Things Behavior & Social Responsibility

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

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 restore the music of words ? (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

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

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 & Enterprise Creativity

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.


Trust & Communication (Juan Munoz)

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

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

Preamble

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.

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

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:

  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 mapping business expectations to supporting systems.
  2. 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).

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 as circling the outer range.

Enterprise Architecture and the loci of change

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:

OpenConcepts_00
Conceptual models as bridges between business processes, and systems.

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.

OKBI_dmProcess
The aim of data mining is to refine raw data into meaningful information

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:

  1. They are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
  2. They are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
  3. 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.

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

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.

KM_OntosCapabs
Ontologies: Purposes & Targets

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:

  1. Observation: understanding of changes in business opportunities.
  2. Orientation: assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations.
  3. Decision: weighting of options with regard to enterprise capabilities and broader objectives.
  4. 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:

OKBI_BIDM
Integration of  operational analytics, business intelligence, and decision-making.

Selected Readings

Detour from Turing Game

Summary

Considering Alan Turing’s question, “Can machines think ?”, could the distinction between communication and knowledge representation capabilities help to decide between human and machine ?

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Alan Turing at 4

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.

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Conveying 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.

Communication Capabilities

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:

  1. Surrogate: KR provides a symbolic counterpart of actual objects, events and relationships.
  2. Ontological commitments: a KR is a set of statements about the categories of things that may exist in the domain under consideration.
  3. Fragmentary theory of intelligent reasoning: a KR is a model of what the things can do or can be done with.
  4. Medium for efficient computation: making knowledge understandable by computers is a necessary step for any learning curve.
  5. 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.

Reference Readings

A. M. Turing, “Computing Machinery and Intelligence”

Davis R., Shrobe H., Szolovitz P., “What is a Knowledge Representation?”

Further Reading

 

 

AI & Embedded Insanity

Summary

Bill Gates recently expressed his concerns about AI’s threats, but shouldn’t we fear insanity, artificial or otherwise ?

vvvv
Human vs Artificial Insanity: chicken or egg ? (Peter Sellers as Dr. Strangelove)

Some clues to answers may be found in the relationship between purposes, designs, and behaviors of intelligent devices.

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 ?

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How to 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.

Symbolic (north) vs non symbolic (south) intelligence
Symbolic (north) vs non symbolic (south) intelligence

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:

  • Unsupervised decision making: device behaviors directly governed by implicit knowledge (a).
  • 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).

Unsupervised (a), embedded (b), and distributed (c) decision making.
Unsupervised (a), embedded (b), and distributed (c) decision making.

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.

Further Readings

Semantic Web: from Things to Memes

“The new soup is the soup of human culture. We need a name for the new replicator, a noun which conveys the idea of a unit of cultural transmission, or a unit of imitation. ‘Mimeme’ comes from a suitable Greek root, but I want a monosyllable that sounds a bit like ‘gene’. I hope my classicist friends will forgive me if I abbreviate mimeme to meme…”

Richard Dawkins

Growing Memes (Wang Xingwei)

The genetics of words

The word meme is the brain child of Richard Dawkins in his book The Selfish Gene, published in 1976, well before the Web and its semantic soup. The emergence of the ill-named “internet-of-things” has brought about a new perspective to Dawkins’ intuition: given the clear divide between actual worlds and their symbolic (aka web) counterparts, why not chart human culture with internet semantics ?

With interconnected digits pervading every nook and cranny of material and social environments, the internet may be seen as a way to a comprehensive and consistent alignment of language constructs with targeted realities: a name for everything, everything with its name. For that purpose it would suffice to use the web to allocate meanings and don things with symbolic clothes. Yet, as the world is not flat, the charting of meanings will be contingent on projections with dissonant semantics. Conversely, as meanings are not supposed to be set in stone, semantic configurations can be adjusted continuously.

Internet searches: words at work

Semantic searches (as opposed to form or pattern based ones) rely on textual inputs (key words or phrase) aiming at specific reality or information about it:

  • Searches targeting reality are meant to return sets of instances (objects or phenomena) meeting users’ needs (locations, people, events, …).
  • Searches targeting information are meant to return documents meeting users’ interest for specific topics (geography, roles, markets, …).

     

What are you looking for ?
Looking for information or instances.

Interestingly, the distinction between searches targeting reality and information is congruent with the rhetorical one between metonymy and metaphor, the former best suited for things, the latter for meanings.

Rhetoric: Metonymy & Metaphor

As noted above, searches can be heeded by references to identified objects, the form of digital objects (sound, visuals, or otherwise), or associations between symbolic representations. Considering that finding referenced objects is basically a technical problem, and that pattern matching is a discipline of its own,  the focus is to be put on the third case, namely searches driven by words. From that standpoint searching the web becomes a problem of rhetoric, namely: how to use language to get rapidly and effectively the most accurate outcome to a query. And for that purpose rhetoric provides two basic contraptions: metonymy and metaphor.

Both metonymy and metaphor are linguistic constructs used to substitute a word (or a phrase) by another without altering its meaning. When applied to searches, they are best understood in terms of extensions and intensions, extensions standing for the actual set of objects and behaviors, and intensions for the set of features that characterize these instances.

Metonymy uses contiguity to substitute target terms for source ones, contiguity being defined with regard to their respective extensions. For instance, given that US Presidents reside at the White House, Washington DC, each term can be used instead.

Metonymy use physical or functional proximity (full line) to match terms (dashed line)
Metonymy use physical or functional proximity (full line) to match extensions (dashed line)

Metaphor uses similarity to substitute target terms for source ones, similarity being defined with regard to a shared subset of features, the others being ignored. Hence, in contrast to metonymy, metaphor is based on intensions.

Metaphors use analogy to maps terms whose intensions share a selected subset of features
Metaphors use analogy (dashed line) to maps terms whose intensions (dotted line) share a selected subset of features

As it happens, and not by chance, those rhetorical constructs can be mapped to categories of searches:

  • Metonymy will be used to navigate across instances of things and phenomena following structural, functional, or temporal associations.
  • Metaphors will be used to navigate across terms and concepts according to similarities, ontologies, and abstractions.

As a corollary, searches can be seen as scaffolds supporting the building of meanings.

Selected metaphors are used to extract occurrences to be refined using metonymies.
The building of meanings, back and forth between metonymies and metaphors

Memes & their making

Today general purpose search engines combine brains and brawn to match queries to references, the former taking advantage of language parsers and statistical inferences, the latter running heuristics on gigantic repositories of data and searches. Given the exponential growth of accumulated data and the benefits of hindsight, such engines can continuously improve the relevancy of their answers; moreover, their advances are not limited to accuracy and speed but also embrace a better understanding of queries. And that brings about a qualitative change: accruing improved understandings to knowledge bases provides search engines with learning capabilities.

Assuming that such learning is autonomous, self-sustained, and encompasses concepts and categories, the Web could be seen as a semantic incubator for the development of meanings. That would bear out Dawkins’ intuition comparing the semantic evolution of memes to the Darwinian evolution of living organisms.

Further Reading

External Links