ai


“A mind is a terrible thing to waste and I dread to think that it will become more so with the assistance of a collective of machines.” – J. Durant

No, my age has absolutely nothing to do with my concerns and apprehensions.  If anything its the result of applied human intelligence and the creative processes that spin off from it.   It is fair to say that today we are into an experimentation cycle and are using research as a means to see just how far we can take the science of artificial intelligence.  There will be some, like Alphabet, who are ahead of many.  Let be perfectly clear that this is a normal learning curve, not just for the innovators but will prove beneficial to later adopters as well.

I do have concern about the notion of intellectual implosion.  Intellectual implosion is where the deployment of AI becomes centered on a closed and narrow application universe.  A simple example would be the use of an AI framework that provides ‘what if guidance’ for business decisions.  It sources of input become limited to a single domain, the company.   Now you might respond to this by saying that we then need to entertain other external sources in order to further elaborate the possibilities both arithmetically as operationally.   But here is where we introduce several factors of concern involving these external sources.  Some of the concerns include;

  • Accessibility and negotiated access
  • Timely (at least as expedient as internal sources) Availability
  • Reliability
  • Balance of understanding (simple definitions)
  • Units of Measure
  • Raw and Deductive Elements

It then becomes a question of need and viewing the concerns with open eyes.  While traditional systems can contain damage, an AI system can propagate even the slightest condition extensively.   It’s not a ‘do not go there’ condition, it means that more regiment must be deployed to check, authenticate, isolate, repair, respond, or release in the course of use.

Beyond Implosion

Let me reiterate that my concerns are not my hesitation to progress.  In fact I am a strong proponent to most things that can make our lives more enriched and productive.  But with that said it places an immense burden on the shoulders of engineers, architects, administrators and management to see to it that we act responsibly.  Our concerns are not just something that is downstream but starts with our present conditions.  Nearly a day goes by when we don’t hear of some technological mishap.

  • Compromises
  • Attacks
  • Failures
  • Denial of Service
  • Efficiency Losses
  • Unreliability
  • Usability Challenges
  • Technological Excesses

are but a few of the things we face today.  How will these challenge what we do tomorrow if we are to advance in the direction of AI?  Overlooking these will be solved by AI, they will be magnified and even acted upon.  The old mnemonics GIGO (Garbage In-Garbage Out) takes on a whole new depth of meaning.  Where humans would act now a rule based action would occur in mostly a non-visible fashion.  To emphasize this point I recently read about experimentation being done at Alphabet where the AI platform had adapted itself to conditions that weren’t set in the rules (came up with its own auto response/reaction).  This for some might be a bit discerning but it shows not only the depth of capability but also the need for the extensive level of human consideration that must be exercised with each an every element.  Catch state, rule limitations, plausible creations elements and redo back checking are but a few of the safety nets that can be considered.

Human Intellectual Depth

As suggested in the previous section there is an elevated level of human thinking that is necessary in AI.  It’s not simply a language form, some rules, data inflow/outflow and a permissive deductive element… it requires real thought, collaboration, postulation, experimentation and a mindset of value generation maintenance.  Present experimentation aside we need to think abnormally.  I think of this is the sense that mere replication of present habits, conditions and outcomes may or may not be the way we need to go.  Why create a robotic arm that emulates the limitations of a human arm or for that matter why create an arm when it is possible that some other form of fetch-release-retain-manipulate mechanism might be better engineered?  In the same context, why think of need or solutions utilizing AI in the same way as we would today?  While it may be comforting that we ‘can do it’, the focus is upon outcomes and growing possibilities.   Even these have a strong potential for change.  The fluidity of AI will change us from thinking in a steady state sort of way to one in which we are driven by rapid adaptation.  The bigger limitation for mankind is the ability of adoption and possibly whether some of the adoption will have to remain vested with the technology.  It remains quite possible that some adoption will remain out of human hands because of the frequency and extent to which it is taking place.

Big Questions

Thinking about the topic should excite us but is also apt to raise up a multitude of questions, concerns and elements for investigation.  Listed below are few of the ones that I have been pondering and I hope that it can be used as a basis for your further inquisitiveness.

  1. How will AI-AI or even Global AI be negotiated?
  2. Will AI-AI/Global AI represent a definable limited and restricted access point(s)?
  3. How does Smart Cities play into AI applications?
  4. To what extent will not AI institutions hinder?  Who will be hindered, AI or the non-AI player or both?
  5. What happens when AI acts cross over that are either wrong or out of control?
  6. Will risk become normality and normality become risk (in the present context)?
  7. Rogue AI threats and issues?
  8. What other present day technologies and practices will put under strain?
  9. How much computing power will be required and the importance levied for comprehensive network connectivity?
  10. How does it affect society and human collateral?
  11. Will the divergence from task to intellectual focus enable or disable societies and companies?
  12. Pervasive and responsible constraint becomes a matter of philosophy.  Should it be regulated, mandated and reshaped?
  13. Convergent roles require collaborative AI interaction (eg. elements of software engineering such as dev-ops, verification & validation (V&V), analysis).  Considered or overlooked or simply ignored?
  14. Extent of human intrusion and at what level of intrusion?
  15. Is there a safe state for change or is it invoked real-time (and should it)?
  16. What paradigms will change, become obsolete or need to be totally created that involve not just AI but also it’s close partnership with Robotic Process Automation (RPA) and advanced analytics?
  17. Speed and quality have plagued businesses, will diametrically different levels of speed give way to quality issues (resolve, mask or create)?
  18. …. Others…. over time there are I’m sure more.  What will your additions be?

“For every complex problem there is an answer that is clear, simple, and wrong.” – H. L. Mencken and as I have often said “A complex problem doesn’t necessarily require a complex solution.” (J. Durant)

Do we move forward with AI…. YES.  Moving forward is not with reckless abandon but still following sound business and engineering tenants.

 

 

 

The maturity of a game changing technology is reflected by the non-academic clarity of how it is characterized and transitioned to the society to which it applies. – J. Durant

I consider myself very lucky that I was given the chance to work with computers at a very young age (1967) and at a time when the concept of automation was more a novelty than a pervasive reality.  It was also quite by coincidence that it was working on a project  (Ford Grant Project/BASIC compiler construction) at an institution (Dartmouth) that was a short distance from my hometown in New Hampshire.  At the time I was fascinated by what could be done with automation and how it could be used as a tool and not as a treat to society.  But I think back to some of the words that came from my mouth, as a babe of technology, and wonder whether they have had some play in all of this.  One example was while working with a bit of machine code I grew frustrated by the never ending barrage of diagnostic error conditions.  I made statements to my mentors (Drs. Kemeny and Kurtz), “if the machine is so smart to tell me I have a problem why isn’t it smart enough to correct itself?”.  While naive it certainly is within the realm of what we envision AI to do and not require the use of time nor intervention to resolve.

Today, some 40 years later, I am no less fascinated by the potential of technology.  At the same time I am also utterly disappointed with the lack of consideration given to transitioning society for its acceptance.   During the course of my life I learned that the best ideas often fail as a result of inept ability to #transition markets (also known to some as market conversion).  Even the humble personal computer had its moments until manufacturers demystified the technology and produced an affordably simplistic paradigm.

A Bit of History

Artificial intelligence (AI) had some early roots at Dartmouth in 1956.  The basis of academic research surrounding the use of computers to perform tasks lead scientists on a pursuit of assimilating thinking (or what we now refer to as learning machines).  Since that time there has been the ebb and tide of AI exploration.  My first exploits (1970s) were with a simple personal computer based package produced by Visi Corp. called ‘VisiExpert’.  It was a rule based solution that would allow you to create lists of conditions and correlate them to one or more rule bases (or in this particular case a simple set of tables).  The example that the package came with was the pairing of wines with cheeses.  I’m sure we could have done something similar with microbreweries and draft choices, or with employment roles and applicants.  The product never got much acclaim not because of its merits but because society was still trying to get their grasp around spreadsheets and word process solutions (databases would come later as they contributed to mailing and label list processing and table feeding for spreadsheet analytic review).  We saw a reappearance of AI in the form of Ada, a high-level Pascal derivative that introduced the concept of ‘object oriented’.  For some the provided a stepping off point from linear programming paradigms and created the potential for reference-able containers (or objects in terms of 1980s nomenclature).  But was this really AI or was it that the reference-able elements created the impression that conditions could invoke established constructs?

Since this time we have seen several what has been referred to ‘AI winters’.  In simple terms these were cooling off periods cause by economics, introduction failures and the reduction in academic and industrial research.  In the corners however there have been those individuals who sustained the course and continued to chip away at the rough hewn work of their predecessors.

Why Now?

There has been allot of topically provocations that have brought AI back to the forefront.  Utilization of big data, robotics advancement from mechanical to intellectual and demand caused by shortfalls in talent resources.   So while society laments about the concerns over loss of jobs and potential infringement on confidentiality the real culprits are in fact self-inflicted.   As I quoted at the onset, the conceptual framework that reflects the interrelationships of technologies (robotics, AI, analytics/data) remains loosely defined and fraught with personal opinion based prejudice.  It isn’t without coincidence that the few models that exist have yet to be proven in either an industrial or an academic research setting. so  how far would you lay trust unless you are also in an exploratory mode?

Let me get to the point on the two concerns voiced by the person on the street.  One – job loss (YES) but this will be replaced not just by people to care and feed AI but also those who will be engaged in different, yet to be defined jobs.  Concern two – confidentiality (YES) but fear not that its a matter of sharing by others but simply a sharing based on the need to know.  Not this sounds a bit like you need to grant permission.  But in today’s world the act of engagement is an act of implied permission.  We must cast away 20th century thinking if we wish to exploit 21st century services.  I recently read a comedic piece posted by Phil Fersht, CEO and Chief Architect of HfS Research involving a phone order being placed for pizza delivery.  It is as follows:

– Hello! Gordon’s pizza?
– No sir it’s Google’s pizza.
– So it’s a wrong number?
– No sir, Google bought it.
– OK. Take my order please .. – Well sir, you want the usual? – The usual? You know me? – According to our caller ID, in the last 12 times, you ordered pizza with cheeses, sausage, thick crust
– OK! This is it
– May I suggest to you this time ricotta, arugula with dry tomato?
– No, I hate vegetables
– But your cholesterol is not good
– How do you know?
– Through the subscribers guide. We have the result of your blood tests for the last 7 years
– Okay, but I do not want this pizza, I already take medicine
– You have not taken the medicine regularly, 4 months ago, you only purchased a box with 30 tablets at Drugsale Network
– I bought more from another drugstore
– It’s not showing on your credit card
– I paid in cash
– But you did not withdraw that much cash according to your bank statement
– I have other source of cash
– This is not showing as per you last Tax form unless you got it from undeclared income source
-WHAT THE HELL? Enough! I’m sick of Google, Facebook, twitter, WhatsApp. I’m going to an Island without internet,where there is no cell phone line and no one to spy on me
– I understand sir, but you need to renew your passport as it has expired 5 weeks ago..

Although totally comedic it remains plausible.  But again my question repeats itself, is this really AI or is it simply a form of what is also being touted by the label of DevOps (Development Operations)?

More Work Required

Aside from the need for framework clarity there also is the question of transitioning of society to embrace the emergent AI paradigm.  People aren’t looking to be sold on the merits or wowed by simple examples.  What societies need certainty about is the clarity of the vision and how control can be maintained.  Images of runaway robots, inaccuracies, false actions and other elements of mistrust abound.  These represent apprehensions created from shortcomings that exist today without AI that can only be elevated with the deployment of a data driven, rule based solution.  Certainty must be earned and shown.

I also believe, in the field of many-many things that need to be done to promote and empower AI, that we need to be looking at things very differently.  If something is done in a certain way do we still need to do that?  Do we need to ask the question if we already have the information or do we need to do it differently or do we need to do it at all?  This isn’t just pertinent to AI but can be equally asked for robotics or even advance analytical applications.  Do we need to perform a six loop analytical calculation or is sufficient for us to simply be alerted and observe a present condition in anticipation of a potential outcome?  It’s these sorts of question but more importantly the thinking mind set that will put advanced technologies like AI into the realm of usable/plausible.

Next?

Of course until finality.   I believe we need AI, not because I’m a technologist that is fascinated by potential but because I am concerned.  I’m not concerned about job losses or confidentiality they have exist, will continue to exist and that is just the way it is.  If you are concerned about job losses you can escape it with being self-employed, you can lose that too with the stroke of a governmental pen or the lack of personal attention to the care and feeding of an enterprise.  You can’t avoid the loss in confidentiality by believing you can lock it away, life in the 21st century is a transparent box whether you want to believe it or not.  If you want total confidentiality then become a hermit but even the hermit has someone who will have your fingerprint.  AI like robotics and analytics has a place to create an opportunity for thinking.  Whether this is focused upon innovation, optimizing, creating or simple recreating the ability to put to task routine parts of our life should be embraced.  Those that chose not to embrace will be destine to a emerging nation paradox where brute force in numbers will overlook efficiency though work augmentation by mechanization.   You can chose to break up concrete with a workforce of thousands or employ a machine that will convert it all to rubble in a matter of hours.  The decision is ours and ours alone.

Yes there will be a next.  ‘The next’ will explore the clear thinking necessary to create a outcome based AI and not a crafted emulation of what we think is human though and logic processes.  A look into the rough constructs necessary to transition from legacy intellect (automation and non-automated systems) to the next forms of creating a durable AI learning machine paradigm.

Till then keep thinking and dreaming (unconventionally).

In the spring of 2011 Frost & Sullivan held a BPO event in Manila.  Held on the heals of the much welcomed attainment of #1 global status as the lead in BPO services the Philippine BPO celebration was in full swing.  There is nothing like a celebration in the Philippines and nothing can dampen the opportunity or the spirit of such revelry.  So it comes as no big surprise that the words I shared at that event were drowned out by the merriment that was being enjoyed.  It was no small feat that rising to this level of achievement over India had taken place.  The future was all bright and there was a sense of invincibility.

While some make predictions on the basis of a hope that no one will remember when it doesn’t take place.  I chose my words careful in issuing a warning that while celebrations are taking place it is also the time to look towards reinvention.  Clients were investing heavily in technology and in doing so it remained committed to solutions that provided long term value and not simply long term reduced cost containment.  This being the case in supporting a month-on-month commitment to manual and voice based BPO support.  The investment in technology would inevitably lead to some form of replacement for shifting from people to an asset invested technology solution.  At that time we weren’t talking artificial intelligence or robots, I was simply looking at a commitment to technologies that could dutifully provided consistent support with a minimum of added capital investment (vs. expensed loss).

Now that the vogue technologies have reached a level of interest concerns have been raised.  IBAP (Information Technology and Business Processing Association of the Philippines – 5/12/2017) is stepping up measures to address the impact of artificial intelligence (AI) on the BPO industry.  Conferences like the Digital Transformation Summit are also adding to the pace by increasing the interchange on what measures need to be taken now in order to combat the effect that might occur on the BPO industry.  Leading Data Analytics Super Star Dan Mayer has weighed in with the importance to embrace the change by shifting focus to data analytics services.  Finally, even the government (DOST – 2/20/2017 – Fortunato dela Peña) has jumped on board to address the real possibilities of AI based impact on the BPO sector.  So the question is why did the message miss being heard?  But even more importantly why didn’t any of the leading analyst groups (Gartner, McKinsey, HfS…) see or hear it either?  It appears as though there was an intermediate fixation with a much broader expanse of technologies dealing with the cloud, Internet-of-Things (IoT), Big Data and Shared Services leaving way for those up-and-coming technologies to remain as background noise.

I don’t want to be too cynical but the story doesn’t end with an acknowledgement of change.  Its not even going to end with a concern and a grass roots movement to follow a correction path.  What is about to happen will be a groundswell of concern by executive management to not only understand the potential accompanied by the ebb-and-tide effect of change.  It will involve significant strategic decisions being made that will lead to tactical plans being crafted.   History however is a horrifying reminder that shifts, such as this have painful paths.  These are littered by potholes of interruptions and a steep learning curves.

There are many questions that remain unanswered.

  • Can #BPO as a service transition to providing high value/high risk value services (this sector was once called ‘knowledge based outsourcing’… #KPO)?
  • Will customers trust offshore institutions with this level of operational intimacy and have the stakes changed for what a provider will be required to maintain (e.g. institutional security)?
  • Cradle to grave time… trained to capability (is it doable given the current state of AI and robotics considerations)?
  • What mindset change will be necessary to make the shift?  Those that have already set an information technology business unit will find the shift easier.  HOWEVER, it isn’t just about technology enable AI, robotics or analytics its about understanding the various industries being serviced.
  • What should be happening now?  Obviously action but what kind?  Waiting is not suitable at the present, a plan needs to be formed.  But more important is the need to understand that success requires intense consideration for #transitioning.

As early as May 2017 the DTI continues to track progress and goals of BPO as though nothing has changed.  This is of concern, it also echos my concern why the messages of dutiful messengers is not reaching those that can address change and the elements necessary to respond to the ever changing technology and commerce worlds.   You cannot rely upon revenue predictions or employment numbers to remain the same, even if you capture a significant segment of the AI/Robotics/Analytics markets.  There will be reductions in head counts, increases in both revenue and cost predictions and an investment in capital assets to support these sciences.  You cannot expect that these will follow the historical ‘life-and-drop’ paradigm of BPO.  There will be issues of trans border data transfer, security, time sensitivity, ground up development, repository control and matters as simple as general understanding of socio-business transference.  In short, allot to be done in a short period of time.

In closing, I remember the day when the Philippines became #1 in the BPO sector.  I remember some of my Indian clients exclaiming that it was always their plan to shift away from BPO (which I considered to be just a sour grapes comment).  Was it that they saw something that others were seeing but from a different perspective, was it that BPO was the low end of the outsourcing food chain in terms of complexity and cost or was it simply ill sentiments?  Regardless, we need to look at conditions not with a focus on critique but with a look towards the potential opportunities that need to be acted upon now.

#BPO #Analytics #AI #Robotics #Transition #TransitionalSciences

From that single organic nodule of package life an offspring is produced, or not.  This week has been particular enriched with insight and wisdom, some unsolicited and others remaining a bit of a quandary.

This week Hubert Dreyfus passed away.  A professor and a human being, a philosopher who challenged us to consider the practical limits of computers.  Aside from his academic acclaim and intense experience as a human being his message was far deeper than the antidotal point on message.  Yes, he asked us to consider the depth of use and application of technology in our personal lives, business and society.  He appears to have know the extent of human temptation and addiction.  Today we are drawn to the light like a moth to a candle, embracing the new and forsaking the tried and true. Is it because we are afraid of being left behind or are we considering the real vs. illusionary value of it all?  Or is it that we are sitting on the edge of restrained obsolesce and the jump seems right even if we might be stepping out into the darkness with hardly a basis of comfort.  Although I never had the chance to meet him in person I enjoy some brief ‘technological’ interchanges to better understand his stance on artificial intelligence.  During my advanced studies he provided invaluable opinions about the difference between machine learning, the need for social contact and interchange, along with some quite private discussions about risk.   It was a bit unnerving to consider to realize which was always in front of my eyes that the success or destruction is not in the device but the enablement provided by the human steward.   To me it wasn’t just a learned opinion, although gifted with experience at MIT and Rand, but his deep and profound thought given on the subject.

The Junk Drawer

Few people do not have a junk drawer.  Whether it be made up of household repair items or kitchen gadgets we all manage to eek out a space to stash away an much anticipated device of salvation.  Likewise we see the emergence of the same for abandoned technology.  Cables, cell phones, chargers and various explored ancillary devices find their way to ‘the drawer’.  We pretty much know that what goes in is unlikely to come and be used, time is not on the side of technology and the obsolescence that occurs.  But our frugal nature suggests that we or someone might find a need or use for these cast aside items.   I mention the junk drawer in a broader context that we have lots of technologies that have come and gone.  Often replaced by what appeared to be superior solutions, that later prove to be less superior that even more future ones.

I think back to my very first expose to artificial intelligence in the 1970s.  It was with a very simple but quite illustrative product called VisiEXPERT produced by the now defunct Visi Corp.  The product was a very rudimentary rule based artificial intelligent (AI) solution.  Its operational example used the pairing of cheese with wine and allowed for the addition of new elements and relationships.  At that time it was robust enough to learn or be driven by inference, it required aggressive assertions in order to advance outcome delivery.  Later in the 1980s we saw the ADA and LISP development as service languages support for data driven behavioral modification processes.  In both cases their emergence was not months but decades in the making and although solidly formed it struggles to produce a groundswell of disciples.

So our junk drawer continues to grow and with this we see the rekindling of interest.  For those in the AI community the drive is not so much from the technology as it is the promotional support of the business community.  AI is talked about in a single breath with learning machines but how does this all fit together with life?


Embryonic Appearance

This past week (May 5, 2017) I read a piece that Scott Ambler a legendary agile disciple wrote about the “Darth of Qualified Agile Coaches”.  The points reflected a condition in which labels get applied but the lack of substantive value creates an abundance of non-value.  Even though the focus was on promoting professional qualifications there remains a quite similar condition as it pertains to AI.  We see a plethora of AI involved entities who for all intense purposes are new market entrants.  But let us not also be fooled by capabilities driven by attending a course, as Scott pointed out, it involves intense and purposeful experience to fulfill the obligations as an expert in a given field.  Even in my case with over 40 years in the information technology field and intensely active engagement in AI related activities I still feel I have lots to learn.  Its from this vantage point I wish to ask the question about capabilities.

When I think in quite simplistic terms as pertaining to AI I think of ‘the seed’.  That kernel catalyst that will drive the growth of technology based learning.  I also think about risks and what level of permissiveness that we should allow the AI model to undertake.  Embedded in that kernel is data and we need to be astutely aware that data is not always clean, controlled and ready for use.  It necessitates sterilization to make it ready and all must be done in as near to real-time as possible.  Momentary lapses in time or hesitation in commitment of cleanliness jeopardizes the AI value proposition.

To further emphasize this point I will refer you back to some earlier writings on did on advanced analytics.  I find that while analytics also makes use of data, it also has the potential to become a close partner with AI and the learning machine.  As stated in this earlier article (10/15/2014) the real next generation is not in preceptor or predictive analytics, it in the real of preemptive which takes action vs. alerting us of or indicating that a condition has the potential to occur.  In short, the model reflected the raw basis of the learning machine.  While not centered on growth of knowledge and more centered on action the elements exists for the feeding the AI model through preemptive analytics.  I also contend that anything short of being soundly grounded preemptive logic, including predictions is really shaky ground for AI.  The basis of this opinion is the potential for runaway illogical reaction by the AI model paradigm.  In the most simple of examples I think of how I look at a situation and react to later discover it was not exactly as I saw it.  If this had been applied to an AI scenario who knows whether reversion would have occurred and if it did was it through intervention or a separate set of rules to deal with error management?

Conclusion… Just a Wee Bit of Fertilizer

No planting would be complete without a bit of care and accelerated nurturing.  AI is no exception.  In this context our growth enhancement hormone is a combination of pragmatic engineering, anticipatory examination and a purposeful examination of our present state of intellectual discourse.  Most would agree that humans make errors and thus anything we do is both prone to error creation but also possibly error propagation caused by what we presently do.  One cannot view human emulated thought process as simplistic.  Even the most rudimentary movements, considerations, evaluations and commitments entail literally thousands of possible paths and choices.  While technology can handle volume and responsiveness it remains the dutiful obligation of humans to craft the paths, the gates of decisions, correlation of relationships and discernment of probable paths with rational and common place or dissenting opposition.  Its for these reasons that the engagement of personnel involved with AI cannot be causal technological Spartans.  Technical proficiency will remain important but the management of the operating intellectual paradigm will remain critically essential.  This involves raw in-sources from data, progressive analytics, paradigm development and deployment to error corrective models as the minimum for sound control.   Even then understanding is bounded by experience and therefore secondary ability to communicate, examine and model will help bolster the skill set needed for application of AI.

To the consumer there may be some worry or dissension, much as was the case with the use of voice response systems.  People are hesitant to embrace what is uncomfortable or viewed as inadequate by comparison to what they have grown accustom.  So while AI proponents dabble in the science there remains a great degree of need for transitioning of people to a new world order.  Some may enjoy a more hidden affront to consumers where others will be challenged regularly by real-time consumerism exchange.  Simply remember that all things are solvable as long as we understand the nature of the best, the human condition.