction.
Abstraction & Meta Models
Taking customers for example, a naive approach would use Customer as a modeling type inheriting from a super-type, e.g Party. But then, if parties are to be uniformly identified (#), that would preclude any agent for playing multiple roles, e.g customer and supplier.
A separate description of parties and roles would clearly be a better option as it would unify the identification of the former without introducing unwarranted constraints on the latter which would then be defined and identified as the realization of a relationship played by a party.
Not surprisingly, that distinction would also be congruent with the one between models and meta-model:
Meta-models will describe generic aspects independently of domain-specific considerations, in particular organizational context (units and roles) and interactions with systems (a).
Models will define Staff, Supplier and Customer according to the semantics of the business considered (b).
That distinction between abstraction scales can also be applied to the conceptual modeling of automation systems.
Abstraction Scales & Conceptual Models
To begin with definitions, conceptual representations could be used for all mental constructs, whereas symbolic representations would be used only for the subset earmarked for communication purposes. That would mean that, contrary to conceptual representations that can be detached of business and enterprise practicalities, symbolic representations are necessarily built on design, and should be assessed accordingly. In our case the aim of such representations would be to describe the exchanges between business processes and supporting systems.
That understanding neatly fits the conceptual modeling of automation systems whose purpose would be to consolidate generic and business specific abstraction scales, the former for symbolic representations of the exchanges between business and systems, the latter symbolic representation of business contents.
At this point it must be noted that the scales are not necessarily aligned in continuity (with meta-models’ being higher and models’ being lower) as their respective ontologies may overlap (Organizational Entity and Party) or cross (Function and Role).
Toward an Ontological Framework for Enterprise Architectures Modeling
Along an analytic perspective, ontologies are meant to determine the categoriesthat can comprehensively and consistently denote the instances of a domain under consideration; applied to enterprise concerns that would entail:
Thesaurus: for the whole range of terms and concepts.
Documents: for documents with regard to topics.
Business: for enterprise organization and business objects and activities.
Engineering: for systems surrogates associated to enterprise organization and business objects and activities
That would open the door to a seamless integration of business intelligence, systems engineering, knowledge management, and decision-making.
The recent and decisive wins of Google’s AlphaGo over the world best Go player have been marked as a milestone on the path to general artificial intelligence, one that would be endowed with the same sort of capabilities as its human model. Yet, such assessment may reflect a somewhat mechanical understanding of human intelligence.
What Machines Can Know
As previously noted, human intelligence relies on three categories of knowledge:
Acquired through senses (views, sounds, smells, touches) or beliefs (as nurtured by our common “sense”). That is by nature prone to circumstances and prejudices.
Built through reasoning, i.e the mental processing of symbolic representations. It is meant to be universal and open to analysis, but it offers no guarantee for congruence with actual reality.
Attained through judgment bringing together perceptions, intuitions, and symbolic representations.
Given the exponential growth of their processing power, artificial contraptions are rapidly overtaking human beings on account of perceptions and reasoning capabilities. Moreover, as demonstrated by the stunning success of AlphaGo, they may, sooner rather than later, take the upper hand for judgments based on fixed sets (including empty ones) of symbolic representations. Would that means game over for humans ?
Maybe not, as suggested by the protracted progresses of IBM’s Watson for Oncology which may come as a marker of AI limits when non-zero-sum games are concerned. And there is good reason for that: human intelligence has evolved against survival stakes, not for games sake, and its innate purpose is to make fateful decisions when faced with unpredictable prospects: while machines look for pointless wins, humans aim for meaningful victories
What Animals Can Win
Left to their own, games are meant to be pointless: winning or losing is not to affect players in their otherwise worldly affairs. As a corollary, games intelligence can be disembodied, i.e detached from murky perceptions and freed from fuzzy down-to-earth rules. That’s not the case for real-life contests, especially the ones that drove the development of animal brains aeons ago; then, the constitutive and formative origins of intelligence were to rely on senses without sensors, reason without logic, and judgment without philosophy. The difference with gaming machines is therefore not so much about stakes as about the nature of built-in capabilities: animal intelligence has emerged from the need to focus on actual situations and immediate decision-making without the paraphernalia of science and technology. And since survival is by nature individual, the exercise of animal intelligence is intrinsically singular, otherwise (i.e were the outcomes been uniform) there could have been no selection. As far as animal intelligence is concerned opponents can only be enemies and winners are guaranteed to take all the spoils: no universal reason should be expected.
So, animal intelligence adds survival considerations to the artificial one, but it lacks symbolic and cooperative dimensions.
How Humans Can Win
Given its unique symbolic capability, the human species have been granted a decisive superiority in the evolution race. Using symbolic representations to broaden the stakes, take into account externalities, and build strategies for a wider range of possibilities, human intelligence clearly marks the evolutionary edge between human and other species. The combined capabilities to process non symbolic (aka implicit) knowledge and symbolic representations may therefore define the playground for human and artificial intelligence. But that will not take the cooperative dimension into account.
As it happens, the ability to process symbolic representations has a compound impact on human intelligence by bringing about a qualitative leap not only with regard to knowledge but, perhaps even more critically, with regard to cooperation. Taking leaves from R. Wright, and G. Lakoff, such breakthrough would not be about problem solving but about social destiny: what characterizes human intelligence would be an ability to assess collective aims and consequently to build non-zero-sum strategies bringing shared benefits.
Back to the general artificial intelligence project, the real challenge would be to generalize deep learning to non-zero-sum competition and its corollary, namely the combination and valuation of heterogeneous yet shared actual stakes.
However, as pointed by Lee Sedol, “when it comes to human beings, there is a psychological aspect that one has to also think about.” In other words, as noted above), human intelligence has a native and inherent emotional dimension which may be an asset (e.g as a source of creativity) as well as a liability (when it blurs some hazards).
Open-plan offices and social networks are often seen as significant factors of collaboration and innovation, breeding and nurturing the creativity of knowledge workers, weaving their ideas into webs of truths, and molding their minds into some collective intelligence.
Yet, as creativity comes with agility, knowledge workflows should give brains enough breathing space lest they get more pressure than pasture.
Collaboration & Thinking Flows
Collaboration is a means to an end. To be of any use exchanges have to be fed with renewed ideas and assumptions, triggering arguments and adjustments, and opening new perspectives. If not they may burn themselves out with hollow considerations blurring clues and expectations, clogging the channels, and finally stemming the thinking flows.
Taking example from lean manufacturing, the first objective should be to streamline knowledge workflows as to eliminate swirling pools of squabbles, drain stagnant puddles of stale thoughts, and gear collaboration to flowing knowledge streams. As illustrated by flood irrigation, the first step is to identify basin levels.
Dunbar Numbers & Collaboration Basins
Studying the grooming habits of social primates, psychologist Robin Dunbar came to the conclusion that the size of social circles that individuals of a living species can maintain is set by the size of brain’s neocortex. Further studies have confirmed Dunbar’s findings, with the corresponding sizes for humans set around 10 for trusted personal groups and 150 for untried social ones. As it happens, and not by chance, those numbers seem to coincide with actual observations: the former for personal and direct collaboration, the latter for social and mediated collaboration.
Based on that understanding, the objective would be to organize knowledge workflows across two primary basins:
On-site and face-to-face collaboration with trusted co-workers. Corresponding interactions would be driven by personal dispositions and attitudes.
On-line and networked collaboration with workers, trusted or otherwise. Corresponding interactions would be based on shared interests and past exchanges.
The aim of knowledge workflows is to process data into information and put it to use. That is to be achieved by combining different kinds of tasks, in particular:
Data and information management: build the symbolic descriptions of contexts, concerns, and means.
Objectives management: based on a set of symbolic descriptions, identify and refine opportunities together with the ways to realize them.
Tasks management: allocate rights and responsibilities across organizations and collaboration frames, public and shallow or personal and deep.
Flows management: monitor and manage actual flows, publish arguments and propositions, consolidate decisions, …
Taking into account constraints and dependencies between the tasks, the aims would be to balance creativity and automation while eliminating superfluous intermediate products (like documents or models) or activities (e.g unfocused meetings).
With regard to dependencies, KM tasks are often intertwined and cannot be carried out sequentially; moreover, as illustrated by the impact of “creative accounting” on accounted activities, their overlapping is not frozen but subject to feedback, changes and adjustments.
With regard to automation, three groups are to be considered: the first requires only raw processing power and can be fully automated; the second also involves some intelligence that may be provided by smart systems; and the third calls for decision-making that can only be done by human agents entitled by the organization.
At first sight some lessons could be drawn from lean manufacturing, yet, since knowledge processes are not subject to hardware constraints, agile approaches should provide a more informative reference.
Iterative Knowledge Processing
A simple preliminary step is to check the applicability of agile principles by replacing “software” by “knowledge”. Assuming that ground is secured, the core undertaking is to consider what would become of cycles and iterations when applied to knowledge processing:
Cycle invariants: tasks would be iterated on given sets of symbolic descriptions applied to the state of affairs (contexts, concerns, and means).
Iterations content: based on those descriptions data would be processed into information, changes would be monitored, and possibilities explored.
Exit condition: cycles would complete with decisions committing changes in the state of affairs that would also entail adjustments or changes in symbolic descriptions.
That scheme meets three of the basic tenets of the agile paradigm, i.e open scope (unknowns cannot be set in advance), continuity of delivery (invariants are defined and managed by knowledge workers), and users in driving seats (through exit conditions). Yet it still doesn’t deal with creativity and the benefits of collaboration for knowledge workers.
Thinking Space & Pace
The scope of creativity in processes is neatly circumscribed by the nature of flows, i.e the possibility to insert knowledge during the processing: external for material flows (e.g in manufacturing), internal for symbolic flows (e.g in software engineering and knowledge processing).
Yet, whereas both software engineering and knowledge processes come with some built-in capability to redefined their symbolic flows on-the-fly, they don’t grant the same room to creativity. Contrary to software engineering projects which have to close their perspectives on the delivery of working products, knowledge processes are meant to keep them open to new understandings and opportunities. For the former creativity is the means to an end, for the latter it’s the end in itself, with collaboration as means.
Such opposite perspectives have direct consequences for two basic agile collaboration mechanisms: backlog and time-boxing:
Backlogs are used to structure and manage the space under exploration. But contrary to software processes whose space is focused and structured by users’ needs, knowledge processes are supposed to play on workers’ creativity to expand and redefine the range under consideration.
Time-boxes are used to synchronize tasks. But with creativity entering the fray, neither space granularity or thinking pace can be set in advance and coerced into single-sized boxes. In that case individuals must remain in full control of the contents and stride of their thinking streams.
It ensues that when creativity is the primary success factor standard agile collaboration mechanisms are falling short and intelligent collaboration schemes are to be introduced.
Creativity & Collaboration Tiers
The synchronization of creative activities has to deal with conflicting objectives:
On one hand the mental maps of knowledge workers and the stream of their thoughts have to be dynamically aligned.
On the other hand unsolicited face-to-face interactions or instant communications may significantly impair the course of creative thinking.
When activities, e.g software engineering, can be streamlined towards the delivery of clearly defined outcomes, backlogs and time-boxes can be used to harness workers’ creativity. When that’s not the case more sophisticated collaboration mechanisms are needed.
Assuming that mediated collaboration has a limited impact on thinking creativity (emails don’t have to be answered, or even presented, instantly), the objective is to steer knowledge workflows across a two-tiered collaboration framework: one personal and direct between knowledge workers, the other social and mediated through enterprise or institutional networks.
On the first tier knowledge workers would manage their thinking flows (content and tempo) independently, initiating or accepting personal collaboration (either through physical contact or some kind of instant messaging) depending on their respective “state of mind”.
The second tier would be for social collaboration and would be expected to replace backlogs and time-boxing. Proceeding from the first to the second tier would be conditioned by workers’ needs and expectations, triggered on their own initiative or following prompts.
From Personal to Collective Thinking
The challenging issue is obviously to define and implement the mechanisms governing the exchanges between collaboration tiers, e.g:
How to keep tabs on topics and contents to be safeguarded.
How to mediate (i.e filter and time) the solicitations and contribution issued by the social tier.
How to assess the solicitations and contribution issued by individuals.
How to assess and manage knowledge deemed to remain proprietary.
How to identify and manage knowledge workers personal and social circles.
Whereas such issues are customary tackled by various AI systems (knowledge management, decision-making, multi-players games, etc), taken as a whole they bring up the question of the relationship between personal and collective thinking, and as a corollary, the role of organization in nurturing corporate innovation.
Conclusion: Collaboration Spaces vs Panopticon
As illustrated by the rising of futuristic headquarters, leading technology firms have been trying to tackle these issues by redefining internal architecture as collaboration spaces. Compared to traditional open spaces, such approaches try to fuse physical and digital spaces into overlapping layers of collaboration spaces, using artificial intelligence to harness cooperation.
Yet, lest uniform and comprehensive transparency brings the worrying shadow of a panopticon within which everyone can be unknowingly observed, working spaces have to be designed as to enhance collaboration without trespassing on privacy.
That could be achieved with a layered transparency set along the nature of collaboration:
Immediate and personal: working cells regrouping 5 to 10 workstations earmarked for a task and used indifferently by teams members.
Delayed and personal: open physical spaces accommodating working cells, with instant messaging and geo-localization; spaces are hinged on domains and focused on shared knowledge.
On-line and networked: digital spaces merging physical spaces and organizational structures.
That mix of physical and virtual spaces could be dynamically redefined depending on activities, projects, location, and organisation.
Data mining explores business opportunities and competitive advantage, requirements analysis considers supporting applications. Both use models, the former’s are predictive and ephemeral, the latter’s descriptive (or prescriptive) and perennial.
As the generalization of digitized environment calls for more integration of business and software engineering processes, understanding the relationship between data mining and requirements analysis could significantly improve processes maturity and agility.
Data vs Requirements Analysis
Nowadays the success of a wide range of enterprises critically depends on two achievements:
Mapping business models to changing environments by sorting through facts, capturing the relevant data, and processing the whole into meaningful and up to date information. That can be achieved through analysis models mapping business expectations to supporting systems.
Putting that information into effective use through business processes and supporting systems. That is done through systems architecture and design models meant to prescribe how to build software artifacts.
Those challenges are converging: under the pressure of markets forces and technological advances most of traditional fences between business channels and IT systems are crumbling, putting the focus on the functional integration between data mining and production systems. That’s where predictive models can help by anchoring descriptive models to moving markets and by cross-feeding analysis and operations. How that can be achieved has been the bread and butter of good corporate governance for some time, but there has been less interest for the third branch, namely how data analysis (predictive models) could “inform” business requirements (descriptive models).
Facts are not given but must be captured through a symbolic description of actual observations. That entails some observer set on task using a mix of conceptual and technical apparatus. Data mining and requirements analysis are practical realizations of that process:
Data mining relies on analytic tools to extract revealing information that could be used to chart opportunities along business models.
Requirements analysis relies on business processes and users’ practice to extract symbolic descriptions that will be used to build models of supporting applications.
If both walk the path from data to information, their objectives are different: the former’s is to improve business decisions by making sense of actual observations; the latter’s is to build system surrogates from the symbolic descriptions of actual business objects and activities.
Anchors & Structures: Plasticity of Business Entities
Perhaps paradoxically, business agility calls for terra firma because nimble trades must be rooted in corporate identity and business continuity. As a consequence, the first step of requirements analysis should be to associate individuals business objects or activities with stable and consistent identification mechanisms, and to group them with regard to that mechanism:
External entities with natural (person) or designed identity (car).
Symbolic entities for roles (customer) or commitments (maintenance contract).
Actual activities (promotion campaign) and events (sale) or business logic (promotion).
Conversely, as the aim of data analysis is to explore every business angle, individual observations are supposed to be moved across groups; yet, since the units identified by data analysis will have to be aligned with the ones described by requirements analysis, moves must also keep track of identities. That dilemma between continuity of identified structures on one side, plasticity of functional aspects on the other side, can be illustrated by banks which, in response to marketing requirements, had to shift from account (internal identification) to customer (external identification) based systems.
That challenge can be overcome by linking the identification of symbolic entities to external anchors.
Profiles & Features: Versatility of Business Opportunities
As noted above, requirements and data analysis are set on the same road but driven by different forces: the former tries to group individuals with regard to identification mechanisms before fleshing them out with relevant features; the latter tries to group individuals with given identities according to features and opportunity profiles. Yet, what could appear as collision courses may become a meeting of minds if both courses are charted with regard to variants analysis.
From the requirements perspective the primary concern is to distinguish between structural and functional variants:
Structural variants are bound to identities, i.e set up-front for the respective life-cycle of individual business objects or transactions. As a consequence they cannot be changed without undermining business continuity. Moreover, being part and parcel of descriptors (e.g types and use cases) their change will affect engineering processes.
Functional variants may vary during the respective life-cycle of individual business objects or transactions. As a consequence they can be changed without undermining business continuity, and changes in descriptors (e.g partitions and scenarii) can be managed without affecting engineering processes.
From the data mining perspective the objective is to improve the benefits of information systems for decision-making processes:
Static: how to classify individuals as to reduce the uncertainty of predictions
Dynamic: how to classify business options as to reduce the uncertainty of decisions.
Since those objectives are set for individuals, constraints on continuity and consistency can be dealt with independently of the description of symbolic surrogates.
It ensues that perspectives can be adjusted by factoring out the constraints of continuity and consistency for business objects (e.g cars), agents (e.g customer) and processes (e.g repair). Profiles for agents (a), behaviors (b), and business options (c) could then be freely explored and tailored with regard to changes in business environment and objectives.
Applying Data Analysis to Requirements
Not surprisingly data analysis techniques can be used to adjust perspectives. For that purpose a sample of individuals (business objects and operations) representing the population targeted by requirements would have to be submitted to basic mining routines. Borrowing a catalog from F. Provost & T. Fawcett:
Classification: estimates the probability for each individual (objects or operations) to belong to a set of classes; can be used to assess the closeness of the variants (respectively power-types or execution paths) identified by requirements analysis.
Regression: reverse classification; estimates how much of individual features valuations can be explained by the proposed classifications.
Similarity: a shallow version of classification; can be used to assess the distance between variants and consolidate the proposed classifications.
Clustering: a deep version of classification; can be used to distinguish between shallow and natural classifications.
Co-occurrence: deals with behavioral variants; can be used to distinguish between functional and structural classifications.
Profiling: reverse of co-occurrence; can be used to consolidate functional and structural classifications.
Links prediction: can be used to define relationships.
Data reduction: eliminate redundant individuals; can be used to consolidate requirements and refine tests scenarii.
Causal modeling: brings together business logic (events and rules) and users decisions; should provide the backbone of tests scenarii.
Besides the direct benefits for requirements, such procedures may help to bridge the span between data and requirements analysis and significantly improve processes’ capability and maturity level.
Data mining being first and foremost about competitive edge, it relies on a timely and effective coupling between enterprises capabilities and business opportunities. But the dilemma between continuity and plasticity described above for business objects and processes reappears at enterprise level: how to conciliate architecture, by nature perennial, with the agility needed to make the best of changing and competitive environments ?
As architectural big bang is arguably a last resort option, answers to that question must be progressive and local: if changes are to be swift and pertinent they must be both circumscribed and leveraged to the relevant parts of architecture. Taking an (amended) leaf of the Zachman framework, its sixth column (“Why” ) could be reset as a line for business and operational objectives that would cross the original five columns instead of the architecture layers. Using a pentagonal representation of enterprise architecture, that line would be set as circling the outer range.
It is worth to note that setting objectives on a line crossing the columns of capabilities instead of a column crossing the lines of layers means that objectives are set at enterprise level and their cascading impact traced and managed through layers.
Conceptual Models & Business Contexts
But even that updated framework doesn’t take into account the fundamental changes in business environments. Once secure behind organizational and technical fences, enterprises must now navigate through open digitized business environments and markets. For business processes it means a seamless integration with supporting applications; for corporate governance it means keeping track of heterogeneous and changing business contexts and concerns while assessing the capability of organizations and systems to cope, adjust, and improve.
As long as environments were a hotchpotch of actual and symbolic artifacts the pros and cons of integration could be balanced. But the generalization of digital flows and transactions has upended the balance: there is no more room or time for latency and enterprises must bring all symbolic representations (business, organization, and systems) under a common conceptual roof:
A canonical approach would be to introduce a conceptual indexing scheme open to extensions but with its footprint defined by business processes and systems functionalities. That would ensure a better integration of processes and supporting engineering but will do nothing for the modeling gap between enterprise architecture and external contexts. That could be achieved with ontologies.
Conceptual Loop: Ontologies & Business Intelligence
As far as data mining is concerned, three kinds of operations are to be considered:
Data understanding gives form and semantics to raw material.
Business understanding charts business contexts and concerns in terms of objects and processes descriptions.
Modeling consolidate data and business understanding into descriptive, predictive, and operational models.
While traditional approaches fall short when tasked with iterative modeling of unstructured data, ontologies may fare better because their explicit aim is only to describe what could exist in a domain of discourse:
They are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
They are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
They are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.
With regard to models, only the second point puts ontologies apart: contrary to models, ontologies are about understanding and are not supposed to be driven by empirical purposes. It ensues that ontologies can be understood as conceptual (aka canonical) models, used as such for business analysis and extended with purposes for systems analysis and design.
In addition to the integration with enterprise architectures, ontologies benefits for business intelligence would be twofold.
On one side, and whatever their use, ontologies could be aligned with the nature of contexts and their impact on business and enterprise governance e.g:
Institutional: mandatory semantics sanctioned by regulatory authority, steady, changes subject to established procedures.
Professional: agreed upon semantics between parties, steady, changes subject to established procedures.
Corporate: enterprise defined semantics, changes subject to internal decision-making.
Social: definedpragmatic semantics, no authority, volatile, continuous and informal changes.
Personal: customary semantics defined by named individuals.
On the other side ontologies could be defined according to the nature of targeted items, namely terms, documents, symbolic representations, or actual objects and phenomena. That would outline four basic concerns that may or may not be combined:
Thesaurus: ontologies covering terms and concepts.
Document Management: ontologies covering documents with regard to topics.
Organization and Business: ontologies pertaining to enterprise organization, objects and activities.
Engineering: ontologies pertaining to the symbolic representation of products and services.
On a broader perspective, ontologies could be influential in spanning the gap between explicit and implicit knowledge. 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, ontologies would be decisive in framing the understanding, assessment, and improvement of the processes.
Operational Loop: Business Intelligence & Decision-making
Once carried out separately and periodically, decision-making is to be carried out iteratively at operational, tactical, and strategic level; while each level is to be set along its own time-frames, all are to rely on data-mining, with cycles following the same pattern:
Observation: understanding of changes in business opportunities.
Orientation: assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations.
Decision: weighting of options with regard to enterprise capabilities and broader objectives.
Action: carrying out of decisions within the relevant time-frame.
Given business new playground, decision-making processes have to weave together material and digitized flows, actual contexts (aka territories) and symbolic descriptions (maps), and overlapping time-frames (operational tactical, strategic). That operational loop could then be coupled with the broader one of business intelligence:
Considering Alan Turing’s question, “Can machines think ?”, could the distinction between communication and knowledge representation capabilities help to decide between human and machine ?
What happens when people interact ?
Conversations between people are meant to convey concrete, partial, and specific expectations. Assuming the use of a natural language, messages have to be mapped to the relevant symbolic representations of the respective cognitive contexts and intentions.
Assuming a difference in the way this is carried on by people and machines, could that difference be observed at message level ?
Communication vs Representation Semantics
To begin with, languages serve two different purposes: to exchange messages between agents, and to convey informational contents. As illustrated by the difference between humans and other primates, communication (e.g alarm calls directly and immediately bound to imminent menace) can be carried out independently of knowledge representation (e.g information related to the danger not directly observable), in other words linguistic capabilities for communication and symbolic representation can be set apart. That distinction may help to differentiate people from machines.
Exchanging messages make use of five categories of information:
Identification of participants (Who) : can be set independently of their actual identity or type (human or machine).
Nature of message (What): contents exchanged (object, information, request, … ) are not contingent on participants type.
Life-span of message (When): life-cycle (instant, limited, unlimited, …) is not contingent on participants type.
Location of participants (Where): the type of address space (physical, virtual, organizational,…) is not contingent on participants type.
Communication channels (How): except for direct (unmediated) human conversations, use of channels for non direct (distant, physical or otherwise) communication are not contingent on participants type .
Setting apart the trivial case of direct human conversation, it ensues that communication capabilities are not enough to discriminate between human and artificial participants, .
Knowledge Representation Capabilities
Taking a leaf from Davis, Shrobe, and Szolovits, knowledge representation can be characterized by five capabilities:
Surrogate: KR provides a symbolic counterpart of actual objects, events and relationships.
Ontological commitments: a KR is a set of statements about the categories of things that may exist in the domain under consideration.
Fragmentary theory of intelligent reasoning: a KR is a model of what the things can do or can be done with.
Medium for efficient computation: making knowledge understandable by computers is a necessary step for any learning curve.
Medium for human expression: one the KR prerequisite is to improve the communication between specific domain experts on one hand, generic knowledge managers on the other hand.
On that basis knowledge representation capabilities cannot be used to discriminate between human and artificial participants.
Returning to Turing Test
Even if neither communication nor knowledge representation capabilities, on their own, suffice to decide between human and machine, their combination may do the trick. That could be achieved with questions like:
Who do you know: machines can only know previous participants.
What do you know: machines can only know what they have been told, directly or indirectly (learning).
When did/will you know: machines can only use their own clock or refer to time-spans set by past or planned transactional events.
Where did/will you know: machines can only know of locations identified by past or planned communications.
How do you know: contrary to humans, intelligent machines are, at least theoretically, able to trace back their learning process.
Hence, and given scenarii scripted adequately, it would be possible to build decision models able to provide unambiguous answers.
Some clues to answers may be found in the relationship between purposes, designs, and behaviors of intelligent devices.
Intelligence is generally understood as the ability to figure out situations and solve problems, with its artificial avatar turning up when such ability is exercised by devices.
Devices being human artifacts, it’s safe to assume that their design can be fully accounted for, and their purposes wholly exhibited and assessed. As a corollary, debates about AI’s threats should distinguish between harmful purposes (a moral issue) on one hand, faulty designs, flawed outcomes, and devious behaviors, (all engineering issues) on the other hand. Whereas concerns for the former could arguably be left to philosophers, engineers should clearly take full responsibility for the latter.
Human, Artificial, & Insane Behaviors
Interestingly, the “human” adjective takes different meanings depending on its association to agents (human as opposed to artificial) or behaviors (human as opposed to barbaric). Hence the question: assuming that intelligent devices are supposed to mimic human behaviors, what would characterize devices’ “inhuman” behaviors ?
From an engineering perspective, i.e moral issues being set aside, a tentative answer would point to some flawed reasoning, commonly described as insanity.
Purposes, Reason, Outcomes & Behaviors
As intelligence is usually associated with reason, flaws in the design of reasoning capabilities is where to look for the primary factor of hazardous devices’ behaviors.
To begin with, the designs of intelligent devices neatly mirror human cognitive activity by combining both symbolic (processing of symbolic representations), and non symbolic (processing of neuronal connections) capabilities. How those capabilities are put to use is therefore to characterize the mapping of purposes to behaviors:
Designs relying on symbolic representations allow for explicit information processing: data is “interpreted” into information which is then put to use as the knowledge governing behaviors.
Designs based on neuronal networks are characterized by implicit information processing: data is “compiled” into neuronal connections whose weights (representing knowledge ) are tuned iteratively based on behavioral feedback.
That distinction is to guide the analysis of potential threats:
Designs based on symbolic representations can support both the transparency of ends and the traceability of means. Moreover, such designs allow for the definition of broader purposes, actual or social.
Neuronal networks make the relationships between means and ends more opaque because their learning kernels operate directly on data, with the supporting knowledge implicitly embodied as weighted connections. They make for more concrete and focused purposes for which symbolic transparency and traceability are less of a bearing.
Risks, Knowledge, & Decision Making
As noted above, an engineering perspective should focus on the risks of flawed designs begetting discrepancies between purposes and outcomes. Schematically, two types of outcome are to be considered: symbolic ones are meant to feed human decisions making, and behavioral ones directly govern devices behaviors. For AI systems combining both symbolic and non symbolic capabilities, risks can arise from:
Embedded decision making: flawed decisions based on symbolic knowledge built from implicit knowledge (b).
Distributed decision making: muddled decisions based on symbolic knowledge built by combining different domains of discourse (c).
Whereas risks bred by unsupervised decision making can be handled with conventional engineering solutions, that’s not the case for embedded or distributed decision making supported by intelligent devices. And both risks may be increased respectively by the so-called internet of things and semantic web.
Internet of Things, Semantic Web, & Embedded Insanity
On one hand the so-called “internet second revolution” can be summarized as the end of privileged netizenship: while the classic internet limited its residency to computer systems duly identified by regulatory bodies, the new one makes room for every kind of device. As a consequence, many intelligent devices (e.g cell phones) have made their coming out into fully fledged systems.
On the other hand the so-called “semantic web” can be seen as the symbolic counterpart of the internet of things, providing a whole of comprehensive and consistent meanings to targeted factual realities. Yet, given that the symbolic world is not flat but built on piled mazes of meanings, their charting is to be contingent on projections with dissonant semantics. Moreover, as meanings are not supposed to be set in stone, semantic configurations have to be continuously adjusted.
That double trend clearly increases the risks of flawed outcomes and erratic behaviors:
Holed up sources of implicit knowledge are bound to increase the hazards of unsupervised behaviors or propagate unreliable information.
Misalignment of semantics domains may blur the purposes of intelligent apparatus and introduce biases in knowledge processing.
But such threats are less intrinsic to AI than caused by the way it is used: insanity is more probably to spring from the ill-designed integration of intelligent and reasonable systems into the social fabric than from insane ones.