E-book on AI systems by Kyield


My ebook “Ascension to a Higher Level of Performance” is now available to the public.

Learn about the background of Kyield and the multi-disciplinary science involved with AI systems, with a particular focus on AI augmentation for knowledge work and how to achieve a continuously adaptive learning organization (CALO).

 

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TABLE OF CONTENTS

INTRODUCTION ……………………………………………………………………………………..

REVOLUTION IN IT-ENABLED COMPETITIVENESS …………………………………………..

POWER OF TRANSDISCIPLINARY CONVERGENCE …………………………………………..

MANAGEMENT CONSULTING ……………………………………………………………………

COMPUTER SCIENCE AND PHYSICS…………………………………………………………….

ECONOMICS AND PSYCHOLOGY ………………………………………………………………..

LIFE SCIENCES AND HEALTHCARE……………………………………………………………

PRODUCTS AND INDUSTRY PLATFORMS…………………………………………………….

KYIELD OS …………………………………………………………………………………………..

THE KYIELD PERSONALIZED HEALTHCARE PLATFORM ………………………………….

ACCELERATED R&D: THE LIVING ONTOLOGY ………………………………………………

SPECIFIC LIFE SCIENCE AND HEALTHCARE USE CASES …………………………………

BANKING AND FINANCIAL SERVICES ………………………………………………………..

THE PILOT PROCESS ……………………………………………………………………………..

EXAMPLE: BANKING, PHASE 1…………………………………………………………………

PHASE 2…………………………………………………………………………………………….

PHASE 3…………………………………………………………………………………………….

PHASE 4…………………………………………………………………………………………….

CONCLUSION: IN THIS CASE THE END JUSTIFIES THE MEANS …………………………21

 

Visit our learning center to download this ebook and view other publications from Kyield at the confluence of AI systems, crisis prevention, risk management, security, productivity and organizational management.

Priority Considerations When Investing in Artificial Intelligence


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After several decades of severe volatility in climatology across the fields involved with artificial intelligence (AI), we’ve finally breached the tipping point towards sustainability, which may also represent the true beginnings for a sustainable planet and humanity.

Recent investment in AI is primarily due to the formation of viable components in applied R&D that came together through a combination of purposeful engineering and serendipity, resulting in a wide variety of revolutionary functionality.  However, since investment spikes also typically reflect reactionary herding, asset allocation mandates, monetary policy, and opaque strategic interests among other factors, caution is warranted.

The following considerations are offered as observations from my perch as an architect and founder who has been dealing with many dozens of management teams over the last few years.  The order of priority will not be the same for each organization, though in practice are usually similar within industries.

Risk Management and Crisis Prevention

The nature of AI when combined with computer networking and interconnected emerging technology such as cryptography, 3D printing, biotech and nanotech represents perhaps the most significant risk and opportunity in history.

While the global warnings on AI are premature, often inaccurate, and appear to be a battle for control, catastrophic risk for individual companies is considerable.  For most organizations the risk should be manageable, though not with traditional strategies and tactics.  That is to say that AI within the overall environment requires aggressive behavioral change outside comfort zones.

Recent examples of multi-billion dollar investments in AI include Google, IBM, and Toyota, though multi-million USD investments now number in the thousands if we include internal investments and venturing.  To be sure much of this investment is reactionary and wasteful, but the nature of the technology only requires a small fraction of the functionality to prove successful, which can be decisive in some markets.

For appreciation of the sea change, common functions employed today were deemed futuristic and decades in the future just three or four years ago.  So it’s not surprising that a majority of senior management teams we’ve engaged in the last two years confirm that AI is among their highest priorities, though I must say some are still moving too slow. We’ve observed a wide range of actions from window dressing for Wall Street to confusing to brilliant.

The highest return on investment possible is prevention of catastrophic events, whether an industrial accident, lone wolf bad actors, systemic fraud, or disruption leading to displacement or irrelevance.  Preventable losses in the tens of billions in single organizations have become common.  Smaller events that require a similar core design to prevent or mitigate are the norm rather than the exception, but are often nonetheless career ending in hindsight, and can be fatal to all but the most capitalized companies.  We’ve experienced several multi-billion dollar events in former management teams that likely could have been prevented if they had moved more quickly, including unfortunately loss of lives, which is what gets me up at 3am.

Talent War

An exponential surge in training is underway in machine learning (ML) along with substantial funding in tools, so we can expect the cost of more common technical skills will begin to subside, while other challenges will escalate.

“In their struggle against the powers of the world around them their first weapon was magic, the earliest fore-runner of the technology of to-day.  Their reliance on magic was, as we suppose, derived from their overvaluation of their own intellectual operations, from their belief in the ‘omnipotence of thoughts’, which, incidentally, we come upon again in our obsessional neurotic patients.’’ — Sigmund Freud, 1932.

The challenge is that the magic of the previous century has evolved and matured by necessity from the efforts of many well meaning scientists, but some of the magicians on stage still suffer from neurosis.  Technology is evolving much faster than humans or organizations.

Examples of talent issues commonly found in our communications:

  • Due to long AI winters followed by the recent tipping point in viability, the number of individuals with extensive experience is very small, and most are at a few tech companies attempting to displace other industries.

  • Industry-specific expertise beyond search and robotics is rare and very specialized with little understanding of enterprise-wide potential.
    An exceptional level of caution is warranted on conflicts in AI counsel due to competency and pre-existing alliances.

  • Despite efforts to exploit emerging opportunity, ability to think strategically in AI systems appears to be almost non-existent.

  • CTOs may win some key battles in tactical applications, but CEOs must win the wars with organizational AI systems.

The talent war for the top tier in AI is so severe with such serious implications that hundreds of millions USD have been invested for key individuals.  Of course very few organizations can compete in talent auctions, which is one reason why the Kyield OS is so important.  We automate many AI functions that will be common in organizations and their networks for the foreseeable future while also making deep dive custom algorithmics simpler and more relevant.

Historic Opportunity

Not only is AI a classic case of ‘offense is the best defense’, when designed and executed well to enhance knowledge workers and customers, the embedded intelligence with prescriptive analytics can accelerate discovery, uncover previously unknown opportunities, providing historically rare potential for new businesses, spin outs, joint ventures and other types of partnering.  Managed well, this is precisely what many companies and national economies need.

Architectural Design

Impacting every part of distributed organizations, the importance of architecture cannot be overstated as it will influence and in many instances determine outcomes in the distributed network environment.  AI is a continuous process, not a one-off project, so it requires pivotal thinking from two decades of fast fail lean innovation that our lab helped pioneer.  Key considerations in architecture we incorporated in the Kyield OS include but are not limited to the following:

  • Optimizing the Internet of Entities

  • Governance, compliance, and data quality

  • Accelerated discovery and innovation

  • Continuous improvement

  • Real-time adaptability

  • Interoperability

  • Business modeling

  • Relationship management

  • Smart contracts

  • Security and privacy

  • Transactions

  • Digital currency

  • Ownership and control of data

  • Audits and reporting

  • Productivity Improvement

A priority outcome for most organizations in competitive environments, productivity improvement is increasingly derived from optimizing embedded intelligence, which is also desperately needed to improve the macro global economic situation.  A large gap remains in most AI strategies with respect to enterprise-wide productivity, which represents the foundation of recurring value to organizations and society, regardless of the specific task of each knowledge worker and organization.

While cultural challenges and defensive efforts are common obstacles to any productivity improvement, strong leadership has proven the ability to triumph.  Internal and external consultants and advisors can help, particularly given the steep learning curve in AI;  just be cautious on unhealthy relationships that may have interests directly opposed to the client organization, as conflicts are pervasive and tactics are sophisticated.

Trust

Just when we thought trust couldn’t become more important, it seems to dominate life on earth.  We’ve come across quite a few trust related issues in our AI voyage. A few examples that come to mind:

Intellectual property:  Trust is a two-way street, particularly when it comes to intellectual assets, so upfront mutual protection is a necessary evil and serves as the first formal step in establishing a trustworthy relationship, without which the other party must presume the worst of intentions.  Once the Kyield OS is installed with partners this problem is effectively eliminated with smart contracts and digital currency based on internal dynamics and verified intelligence (aka evidence).

Fear of displacement:  Since AI is new for most, suffice to say that fear is omnipresent and must be dealt with in a transparent and intelligent manner. At the knowledge worker level we overcome the problem with transparency, which makes it obvious that the Kyield OS is likely their strongest ally.

Modeling:  While motivation to change is often needed from external sources such as regulatory or competition, it’s probably not a good idea to trust a company that has the capability, desire, culture and incentive to displace customers.  Another problem to avoid at the confluence of networked computing and AI is lock-in from technology or talent, including service models.  Beware the overfunded offering that attempts to buy adoption and/or over-reliance on marketing hype.

Authenticity:  Apart from the serious structural economic problems caused by copying or theft of intellectual work, consider the trustworthiness of those who would do so and how much know-how is withheld because of this problem.  Authenticity is especially important in this field due to the length of time required to understand the breadth and depth of implications across the organization and network economy.

Conclusion

Given the strategic implications to organizations, AI should be a top priority led by senior management.  However, since supply chains face similar challenges with AI, traditional methods and channels to technology adoption may not necessarily serve organizations well, and in some cases may be high risk.  Whether for strategic intent, financial return, operational necessity or any combination thereof, investing well in AI is not a trivial undertaking. Integrity, experience, knowledge and freedom from conflicts are therefore critical in choosing partners and investments.

About the author

Mark Montgomery is the founder and CEO of Kyield, which is based on two decades of self-funded R&D.  The Kyield OS is designed around his patented AI system, which tailors data to each entity in the digital network environment.

We must empower a more diversified economy in 2016


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Those of us growing up in the 1960s and 1970s experienced tumultuous times that had some similarities to the last decade. Among many other contributions from our generation—which include both positive and negative influences—were some great artists, one of whom Bob Dylan is featured in a massive IBM ad campaign. Dylan’s poetry is timeless and quite relevant today:

The post WW2 era we grew up in provided the best economic conditions the world has ever known. The baby boom population explosion, of which I am at the tail end of, combined with vast sequential gains in productivity to create the ‘miracles’ of economies in the U.S., Japan, Germany, and China among others, or so it seemed.

Although a few credible experts have warned all along that the world’s trajectory wasn’t sustainable, and perhaps most of us intuitively realized same, the financial crisis contained a potential silver lining in revealing the stark naked truth: much of that ‘success’ in the post war era came at the direct expense of the future, and the bills are coming due.

Although woefully deficient in ethics with poor visibility of systemic risk—even in cases where desire for prevention existed, master politicians and financial engineers in both the public and private sector have masked structural problems in the economy for decades—from the public and each other, by employing ever-more complex short-term remedies in a misguided game of musical chairs.

Unfortunately, the resolution of the financial crisis has consisted primarily of the very same type of financial engineering—it’s the only hammers central banks have in their toolbox. While central bankers are justified in pointing fingers at political and fiscal malfeasance, it’s up to humble citizens like me to hold up a mirror and suggest that they take a look to see that such malfeasance would not be possible if not empowered by monetary policy.

One certainty is that the super majority of consolidated malfeasance in much of the world has been transferred to the balance sheets of central banks and national debt at direct cost to billions of people, many of whom followed the rules, not least those who saved all their lives just as their public institutions recommended.  Those savings have been taxed for nearly a decade now by monetary policy rather than a democratic process; by devaluation of currency, record low (or negative) interest rates, inflation from asset bubbles such as commodities and housing, and the need for hundreds of millions to tap their principal for survival. Also certain is that regardless of whether or which stimulus measures were necessary, one outcome has been a dangerous expansion of the wealth gap now at record level in the modern era.

It’s very important to better understand that the previous wealth gap peak in the 1920s was partially causal to the Great Depression and WW2, among other earlier great revolutions and loss of life. Today’s billionaires seem to understand the moral hazard and potential for backlash, which is presumably one of the reasons for the philanthropic pledges. A nice gesture that will hopefully do much good, philanthropy is not an alternative for economic diversification, though can help if targeted for that purpose.

The financial crisis represents precisely how corrosive moral hazard is realized at dangerous levels that can reach critical mass, which could be triggered by unforeseen events.  Moral hazard is a psychological phenomenon, which occurs from regulatory, governance and policy failures that then combine with the ensuing economic weakness to cause the next crisis.  In this case the trigger was regulatory failure followed by heavy-handed resolution that caused massive collateral damage, further harming innocent citizens worldwide. In such cases where the non-virtuous (aka vicious) cycle is not interrupted by a moral realignment, typically through accountability by the justice system, strong credible governance, and adoption of new systems that punishes crimes and rewards beneficial behavior, then civilizations can and do rapidly decline.

In hindsight from a high level view, from a hopefully wiser former business consultant who has studied related phenomenon for decades now, it appears that we enjoyed a long period of one-off exploitations of planet and people combined with ever-increasing public debt and corruption supporting promises by politicians and institutions that were far beyond their means to deliver.

The bad news is that the combination of public debt and future liabilities tragically promised by politicians—and now expected—some portion of which is necessary to survive in the high cost modern economy caused by these policies, can’t possibly be paid by the current economy.

The good news is that not all of that massive spend on R&D over decades has gone to waste, and we now have much more accountable systems that can indeed prevent the super majority of future crises, if only we can muster the courage to adopt them. We are also seeing dramatic improvements in systems that have the capacity for exponential productivity growth over time, which is the only method in our current economic system to cover national debt, unfunded liabilities, and the needs of a quickly aging global population, given the immense future needs in healthcare, environment and economics.

So my plan for 2016 is to tap the exponentially decreased cost and performance improvement in computing hardware and algorithmics to extend our networked artificial intelligence system to the mid-market, NGOs, and governments to provide them with a world class system unavailable to anyone at any cost until very recently. My hope is that our Kyield OS will help even the playing field and lead to a more dynamic and robust economy of the type that is only possible with healthy balance of diversification. Soon thereafter we plan to do the same directly for small business and individuals.

“If your time to you is worth savin’
you better start swimmin’, or you’ll sink like a stone
For the times they are a-changin’”
– Bob Dylan

Discover Kyield on the voyage to CALO (continuously adaptive learning organization)


tidal pool

A tidal pool ecosystem in Half-Moon Bay, CA.

For those involved with the art, science, and mechanics of organizational management, the 1990s was an exciting if humbling decade. The decade was ushered in with new thinking from Peter Senge in The Fifth Discipline, which introduced The Learning Organization to many. The concept sounded absolutely refreshing to those in the trenches—who could disagree with such logic?

I was deeply engaged at the time in a series of turn-arounds in mid-market companies. The particular business case offered for consideration was a union shop that was bankrupt from the day it opened nearly a decade earlier, and had never made a payment on many millions of dollars of debt.

The new owner had acquired the operating company out of bankruptcy with assumptions that were based on experiences that did not apply to the subject or market. Due to a low discounted acquisition value, the buyers had little to lose if the company continued to underperform, but a great deal to gain if the company could be brought up to a competitive market position.

Long-story short, it was a very challenging yearlong engagement during which time we surpassed everyone’s short-term expectations by a significant degree, including my own. Before I share what went wrong, let’s review a few things we did right:

  • The subject called for and we received an unusual level of authority from owners, which required exceptional levels of mutual trust, and a great deal of credibility.

  • We put together a strong team with a mix of experience relevant to the specific case. Each could recognize the opportunity, and even though under-resourced, we were able to create one of the industry’s top performances that year.

  • We were able to gain the trust and support of the union—which enjoyed a double-digit increase in membership, with most members experiencing significant increases in compensation.

  • Though never union members, senior management to include the owners had personal experience with labor, which was demonstrated alongside workers at critical times and helped gain their support (we walked the talk).

  • The few remaining core customers were so desperate for improved product and service that a modest upgrade and competency improved sales as well as the reputation, from which new and larger customers were gained.

  • The communities involved were relieved to see a well-managed company emerge from years of bankruptcy, churn of management, and under-investment, so we gained support of regional governments (important in this case), which also enjoyed increased tax receipts.

  • The pricing of products and services were well below market, so after modest investment with competent management, I was able to raise prices significantly while increasing sales volume, resulting in a substantial, growing profit.

  • We were able to double the value of the company in one year based on cash flow and future orders, which dramatically improved the position of the parent company, allowing refinancing at more attractive rates.

Now allow me to share why I consider this engagement (with others in this era) to be among my greatest mistakes.

Failure to learn, adapt, and seize the more important opportunity

Many of us wrongly assumed that the parent company would learn from the experience and use the success as a platform to seize other opportunities, which would have required a change in the structure, type, and talent of the company.

Despite considerable coaching combined with all that accompanies short-term financial success, the parent company did not learn the most valuable lessons from this intense experience, so they were not able to adapt to rapidly changing markets. Motivation and desire also clearly contributed, so after a final attempt to convince the parent company owners to transform into a competitive model, we moved on after contract.

My final report to owners and lenders concluded that the company had probably hit a ceiling, recommending that they either embrace the transformation strategy or sell the company. We could take them no further in current form. Just one example of why—a colleague who was a key team member had received a far superior offer from a great company in a location he and his family preferred.

The client’s response was way below market at a tiny fraction of what the operations specialist had created for the client. The decision on my colleague was surprising, as he was one of the pillars, so it confirmed the ceiling for me. The owners were left with a rising star in a subsidiary that kept shining for a short period, during which time likely created profits far exceeding investment, but then began to fade. Unlike the other holdings of the parent company, this subsidiary required intensive, sophisticated, and experienced management.

A decade later I read where the subject had fallen back to a similar performance level to when the parent company acquired it. It gives me no pleasure to share that hindsight has demonstrated the period of our engagement to be the peak of the parent company’s 50-year history. We worked very hard to provide the opportunity for an enduring success.

While we enjoyed deep mutual respect with the parent company, which appeared adaptive in this acquisition and others we brought to them, they weren’t willing to transform their organization. They were opportunistic on a one-off basis, which is best suited for the flipping model, not for an enduring business. Even then technology played a big role through IBM mainframes, inventory management, and transactions over networks. Today of course most companies are facing more dramatic change in an environment that is far more talent and technology dependent.

The learning organization struggles in the 1990s

A few years and dozens of assignments later we had established a tech lab and incubator to explore and test opportunities due to Internet commercialization, which catapulted the economy with significant g-force into the network era. By the mid-1990s a few operational consultants had become critical of the apparent naiveté with the learning organization theory, which like knowledge management had a philosophy that many could embrace, but in practice found difficult to achieve given real-world constraints, including legal and physical, not just cultural or soft issues.

A few researchers pointed out that it was difficult to find actual cases of learning organizations (Kerka 1995). In papers, textbooks, and Ph.D. theses I was invited to review, the same few cases and papers were cited relentlessly, often comparing apples to oranges. One example was a study published by Finger and Brand in 1999, which found that systems needed to be non-threatening. That may have been the case for their subject at the Swiss Postal Service, but would be unrealistic where threats are among few constants, increasingly to include government and academia. Peter Senge apparently took notice of the criticism, reflected in the subtitle of his book The Dance of Change (1999); “The Challenges of Sustaining Momentum in Learning Organizations”.

By the end of the 1990s I had become vocal on the primitive state of systems and tools that could achieve the goals of the learning organization, and more importantly adapt in a sufficient time frame. My perspective was one from operating a live knowledge systems lab that was building, operating, and testing learning networks, which included daily forums that discussed hundreds of real-world cases in real-time over several years. Many were complaining about poor technology and lack of much needed innovation, yet few were focused on improvement from within organizations that would allow innovation to make it to market.

Some researchers have since suggested that in order to achieve the learning organization, it was first necessary for knowledge workers to abandon self-interest, while others claimed that ‘collective accountability’ is the key. In updating myself on this research recently, it sometimes seemed as if organizational management consultants and researchers were attempting to project a vision over actual evidence, denying the historical importance of technology for survival of our species, as well as mathematics, physics, and economics. Consider the message to an AI programmer or pharma scientist today coming from a tenured professor or government employee with life-long security on ‘the need to abandon self-interest’. This has been tested continuously in competitive markets and simply isn’t credible. The evidence is overwhelmingly polar to such advice.

Current state of the CALO

We may have been experiencing devolution and evolution concurrently, yet humans kept working to overcome problems, representing all major disciplines. My company Kyield is among them.

Significant progress has been made across and between all disciplines, including with understanding and engineering the dynamical components of modern organizations.

The network economy

No question that the structure of an organization greatly influences the ability to adapt even if having learned lessons well, including legal, tax, reporting, incentives, physical, and virtual. The network economy has altered the very foundational structure many organizations operate on top of.

While each structure needs to be very carefully crafted, the structural changes vary from the rare ‘no change is needed’ to increasingly common ‘bold change required to survive’. Though it need not be so, it is increasingly more efficient to disrupt and displace than to change from within.

Globalization

While we are experiencing a repatriation and regionalization trend, globalization radically changed the way organizations learn and how they must adapt. Several billion more people are driving the network economy than in 1990, with a significant portion moving from extreme poverty to the middle class.

The global financial crisis (GFC)

Suffice to say for this purpose that the GFC has altered the global operating and regulatory landscape for many businesses and governments—in some cases radically, and is still quite fluid. Currency swings in response to unprecedented monetary policies are the most recent example of this chaotic process, though only one of many organizations must navigate. If the regulatory agencies were CALOs, much of the pain would be mitigated.

Machine learning (ML)

Although quite early in commercialization and most cases still confidential, ML combined with cognitive computing and AI assisted augmentation is rapidly improving. Deep learning is an effective means of achieving a CALO in a pure network environment that interacts with customers such as search and social networking, though is being adopted widely now.

One of the largest continuous learning projects is Orion by UPS, which was kind enough to share some detail in public. Orion provides an excellent case for many to consider as it overlaps the physical world with advanced networks and large numbers of employees worldwide. Unlike Google or Facebook that began as virtual companies running on computer networks, UPS represents a large transformation of the type most organizations need to consider. In the case of UPS of course, they have massive logistical operations with very high volume of semi-automated and automated data management.

Having developed deeply tailored use case scenarios for each sector in Kyield’s customer pipeline, numbering in the dozens, I can offer a few words of advice in public.

  1. While all public cases should be considered, remember that few are shared in public for good reason. Few if any companies have the business model, need, resources, or capacity of Google or UPS, which is why they can share the information.

  2. Rare is the case when enormous custom projects should be copied. The craftwork of planning and design is substantially about taking available lessons, combining with technology, systems, and talent in a carefully tailored manner for the client.

  3. Regardless of whether a large custom project, completely outsourced, or anything in-between, board level supervision is necessary to avoid switching dependencies from one vendor or internal department to another. I see this occurring with new silos popping up in open source models, data science, statistics, and algorithms. The goal for most should be to reduce dependencies, which is nontrivial when dealing with high-level intellectual capital embedded in advanced technology, particularly given talent wars, level of contracting in these functional roles, and churn.

  4. Since few will be able to develop and maintain competitive custom systems, the goal should be to seek an optimal, adaptive balance between the benefits of custom tailored software systems (Orion or Google), with the efficiency of write once and adopt at scale in software development. This is one of several areas where Kyield can really make the difference on the level of ROI realized. Our continuously adaptive data management system (patented) is automatically tailored to each entity with semi-automated functions restricted to regulatory and security parameters at the corporate and unit level, with the option for individual knowledge workers to plan projects and goals.

  5. Plan from inception to expand the system to the entire organization, ecosystem, and Internet of Entities. Otherwise, the organization will be physically restricted from achieving much of the value any such system can offer, particularly in risk management. One of the biggest errors I see being made, including in some of the most sophisticated tech companies, is approaching advanced analytics as ‘only’ a departmental project. Of course it is wise to take the low hanging fruit through use of pre-existing department budgets where authority and ROI are simple, but it is a classic mistake for CIOs and IT to consider such projects strategic, with very rare exception such as for a strategic project.

  6. Optimize relationship management. Just one example is when our adaptive data management is combined with advanced ML algorithms that include predictive capabilities, which among other functions weighs counter party risk. A similar algorithm can be run for identifying business opportunities.

Concluding thoughts

While it is more challenging to achieve buy-in for organization-wide systems, it is physically impossible to achieve critical use cases otherwise, some of which have already proven to be very serious, and can be fatal. Moreover, very few distributed organizations can become a CALO without a holistic system design across the organization’s networks, particularly in the age of distributed network computing.

If this isn’t sufficient motivation to engage, consider that learning algorithms are very likely (or soon will be) improving the intelligence quotient and operational efficiency of your chief competitors at an extremely rapid rate. Lastly, if the subject organization or entire industry is apathetic and slow to change, it is increasingly likely that highly sophisticated, well-financed disrupters are maturing plans to deploy this type of technology to displace the subject, if not the entire industry.

Bottom line: Move towards CALO rapidly or deal with the consequences. 

Mark Montgomery is founder and CEO of http://www.kyield.com, which offers an advanced distributed operating system and related services centered around Montgomery’s AI systems invention.

White board video presentation on Kyield Enterprise


Kyield founder Mark Montgomery provides a 14 minute white board presentation on Kyield Enterprise and the Kyield Enterprise Pilot

Converting the Enterprise to an Adaptive Neural Network


Those tracking business and financial news may have observed that a little bit of knowledge in the corner office about enterprise architecture, software, and data can cause great harm, including for the occupant, often resulting in a moving van parked under the corner suite of corporate headquarters shortly after headlines on their latest preventable crisis. Exploitation of ignorance in the board room surrounding enterprise computing has become mastered by some, and is therefore among the greatest of many challenges for emerging technology that has the capacity for significant improvement.

The issues surrounding neural networks requires total immersion for extended duration. Since many organizations lack the luxury of time, let’s get to it.

Beware the Foreshadow of the Black Swan

A recent article by Reuters confirms what is perhaps the worst kept secret in the post printing press era: Many Wall Street executives say wrongdoing is necessary: survey. A whopping 25% of those surveyed believe that in order to be personally successful, they must conduct themselves in an unethical manner to include breaking important laws, some of which are intended to defend against contagion; a powerful red flag warning even if only partially true.

This reminds me of a situation almost a decade ago when I had the unpleasant task of engaging the president of a leading university about one of their finance professors who may have been addressing a few respondents to this very survey when he lectured: “if you want to survive in finance, forget ethics”. Unfortunately for everyone else, even if that curriculum served the near-term interests of the students, which is doubtful given what has transpired since, it cannot end well for civilization. Fortunately, in this case the university president responded immediately, and well beyond expectation, after I sent an email stating that I would end my relationship with the university if that philosophy was shared by the institution.

For directors, CEOs, CFOs, and CROs in any sector, this latest story should only confirm that if an individual is willing to risk a felony for his/her success, then experience warns that corporate governance rates very low on their list of priorities. Black Swan events should therefore be expected in such an environment, and so everything possible should be planned and executed to prevent them, which requires mastering neural networks.

Functional Governance: As simple as possible; as complex as necessary

Functional governance and crisis prevention in the modern complex organization requires deep understanding of the organizational dynamics embedded within data architecture found throughout the far more complex environment of enterprise networks and all interconnected networks.

Kyield Enterprise Diagram 2.7 (protected by Copyright and U.S. Patent)

Are you thinking what I think you may be thinking about now? In fact adaptive neural networks in a large enterprise is quite comparable to the complexity found in brain surgery or rocket science, and in some environments even more so. The largest enterprise neural networks today far outnumber comparable nodes, information exchanges, and memory of even the most exceptional human neurological system. Of course biological systems are self-contained with far more embedded intelligence that adapt to an amazing variety of change, which usually enables sustainability throughout a complete lifecycle—our lives, with little or no external effort required. Unfortunately, even the most advanced enterprise neural networks today are still primitive by comparison to biological systems, are not adaptive by design, and are subject to a menagerie of internal and external influences that directly conflict with the future health of the patient, aka the mission of the organization.

So the next question might be, where do we start?

The simple answer is that most organizations started decades ago with the emergence of computer networking and currently manage a very primitive, fragmented neural network that wasn’t planned at all, but rather evolved in an incremental manner where proprietary standards became commoditized and lost the ability to provide competitive differentiation, yet are still very expensive to maintain. Those needing a more competitive architecture have come to the right place at the right time as we are deeply engaged in crafting tailored action plans for several organizations at various stages of our pilot program for Kyield enterprise, which is among the best examples of a state-of-the-art, adaptive enterprise neural network architecture I am aware of. We’ve recently engaged with large to very large organizations in banking, insurance, biotech, government, manufacturing, telecommunications, engineering and pharmaceuticals in the early stages of our pilot process.

Tailored Blueprint

Think of the plan as a combination of a technical paper, a deeply tailored use case for each organization, and a detailed time-line spanning several years. In some ways it serves as sort of a redevelopment blueprint for a neighborhood that has been locked-in to ancient infrastructure with outdated electrical, plumbing, and transportation systems that are no longer compatible or competitive. Most have either suffered a crisis, or wisely intent on prevention, while seeking a significant competitive advantage.

The step-by-step process we are tailoring for each customer serves to guide collaborative teams through the conversion process from the ‘current architecture’ to an ‘adaptable neural enterprise network’, starting with the appropriate business unit and extending throughout subsidiaries over weeks, months and years in careful orchestration according to the prioritized needs of each while preventing operational disruptions. Since we embrace independent standards with no lock-in or maintenance fees and offer attractive long-term incentives, the risk for not engaging in our pilot program appears much greater than for those who do. In some cases it looks like we may be able to decrease TCO substantially despite generational improvement in functionality.

Those who are interested and believe they may be a good candidate for our pilot program are welcome to contact me anytime.