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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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.

New E-Book: Ascension to a Higher Level of Performance


Power of Transdisciplinary Convergence (Copyright 2015 Kyield All RIghts Reserved)

Power of Transdisciplinary Convergence

Ascension to a Higher Level of Performance 

The Kyield OS: A Unified AI System

By Mark Montgomery
Founder & CEO
Kyield

I just completed an extensive e-book for customers and prospective customers, which should be of interest to all senior management teams in all sectors as the content impacts every aspect of individual and corporate performance.

Our goals in this e-book are fivefold:

  1. Provide a condensed story on Kyield and the voyage required to reach this stage.
  2. Demonstrate how the Kyield OS assimilates disparate disciplines in a unified manner to rapidly improve organizations and then achieve continuous improvement.
  3. Discuss how advances in software, hardware and algorithmics are incorporated in our patented AI system design to accelerate strategic performance and remain competitive.
  4. Detail how a carefully choreographed multi-phase pilot of the Kyield OS can provide the opportunity for an enduring competitive advantage by establishing a continuously adaptive learning organization (CALO).
  5. Educate existing and prospective customers on the Kyield OS as much as possible without disclosing unrecoverable intellectual capital, future patents and trade secrets.
TABLE OF CONTENTS
INTRODUCTION  1
REVOLUTION IN IT-ENABLED COMPETITIVENESS  2
POWER OF TRANSDISCIPLINARY CONVERGENCE  3
MANAGEMENT CONSULTING  4
COMPUTER SCIENCE AND PHYSICS  5
ECONOMICS AND PSYCHOLOGY  9
LIFE SCIENCE AND HEALTHCARE 10
PRODUCTS AND INDUSTRY PLATFORMS 11
THE KYIELD OS 11
THE KYIELD PERSONALIZED HEALTHCARE PLATFORM 12
ACCELERATED R&D 13
SPECIFIC LIFE SCIENCE AND HEALTHCARE USE CASES 13
BANKING AND FINANCIAL SERVICES 14
THE PILOT PROCESS 15
EXAMPLE: BANKING, PHASE 1 17
PHASE 2 18
PHASE 3 18
PHASE 4 18
CONCLUSION: IN THIS CASE THE END JUSTIFIES THE MEANS  21

To request a copy of this e-book please email me at markm@kyield.com from your corporate email account with job title and affiliation.

Transforming Healthcare With Data Physics


I just completed an in-depth paper on how our work and system can help life science and healthcare companies overcome the great challenges they face, so I wanted to share some thoughts while still fresh. The paper is part of our long-term commitment to healthcare and life sciences, requiring a deep dive over the past several weeks to update myself on the latest research in behavioral psychology, machine learning, deep learning, genetics, chemicals, diagnostics, economics, and particle physics, among others. The review included several hundred papers as well as a few dozen reports.

Kyield Distributed OS - Life Science and Healthcare

The good news is that the science is improving rapidly. An important catalyst to accelerated learning over the past 20 years has been embracing the multi-disciplinary approach, which academia resisted for many years despite the obvious benefits, but is now finally mainstream with positive impact everywhere one looks.

The bad news is that the economics of U.S. healthcare has not noticeably improved. For a considerable portion of the population it has deteriorated. The economic trajectory for the country is frankly grim unless we transform the entire healthcare ecosystem.

A common obstacle to vast improvement in healthcare outcomes that transcends all disciplines with enormous economic consequences is data management and analytics, or perhaps more accurately; the lack thereof. There is no doubt that unified networks must play a lead role in the transformation of healthcare. A few clips from the paper:

“By structural we mean the physics of data, including latency, entropy, compression, and security methodology. The Kyield system is intended to define structural integrity in NNs, continually exploring and working to improve upon state-of-the-art techniques.”

“While significant progress has been made with independent standards towards a more sustainable network economy, functionality varies considerably by technology, industry, and geography, with variety of data types and models remaining among the greatest obstacles to discovery, cost efficiency, performance, security, and personalization.”

Life science and healthcare are particularly impacted by heterogeneous data, which is one reason why networked healthcare is primitive, expensive, slow, and alarmingly prone to error.

“Biodiversity presents a unique challenge for data analytics due to its ambiguity, diversity, and specialized language, which then must be integrated with healthcare and data standards as well as a variety of proprietary vendor technology in database management systems, logistics, networking, productivity, and analytics programs.”

“Due to the complexity across LS and healthcare in data types, standards, scale, and regulatory requirements, a functional unified network OS requires specific combinations of the most advanced technology and methods available.”

Among the most difficult challenges facing management in mature life science companies are cultures that have been substantially insulated from economic reality for decades, only recently feeling the brunt of unsustainable economic modeling throughout the ecosystem, typically in the form of restructures, layoffs, and in some cases closure. This uncertainty particularly impacts individuals who are accustomed to career security and relatively high levels of compensation. I observed this often during a decade of consulting. The pain caused by a dysfunctional economic system is similar to the diseases professionals spend their careers fighting; often unjustly targeting individuals in a seemingly random manner, which of course has consequences.

“Among many changes for knowledge workers associated with the digital revolution and macro economics are less security, more free agency, more frequent job changes, much higher levels of global venture funding, less loyalty to corporate brands and mature industry models, and considerably increased motivation and activism towards personal passionate causes.”

Healthcare is a topic where I have personal passion as it cuts to the core of the most important issues to me, including family, friends, colleagues, and economics, which unfortunately in U.S. healthcare represents a highly self-destructive model. My brother was diagnosed with Lou Gehrig’s disease (amyotrophic lateral sclerosis/ALS) in 1997 not long after his only child was born. I’ll never forget that phone call with him or what he and his family endured over the next three years even though his case was a fine example of dedicated people and community. My father passed a decade later after a brutal battle with type 2 diabetes; we had an old friend pass from MS recently, and multiple cancers as well as epilepsy are ongoing within our small group of family and friends. So it would be foolhardy to deny the personal impact and interest. Healthcare affects us all whether we realize it or not, and increasingly, future generations are paying for the current generation’s unwillingness to achieve a sustainable trajectory. Unacceptable doesn’t quite capture the severity of this systemic failure we all own a part of.

The challenge as I see it is to channel our energy in a positive manner to transform the healthcare system with a laser focus on improved health and economic outcomes. This of course requires a focus on prevention, reduced complexity throughout the ecosystem, accelerated science, much improved technology, and last but not least; rational economic modeling to included increased competition. The latter will obviously require entirely new distribution systems and business models more aligned with current science and economic environment. Any significant progress must include highly evolved legislation reflecting far more empowerment of patients and dramatic improvement in fiscal discipline for the ultimate payer we call America while there is still time to manage the disease. If we continue to treat only the symptoms of healthcare in America it may well destroy the quality of life for the patient, if indeed the patient as we know it survives at all. This essentially represents my diagnosis.

A few of the 80 references I cited in the paper linked below are good sources to learn more:

Beyond borders: unlocking value. Biotechnology Industry Report 2014, EY
http://www.ey.com/GL/en/Industries/Life-Sciences/EY-beyond-borders-unlocking-value

Dixon-Fyle, S., Ghandi, S., Pellathy, T., Spatharou, A., Changing patient behavior: the new frontier in healthcare value (2012). Health International, McKinsey & Company.
http://healthcare.mckinsey.com/sites/default/files/791750_Changing_Patient_Behavior_the_Next_Frontier_in_Healthcare_Value.pdf

Thessen A., Cui H., Mozzherin D. Applications of Natural Language Processing in Biodiversity Science Adv Bioinformatics.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364545/

Top 10 Clinical Trial Failures of 2013. Genetic Engineering & Biotechnology News.
http://www.genengnews.com/insight-and-intelligence/top-10-clinical-trial-failures-of-2013/77900029/

Begley, C.G., Ellis, L.M. (2012) Drug development: raise standards for preclinical cancer research. Nature 483 http://www.nature.com/nature/journal/v483/n7391/pdf/483531a.pdf

Cambria, E., and White, B. Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9:1–28, 2014.
http://www.krchowdhary.com/ai/lects/nlp-research-com-intlg-ieee.pdf

Montgomery, M. Diabetes and the American Healthcare System. Kyield, Published online May 2010
http://www.kyield.com/images/Kyield_Diabetes_Use_Case_Scenario.pdf

All quotes above are mine from Kyield’s paper of 8-15-2014:

Unified Network Operating System
With Adaptive Data Management Tailored to Each Entity
Biotech, Pharmaceuticals, Healthcare, and Life Sciences

New Report: Adaptive Unification for Life Science Ecosystems


First, I want to apologize for not being able to keep up with my blog as much as I would like, or to share as much in public as I would prefer. The reasons are twofold. We’ve been very busy at Kyield, and testing has increasingly confirmed that while competitors in our industry invest heavily in web information (CI), most customers do not; at least for enterprise-wide systems like Kyield.  So I have regrettably pulled back on detailed public writing, or rather– have replaced with more formal papers and presentations with customers.

A good example of our efforts is the new report below, which is a hybrid of an academic paper with citations supporting our claims and a detailed brochure for senior managers in pharmaceuticals, biotech, and healthcare–particularly those pursuing personalized medicine and significant improvement in operational efficiency:

Adaptive Unification  for  Life Science  Ecosystems 

Kyield report: Adaptive Unification for Life Science Ecosystems

The paper highlights the challenges facing the industry with considerable detail on how Kyield is unique in the world with respect to ability to overcome these challenges. Essentially, in order to overcome systemic challenges it requires a systemic solution, and in terms of distributed organizations it requires a very particular type of systemic solution that can address each of the challenges. Due to the high values involved, the result is that Kyield may well be the best investment option in the world today for life science executives.

For those who would prefer more frequent updates, the best methods to track either Kyield or my activity are as follows:

Connect with me on LinkedIn:      http://www.linkedin.com/in/markamontgomery/

Follow Kyield on LinkedIn:              

Follow @kyield on Twitter:             https://twitter.com/kyield

And of course visit our web site regularly at www.kyield.com

Kind regards,

Mark Montgomery

Best of Kyield Blog Index


I created an index page containing links to the best articles in our blog with personal ratings:

Best of Kyield Blog Index.

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

Regulatory Failure on the Web; Consequences and Solutions


I have argued consistently since the mid 1990s that the global medium (combined Internet and Web) increasingly reflects the global economy and that rational, functional regulation is essential. I started this journey then with a very similar ideology to Alan Greenspan’s before the financial crisis; that self-regulation should be sufficient to prevent systemic crises, but unfortunately in practice it has failed to do so.

Most of the actual regulation in computer networking today is accomplished via manipulation of architecture in one form or another, but technical standards on the web are voluntary, as the Tech Review article The Web is Reborn highlights, which was apparently in response to the article The Web is Dead at Wired earlier in the year. In the U.S. we are really reliant primarily on one form of regulation on the web, other than proprietary architecture and voluntary standards, which is social. Social regulation has evolved with the consumer web, occasionally demonstrating some power—as was recently demonstrated with Facebook security issues, but social regulation has also proven to be self-destructive at times, particularly regarding sustainable economics and jobs. Few if any consumers can see how their actions on the web are impacting their own regional economy or industry, meaning that the blind is often leading the blind towards dangerous hazards in a similar fashion to the housing crisis. Ignorance is being exploited.

Two eras, opposing needs, yet same reaction

In order to provide some context with continuity let’s begin with the PC revolution about 30 years ago when the lack of interoperability allowed Microsoft to extend its growth from the operating system into productivity, communications, and eventually networking, forming one of the strongest strategic partnerships and business ecosystems in history.

During the PC era regulation was essentially outsourced to industry which employed a combination of proprietary computer code, strategic alliances and the failure of others to work together in a competitive manner to establish the standard, which of course led to a monopoly. The political, social and cultural dynamics at play were very interesting at the time, dominated by the view still common today that the only other option was government which couldn’t be relied upon to regulate technology.  It turns out that many other forms of regulation exist that can be learned from in natural sciences, physical sciences, social sciences, and architecture, among others.

Among the most important business lessons in the PC era was that Microsoft bet against the abilities of others to work together which when combined with strong execution was rewarded at historic levels. At the time I clearly recall arguments from investors, customers, government officials and even competitors agreeing with Microsoft that the PC revolution was too young for external regulation (which I heard again this week regarding the web), so “let the best man win”.  I myself said much the same then—not having the benefit of observing this case (and many others) in what is a very complex, quickly evolving environment.

In hindsight I believe we were correct regarding regulation in the PC era, but only for a very brief time—less than a decade; that’s how fast the big innovation door opened, scaled, and began to close. Our society cannot respond that quickly. This was new territory, just as the web would be a decade later.

In a very similar manner to the PC era the lack of regulation on the early web fostered a highly innovative environment during the very early days, but the era peaked much quicker on the web due to the toxic flood of capital during the inflation of the dot-com bubble; and the web was very quickly taken over by entrenched interests.  Opportunity still exists of course, but the dirty secret few discuss is that the failure rate for new IT ventures is very likely at an all time high—no credible statistics exist on the entire ecosystem to my knowledge; only portions thereof.  Of course knowledge, experience, relationships, resources and luck play a big role on outcomes, as usual, but lack of effective regulation generally favors and rewards predatory behavior.

The PC was sustaining innovation; the web is disruptive innovation

It’s important to understand that in the pre-network era economic alignment in desktop computing was primarily positive for everyone except direct competitors to Microsoft (or Intel in semiconductors), which was managed masterfully by a brilliant entrepreneur who became the world’s wealthiest human, and supported by many other brilliant people.  The world needed a standard for interoperability, and since few were negatively impacted the increases in productivity from authoritarian rule were viewed largely as positive within the social regulation realm, even if only for a brief time.  In hindsight government regulation failed not only to prevent the monopoly but also in resolving it. Government was then and still is today complicit in the creation and protection of  monopolies, regardless of how they form, particularly in the U.S. and EU within the IT cluster, which is I think driving future industry leaders to other countries.

Once monopoly power sets in it can be very destructive, including to the long-term company culture within the monopoly itself, which provides a strong case to manage market share very carefully.  The largest impact, however, is invisible, which usually manifests as aborted innovation within the specific market and industry, lack of adoption of competing technologies, and failed investment, which is evident today in most consolidated industries as reflected by very poor economic performance.

Failed regulation often leads to market failure, which is a real possibility for the web unless a sustainable economic structure is formed. This is essentially the argument behind the claim the “Web is dead”—with Chris Anderson suggesting that the Internet was moving over to wireless devices where a more viable economic structure is forming; customers are far more likely to pay for services rendered in the iPhone structure than the web structure.

As I have often argued since the commercialization of the web, the advertising industry is not nearly large enough to compensate for the economic displacement of industries from the disruption, particularly in the service dominated economies in the West.  Silicon Valley, Madison Ave, Wall St. and D.C. cultures still don’t seem to fully understand this reality and equation, or presumably policy would reflect it.  China and Germany on the other hand seem to understand the issue with abundant clarity, and are exploiting the situation brilliantly, as is India and others.  A nation does not want to be dependent upon a service economy within a global economy that is increasingly delivering services over an ad supported medium; particularly a nation deep in debt that is challenged educationally.

The often misunderstood lesson here is that the PC ecosystem was not a disruptive innovation but rather a sustaining innovation—meaning that it threatened very few. In direct contrast the web is very disruptive—not only to specific companies, but to entire industry clusters, regional, and national economies, which affects everyone on the planet.

Despite this extremely important difference, regulatory schemes reacting to the two very different situations are essentially the same, and will very likely result in a similar outcome unless regulation improves quickly, particularly relating to technical standardization.  As market share becomes more dominant in corporate cultures, so does hubris—the cultures become increasingly less influenced by voluntary standard processes or social regulation.  Eventually, as we’ve seen in our recent past, the monopoly cultures can even directly challenge the authority of sovereign governments with the potential exist for global companies to actually dominate national policy.  Currently Ireland provides a fine case study of why such a situation should be avoided.

I maintain that the winner-takes-all approach of the PC era would be catastrophic for the web and the global economy, perhaps even leading indirectly to civil disruption and conflict. Many wars have been fought over far less economic disruption so in a very real sense this issue is one of national and global security.

So is the web dead or reborn?

The web is primarily lost in a sea of confusion from lack of structure, which is largely due to the lack of effective regulation, which is in turn due to spin from those who benefit from the lack of regulation, and perhaps the impact of that spin on the ideology within our culture.  As in all previous standards wars free from effective regulation, a continuous battle rages, albeit somewhat more rational given the global nature of the beast than in previous sector or geographic standards wars.

In an invited letter to the editor in an upcoming issue of a leading publication, I will argue for functional regulation of the web relating to the creation and enforcement of technical standards, which are necessary to achieve security, privacy, and a host of other essential issues, including some degree of certainty for investors and entrepreneurs like myself.  It is far more important that credible independent standards exist than what the specific standards are, which is lost on the academic CS community almost entirely.  The current scheme is without power, glacial, and entirely without dependability, the latter of which is synonymous with credibility outside of academia.

I will save my detailed suggestions on how such a regulatory body might be structured for my book, but there is an emerging regulatory scheme on the web worth noting within the largest industry.  The U.S. health care legislation, as messy as it was, did empower the HHS to determine technical standards for electronic health records, which was tied to funding and reimbursement.  While substantially less than perfect, this standards process does appear to have the ability to gain traction due to a combination of initial funding, need for interoperable data, and leverage from other governments around the world to achieve a functional global standard.

Just one example of how this may occur is the relationship between life sciences, government regulation over drugs and devices, and the delivery of health care, all of which will require interoperability in order to function with any degree of efficiency.  In the current environment the health care technical standards process appears to be the most functional regulatory path towards adoption of a more intelligent web, aka the semantic web.

While we are all aware of the messiness of democracy, this alternate path towards regulation of standards on the web should not be viewed as a substitute for a rational, long-term solution.  Welcoming luck once it occurs is one thing; depending on it for survival quite another.  Our economy is too fragile and complex to depend on luck alone.  Conflicted interests simply cannot be trusted, whether corporate, academic, or otherwise.

How can information technology improve health care?


I recall first asking this question in leadership forums in our online network in 1997, hoping that a Nobel laureate or Turing Award winner might have a quick answer.  A few weeks earlier I had escorted my brother Brett and his wife from Phoenix Sky Harbor airport to the Mayo Clinic in Scottsdale, seeking a better diagnosis than the three-year death sentence he had just received from a physician in Washington. Unfortunately, Mayo Clinic could only confirm the initial diagnosis for Amyotrophic lateral sclerosis (ALS).

In my brother’s case, the health care system functioned much better than did the family; it was the dastardly disease that required a cure, along with perhaps my own remnant hubris, but since his employer covered health care costs we were protected from most of the economic impact. I then immersed myself in life science while continuing the experiential learning curve in our tech incubator. It soon became apparent that solving related challenges in research would take considerably longer than the three years available to my brother, his wife, and their new son. Close observation of health care has since revealed that research was only part of the challenge.

Symptoms of an impending crisis

During my years in early stage venture capital, symptoms of future economic crisis in health care appeared in several forms, including:

  • R&D failed to consider macro economics
  • Technology that increased costs were most likely to be funded and succeed
  • Technology that decreased costs were often unfunded and/or not adopted
  • Cultural silos in scientific disciplines were entrenched as effective guilds
  • Professional compensation packages were growing rapidly
  • Regulatory bureaucracy was devolving
  • The valley of death was expanding rapidly
  • Trajectory of HC costs and customer means were in opposing X formation

Over the course of the following decade, while observing my father’s experience with diabetes—including billing, it became obvious that few stakeholders in the life science and health care ecosystem were provided with a financial incentive for preserving the overall system; meaning the challenge was classically systemic. Clayton Christensen sums up the situation in health care succinctly: “clearly, systemic problems require systemic solutions.”

12 years to design an answer

When looking at the challenges within information technology and health care first as individual systems, and then combined as a dynamic integrated system, we came to several conclusions that eventually led to the design of our semantic healthcare platform.

Ten essentials:

  1. Patients must manage their own health, including data
  2. Universal computing standards; likely regulated in health care
  3. A trustworthy organization and architecture
  4. Simple to use for entry level skills
  5. Unbiased, evidenced-based information and learning tools
  6. Highly structured data from inception
  7. High volume data meticulously synthesized from inception
  8. Integrated professional and patient social networking
  9. Mobile to include automation, analytics, and predictive
  10. Anonymous data should be made available to researchers

Probable benefits

While we must build, scale, and evaluate our system to confirm and measure our predictions, we anticipate measurable benefits to multiple stakeholders, including:

  • Higher levels of participation in preventative medicine
  • More timely and accurate use of diagnostics
  • Reduced levels of unnecessary hospital visitation
  • Higher levels of nutrition
  • Lower levels of over-prescription
  • Reduced levels of human error
  • Improved physician productivity
  • Reduced overall cost of health care
  • Much greater use of analytics and predictive algorithms
  • Expedited paths to discovery for LS researchers

In May of 2010, we unveiled our semantic health care platform with a companion diabetes use case scenario, which is written in a story telling format, and is freely available in PDF.

Preparing for the big data storm in mHealth


Imagine the gasp from the family physician when he is confronted with the first text message from a patient asking whether they are dying due to the asynchronistic patterns displayed by their new mobile app, which is being downloaded by the millions daily.  Or consider the cardiologist who suddenly has the opportunity to observe a continuous mobile data stream 24/7/365 sourced from multiple sensors on 95% of her patients, creating more data in a day than the previous decade.

For some time now I have been thinking about structures, classifications, compression, security, scaling, and synthesis of data for the anticipated big data storm just beginning to form in mobile health. Even with a healthy dose of de-hyped skepticism, the mobile health data storm promises record sustained winds, with much higher gusts.  An extension of the Internet and Web, which is often compared to a global electric grid, mobility also contains dynamics more comparable to solar winds or sea plankton, complete we intend with personalized recipes that will positively impact human behavior, diagnostics, and therapies.

While specific outcomes can only be fully understood with high-scale testing, several assumptions warrant serious consideration, including:

  • Several billion people worldwide will be introduced to lab-quality, real-time data for the first time
  • To avert chaos, mHealth data must be highly structured from inception
  • Care givers will quickly become far more conversant in computing
  • Care facilities and organizations will be transformed, with multiple new disruptive models emerging
  • Multi-dimensional visualization of human physiology will become ubiquitous from global to nano scale
  • Self-managed care will rapidly scale, as will expectations
  • Sensory implants and implanted sensors will normalize
  • Advanced data management engines will become essential cores of healthcare organizations
  • The gap between life science research and health care will begin to close
  • The velocity of discoveries will rapidly expand

In looking out to this new galaxy, it’s relatively easy to see far more dramatic change than is commonly discussed. Medicine has avoided much of the revolutionary change affecting other industries from rapid technological evolution, in large part by regulatory management, leading to a model that is no longer affordable. How might health care change when ‘democratized’ by billions of people? Who knows; a considerable amount is probably a fair guess. I am far less certain of how mobile health will change regulation than the necessity to properly manage the data, or the need for data structure, in order to optimize decision making and realize the potential of personalized medicine.

The mobile phenomenon is much different than the emergence of enterprise networks, the consumer web, or big data found within intelligence and research, but is rather more like a hybrid that borrows from each, with the extraordinarily complex individual human brain increasingly in the driver’s seat. It should prove to be a journey full of adventure, hidden obstacles, and exciting discovery.