Archive for the ‘Artificial Intelligence’ Category

Self-driving Cars & Turing’s Imitation Game

May 27, 2018

Self-driving vehicles should behave like humans, here is how to teach them so.

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Preamble

The eventuality of sharing roads with self-driven vehicles raises critical issues, technical, social, or ethical. Yet, a dual perspective (us against them) may overlook the question of drivers’ communication (and therefore behavior) because:

  • Contrary to smart cars, human drivers don’t use algorithms.
  • Contrary to humans, smart cars are by nature unethical.

If roads are to become safer when shared between human and self-driven vehicles, enhancing their collaboration should be a primary concern.

Driving Is A Social Behavior

The safety of roads has more to do with social behaviors than with human driving skills, as it depends on human ability, (a) to comply with clearly defined rules and, (b) to communicate if and when rules fail to deal with urgent and exceptional circumstances. Given that self-driving vehicles will have no difficulty with rules compliance, the challenge is their ability to communicate with other drivers, especially human ones.

What Humans Expect From Other Drivers

Social behavior of human drivers is basically governed by clarity of intent and self-preservation:

  1. Clarity of intent: every driver expects from all protagonists a basic knowledge of the rules, and the intent to follow the relevant ones depending on circumstances.
  2. Self-preservation: every driver implicitly assumes that all protagonists will try to preserve their physical integrity.

As it happens, these assumptions and expectations may be questioned by self-driving cars:

  1. Human drivers wouldn’t expect other drivers to be too smart with their interpretation of the rules.
  2. Machines have no particular compunction with their physical integrity.

Mixing human and self-driven cars may consequently induce misunderstandings that could affect the reliability of communications, and so the safety of the roads.

Why Self-driving Cars Have To Behave Like Human Drivers

As mentioned above, driving is a social behavior whose safety depends on communication. But contrary to symbolic and explicit driving regulations, communication between drivers is implicit by necessity; if and when needed, it is in urgency because rules are falling short of circumstances: communication has to be instant.

So, since there is no time for interpretation or reasoning about rules, or for the assessment of protagonists’ abilities, communication between drivers must be implicit and immediate. That can only be achieved if all drivers behave like human ones.

Turing’s Imitation Game Revisited

Alan Turing designed his Imitation Game as a way to distinguish between human and artificial intelligence. For that purpose a judge was to interact via computer screen and keyboard with two anonymous “agents”, one human and one artificial, and to decide which was what.

Extending the principle to drivers’ behaviors, cars would be put on the roads of a controlled environment, some driven by humans, others self-driven. Behaviors in routine and exceptional circumstances would be recorded and analyzed for drivers and protagonists.

Control environments should also be run, one for human-only drivers, and one with drivers unaware of the presence of self-driving vehicles.

Drivers’ behaviors would then be assessed according to the nature of protagonists:

DeciMakingTaxo

  • H / H: Should be the reference model for all driving behaviors.
  • H / M: Human drivers should make no difference when encountering self-driving vehicles.
  • M / H: Self-driving vehicles encountering human drivers should behave like good human drivers.
  • Ma / Mx: Self-driving vehicles encountering self-driving protagonists and recognising them as such could change their driving behavior providing no human protagonists are involved.
  • Ma / Ma: Self-driving vehicles encountering self-driving protagonists and recognising them as family related could activate collaboration mechanisms providing no other protagonists are involved.

Such a scheme could provide the basis of a driving licence equivalent for self-driving vehicles.

Self-driving Vehicles & Self-improving Safety

If self-driving vehicles have to behave like humans and emulate their immediate reactions, they may prove exceptionally good at it because imitation is what machines do best.

When fed with data about human drivers behaviors, deep-learning algorithms can extract implicit knowledge and use it to mimic human behaviors; and with massive enough data inputs, such algorithms can be honed to statistically perfect similitude.

That could set the basis of a feedback loop:

  1. A limited number of self-driving vehicles (properly fed with data) are set to learn from communicating with human drivers.
  2. As self-driving vehicles become better at the imitation game their number can be progressively increased.
  3. Human behaviors improve influenced by the growing number of self-driving vehicles, which adjust their behavior in return.

That is to create a virtuous feedback for roads safety.

Further Reading

 

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Ontologies as Productive Assets

January 22, 2018

Preamble

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

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

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

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

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

Ontologies as Assets

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

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

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

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

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

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

Ontologies Benefits

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

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

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

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

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

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

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

A Proof of Concept

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

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

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

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

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

CaKe_DataInfoKnow

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

Further Reading

External Links

2018: Clones vs Octopuses

December 4, 2017

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

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

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

Machine Learning & The Economics of Clones

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

Knowledge & Competition

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

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

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

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

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

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

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

Organization & Innovation

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

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

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

That organizational paradigm puts a double restraint on innovation:

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

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

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

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

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

Learning from the Octopus

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

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

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

Further Reading

External Links

Why Virtual Reality (VR) is Late

July 25, 2017

Preamble

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

Detached Reality (N.Ghesquiere, G.Coddington)

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

Virtual vs Augmented

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

Consciousness Cannot Wait

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

Aesop’s Lesson

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

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

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

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

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

Further Reading

External Links

Deep Blind Testing

March 21, 2017

Preamble

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

Unexpected Outcome (Ariel Schlesinger)

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

What to Look For

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

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

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

Automated Software Testing

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

Reconnaissance: Known Knowns

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

Exploration: Known Unknowns

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

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

Blind Errands: Unknown Unknowns

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

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

From Non-regression to Self-improvement

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

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

Further Reading

 

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)

Silent Sounds (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

2017: What Did Your Learn Last Year ?

December 15, 2016

Sometimes the future is best seen through rear-view mirrors; given the advances of artificial intelligence (AI) in 2016, hindsight may help for the year to come.

(J.Bosh)

Deep Mind Learning (J.Bosh)

Deep Learning & the Depths of Intelligence

Deep learning may not have been discovered in 2016 but Google’s AlphaGo has arguably brought a new dimension to artificial intelligence, something to be compared to unearthing the spherical Earth.

As should be expected for machines capabilities, artificial intelligence has for long been fettered by technological handcuffs; so much so that expert systems were initially confined to a flat earth of knowledge to be explored through cumbersome sets of explicit rules. But exponential increase in computing power has allowed neural networks to take a bottom-up perspective, mining for implicit knowledge hidden in large amount of raw data.

Like digging tunnels from both extremities, it took some time to bring together top-down and bottom-up schemes, namely explicit (rule-based) and implicit (neural network-based) knowledge processing. But now that it comes to fruition, the alignment of perspectives puts a new light on the cognitive and social dimensions of intelligence.

Intelligence as a Cognitive Capability

Assuming that intelligence is best defined as the ability to solve problems, the first criterion to consider is the type of input (aka knowledge) to be used:

  • Explicit: rational processing of symbolic representations of contexts, concerns, objectives, and policies.
  • Implicit: intuitive processing of factual (non symbolic) observations of objects and phenomena.

That distinction is broadly consistent with the one between humans, seen as the sole symbolic species with the ability to reason about explicit knowledge, and other animal species which, despite being limited to the processing of implicit knowledge, may be far better at it than humans. Along that understanding, it would be safe to assume that systems with enough computing power will sooner or later be able to better the best of animal species, in particular in the case of imperfect inputs.

Intelligence as a Social Capability

Alongside the type of inputs, the second criterion to be considered is obviously the type of output (aka solution). And since classifications are meant to be built on purpose, a typology of AI outcomes should focus on relationships between agents, humans or otherwise:

  • Self-contained: problem-solving situations without opponent.
  • Competitive: zero-sum conflictual activities involving one or more intelligent opponents.
  • Collaborative: non-zero-sum activities involving one or more intelligent agents.

That classification coincides with two basic divides regarding communication and social behaviors:

  1. To begin with, human behavior is critically different when interacting with living species (humans or animals) and machines (dumb or smart). In that case the primary factor governing intelligence is the presence, real or supposed, of beings with intentions.
  2. Then, and only then, communication may take different forms depending on languages. In that case the primary factor governing intelligence is the ability to share symbolic representations.

A taxonomy of intelligence with regard to cognitive (reason vs intuition) and social (symbolic vs non-symbolic) capabilities may help to clarify the role of AI and the importance of deep learning.

Between Intuition and Reason

Google’s AlphaGo astonishing performances have been rightly explained by a qualitative breakthrough in learning capabilities, itself enabled by the two quantitative factors of big data and computing power. But beyond that success, DeepMind (AlphaGo’s maker) may have pioneered a new approach to intelligence by harnessing both symbolic and non symbolic knowledge to the benefit of a renewed rationality.

Perhaps surprisingly, intelligence (a capability) and reason (a tool) may turn into uneasy bedfellows when the former is meant to include intuition while the latter is identified with logic. As it happens, merging intuitive and reasoned knowledge can be seen as the nexus of AlphaGo decisive breakthrough, as it replaces abrasive interfaces with smart full-duplex neural networks.

Intelligent devices can now process knowledge seamlessly back and forth, left and right: borne by DeepMind’s smooth cognitive cogwheels, learning from factual observations can suggest or reinforce the symbolic representation of emerging structures and behaviors, and in return symbolic representations can be used to guide big data mining.

From consumers behaviors to social networks to business marketing to supporting systems, the benefits of bridging the gap between observed phenomena and explicit causalities appear to be boundless.

Further Reading

External Links

Business Agility vs Systems Entropy

November 28, 2016

Synopsis

As already noted, the seamless integration of business processes and IT systems may bring new relevancy to the OOAD (Observation, Orientation, Decision, Action) loop, a real-time decision-making paradigm originally developed by Colonel John Boyd for USAF fighter jets.

Agility: Orientation (Lazlo Moholo-Nagy)

Agility & Orientation (Lazlo Moholo-Nagy)

Of particular interest for today’s business operational decision-making is the orientation step, i.e the actual positioning of actors and the associated cognitive representations; the point being to use AI deep learning capabilities to surmise opponents plans and misdirect their anticipations. That new dimension and its focus on information brings back cybernetics as a tool for enterprise governance.

In the Loop: OOAD & Information Processing

Whatever the topic (engineering, business, or architecture), the concept of agility cannot be understood without defining some supporting context. For OODA that would include: territories (markets) for observations (data); maps for orientation (analytics); business objectives for decisions; and supporting systems for action.

OODA loop and its actual (red) and symbolic (blue) contexts.

OODA loop and its actual (red) and symbolic (blue) contexts.

One step further, contexts may be readily matched with systems description:

  • Business contexts (territories) for observations.
  • Models of business objects (maps) for orientation.
  • Business logic (objectives) for decisions.
  • Business processes (supporting systems) for action.
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The OODA loop and System Perspectives

That provides a unified description of the different aspects of business agility, from the OODA loop and operations to architectures and engineering.

Architectures & Business Agility

Once the contexts are identified, agility in the OODA loop will depend on architecture consistency, plasticity, and versatility.

Architecture consistency (left) is supposed to be achieved by systems engineering out of the OODA loop:

  • Technical architecture: alignment of actual systems and territories (red) so that actions and observations can be kept congruent.
  • Software architecture: alignment of symbolic maps and objectives (blue) so that orientation and decisions can be continuously adjusted.

Functional architecture (right) is to bridge the gap between technical and software architectures and provides for operational coupling.

Business Agility: systems architectures and business operations

Business Agility: systems architectures and business operations

Operational coupling depends on functional architecture and is carried on within the OODA loop. The challenge is to change tack on-the-fly with minimum frictions between actual and symbolic contexts, i.e:

  • Discrepancies between business objects (maps and orientation) and business contexts (territories and observation).
  • Departure between business logic (objectives and decisions) and business processes (systems and actions)

When positive, operational coupling associates business agility with its architecture counterpart, namely plasticity and versatility; when negative, it suffers from frictions, or what cybernetics calls entropy.

Systems & Entropy

Taking a leaf from thermodynamics, cybernetics defines entropy as a measure of the (supposedly negative) variation in the value of the information supporting the control of viable systems.

With regard to corporate governance and operational decision-making, entropy arises from faults between environments and symbolic surrogates, either for objects (misleading orientations from actual observations) or activities (unforeseen consequences of decisions when carried out as actions).

So long as architectures and operations were set along different time-frames (e.g strategic and tactical), cybernetics were of limited relevancy. But the seamless integration of data analytics, operational decision-making, and IT supporting systems puts a new light on the role of entropy, as illustrated by Boyd’s OODA and its orientation component.

Orientation & Agility

While much has been written about how data analytics and operational decision-making can be neatly and easily fitted in the OODA paradigm, a particular attention is to be paid to orientation.

As noted before, the concept of Orientation comes with a twofold meaning, actual and symbolic:

  • Actual: the positioning of an agent with regard to external (e.g spacial) coordinates, possibly qualified with the agent’s abilities to observe, move, or act.
  • Symbolic: the positioning of an agent with regard to his own internal (e.g beliefs or aims) references, possibly mixed with the known or presumed orientation of other agents, opponents or associates.

That dual understanding underlines the importance of symbolic representations in getting competitive edges, either directly through accurate and up-to-date orientation, or indirectly by inducing opponents’ disorientation.

Agility vs Entropy

Competition in networked digital markets is carried out at enterprise gates, which puts the OODA loop at the nexus of information flows. As a corollary, what is at stake is not limited to immediate business gains but extends to corporate knowledge and enterprise governance; translated into cybernetics parlance, a competitive edge would depend on enterprise ability to export entropy, that is to decrease confusion and disorder inside, and increase it outside.

Working on that assumption, one should first characterize the flows of information to be considered:

  • Territories and observations: identification of business objects and events, collection and analysis of associated data.
  • Maps and orientations: structured and consistent description of business domains.
  • Objectives and decisions: structured and consistent description of business activities and rules.
  • Systems and actions: business processes and capabilities of supporting systems.
cccc

Static assessment of technical and software architectures for respectively observation and decision

Then, a static assessment of information flows would start with the standing of technical and software architecture with regard to competition:

  • Technical architecture: how the alignment of operations and resources facilitate actions and observations.
  • Software architecture: how the combined descriptions of business objects and logic facilitate orientation and decision.

A dynamic assessment would be carried out within the OODA loop and deal with the role of functional architecture in support of operational coupling:

  • How the mapping of territories’ identities and features help observation and orientation.
  • How decision-making and the realization of business objectives are supported by processes’ designs.
ccccc

Dynamic assessment of decision-making and the realization of business objectives’ as supported by processes’ designs.

Assuming a corporate cousin of  Maxwell’s demon with deep learning capabilities standing at the gates in its OODA loop, his job would be to analyze the flows and discover ways to decrease internal complexity (i.e enterprise representations) and increase external one (i.e competitors’ representations).

That is to be achieved with the integration of  operational analytics, business intelligence, and decision-making.

OKBI_BIDM

Seamless integration of operational analytics, business intelligence, and decision-making.

Further Readings

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