A 20 year trek in AI systems


From theorem to market through multiple valleys of death and beyond

Mark Montgomery_Kyield_office_2007

Mark Montgomery at AZ office in 2007

This is a personal story about our real-world experience, which contains little resemblance to most of what is written about entrepreneurism and technology commercialization. While our journey has been longer than most, scientific commercialization (aka deep tech) typically requires two decades or more from theory to market. Even more rare in our case is that the R&D journey has been self-funded and very lean. Although my route was different, my peers in R&D have been scientists in a handful of labs—primarily universities, a few corporations, non-profit institutes and national labs. While our independence through the entire process has been difficult, this model has allowed us to develop one of very few unified AI systems in pure native form free from institutional and other conflicts that too-often kill or ruin much-needed technology and companies based on them.

I’ll begin in the Puget Sound area where my wife Betsy and I met in 1980 while working at Mt. Rainier. A couple years later we started a traditional business. After selling our business Betsy went into banking and I started a consulting firm that worked with a variety of different clients across the Pacific Northwest. We moved to Arizona in 1992 in part due to consulting work cleaning up the S&L crisis for private owners. In 1995 we decided to test the emerging web with a self-guided management system that was distributed in hard copy. That effort became one of the leading networks for small business. It was a first so we experimented with all models. The venture grew rapidly in organic form but needed significant growth capital to reach sustainable maturity. Unlike San Francisco and the Seattle area where nearly every good scalable business was funded, flyover states had little infrastructure to support scalable businesses, even when risk had been mitigated in sustainable form, so we sold prematurely.

The lean KS lab

First office in AZ - Kyield

Kyield’s first office in AZ

The experience with our first online venture was so intense with such profound implications that I converted our consulting firm to one of the original lean venture labs. I retrained in computer programming and built a lab and data center in the building above on our property in Northern Arizona. Our specialty was in knowledge systems (KS — arm of artificial intelligence / AI). Stanford has a well-known KS lab—one of few at the time. Although hundreds of billions USD were wasted on me-too dotcoms in the 1990s, AI was still in an ice age (aka AI winter).

Two decades ago this year I was working in the lab operating a learning network I designed called GWIN (Global Web Interactive Network), which was the most advanced of several experiments we developed from scratch. Primitive by today’s standards, GWIN was a cutting edge network at the time that attracted an impressive membership of leaders in science, business, NGOs and government. Tech CEOs and VCs were among our closest followers, though we had entire boards of in the Fortune 500, intelligence agencies, and hundreds each of professors, investment houses, analysts, NGOs, and editors. Log activity from Air Force One was not uncommon. A nun reporting from the Amazon jungle was one our most interesting members.

The most promising program in GWIN was ‘Lookout’, which was a primitive early digital assistant that delivered personalized news clips sourced from the web containing brief human analysis accompanied by discussion. Although we offered web discussion and chat, email lists were preferred at the time.

GWIN was a fascinating experience that was also producing enormous value. One of many examples was a network-wide warning on hurricane Mitch—second most deadly Atlantic hurricane. A life-long weather geek, I typically had a monitor running radar and sat loops so I watched as Mitch grew into a dangerous slow moving cat 5 heading right for high risk areas, so I issued a warning. Between a few members in Central America, media, corporate and government members with operations in the region, our warning on Mitch spread rapidly. I can still recall the satisfaction in receiving messages from members conveying that by distributing a few lines of text in GWIN we helped save lives and prevent unnecessary losses. Prevention has been one of my personal passions all along. When planned and executed well, prevention can provide the highest possible ROI—in dollars and lives.

Most of the GWIN members didn’t realize that although a team of remote developers helped build the network, I was operating and improving it solo 24/7/365 from my office in our onsite data center. My wife Betsy and I were paying for almost all of the efforts personally other than a small investment at the time from my late partner and friend Dr. Russell E. Borland. By that time a tsunami of capital had arrived in Silicon Valley and Wall Street causing the infamous dotcom bubble, resulting in enormous levels of predation, subsidies, and losses, much of which I considered fraud. Few would pay for online services because of it. It was the largest consumer price war in history so we focused our efforts on deep tech and business rather than consumer.

A new theorem

A few months after the launch of GWIN I received a life-changing call from my brother Brett telling me that he had been diagnosed with ALS. I then dedicated as much time as possible attempting to find promising therapies or tools that could accelerate R&D. Tragically, I discovered that we were a long way from even understanding ALS or obtaining technology that could significantly accelerate effective therapies. Brett passed away three years later within a few days of the estimate by doctors at Mayo Clinic in Scottsdale who confirmed his diagnosis. My quest to find, test and develop more intelligent tools led to a new theorem ‘yield management of knowledge’, which was then followed by piecing together components of a unified AI system in our Kyield OS.

The pathway to the theorem began with a classic aha moment after an extended period of intense work on information overload in operations and research, including testing promising search engines and other methods as they became published. I’m still refining the equation, but it essentially details key factors in optimizing the knowledge yield curve given the needs and constraints of each entity. Although the human brain is amazingly powerful, it does have finite limits beyond which it begins to malfunction, which I first discovered at 30-something in the lab. We were clearly faced with a highly complex systemic problem requiring a systemic solution with the capacity to effectively manage the complexities involved. To help clarify I posed the following question:

 If a hypothetical perfect Chief Knowledge Officer (CKO) existed, how would we optimally achieve his/her mission in a network environment, how would it be designed, and what essential components would be required?

That question eventually led to our CKO Engine, which provides governance and security for the entire distributed network. Administration in the Kyield OS is through a simple natural language interface with multiple security levels and methods, some of which are kept secret for security.

It was discovered that multiple obstacles could best be overcome within a single holistic architecture; and without which none of the problems can be fully overcome:

  1. If we do not resolve the problem of information overload, then creativity and productivity suffer.

  2. If we do not resolve the problem of ownership of original work, then innovation suffers.

  3. If we do not provide accurate metrics, then meritocracy cannot function properly.

  4. If we do not provide adaptability, then differentiation and continual improvement will be very difficult to achieve.

  5. If we do not embed intelligence into the files, the most relevant search queries cannot be returned even by the most improved algorithms, thus negatively impacting productivity and innovation.[i]

We realized that it would be at least a decade before essential components matured sufficiently to begin to effectively manage knowledge yield over computer networks. A continuation of Moore’s Law in semiconductors in combination with rapid improvements in bandwidth and algorithmics would be required over an extended period before the theorem could be fully realized in applied form as intended. However, I was confident it would be achievable in my lifetime, even if imperfect.

We were able to test components of the standard system and verify supercomputing results of similar scale and data structure in early 2000s, but scale challenges and bandwidth bottlenecks prevented the ability to deliver functionality to individuals and devices. By the mid-2000s Kyield had matured into a distributed operating system (hence Kyield OS) and essential pieces of the puzzle began to coalesce, so I submitted my AI systems patent application “Modular system for optimizing knowledge yield in the digital workplace.” The 2006 application was granted in 2011 representing about 25% of the total IP/IC at the time.[ii] I viewed the patent as additional insurance.

Initium Capital

In late 2007 I met with Craig Barrett at his office in Chandler Arizona. Although Craig and I were both active with local universities and tech groups in Arizona we had never met, so a mutual friend Les Vadasz introduced us. I won’t go into detail on what we discussed in our one-on-one meeting other than to say it was open, honest, and friendly. Craig may have been approaching mandatory retirement age but he was impressive, helpful, and obviously still at peak performance. A few years earlier I had spearheaded a VC firm (Initium). It had proven suicidal to build high cap ventures in flyover states that depend on capital centers for growth funding. In addition to rare private efforts like our small lab, universities, federal and state governments were investing enormous sums in R&D just to see ventures copied or cherry picked primarily by California (more recently China). In New Mexico most of the spinout ventures from national labs were exported, perpetuating a long-term trend in one of the worst state economies. I warned often of an economic balkanization underway. Few seemed to understand that if that wasn’t fixed most other problems would be trivial.

Our efforts to build Initium hit a similar capital ceiling as individual ventures in the form of lack of regional support. We had one of the strongest teams ever assembled in a flyover state with an unusually large inaugural fund target of $250 million. The fund structure contained a flexible 40% dedicated to the region and 60% with no geographic restrictions. While we earned a place on the emerging leader radar, history had painfully demonstrated the need for key local support and investment. To the extent such regional investment existed it was rare, too risk averse for deep tech and/or unqualified. So we reluctantly sized down Initium and explored merger interest from Bay area firms. Betsy and I liquidated everything but our property and relocated to Half Moon Bay during the first week of 2008, just in time for the financial crisis.

We enjoyed many aspects of living in the Bay area, not least living a block from the ocean after 15 years in the desert, though we found the economic situation troubling. Home prices were several times the cost of where we lived in Arizona and all other costs were much higher as well. It was quite clear why VC investment was so high in SV, contributing to sharply increasing failure rates. The number of homeless served as a constant reminder of just how out of whack the local economy was. Betsy took her first year off work to pursue a hobby in art and wound up working for non-profits as a volunteer attempting to fill some of the massive unmet social needs.

We had a one-year window during which time the financial crisis became increasingly worse and the future of the other firms and investors increasingly uncertain. We were also in discussions with market leaders for OEM-type relationships, but they were clearly not yet prepared for AI systems or Kyield. So after the most costly year of our financial lives other than not investing in pre IPO Microsoft or pre investment in Google (among others), we walked away from a merger that teed up a significant investment in Kyield. Hindsight suggests that our instincts were functioning well as Kyield and the markets were still premature a few years later. It’s unlikely that Kyield would have survived in the SV VC model at the time. Machine learning really took off in 2015 with investment in the tech stack that improved performance and value for majority of use case scenarios.

The city different in the land of (serendipitous) enchantment

Upon arrival at our property back in Arizona in early 2009 we discovered that the caretakers had trashed our property, so we took another financial hit and turned it over to a management company. We then decided to go on a road trip to find a rational place to ride out the financial crisis while maturing Kyield R&D. The plan was to do a loop starting in Tucson, then through New Mexico (NM) to Colorado, perhaps Wyoming and Montana and back through Utah to Arizona. My expectation was to lease a place in Colorado, but fate intervened in the form of a car pulling a u-turn right in front of us outside of Albuquerque on the way to meet a realtor for a house showing. The ensuing collision almost totaled both cars but no one was injured and the driver was very nice as were the police. We were on a schedule, however, so had to rent a car and move on to Santa Fe where the first house we looked at seemed perfect for us and our dogs, so we took it.

We have history in Santa Fe dating back to our first trip in 1985 and also an informal relationship with the Santa Fe Institute (SFI) from our GWIN days that share many others. I also had some interaction with national labs due to Initium. We performed consulting work in NM that included market audits in the 1990s and also covered in VC, so I was familiar with the strengths and weaknesses. One of the world’s leading research centers—more so than most realize, NM is also famously difficult for growing scalable businesses of the type that occasionally emerge from that investment. Despite hundreds of billions of dollars invested in research within NM and large numbers of spinouts, the state has never produced a significant business success in tech. Suffice to say that accidents normally occur with far more frequency.

Patio at SFI

Terrace at the Santa Fe Institute

I spent quite a bit of time at SFI over the next several years meeting with leading scientists from around the world working on similarly challenging problems in physics, computer science, biology, economics and sociology, which helped indirectly in ways difficult to capture or fully understand. SFI is unique in the world in many respects.

In early 2012 we began presenting Kyield to management in the few organizations that had a supercomputer, sufficient budget and the internal talent to even consider Kyield in organic fashion at the time. Significant progress has since allowed us to steadily expand our focus to mid-market and government markets. When the managed services model is completed as originally intended most markets should be viable.

Byproducts of the voyage (not including R&D pipeline)

IoE (Internet of Entities)

Since the early days of our R&D I have looked at networks as being comprised of entites, not things. The reasons should be self-evident—to the degree they aren’t speaks to the influence on structural issues in the network economy we are working to resolve, some of which are causing serious economic and social damage—namely the business models applied to the web.

Our old colleagues who designed the Internet are the first to admit that it was never designed for many of the tasks required of it today, including commerce or security. Public networks involve many different legal entities, including individual humans and organizations, each of which has unique needs and legal rights. The data carried over networks represents those rights (or should). Even sensors on the network are owned and governed by entities, and they are rapidly becoming more intelligent, hence the need to view networks as entities that contain appropriately engineered governance structure to manage relationships between entities.

Today we offer a suite of IoE options built upon the Kyield OS to manage an enterprise network easily extendable to partners, customers and things (sensors). This is the wisest path from my perspective for managing networks in government, industries, homes, autos, ships, planes, etc. The Kyield OS offers critical elements for optimizing intelligent networks.

The standard Kyield OS

Kyield OS Diagram - CALO

CALO (Continuously Adaptive Learning Organization) is the manifestation of the original modular system invention as applied with state-of-the-art components and algorithms. Recent improvements in machine learning combined with more sophisticated statistical processes and algorithmics within the distributed Kyield OS enable customer organizations to achieve a CALO. The Kyield OS operates substantially in the background with semi-automated controls for each organization, group and individual. Unlike earlier management concepts, CALO is executable.

Health Management Platform

Kyield healthcare platform

Kyield Health Managment

First unveiled in our diabetes use case scenario paper in 2010 still in futuristic form, which has since been downloaded in the seven figures, the U.S. has yet to deal with the healthcare fiscal time bomb. The sector has evolved over decades to build resistance to efficiencies, cost management and/or patient-centric systems, resulting in the highest cost healthcare system in the world, which provides less quality than others at half the cost. Little progress can be made in U.S. healthcare until reformed by Congress, without which we are limited primarily to the self-insured in the U.S.

HumCat (Prevention of Human Caused Catastrophes)

After many years of focused R&D we announced our HumCat program powered by the Kyield OS. The HumCat program pioneers new territory at the confluence of distributed AI systems, risk mitigation and prevention. By bundling more powerful computing and algorithmics in the Kyield OS with financial incentives and risk transfer through bonds, reinsurance, and other vehicles, we can significantly improve the risk profiles of individuals and organizations and thus lower costs.

It is now possible to prevent many if not most human-caused crises, including accidents, fraud and/or malintent, whether in physical or cyber form. While each organization has unique characteristics requiring bespoke structuring, it is possible to offer select clients limited upfront guarantees that finance and cover the cost of the entire program over a defined period (1-5 years). Higher risk organizations can likely reduce costs significantly and may be able to improve ratings over time as reduced risk is demonstrated with more accurate analytics offered by the Kyield OS. As interest rates rise ratings will become even more critical for corporations and governments.

The HumCat program targets the highest possible ROI events while bundling the individual functions in the Kyield OS such as enhanced security and productivity, representing a significant breakthrough in value to clients and society. We have a great deal of interest in the HumCat program for what are hopefully obvious reasons.

Knowledge Currency

A byproduct of the architecture necessary to execute functionality within the Kyield OS is deep intelligence on workflow and work products from each entity. While Kyield makes no claim on the data ownership or control beyond required by law and as pre-agreed with customers for specific needs, that intelligence does allow us to create and manage an exceptionally valuable digital currency, or knowledge currency. The creation and offering of Kyield’s knowledge currency (KYC) opens up many positive benefits for and between customers, including more accurate valuation of individual, team, and corporate knowledge capital, the ability to be compensated fairly for knowledge work, and the ability to transact and trade intellectual work products in a more rational and accurate manner. In addition to knowledge products created, KYC can be used to value and transact knowledge about an entity, such as health information. At large scale the KYC could have profound economic benefits by substantially overcoming the serious problems across our society caused by problems with the ad model. KYC has been in our R&D pipeline since the early 2000s.

Where is Kyield today?

We are in discussions and negotiations on various options to build out and scale the Kyield OS in the hybrid managed services model as originally intended. While the system can be installed on top of the infrastructure of others such as AWS, Azure, Google, IBM, and Oracle, we have some proprietary technology that must be installed on our own hardware for an optimal unified Kyield OS. The hybrid configuration typically includes an installed custom computer within the client data center, private cloud or a multi-cloud scenario. This allows us to offer the pre-engineered Kyield OS and additional products while protecting our security as well as customers, reduce unnecessary and costly integration costs, and reduce or eliminate redundancies. In order to help facilitate this transition to the managed services model we recently announced that Marc Spezialy joined Kyield as our first CFO. Working from his Denver office Marc will be spearheading and executing the financial needs, investor relationships and reporting.

Happy Thanksgiving 2017!

Mark Montgomery

Mark and Betsy Montgomery_30th anniversary

Mark and Betsy Montgomery 5/15/2014

Five related articles

[i] Montgomery M “Unleash the Innovation Within” Kyield, November 2008

URL: http://www.kyield.com/images/Unleash_the_Innovation_Within_-_A_Kyield_White_Paper.pdf

[ii] Montgomery, Mark. “Modular system for optimizing knowledge yield in the digital workplace.” US Patent 800577823 August 2011. http://www.google.com/patents/US8005778

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Why Every Company Needs a New Type of Operating System Enhanced with Artificial Intelligence


IMG_20150909_130429

Kyield founder Mark Montgomery on top of NM Oct., 2016, taken by Betsy Montgomery with their dog Austin

 

The Amazon acquisition of Whole Foods represents yet another confirmation of our rapidly changing business environment driven by opportunities at the confluence of technology and network dynamics. Although only the latest in a powerful trend initially impacting in this case the grocery industry, the business and technology issues driving the strategy are relevant to most and serves as a reminder that digital convergence is not confined to traditional thinking or industry lines.

A sharp devaluation of public grocery companies followed, so apparently many investors share concerns highlighted in the current HBR article “Managing Our Hub Economy”, which warns that “most companies will not become hubs, and they will need to respond astutely to the growing concentration of hub power”. The devil in the details for management is how to respond astutely. The answer is largely an AI OS.

A recent article at MIT SMR describes the complex operating environment: “The Five Steps All Leaders Must Take in the Age of Uncertainty”:

These ecosystems are nested complex adaptive systems: multilevel, interconnected, dynamic systems hosting local interactions that can give rise to unpredictable global effects and vice versa. Acknowledging the unpredictability, nonlinearity, and circularity of cause-and-effect relationships within these systems is a notable departure from the simpler, linear models that underpin traditional mechanistic management thinking.

One of the reasons for the attention in this latest combination is the integration of virtual networks with physical locations, which has been a priority for many companies, including Kroger, which shares many of the same zip codes as Whole Foods. A few days following the announcement Kroger Chairman and CEO Rodney McMullen revealed that he wasn’t surprised: “you could tell that Amazon wanted to do something from a physical asset standpoint and I think Whole Foods is a great fit for them.” Kroger is a well-managed company known for wise use of analytics, which is reflected in McMullen’s advice to investors: “you should assume that we look at any potential opportunities”.

We needed a new operating system” – Doug McMillion, CEO of Wal-Mart Stores, Inc.

The question is will traditional methods be sufficient moving forward? The answer may be found in part by looking at the world’s largest retailer. Wal-Mart was viewed as one of the most stable companies before Amazon entered their core lines of business, eventually leading to the recent conclusion by Wal-Mart CEO Doug McMillon: “We needed a new operating system”. The company recently paid $3 billion for an e-commerce component of a new OS in Jet.com, which may seem excessive to those of us unaccustomed to managing a half-trillion USD in annual revenue, though represents a relatively minor investment if it works well.

Unfortunately for 99.99% of businesses, investing $3 billion in a native e-commerce platform is not an option, particularly one experiencing significant losses. Very few of the remaining .01% could consider doing so for a partial OS. Even Walmart’s bold actions appear insufficient when we consider that the acquisition of Whole Foods was powered less by the core business of Amazon or Whole Foods than the bold manifestation of what was previously learned, resulting in an entirely new and much better business model in AWS (see article on spinning off AWS). Amazon’s strength is its ability to learn rapidly, recognize potential, and then convert and realize interconnected opportunity to new offerings in a fiercely competitive manner.

Competing in such a hypercompetitive and rapidly changing environment can be especially difficult for companies thinking and behaving in a linear manner. The retraining for me personally that began in our lab in the 1990s was a profound voyage initiating from a relatively high level. The technical training and transition involved a sharp learning curve that has only become steeper and more intense with time.

Native platforms are quite different than corporate networks that have evolved incrementally over decades. Understanding related opportunities and risk many years in advance is a critical challenge. One must peer through an asymmetrical prism constructed from tens of thousands of hours of total immersion and make bold bets that are well timed, particularly with AI systems.

Among many lessons learned is that the network economy is not only interconnected, it is also multidimensional and pre-programmed. When managed optimally and competitively the entire experience of the customer is an obsession with little deference for traditional lines.

Important considerations for an AI OS

1 – A competitive AI OS will be necessary for most to survive

Essentially all the evidence we see with mid-size companies to market leaders across most industries is that a strong AI OS is rapidly becoming the new competitive bar. If a company doesn’t have a competitive AI OS platform and the competition does, it will likely negatively impact the entire organization and each individual within it, as well as customers and partners. Google and Amazon are examples of companies that appear to be employing some functions similar to those found in our Kyield OS. While leadership and corporate strengths are critical, employing advanced AI systems is among the most important improvements any organization can make. The question really is how and when.

2  – An organization OS is not a computer OS

Many different types of operating systems exist. A few minutes of reading my book (condensed version) Ascension to a Higher Level of Performance will highlight the difference between the Kyield OS and a computer OS. The standard system is focused on universal issues common to all organizations, individuals, and networks. We have good reason to believe Kyield is among the world’s competency leaders in knowledge systems, which is a sub-specialty of artificial intelligence.

Our focus is a thin yet broad and very deep specialty with little overlap to most others, including AWS, Azure, and Google (Kyield OS integrates well with most others). Although executed with software, the Kyield OS is a ‘low code’ system compared to a computer OS and more dependent upon data and algorithmics. The system operates in the background with a simple natural language interface for corporate, group, and individual administration. The Kyield OS is transparent, non-intrusive, and interoperable so that any function can be added as needed in a highly efficient manner.

A recent note from a Fortune 50 CXO exemplifies the need from a slightly different perspective in response to our recently released HumCat offering—a new model for prevention for human-caused catastrophes, including cyber prevention.

I like your idea of an Operating System. I’m so convinced that the world is too complex and getting more complex every second that human beings cannot manage it in the right way anyway… Now, it is time (for the Kyield OS), otherwise we are on the hook of dark side of cybernetics—cybercrime or cyberwar and nobody can defend us.

3  –  Reinventing AI system wheels is not wise

As I shared with a Fortune 20 team recently, while it may be extraordinarily easy to underestimate the amount of tradecraft and secrets for such an endeavor, it is nonetheless foolhardy to do so (hence the AI talent wars). Fortunately for our customers, we’ve done the bulk of the heavy lifting. It was two decades ago this year that Kyield was originally conceived in the lab as an authentic invention (Optimizing knowledge yield in the digital workplace).

Many research and consulting reports on AI systems are available, and they have improved significantly over the past two years (See reports by MIT SMR & BCG and Nordea as recent examples), but caution is warranted. Some consulting firms are still advising to start small and experiment in areas that no longer need experimentation. Although generally appropriate five years ago, it is increasingly dangerous today as the competitive gap due to AI systems is expanding rapidly.

A good example of an ongoing experiment was highlighted in the WSJ CIO Journal: “Swiss Re Bets AI Can Help Workers Cope with Complexity of Reinsurance”. The goal is admirable, achievable and sounds impressive until reading the subtitle: “The company’s 100 data scientists and AI experts are building software that can read documents on their own.” This is not a new technology. If the article is accurate it appears that Swiss Re is spending between 10-100 times more for a small fraction of the functionality found in our Kyield OS. Other options also exist for the specific function described that would likely be much more wise than a custom effort.

Our friends at Swiss Re are far from alone. Munich Re publishes an IT radar report (with a nice diagram) based on research that “systematically analyzes opportunities, trends and technologies, and provides an ongoing insight into which technologies could be relevant for Munich Re and our customers from a very early phase”. In the current 2017 report Munich Re places predictive analytics in adoption phase while advanced machine learning and robotics process automation in the trial phase. These and other recommendations may raise some eyebrows. Predictive analytics has been deployed for many years as has advanced machine learning for specific purposes. If one is competing with a technical leader—and increasingly most are, waiting too long can be a fatal error. The first mover position, however, is not always an advantage, so such decisions need to be situation-specific.

4  – Method and sequence of adoption

To date the super majority of investment in AI systems have been strategic resulting in a few notable successes. We have also witnessed large and costly errors, including in M&A, VC, internal development, and in system designs and business models by vendors.

Horizontal systems like our Kyield OS serve as an efficient platform to unify the organization and ecosystem. Ideally a native AI OS should be adopted first. Quite apart from significant IP liability risk, since our standard system is focused on universal issues for every type of organization, with improved productivity, security, and prevention, it is difficult at best to justify internal custom efforts that replicate any of this functionality. Strategic functions are best built on top and/or integrated with our networked platform OS so that the organization and ecosystem operate in an optimal manner.

All is not lost, however, for those who have experimented in overlapping areas. The modules within the Kyield OS creates the data structure needed for compliance and then populates across the network in a manner designed to execute the functionality within the system as efficiently as possible, including for accuracy, integration and financial efficiency.

Conclusion

As important as external competition can be in this environment, the degree to which displacement will occur is dependent on a number of factors. All things being equal otherwise, the outcome primarily depends on the incumbent’s actions, its people, systems, and processes. Even though some companies may seem well positioned, the fundamental economic and business environment is rapidly changing. To the best of my awareness, survival from this point forward will essentially require a strong AI OS for the super majority of organizations.

Mark Montgomery is the founder and CEO of Kyield, originator of the theorem ‘yield management of knowledge’, and inventor of the patented AI system that serves as the foundation for Kyield: ‘Modular System for Optimizing Knowledge Yield in the Digital Workplace’. He can be reached at markm@kyield.com.

Why the U.S. Must Lead the World with Intelligent Infrastructure


Americans have been through a great deal in the last two decades. In addition to the network effect that consolidated wealth in a few zip codes, we endured rapid globalization that benefited other nations at the expense of our own, the 9/11 terrorist attacks, multiple wars, and the global financial crisis, all within the first few years of this millennium.

To put this series of interconnected events in perspective, the collective shock is roughly comparable to the impact of a small super volcano, a minor asteroid, or a limited nuclear war. Catastrophes of this scale are thought to be of sufficient size to change the course of modern civilization, depending of course upon our response.

The most recent CBO report forecasts the U.S. federal debt at 150 percent of gross domestic product in 2047, which would place the U.S. as the third most indebted nation in 2017 between Greece and Lebanon. This is obviously not where the U.S. wants to be in 30 years. Fortunately, such a decline is unnecessary and well within our power to avoid, though the path is narrow and hazards are many.

Catching up on deferred maintenance is a necessary but insufficient plan for the challenges facing Americans. A modern strategic infrastructure plan should be focused on unleashing the current national economy similar to previous eras with the intercontinental railroad, interstate highway system, or electric grid.

The focus should be maximize benefits from our inventions, engineered systems and technologies to recreate a sustainable competitive advantage. One benefit of lagging behind other countries in infrastructure is that much progress has been made in recent years. Future projects can be embedded with hardware that enable intelligent networks, which can then be managed with distributed operating systems enhanced with artificial intelligence (AI) to meet the diverse needs of our society.

AI systems can substantially resolve many of the destructive forces and high-risk areas facing the modern economy, including the ability to provide far more effective governance in a highly complex data-driven world, prevention of most types of human-caused catastrophes, improved workplace productivity, more effective security, and reversal of the dangerous trajectory in healthcare costs.

In order to realize the full potential of a national intelligent infrastructure strategy, it must be planned in a highly specific manner. Intelligent infrastructure is driven by physics and engineering, which can be easily damaged by misguided or corrupted politics. The value of AI systems is substantially dependent upon the availability and integrity of data. Important priorities include but are not limited to interoperability, security, privacy, business modeling, cost of ongoing maintenance, and adaptability for future innovations.

The combination of technical viability in AI systems with the current macro economic scenario has created a perfect storm for public-private partnerships. Funds with trillions USD under management are in search of improved yields in mid to long-term bonds that offer lower risk profiles and diversification, which allows risk transfer to investors for specific projects rather than tacked on to an unsustainable national debt trajectory. Moreover, the combination of intelligent infrastructure with AI systems can improve productivity, provide attractive return on investment, and create new high paying jobs that will be competitive far into the future.

The federal government should act as the policy and standards body to avoid hard lessons learned in previous national infrastructure programs. A plug-and-play architecture is needed that encourages economic diversification in all states, fosters new business formation and new wealth creation that has the capacity for reversing the increasingly historic wealth gap.

In today’s fast moving hyper competitive world, the U.S. cannot afford to wait a century to unravel the type of monopolies that were cobbled together during the formation of the electric grid. The network economy increasingly represents the entire economy so must be as diversified and dynamic as society if to remain healthy and sustainable.

If the Trump administration and U.S. Congress seize this historic opportunity for a strategic intelligent infrastructure plan, they should find bipartisan support as it can positively impact every zip code in America, which could serve to reunify the nation around common good. Executed well, a strategic intelligent infrastructure plan can serve as a solid foundational platform to solve many of the current and future challenges facing America and the world.

Mark Montgomery is the founder and CEO of Kyield, originator of the theorem ‘yield management of knowledge’, and inventor of the patented AI system that serves as the foundation for Kyield: ‘Modular System for Optimizing Knowledge Yield in the Digital Workplace’. He can be reached at markm@kyield.com.

We must empower a more diversified economy in 2016


Austin Christmas Hat 1

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.

Key patent issued


My key patent for Kyield was issued today by the USPTO as scheduled earlier this month.

Title: Modular system for optimizing knowledge yield in the digital workplace

Abstract: A networked computer system, architecture, and method are provided for optimizing human and intellectual capital in the digital workplace environment.

To view our press release go here

To view the actual patent  go here

I will post an article when time allows on the importance of this technology and IP, and perhaps one on the experience with the patent system. Thanks, MM

New Semantic Health Care Platform


Kyield Unveils New Semantic Health Care Platform

Read the release at Business Wire or the diabetes use case scenario

Kyield AWSE

Diabetes and the American Healthcare System


I am pleased to share our just completed healthcare use case scenario in story telling format.

We selected diabetes mellitus (type 2) as a scenario to demonstrate the value of the Kyield platform to healthcare. Given the very high cost of healthcare in the U.S. currently with an unsustainable economic trajectory, it’s essential that costs be driven lower while improving care. Diabetes type 2 has direct costs exceeding $200 billion annually in the U.S. alone, the majority of which is entirely preventable.

The most obvious method to overcome this significant challenge is with far more intelligent HIT systems. It is not surprising that the legislation appears to be perfectly matched for the Kyield PaaS– nor is it entirely accidental as our mission aligns well with the needs in healthcare; an R&D process that began more than a decade ago.

This was a challenging scenario to develop and write due to the complexity of the disease, large body of regulations, incomplete standards, and varying interests between the partners in the ecosystem.  A bit of extra personal motivation for me was that my father died a few years ago from complications from diabetes, which was diagnosed shortly after my brother died of ALS. Ever since the shocking phone call from my brother informing me of his “death sentence” in the summer of 1997, I have followed ALS research; among the most complex and brutal diseases.

Diabetes type 2 is also complex, but unlike ALS and many other diseases, diabetes type 2 is largely preventable with a relatively modest change in behavior and lifestyle– modest indeed particularly compared to the later stage affects of the disease in absence of prevention, which we highlight in this use case. It’s difficult to understand after watching my father’s disease progress for a decade why anyone would not want to prevent diabetes– it literally destroys the human body.

I hope you find the case interesting and valuable. I am confident that if followed in a similar path as outlined in this scenario, the platform will contribute to significantly more effective prevention and healthcare delivery at a lower cost.

Diabetes Use Case Scenario (PDF)

Mark Montgomery

Toyota’s failure to act on the dots


It seems that every few weeks we hear of another major series of tragedies leading to a crisis that could have been substantially mitigated, if not entirely prevented. Among the most disturbing to discover, and most costly, are those involving safety issues that either threaten or cause loss of life.

Often has been the case during the past decade when the organization involved has been a government agency, but this quarter Toyota wins the prize for failure to act on the digital dots in the enterprise (in fairness Toyota should share the prize with NHTSA—also the front runners for the annual prize—it’s still early in the year).

In the case of  Toyota, we may never know how many digital red flags existed within the enterprise network, but the clear pattern in other cases suggests that the probabilities are high that warnings were common within communications, documents, and web servers indexed by enterprise search engines. Similar to previous events, these problems were known for years by the corporation and the agencies charged with regulation, yet they collectively failed to prevent loss of life, resulting in personal tragedy for a few and far reaching economic destruction in one of the world’s premier corporations.

While growing too fast may have contributed to this problem, it was organizational system design that allowed it to become one of the worst crises in the company’s history, and a serious threat to the future. Toyota has done a good job with innovation in product design compared to most of its competitors, but failed in adopting innovation in organizational management that would sustain their success.

When will leaders of the largest organizations finally understand that failing to invest in how organizations function is often the most expensive mistake executives will ever make? Given the size and impact of industry leaders, combined with the systemic nature of the global economy, these types of failures are unacceptable to growing numbers of communities, companies, and consumers.

An article in the WSJ sums it up well with this quote:

Liu Jiaxiang, a Toyota dealer in Shenzhen, said sales at his stores in southern China have been “steady” since late January. “What keeps me awake at night is a possible long-term erosion of customer confidence in the Toyota brand” because of the recall problem, he said.

Denial is a powerful force in humans, particularly when combined with peer pressure, social herding, politics, bureaucracy, contradictory information, and sheer inertia that seems unstoppable at times. It’s also true that cultures of invincibility have a consistent track record of experiencing crisis and decline soon thereafter.

Mr. Toyoda, if this post finds you, I invite your team to revisit our publications and jointly explore how our holistic semantic enterprise platform could have reduced or prevented this series of recalls. Even if  Toyota had deployed Kyield when your domain first appeared in our Web logs, we almost certainly could have contained this event to a multi-million dollar product defect issue, rather than the multi-billion dollar corporate crisis that it has become.

Mark Montgomery
Founder & CEO – Kyield
Web: http://www.kyield.com
Blog: https://kyield.wordpress.com
email: markm@kyield.com
Twitter: @kyield