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

Advertisements

Marc Spezialy joins Kyield as our first CFO


IMG_7892

I am delighted to announce that Marc Spezialy has agreed to become CFO for Kyield in preparing for expansion as we scale the Kyield OS in hybrid managed services model.

Originally from Alaska, Marc attended the University of San Francisco where he graduated with a double major in accounting and finance before joining PwC in Austin as an auditor.  At PwC Marc was a manager that focused on technology clients. Marc then joined a new oil business as CFO that included a turn-around leading to a small public company. His next role was VP of Finance for a successful aerospace asset management company that involved complex international finance and tax. While in Austin, Marc was also a board member and treasurer of Habitat for Humanity Texas.

Marc and his wife relocated to Denver to be closer to family in raising their children where he has been consulting and served as CFO for a retail start-up during their growth phase. He will work out of Denver on an interim basis for the next few months to lead our financing and accounting efforts. I look forward to working with Marc to refine and execute our business plan at this critical expansion stage for Kyield.

Mark Montgomery
Founder & CEO
Kyield

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.

An open letter to Fortune 500 CEOs on AI systems from Kyield’s founder


IMG_1532

Since we offer an organizational and network operating system—technically defined as a modular artificial intelligence system, we usually deal with the most important strategic and operational issues facing organizations. This is most obvious in our new HumCat offering, which provides advanced technology for the prevention of human-caused catastrophes. Short of preventing an asteroid or comet collision with earth, this is among the most important work that is executable today. Please keep that in mind while reading.

In our efforts to assist organizations we perform an informal review on our process with the goal of improving upon the experience for all concerned. In cases where we invest a considerable amount of time, energy, and money, the review is more formal and extensive, including SWOT analysis, security checks and reviews, and deep scenario plans that can become extremely detailed down to the molecular level.

We are still a small company and I am the founder who performed the bulk of R&D, so by necessity I’m still involved in each case. Our current process has been refined over the last decade in many dozens of engagements with senior teams on strategic issues. In so doing we see patterns develop over time that we learn from and I share with senior executives when behavior causes them problems. This is still relatively new territory while we carefully craft the AI-assisted economy.

I just sent another such lessons learned to a CEO in a public company this morning. Usually this is done in a confidential manner to very few and never revealed otherwise, but I wanted to share a general pattern that is negatively impacting organizations in part due to the compounding effect it has on the broader economy. Essentially this can be reduced to misapplying the company’s playbook in dealing with advanced technology.

The playbook in this instance for lack of a better word can be described as ‘industry tech’, as in fintech or insurtech. While new to some in banking and insurance, this basic model has been applied for decades with limited, mixed results over time. The current version has switched the name incubators for accelerators, innovation shops now take the place of R&D and/or product development, and corporate VC is still good old corporate VC. Generally speaking this playbook can be a good thing for companies in industries like banking and insurance where the bulk of R&D came from a small group of companies that delivered very little innovation over decades, or worse as we saw in the financial crisis, which delivered toxic innovation. Eventually the lack of innovation can cause macro economic problems and place an entire industry at risk.

A highly refined AI system like ours is considered by many today to be among most important and valuable of any type, hence the fear however unjustified in our case. Anyone expecting to receive our ideas through open innovation on these issues is irresponsible and dangerous to themselves and others, including your company. That is the same as bank employees handing out money for free or insurance claims adjusters knowingly paying fraudulent claims at scale. Don’t treat the issue of intellectual capital and property lightly, including trade secrets, or it will damage your organization badly.

The most common mistake I see in practice is relying solely on ‘the innovation playbook’. CEOs especially should always be open to opportunity and on the lookout for threats, particularly any that have the capacity to make or save the company. Most of the critical issues companies face will not come from or fit within the innovation playbook. Accelerators, internal innovation shops and corporate VC arms are good tools when used appropriately, but if you rely solely on them you will almost certainly fail. None of the most successful CEOs that come to mind rely only on fixed playbooks.

These are a few of the more common specific suggestions I’ve made to CEOs of leading companies in dealing with AI systems, in part based on working with several of the most successful tech companies at similar stage, and in part in engaging with hundreds of other organizations from our perspective. As you can see I’ve learned to be a bit more blunt.

  1. Don’t attempt to force a system like Kyield into your innovation playbook. Attempting to do so will only increase risk for your company and do nothing at all for mine but waste time and money. Google succeeded in doing so with DeepMind, but it came at a high price and they needed to be flexible. Very few will be able to engage similarly, which is one reason why the Kyield OS is still available to customer organizations.

  2. With very few exceptions, primarily industry-specific R&D, we are far more experienced in AI systems than your team or consultants. With regard to the specific issues, functionality, and use cases within Kyield that we offer, no one has more experience and intel I am aware of. We simply haven’t shared it. Many are expert in specific algorithmics, but not distributed AI systems, which is what is needed.

  3. A few companies have now wasted billions of USD attempting to replicate the wheel that we will provide at a small fraction of that cost. A small number of CEOs have lost their jobs due to events that cost tens of billions I have good reason to believe our HumCat could have prevented. It therefore makes no sense at all not to adopt.

  4. The process must be treated with the respect and priority it deserves. Take it seriously. Lead it personally. Any such effort requires a team but can’t be entirely delegated.

  5. Our HumCat program requires training and certification in senior officers for good reasons. If it wasn’t necessary it wouldn’t be required.

  6. Don’t fear us, but don’t attempt to steal our work. Some of the best in the world have tried and failed. It’s not a good idea to try with us or any of our peers.

  7. Resist the temptation to customize universal issues that have already been refined. We call this the open checkbook to AI system hell. It has ended several CEO careers already and it’s early.

  8. Since these are the most important issues facing any organization, it’s really wise to let us help. Despite the broad characterization, not all of us are trying to kill your organization or put your people out of work. Quite the contrary in our case or we would have gone down a different path long ago.

  9. We are among the best allies to have in this war.

  10. It is war.

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.

A Million in Prevention can be Worth Billions of Cure with Distributed AI Systems


Deep Water Horizon Rig (NOAA)

DeepWater Horizon Rig, April 2010 (NOAA News)

Every year, natural catastrophes (nat cat) are highly visible events that cause major damage across the world. In 2016 the cost of nat cats were estimated to be $175 billion, $50 billion of which were covered by insurance, reflecting severe financial losses for impacted areas.[i]  The total cost of natural catastrophes since 2000 was approximately $2.3 trillion.[ii]

Much less understood is that human-caused catastrophes (hum cat) have resulted in much greater economic damage during the same period and have become increasingly preventable. Since 2000 the world has experienced two preventable hum cat events of $10 trillion or more: the 9/11 terrorist attacks and the global financial crisis. In addition, although the Tōhoku earthquake in Japan was unavoidable, the Fukushima Daiichi nuclear disaster was also preventable, now estimated at $188 billion excluding widespread human suffering and environmental damage.[iii]

One commonality in these and other disasters is that experts issued advanced and accurate evidence-based warnings only to be ignored. The most famous and costly such example was the Phoenix memo issued on July 10, 2001 by Special Agent Kenneth J. Williams.[iv]  The FBI memo was described as “chilling” by the first journalist who reviewed it due to specificity in describing terrorist-linked individuals who were training to fly commercial aircraft.

Williams was a seasoned terrorism expert who followed the prescribed use of the FBI’s rules-based system, yet during the two-month period prior to the 9/11 attacks no relevant action was taken. If the lead had been pursued the terrorist attacks and ensuing events would very likely have been avoided, including two wars with massive casualties and continuing hostilities.

Government agencies have invested heavily in prevention since that fateful day of September 11, 2001 so hopefully similar events will be prevented.

In corporate catastrophes, however, prevention scenarios are usually more complex than the Phoenix memo case. They are also occurring with increasing frequency and expanding in scale and cost.

The remainder of this article can be viewed at Cognitive World.

View brief video by Mark related to this article and Kyield’s new HumCat product.

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.

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).

 

ebook-kyield-ascension-to-higher-level

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


IMG_7892.JPG

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


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

Why go to the Moon when what your company really needs is in the Rockies? (AI, Watson, Kyield)


Mark Betsy Austin on summit

Mark, Betsy & Austin on top of NM – 10-2015

.

This post is in response to an excellent article Tom Davenport wrote for the WSJ (now on LinkedIn) ‘Lessons from the Cognitive Front Lines: Early Adopters of IBM’s Watson’.

Tom is a long-term advocate for increasing jobs related to analytics, particularly in the service sector, and is an advisor to Deloitte, which is a strong alliance partner with IBM, and Deloitte is a sponsor of WSJ CIO. Like most in our industry, we are in constant discussions, but as of now Kyield has no formal alliances or conflicts with any of the people or organizations mentioned in this article.

I too am an advocate for jobs, though not necessarily for IT incumbents, but rather for customers and the broader economy. Smaller companies create most jobs and most job losses come from incumbent consolidation, which is a credible place from which to start this discussion. I think Tom’s article and most of the related strategy is about protecting a few very specific jobs at Armonk, NY, and perhaps at a few alliance partners—not creating them for customers or the broader economy. Modern job creation is no mystery; it’s very well documented.

This article triggered a great many thoughts so I felt compelled to blog about it very early in the morning from my perch in Santa Fe, NM. You see I am the founder of a company called Kyield with an authentic invention based on a theory I developed in our small lab 20 years ago (yield management of knowledge), which looks and sounds increasingly like what Watson has been attempting to become over the last few years. Our company is self-funded almost entirely by me and my wife (well into 7 figures), and frankly I’m feeling just a bit over-exploited at the moment, so hang in there as I poke some fun at the expense of our esteemed colleagues on the east coast and hopefully share something valuable in the process.

Several very important clues and quotes were revealed in this article. I highly recommend reading it carefully as Tom is one of the most experienced and knowledgeable observers in related overlapping domains. First, let’s dissect the lessons the article shares with us including that only one of the organizations interviewed in the article was a customer of Watson, which was University of Texas MD Anderson Cancer Center (MDACC), three were a “partner/co-developer” or “Watson ecosystem partners”, and the undisclosed health insurance company’s relationship type was also not disclosed. Those of us enlightened on the complex relationships in enterprise IT will of course immediately wonder what the terms of these relationships are, who is paying for what, and most importantly why.

Some things we can confirm. IBM has disclosed a billion dollar investment in Watson, often claims to be betting the farm on Watson and/or the cloud, and is obviously spending enormous sums on marketing, partnerships and sales. I too have a lot riding on my company Kyield so IBM’s CEO Ginni and I share that in common—our jobs and future wealth are riding on our respective systems. IBM is constantly reminding us that Watson and healthcare in particular are “moon shots” for IBM, but I’m seriously beginning to wonder if this moon shot is a prudent business decision for IBM and its customers, or a science project that needs another two decades of R&D like we performed with Kyield before attempting to unleash it on customers—particularly business customers (more on basic vs. applied research in a minute).

In order to get our arms around this topic we need to understand a few of the business and technical issues, which for me dates back to the early 1980s to include discussions with IBM and most other industry leaders off and on the entire time. As you may be aware, IBM has been substantially dependent upon the high-end service model since the previous major transformation led by Lou Gerstner in the early 1990s.

What is less known is that IBM grew to over 400,000 employees in that model with more in India than in the U.S. While a brilliant turn-around model in Lou’s time that probably saved the company, 20+ years later the service model has in my view grown far beyond the means to pay for it, and become a big part of the problem in IT for customers and the macro economy. I think IBM understands this well, but it’s a slow and difficult transformation. Ginni herself often states in interviews the challenge is whether IBM “can make the transformation in time”. I suspect with some private confirmation that time may be growing short.

IBM is not alone. All system integrators and many IT consultants share this misalignment of interest challenge often discussed today regarding both internal and external IT investments. The IT services sector represents something like a third of the now almost $4 trillion global IT industry, but drives spending in the majority, which is one reason why the enterprise cloud market is exploding. We then need to understand that IBM’s quarterly revenue has been falling like a rock for several years and so too has the company’s value, during which time competitors like AWS are experiencing record rapid growth, which places a great deal of pressure on the company, partners and loyal customers, not to mention investors and employees. While I am empathetic with IBM’s challenge and especially employees, rest assured that whatever pressure IBM is under it cannot compare to a self-funded entrepreneur.

We have our challenges as well to include the fallout from IBM’s problem in the marketplace, not least of which is a massive ad spend and sales force, with a combined millions of individuals in shareholders, employees and partners all over the world clicking on articles about Watson, which just incentivizes publishers to write more articles with the keyword Watson. Unfortunately, all that attention and spending isn’t necessarily good for customers, the economy, or even IBM—in this regard I may have as much or more relevant experience in my background as our friends at IBM as the challenge to overcome such an advantage in small companies is substantially greater than defense in an incumbent.

The namesake Watson by the way was not a scientist, but a famous salesman who built the early IBM by going door to door selling machines—trust me I respect that, as well as IBM, and many I know and have known at the company. This may provide a clue however regarding the dual branding definition in the name Watson. Prior to becoming CEO of IBM, Ginni was SVP Sales, Marketing, and Strategy at IBM (I too am guilty as I was a CSO of smaller turn-around companies long before changing paths before Lou’s time).

Let’s talk technology

With business strategy, misalignment of interests, and potential business model conflicts out of the way, let’s now take a brief look at the science and technology involved with Watson, which like my company Kyield and our OS is based on AI—in fact my core patent was labeled an AI system by the USPTO. Ginni is stepping back from using the term AI now (“a small part of it”) presumably due to the fear of job displacement out there and other nonsense perpetuated by famous brands with all manner of agendas, which is certainly understandable. I’ve contributed to that learning curve myself over at Wired, but make no mistake Watson is AI even if most of the revenue may come from some other stream like system integration, solutions, and consulting.

While defining AI is not a perfect science, the consensus among scientists is that AI can be divided into two forms, which is extremely important to understand and directly relevant to almost everything discussed here and elsewhere on AI:

  1. General AI (AGI), which is also referred to by some leading AI scientists and authors who cover the field as ‘super intelligence’, or strong AI.
  2. Narrow AI, which is also called weak, narrow or applied AI.

Augmentation or enhancement is by extension applied AI as it is very narrow and highly specific, particularly in our case for each individual entity down to the molecular level when necessary (as in personalized healthcare). Kyield is without question one of the world’s competency leaders at the confluence of human and artificial intelligence, if not the leader.

Now let’s delve into the carefully structured quotes in Tom’s article to glean some additional intelligence.

“It’s an apprenticeship form of training that takes years—there are lots of subtleties that Watson has to learn,” said Dr. Kris at MSKCC.

This is inherent with any deep learning (DL) application including source code shared by researchers with the public and those in our system, but the challenge frankly has always been with system design, algorithmics, and hardware, not marketing. DL is now widely available for anyone who has the talent, providing a long-term benefit across all sectors, and certainly not dependent on Watson, Kyield or any other system. Rather it’s a function within the system. The difference with Kyield is that while DL is tapped for continuous learning over time, other critically important functionality in the basic core provides immediate value to customers, like increased productivity and crisis prevention.

It was not a trivial undertaking to design the Kyield OS in a simple to use fashion. I doubt that it would have been possible if funded by a conflicted organization of any kind, including the super majority of corporations, foundations or government R&D programs. We sacrificed to remain independent throughout the long voyage across the valley of death in large part to avoid such conflicts and be free to focus only on the needs of customers and the specific tasks at hand, not least of which is to prevent crises sourced within large organizations.

“But the problem comes when the needed knowledge isn’t in the corpus. Dr. Kris at MSKCC comments: We had three drugs approved in lung cancer this year. None of them are in the literature yet. And definitions of cancer and its variations are being redefined all the time as we understand the biological characteristics of each one. The science is changing more rapidly than the published literature.”

This is a good example of many specific types of intellectual obstacles we were forced to overcome early in our R&D, and the solution is frankly partially represented in our patented design. The complete solution also includes tradecraft and secrets to include expected future patents, and like IBM we are dependent on intellectual property for survival, so I can’t disclose further except to say that it was a very difficult and expensive problem to overcome; one deemed necessary prior to offering to customers.

“MDACC (UT MD Anderson Cancer Center) actually referred to its project as a moon shot.”….. “An application like OEA cannot deliver on its intended impact of improving patient outcomes worldwide without addressing the necessary network infrastructure, security and regulatory controls, data sharing/access/use contracts, and reimbursement, not to mention the culture of medicine and clinical adoption. Only through addressing these non-technical challenges, we will be able to translate a piece of technology, like OEA, into impact. That is what separates an innovation from a transformation…that is what makes it a moon shot.” — Dr. Lynda Chin, who led the Watson-based project at MDACC.

I see this as the most mission oriented statement in Tom’s article, coming from the only customer disclosed. MDACC has a clear mission in scientific research to eventually eliminate cancer so related ‘moon shots’ fall well within the organization’s responsibility. Most organizations to include most businesses do not share a similar mission as MDACC, which is why we took our R&D much further in applied form and removed as much risk as possible for customers prior to offering, even if admittedly lacking billions USD in development, marketing or sales.

Definition of Moonshot:

A moonshot, in a technology context, is an ambitious, exploratory and ground-breaking project undertaken without any expectation of near-term profitability or benefit and also, perhaps, without a full investigation of potential risks and benefits.”

If you concluded from reading this article that Kyield doesn’t claim to be a moonshot, that would be correct. Kyield does not provide artificial general intelligence, but rather offers a highly evolved system with as much complexity driven out of it as possible, so it is very much an applied system with a laser focus. While Kyield was a moon shot in the mid 1990s when developing the theorem in our lab, it is now a viable product and system at a very attractive price with a reasonably good probability of achieving an outstanding ROI.

Bringing discussion back to earth

Back down here on earth at the southern tip of the Rocky Mountains in the Land of Enchantment is a City Different called Santa Fe, which is over 400 years old. This area is known for history, art, culture, climate and science, the combination of which is why we brought Kyield here from the Bay area seven years ago to mature our R&D. While NM has vast open spaces made famous by Georgia O’Keeffe among others, we also have one of the highest concentrations of intellectual capital in the known universe, including of course the Moon!

I have a suggestion that is entirely compatible with moon shots of the Watson kind, which local theorists understand better than most, and that is to adopt a very pragmatic AI system that follows the rules of laws, physics and economics. We engaged in this process by the book, took massive risk, played strictly by the rules of engagement, invented an authentic system from scratch, and are now offering the world’s most advanced system at the confluence of human and artificial intelligence, which can be adopted at a tiny fraction of the cost as those described in Tom’s article.

In addition, while almost every single one of the Fortune 100 has benefited greatly from the science here in NM, most of which was produced with taxpayer’s money (Kyield is a rare exception in that regard), that value is almost always exported in the form of spinouts, flips and M&A, usually to the coasts and occasionally off-shore. This commercialization (aka tech transfer) model that generates considerable wealth for a very few has not manifested into benefitting NM from the beneficiaries of the R&D in the private sector, otherwise the numbers would be very different.

Seven decades after the Manhattan Project and hundreds of billions of dollars later, NM has yet to experience a significant business success, will soon surpass WV to rank dead last in unemployment, and has among the highest rates of poverty and crime in the U.S. And it isn’t just about education as many with advanced degrees are unemployed or underemployed here. Part-time wait staff at local hospitality establishments or gift shops holding doctorates is not uncommon.

So my suggestion is to come on out and visit Santa Fe just as hundreds of the leading minds in the world do each year, and we can then discuss in greater detail how our applied science in the form of the Kyield OS can help your organization ascend to a higher level of performance, and do the right thing for your career, organization and the economy in the process. In so doing you will empower us to empower NM and perhaps the rest of the global economy to ascend to the next level, which would be a good thing for everyone.

Oh, about that Watson we keep reading about? No worries, it’s likely compatible with the Kyield OS—that’s what all those APIs are for. And who knows, one of these days that moon shot may just pay off. In the interim we can help your organization ascend almost immediately following adoption of the Kyield OS!

Mark Montgomery