Archive for the ‘Semantic Web’ Category

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

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Things Speaking in Tongues

January 25, 2017

Preamble

Speaking in tongues (aka Glossolalia) is the fluid vocalizing of speech-like syllables without any recognizable association with a known language. Such experience is best (not ?) understood as the actual speaking of a gutted language with grammatical ghosts inhabited by meaningless signals.

The man behind the tongue (Herbert List)

Do You Hear What I Say ? (Herbert List)

Usually set in religious context or circumstances, speaking in tongue looks like souls having their own private conversations. Yet, contrary to extraterrestrial languages, the phenomenon is not fictional and could therefore point to offbeat clues for natural language technology.

Computers & Language Technology

From its inception computers technology has been a matter of language, from machine code to domain specific. As a corollary, the need to be in speaking terms with machines (dumb or smart) has put a new light on interpreters (parsers in computer parlance) and open new perspectives for linguistic studies. In due return, computers have greatly improve the means to experiment and implement new approaches.

During the recent years advances in artificial intelligence (AI) have brought language technologies to a critical juncture between speech recognition and meaningful conversation, the former leaping ahead with deep learning and signal processing, the latter limping along with the semantics of domain specific languages.

Interestingly, that juncture neatly coincides with the one between the two intrinsic functions of natural languages: communication and representation.

Rules Engines & Neural Network

As exemplified by language technologies, one of the main development of deep learning has been to bring rules engines and neural networks under a common functional roof, turning the former unfathomable schemes into smart conceptual tutors for the latter.

In contrast to their long and successful track record in computer languages, rule-based approaches have fallen short in human conversations. And while these failings have hindered progress in the semantic dimension of natural language technologies, speech recognition have strode ahead on the back of neural networks fueled by increasing computing power. But the rift between processing and understanding natural languages is now being fastened through deep learning technologies. And with the leverage of rule engines harnessing neural networks, processing and understanding can be carried out within a single feedback loop.

From Communication to Cognition

From a functional point of view, natural languages can be likened to money, first as medium of exchange, then as unit of account, finally as store of value. Along that understanding natural languages would be used respectively for communication, information processing, and knowledge representation. And like the economics of money, these capabilities are to be associated to phased cognitive developments:

  • Communication: languages are used to trade transient signals; their processing depends on the temporal persistence of the perceived context and phenomena; associated behaviors are immediate (here-and-now).
  • Information: languages are also used to map context and phenomena to some mental representations; they can therefore be applied to scripted behaviors and even policies.
  • Knowledge: languages are used to map contexts, phenomena, and policies to categories and concepts to be stored as symbolic representations fully detached of original circumstances; these surrogates can the be used, assessed, and improved on their own.

As it happens, advances in technologies seem to follow these cognitive distinctions, with the internet of things (IoT) for data communications, neural networks for data mining and information processing, and the addition of rules engines for knowledge representation. Yet paces differ significantly: with regard to language processing (communication and information), deep learning is bringing the achievements of natural language technologies beyond 90% accuracy; but when language understanding has to take knowledge into account, performances still lag a third below: for computers knowledge to be properly scaled, it has to be confined within the semantics of specific domains.

Sound vs Speech

Humans listening to the Universe are confronted to a question that can be unfolded in two ways:

  • Is there someone speaking, and if it’s the case, what’s the language ?.
  • Is that a speech, and if it’s the case, who’s speaking ?.

In both case intentionality is at the nexus, but whereas the first approach has to tackle some existential questioning upfront, the second can put philosophy on the back-burner and focus on technological issues. Nonetheless, even the language first approach has been challenging, as illustrated by the difference in achievements between processing and understanding language technologies.

Recognizing a language has long been the job of parsers looking for the corresponding syntax structures, the hitch being that a parser has to know beforehand what it’s looking for. Parser’s parsers using meta-languages have been effective with programming languages but are quite useless with natural ones without some universal grammar rules to sort out babel’s conversations. But the “burden of proof” can now be reversed: compared to rules engines, neural networks with deep learning capabilities don’t have to start with any knowledge. As illustrated by Google’s Multilingual Neural Machine Translation System, such systems can now build multilingual proficiency from sufficiently large samples of conversations without prior specific grammatical knowledge.

To conclude, “Translation System” may even be self-effacing as it implies language-to-language mappings when in principle such systems can be fed with raw sounds and be able to parse the wheat of meanings from the chaff of noise. And, who knows, eventually be able to decrypt languages of tongues.

Further Reading

External Links

NIEM & Information Exchanges

January 24, 2017

Preamble

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.”

(Alfred Jensen)

NIEM’s model makes no difference between data and information (Alfred Jensen)

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.

Data is meaningless, information meaning is set by semantic domains.

As an exchange, NIEM 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.

Data is meaningless, information meaning is set by semantic domains.

Data is meaningless, information meaning is set by semantic domains.

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.

vv

Shared or alternative meanings cannot be managed with lexicons

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).
vvv

NIEM’s Lexicon: Core (a) and specific (b) and associated core (c) and specific (d) properties

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.
Lexical items are meshed disregarding semantics

Lexical items are chartered arbitrarily

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.
nformation exchanges are best supported by dictionaries organized around roots and aspects

Information exchanges are best supported by dictionaries organized around roots and aspects

As it happens that distinction can be neatly mapped to core concepts of software engineering.

Further Reading

External Links

Things Behavior & Social Responsibility

October 27, 2016

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.

robot_waynemiller

Device & Social Identity (Wayne Miller)

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.

Further Reading

External Links

Brands, Bots, & Storytelling

May 2, 2016

As illustrated by the recent Mashable “pivot”, meaningful (i.e unbranded) contents appear to be the main casualty of new communication technologies. Hopefully (sic), bots may point to a more positive perspective, at least if their want for no no-nonsense gist is to be trusted.

(Latifa Echakhch)

Could bots repair gibberish ? (Latifa Echakhch)

The Mashable Pivot to “branded” Stories

Announcing Mashable recent pivot, Pete Cashmore (Mashable ‘s founder and CEO) was very candid about the motives:

“What our advertisers value most about
 Mashable is the same thing that our audience values: Our content. The
 world’s biggest brands come to us to tell stories of digital culture, 
innovation and technology in an optimistic and entertaining voice. As 
a result, branded content has become our fastest growing revenue 
stream over the past year. Content is now at the core of our ad 
offering and we plan to double down there.

”

Also revealing was the semantic shift in a single paragraph: from “stories”, to “stories told with an optimistic and entertaining voice”, and finally to “branded stories”; as if there was some continuity between Homer’s Iliad and Outbrain’s gibberish.

Spinning Yarns

From Lacan to Seinfeld, it has often been said that stories are what props up our world. But that was before Twitter, Facebook, YouTube and others ruled over the waves and screens. Nowadays, under the combined assaults of smart dummies and instant messaging, stories have been forced to spin advertising schemes, and scripts replaced  by subliminal cues entangled in webs of commercial hyperlinks. And yet, somewhat paradoxically, fictions may retrieve some traction (if not spirit) of their own, reprieved not so much by human cultural thirst as by smartphones’ hunger for fresh technological contraptions.

Apps: What You Show is What You Get

As far as users are concerned, apps often make phones too smart by half: with more than 100 billion of apps already downloaded, users face an embarrassment of riches compounded by the inherent limitations of packed visual interfaces. Enticed by constantly renewed flows of tokens with perfunctory guidelines, human handlers can hardly separate the wheat from the chaff and have to let their choices be driven by the hypothetical wisdom of the crowd. Whatever the outcomes (crowds may be right but often volatile), the selection process is both wasteful (choices are ephemera, many apps are abandoned after a single use, and most are sparely used), and hazardous (too many redundant dead-ends open doors to a wide array of fraudsters). That trend is rapidly facing the physical as well as business limits of a zero-sum playground: smarter phones appear to make for dumber users. One way out of the corner would be to encourage intelligent behaviors from both parties, humans as well as devices. And that’s something that bots could help to bring about.

Bots: What You Text Is What You Get

As software agents designed to help people find their ways online, bots can be differentiated from apps on two main aspects:

  • They reside in the cloud, not on personal devices, which means that updates don’t have to be downloaded on smartphones but can be deployed uniformly and consistently. As a consequence, and contrary to apps, the evolution of bots can be managed independently of users’ whims, fostering the development of stable and reliable communication grammars.
  • They rely on text messaging to communicate with users instead of graphical interfaces and visual symbols. Compared to icons, text put writing hands on driving wheels, leaving much less room for creative readings; given that bots are not to put up with mumbo jumbo, they will prompt users to mind their words as clearly and efficiently as possible.

Each aspect reinforces the other, making room for a non-zero playground: while the focus on well-formed expressions and unambiguous semantics is bots’ key characteristic, it could not be achieved without the benefits of stable and homogeneous distribution schemes. When both are combined they may reinstate written languages as the backbone of communication frameworks, even if it’s for the benefits of pidgin languages serving prosaic business needs.

A Literary Soup of Business Plots & Customers Narratives

Given their need for concise and unambiguous textual messages, the use of bots could bring back some literary considerations to a latent online wasteland. To be sure, those considerations are to be hard-headed, with scripts cut to the bone, plots driven by business happy ends, and narratives fitted to customers phantasms.

Nevertheless, good storytelling will always bring some selective edge to businesses competing for top tiers. So, and whatever the dearth of fictional depth, the spreading of bots scripts could make up some kind of primeval soup and stir the emergence of some literature untainted by its fouled nourishing earth.

Further Readings

Out of Mind Content Discovery

April 20, 2016

Content discovery and the game of Go can be used to illustrate the strengths and limits of artificial intelligence.

(Pavel Wolberg)

Now and Then: contents discovery across media and generations (Pavel Wolberg)

Game of Go: Closed Ground, Non Semantic Charts

The conclusive successes of Google’s AlphaGo against world’s best players are best understood when  related to the characteristics of the game of Go:

  • Contrary to real life competitions, games are set on closed and standalone playgrounds  detached from actual concerns. As a consequence players (human or artificial) can factor out emotions  from cognitive behaviors.
  • Contrary to games like Chess, Go’s playground is uniform and can be mapped without semantic distinctions for situations or moves. Whereas symbolic knowledge, explicit or otherwise, is still required for good performances, excellence can only be achieved through holistic assessments based on intuition and implicit knowledge.

Both characteristics fully play to the strengths of AI, in particular computing power (to explore playground and alternative strategies) and detachment (when decisions have to be taken).

Content Discovery: Open Grounds, Semantic Charts

Content discovery platforms like Outbrain or Taboola are meant to suggest further (commercial) bearings to online users. Compared to the game of Go, that mission clearly goes in the opposite direction:

  • Channels may be virtual but users are humans, with real emotions and concerns. And they are offered proxy grounds not so much to be explored than to be endlessly redefined and made more alluring.
  • Online strolls may be aimless and discoveries fortuitous, but if content discovery devices are to underwrite themselves, they must bring potential customers along monetized paths. Hence the hitch: artificial brains need some cues about what readers have in mind.

That makes content discovery a challenging task for artificial coaches as they have to usher wanderers with idiosyncratic but unknown motivations through boundless expanses of symbolic shopping fields.

What Would Eliza Say

When AI was still about human thinking Alan Turing thought of a test that could check the ability of a machine to exhibit intelligent behaviors. As it was then, available computing power was several orders of magnitude below today’s capacities, so the test was not about intelligence itself, but with the ability to conduct text-based dialogues equivalent to, or indistinguishable from, that of a human. That approach was famously illustrated by Eliza, a software able to beguile humans in conversations without any understanding of their meanings.

More than half a century later, here are some suggestions of leading content discovery engines:

  • After reading about the Ecuador quake or Syrian rebels one is supposed to be interested by 8 tips to keep our liver healthy, or 20 reasons of unsuccessful attempts at losing weight.
  • After reading about growing coffee in Ethiopia one is supposed to be interested by the mansions of world billionaires, or a Shepard pup surviving after being lost at sea for a month.

It’s safe to assume that both would have flunked the Turing Test.

Further Reading

External Links

Selfies & Augmented Identities

March 31, 2016

As smart devices and dumb things respectively drive and feed internet advances, selfies may be seen as a minor by-product figuring the scenes between reasoning capabilities and the reality of things. But then, should that incidental understanding be upgraded to a more meaningful one that will incorporate digital hybrids into virtual reality.

Actual and Virtual Representations (N. Rockwell)

Portraits, Selfies, & Social Identities

Selfies are a good starting point given that their meteoric and wide-ranging success makes for social continuity of portraits, from timeless paintings to new-age digital images. Comparing the respective practicalities and contents of traditional and digital pictures  may be especially revealing.

With regard to practicalities, selfies bring democratization: contrary to paintings, reserved to social elites, selfies let everybody have as many portraits as wished, free to be shown at will, to family, close friends, or total unknowns.

With regard to contents, selfies bring immediacy: instead of portraits conveying status and characters through calculated personal attires and contrived settings, selfies picture social identities as snapshots that capture supposedly unaffected but revealing moments, postures, entourages, or surroundings.

Those selfies’ idiosyncrasies are intriguing because they seem to largely ignore the wide range of possibilities offered by new media technologies which could (and do sometimes) readily make selfies into elaborate still lives or scenic videos.

Likewise is the fading-out of photography as a vector of social representation after the heights achieved in the second half of the 19th century: not until the internet era did photographs start again to emulate paintings as vehicles of social identity.

Those changing trends may be cleared up if mirrors are introduced in the picture.

Selfies, Mirrors, & Physical Identities

Natural or man-made, mirrors have from the origin played a critical part in self-consciousness, and more precisely in self-awareness of physical identity. Whereas portraits are social, asynchronous, and symbolic representations, mirrors are personal, synchronous, and physical ones; hence their different roles, portraits abetting social identities, and mirrors reflecting physical ones. And selfies may come as the missing link between them.

With smartphones now customarily installed as bodily extensions, selfies may morph into recurring personal reflections, transforming themselves into a crossbreed between portraits, focused on social identification, and mirrors, intent on personal identity. That understanding would put selfies on an elusive swing swaying between social representation and communication on one side, authenticity and introspection on the other side.

On that account advances in technologies, from photographs to 3D movies, would have had a limited impact on the traction from either the social or physical side. But virtual reality (VR) is another matter altogether because it doesn’t only affect the medium between social and physical aspects, but also the “very” reality of the physical side itself.

Virtual Reality: Sense & Sensibility

The raison d’être of virtual reality (VR) is to erase the perception fence between individuals and their physical environment. From that perspective VR contraptions can be seen as deluding mirrors doing for physical identity what selfies do for social ones: teleporting individual personas between environments independently of their respective actuality. The question is: could it be carried out as a whole, teleporting both physical and social identities in a single package ?

Physical identities are built from the perception of actual changes directly originated in context or prompted by our own behavior: I move of my own volition, therefore I am. Somewhat equivalently, social identities are built on representations cultivated innerly, or supposedly conveyed by aliens. Considering that physical identities are continuous and sensible, and social ones discrete and symbolic, it should be possible to combine them into virtual personas that could be teleported around packet switched networks.

But the apparent symmetry could be deceitful given that although teleporting doesn’t change meanings, it breaks the continuity of physical perceptions, which means that it goes with some delete/replace operation. On that account effective advances of VR can be seen as converging on alternative teleporting pitfalls:

  • Virtual worlds like Second Life rely on symbolic representations whatever the physical proximity.
  • Virtual apparatuses like Oculus depend solely on the merge with physical proximity and ignore symbolic representations.

That conundrum could be solved if sense and sensibility could be reunified, giving some credibility to fused physical and social personas. That could be achieved by augmented reality whose aim is to blend actual perceptions with symbolic representations.

From Augmented Identities to Extended Beliefs

Virtual apparatuses rely on a primal instinct that makes us to believe in the reality of our perceptions. Concomitantly, human brains use built-in higher level representations of body physical capabilities in order to support the veracity of the whole experiences. Nonetheless, if and when experiences appear to be far-fetched, brains are bound to flag the stories as delusional.

Or maybe not. Even without artificial adjuncts to the brain chemistry, some kind of cognitive morphing may let the mind bypasses its body limits by introducing a deceitful continuity between mental representations of physical capabilities on one hand, and purely symbolic representations on the other hand. Technological advances may offer schemes from each side that could trick human beliefs.

Broadly speaking, virtual reality schemes can be characterized as top-down; they start by setting the mind into some imaginary world, and beguiles it into the body envelope portraying some favorite avatar. Then, taking advantage of its earned confidence, the mind is to be tricked on a flyover that would move it seamlessly from fictional social representations into equally fictional physical ones: from believing to be with superpowers into trusting the actual reach and strength of his hand performing corresponding deeds. At least that’s the theory, because if such a “suspension of disbelief” is the essence of fiction and art, the practicality of its mundane actualization remains to be confirmed.

Augmented reality goes the other way and can be seen as bottom-up, relying on actual physical experiences before moving up to fictional extensions. Such schemes are meant to be fed with trusted actual perceptions adorned with additional inputs, visual or otherwise, designed on purpose. Instead of straight leaps into fiction, beliefs can be kept anchored to real situations from where they can be seamlessly led astray to unfolding wonder worlds, or alternatively ushered to further knowledge.

By introducing both continuity and new dimensions to the design of physical and social identities, augmented reality could leverage selfies into major social constructs. Combined with artificial intelligence they could even become friendly or helpful avatars, e.g as personal coaches or medical surrogate.

Further Readings

External Links

Agile Collaboration & Social Creativity

February 22, 2016

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.

Brains need some breathing space

Open-plan offices, collaboration, and knowledge workers creativity

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.

Knowledge Workflows

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.

Further Readings

External Links

AlphaGo: From Intuitive Learning to Holistic Knowledge

February 1, 2016

Brawn & Brain

Google’s AlphaGo recent success against Europe’s top player at the game of Go is widely recognized as a major breakthrough for Artificial Intelligence (AI), both because of the undertaking (Go is exponentially more complex than Chess) and time (it has occurred much sooner than expected). As it happened, the leap can be credited as much to brawn as to brain, the former with a massive increase in computing power, the latter with an innovative combination of established algorithms.

(Kunisada)

Brawny Contest around Aesthetic Game (Kunisada)

That breakthrough and the way it has been achieved may seem to draw opposite perspectives about the future of IA: either the current conceptual framework is the best option, with brawny machines becoming brainier and, sooner or later, will be able to leap over  the qualitative gap with their human makers; or it’s a quantitative delusion that could drive brawnier machines and helpless humans down into that very same hole.

Could AlphaGo and its DeepMind makers may point to a holistic bypass around that dilemma ?

Taxonomy of Sources

Taking a leaf from Spinoza, one could begin by considering the categories of knowledge with regard to sources:

  1. The first category is achieved through our senses (views, sounds, smells, touches) or beliefs (as nurtured by our common “sense”). This category is by nature prone to circumstances and prejudices.
  2. The second is 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.
  3. The third is attained through philosophy which is by essence meant to bring together perceptions, intuitions, and symbolic representations.

Whereas there can’t be much controversy about the first ones, the third category leaves room for a wide range of philosophical tenets, from religion to science, collective ideologies, or spiritual transcendence. With today’s knowledge spread across smart devices and driven by the wisdom of crowds, philosophy seems to look more at big data than at big brother.

Despite (or because of) its focus on the second category, AlphaGo and its architectural’s feat may still carry some lessons for the whole endeavor.

Taxonomy of Representations

As already noted, the effectiveness of IA’s supporting paradigms has been bolstered by the exponential increase in available data and the processing power to deal with it. Not surprisingly, those paradigms are associated with two basic forms of representations aligned with the source of knowledge, implicit for senses, and explicit for reasoning:

  • Designs based on symbolic representations allow for explicit information processing: data is “interpreted” into information which is then put to use as knowledge governing behaviors.
  • Designs based on neural networks are characterized by implicit information processing: data is “compiled” into neural connections whose weights (pondering knowledge ) are tuned iteratively on the basis of behavioral feedback.

Since that duality mirrors human cognitive capabilities, brainy machines built on those designs are meant to combine rationality with effectiveness:

  • Symbolic representations support the transparency of ends and the traceability of means, allowing for hierarchies of purposes, actual or social.
  • Neural networks, helped by their learning kernels operating directly on data, speed up the realization of concrete purposes based on the supporting knowledge implicitly embodied as weighted connections.

The potential of such approaches have been illustrated by internet-based language processing: pragmatic associations “observed” on billions of discourses are progressively complementing and even superseding syntactic and semantic rules in web-based parsers.

On that point too AlphaGo has focused ambitions since it only deals with non symbolic inputs, namely a collection of Go moves (about 30 million in total) from expert players. But that limit can be turned into a benefit as it brings homogeneity and transparency, and therefore a more effective combination of algorithms: brawny ones for actual moves and intuitive knowledge from the best players, brainy ones for putative moves, planning, and policies.

Teaching them how to work together is arguably a key factor of the breakthrough.

Taxonomy of Learning

As should be expected from intelligent machines, their impressive track record fully depends of their learning capabilities. Whereas those capabilities are typically applied separately to implicit (or non symbolic) and explicit (or symbolic) contents, bringing them under the control of the same cognitive engine, as humans brains routinely do, has long been recognized as a primary objective for IA.

Practically that has been achieved with neural networks by combining supervised and unsupervised learning: human experts help systems to sort the wheat from the chaff and then let them improve their expertise through millions of self-play.

Yet, the achievements of leading AI players have marked out the limits of these solutions, namely the qualitative gap between playing as the best human players and beating them. While the former outcome can be achieved through likelihood-based decision-making, the latter requires the development of original schemes, and that brings quantitative and qualitative obstacles:

  • Contrary to actual moves, possible ones have no limit, hence the exponential increase in search trees.
  • Original schemes are to be devised with regard to values and policies.

Overcoming both challenges with a single scheme may be seen as the critical achievement of DeepMind engineers.

Mastering the Breadth & Depth of Search Trees

Using neural networks for the evaluation of actual states as well as the sampling of policies comes with exponential increases in breath and depth of search trees. Whereas Monte Carlo Tree Search (MCTS) algorithms are meant to deal with the problem, limited capacity to scale up the processing power will nonetheless lead to shallow trees; until DeepMind engineers succeeded in unlocking the depth barrier by applying MCTS to layered value and policy networks.

AlphaGo seamless use of layered networks (aka Deep Convolutional Neural Networks) for intuitive learning, reinforcement, values, and policies was made possible by the homogeneity of Go’s playground and rules (no differentiated moves and search traps as in the game of Chess).

From Intuition to Knowledge

Humans are the only species that combines intuitive (implicit) and symbolic (explicit) knowledge, with the dual capacity to transform the former into the latter and in reverse to improve the former with the latter’s feedback.

Applied to machine learning that would require some continuity between supervised and unsupervised learning which would be achieved with neural networks being used for symbolic representations as well as for raw data:

  • From explicit to implicit: symbolic descriptions built for specific contexts and purposes would be engineered into neural networks to be tried and improved by running them on data from targeted environments.
  • From implicit to explicit: once designs tested and reinforced through millions of runs in relevant targets, it would be possible to re-engineer the results into improved symbolic descriptions.

Whereas unsupervised learning of deep symbolic knowledge remains beyond the reach of intelligent machines, significant results can be achieved for “flat” semantic playground, i.e if the same semantics can be used to evaluate states and policies across networks:

  1. Supervised learning of the intuitive part of the game as observed in millions of moves by human experts.
  2. Unsupervised reinforcement learning from games of self-play.
  3. Planning and decision-making using Monte Carlo Tree Search (MCTS) methods to build, assess, and refine its own strategies.

Such deep and seamless integration would not be possible without the holistic nature of the game of Go.

Aesthetics Assessment & Holistic Knowledge

The specificity of the game of Go is twofold, complexity on the quantitative side, simplicity on  the qualitative side, the former being the price of the latter.

As compared to Chess, Go’s actual positions and prospective moves can only be assessed on the whole of the board, using a criterion that is best defined as aesthetic as it cannot be reduced to any metrics or handcrafted expert rules. Players will not make moves after a detailed analysis of local positions and assessment of alternative scenarii, but will follow their intuitive perception of the board.

As a consequence, the behavior of AlphaGo can be neatly and fully bound with the second level of knowledge defined above:

  • As a game player it can be detached from actual reality concerns.
  • As a Go player it doesn’t have to tackle any semantic complexity.

Given a fitted harness of adequate computing power, the primary challenge of DeepMind engineers is to teach AlphaGo to transform its aesthetic intuitions into holistic knowledge without having to define their substance.

Further Readings

External Links

Data Mining & Requirements Analysis

October 24, 2015

Preamble

Data mining explores business opportunities and competitive advantage, requirements analysis describe supporting applications. Both use models, the former’s are predictive and ephemeral, the latter’s descriptive (or prescriptive) and perennial.

(Andreas Gursky)

Data mining: sorting business wheat from world chaff (Andreas Gursky)

Understanding how they are related could significantly improve processes maturity.

Data vs Requirements Analysis

Nowadays the success of a wide range of enterprises critically depends on two achievements:

  1. 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 meant to described business expectations with regard to supporting systems.
  2. Putting that information into effective use through their business processes and supporting systems. That is done by systems architecture and design models meant to prescribe how to build software artifacts.
vv

From data analysis to systems requirements and software design

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).

From Data to Information

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).
Anchors

Anchors

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.

From account (left) to customer (right) centered systems

It’s easier to market insurance from customer centered systems (right) than from account centered ones (left)

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.

Identified individuals with profiles for customers (a), their behaviors (b), and conciliatory gestures (c)

Identified individuals with profiles for customers (a), their behaviors (b), and promotional gestures (c)

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:

  1. 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.
  2. Regression: reverse classification; estimates how much of individual features valuations can be explained by the proposed classifications.
  3. Similarity: a shallow version of classification; can be used to assess the distance between variants and consolidate the proposed classifications.
  4. Clustering: a deep version of classification; can be used to distinguish between shallow and natural classifications.
  5. Co-occurrence: deals with behavioral variants; can be used to distinguish between functional and structural classifications.
  6. Profiling: reverse of co-occurrence; can be used to consolidate functional and structural classifications.
  7. Links prediction: can be used to define relationships.
  8. Data reduction: eliminate redundant individuals; can be used to consolidate requirements and refine tests scenarii.
  9. 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.

Business Objectives & Enterprise Architecture Capabilities

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 on the outer range.

ccc

Enterprise Architecture and the loci of change

It must be reminded 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.

Symbolic Systems vs World

Nowadays the life of enterprises fully depends on the ability of their systems to deal with their environment by making sense of data and supporting production systems. 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 facts and transactions has upended the balance: there is no more room or time for latency and enterprises must unify the symbolic representation of their business models, organization, and systems. That should be the role of conceptual models but the challenge is to avoid flights to abstraction and rainbow chases.

OpenConcepts_00

Conceptual models as bridges between environments, processes, and systems.

That could be done by introducing a conceptual indexing scheme open to extensions but with its footprint defined by business processes and systems functionalities.

Selected Readings