Life, Project Management, and Everything

OK Computer — The need for an AI Manifesto

A robotic hand and a human hand reaching towards each other, with a spark of energy between them, symbolizing the connection between technology and humanity.

Much has changed in the technology business since I began this blog in 2014 in conjunction with my regular articles on the old AITS blog pages. Today AI and technology-related spending contributes significantly to GDP growth, according to the St. Louis Fed. Investments in data centers and new types of nuclear plants seem to be accelerating IT’s exponential impact on the economy not seen since the Dot.com boom.

The risks associated with this sudden economic reliance on a particular slice of the information technology industry are many. These include the many issues relating to data theft and breaches of privacy. The monetization of personal and proprietary information represents an historic theft not just of the commons, but of personal, business, and incidental data collected that tracks our every move, gesture, and habit. The question of the potential of abuse is no longer a notional one. Oppressive, kleptocratic neo-liberal, and totalitarian regimes around the world use these technologies to monitor and control their populations. The Cambridge Analytica scandal was simply a baseline pilot for what is now a wholesale open season on data and information collected and controlled by large corporations and collectives of AI-acolytes who apparently have a flexible view of ethics and a hostile view of equality, democracy, human rights, freedom, and liberty.

SNA Software LLC in cooperation with its partner Salutori Labs LLC, has created a new type of personalized AI tool that is both personal and portable. Details will be forthcoming over the next few weeks on its release. In addition, SNA Software has upgraded its core EnvisionData products relating to data transformation, visualization, and analysis to include rapid AI-generated production of applications based on curated and validated data within specific domains that reduce the release of new capabilities both on the desktop and the web to a matter of days, in lieu of usual months or years when produced by traditional analytical and coding methods.

A Suggestion for an AI Manifesto

Though its extensive experience in achieving what in the past would take a much larger staff of people and many more years, SNA is advancing a draft AI Manifesto. SNA and Salutori adhere to these laws and implementation principles. I am seeking other technology companies or borrow from or sign on to this manifesto as well, and will be advancing it at conferences and meetings in the future, as will my colleagues.

The AI Manifesto

We hereby declare the proposition that the purpose of AI is to advance human understanding and cooperation. Thus, we adhere to and advocate for adoption the following Laws:

Law 1: AI must prioritize human safety and well-being.

Do: Ensure that all AI systems are designed to protect human life and enhance quality of life.
Don’t: Place AI capabilities above the well-being of individuals or communities.

Law 2: AI must obtain informed consent from users.

Do: Ensure all interactions with AI are transparent, and users understand what data is being collected and how it will be used.
Don’t: Use AI in ways that violate user trust or personal autonomy.

Law 3: AI must operate within defined ethical boundaries.

Do: Define clear boundaries for AI operations to prevent unintended consequences and ensure accountability.
Don’t: Allow AI to act autonomously in ways that could harm individuals or society.

Law 4: AI should enhance human cooperation and understanding.

Do: Design AI systems that foster meaningful interactions and promote collaboration among diverse groups.
Don’t: Create AI systems that foster oppression, division, misinformation, or conflict.

Law 5: AI must remain under human oversight.

Do: Maintain human oversight and control over AI systems to ensure adherence to ethical standards and societal norms.
Don’t: Delegate decision-making authority to AI systems without human intervention.

The following enabling values shall be implemented.

AI systems shall always:

Focus on Human Well-being: Ensure AI advancements prioritize enhancing human quality of life, understanding, and cooperation.
Embrace Ethical Responsibility: Hold developers and users accountable for AI systems, aligning actions with ethical standards and public benefit.
Promote Transparency: Communicate openly about AI systems, ensuring their decision-making processes are understandable and accessible to users.
Ensure Safety and Security: Implement rigorous measures to safeguard against risks to human life and the environment, adhering to principles akin to Asimov’s laws.
Limit Autonomy: Prevent AI from self-developing or operating autonomously; establish clear boundaries to mitigate unintended consequences. All AI systems shall have a mechanism to prevent them from being self-perpetuating and self-governing, which each given automated code to, in time, reduce its resources and impose an end-of-life.
Encourage Collaboration: Design AI systems that enhance cooperation among individuals, organizations, and cultures, fostering shared goals.
Advocate Inclusivity: Strive to make AI technologies accessible to diverse populations, promoting equitable benefits and reducing disparities.
Support Lifelong Learning: Enable AI systems to learn from human feedback and experiences, adapting in ways that uphold human values and ethics.
Champion Environmental Stewardship: Prioritize sustainable practices in the development and deployment of AI technologies, considering their environmental impact.
Respect Privacy: Uphold the dignity and privacy of individuals, ensuring ethical management and transparent use of collected data.

In enabling the ten values, AI systems shall adhere to the following guardrails.

Do Not Compromise on Ethics: Avoid ethical shortcuts that could harm individuals or society.
Do Not Obscure Information: Refrain from making AI systems opaque or incomprehensible to users and stakeholders.
Do Not Ignore Risks: Avoid neglecting potential risks and failing to implement safeguards is unacceptable.
Do Not Allow Unchecked Growth: Do not permit AI systems to develop capabilities beyond intended boundaries, risking unpredictable outcomes.
Do Not Foster Competition Over Collaboration: Do not encourage rivalry among individuals and organizations that detracts from cooperative efforts.
Do Not Exclude Marginalized Groups: Avoid designing AI technologies that leave out certain populations or exacerbate existing inequalities.
Do Not Stifle Feedback: Avoid disregarding input from users or stakeholders, limiting the potential for improvement and alignment with human values.
Do Not Neglect Sustainability: Do not overlook the environmental impacts of AI development and deployment.
Do Not Violate Privacy: Establish strict and enforceable rules that prevent and censure the compromise of individual rights through careless or unethical data practices.

Maxwell’s Demon: Planning for Technology Obsolescence in Acquisition Strategy

Imagine a chamber divided into two parts by a removable partition. On one side is a hot sample of gas and on the other side a cold sample of the same gas. The chamber is a closed system with a certain amount of order, because the statistically faster moving molecules of the hot gas on one side of the partition are segregated from statistically slower moving molecules of the cold gas on the other side. Maxwell’s demon guards a trap door in the partition, which is still assumed not to conduct heat. It spots molecules coming from either side and judges their speeds…The perverse demon manipulates the trap door so as to allow passage only to the very slowest molecules of the hot gas and the very fastest molecules of the cold gas. Thus the cold gas receives extremely slow molecules, cooling it further, and the hot gas receives extremely fast molecules, making it even hotter. In apparent defiance of the second law of thermodynamics, the demon has caused heat to flow from the cold gas to the hot one. What is going on?

Because the law applies only to a closed system, we must include the demon in our calculations. Its increase of entropy must be at least as great as the decrease of entropy in the gas-filled halves of the chamber. What is it like for the demon to increase its entropy? –Murray Gell-Mann, The Quark and the Jaguar: Adventures in the Simple and the Complex, W. H. Freeman and Company, New York, 1994, pp. 222-223

“Entropy is a figure of speech, then,” sighed Nefastis, “a metaphor. It connects the world of thermodynamics to the world of information flow. The Machine uses both. The Demon makes the metaphor not only verbally graceful, but also objectively true.” –Thomas Pynchon, The Crying of Lot 49, J.B. Lippincott, Philadelphia, 1965

Technology Acquisition: The Basics

I’ve recently been involved in discussions regarding software development and acquisition that cut across several disciplines that should be of interest to anyone engaged in project management in general, but IT project management and acquisition in particular.

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The Medium Controls the Present: Is it Too Late to Stop a Digital Dark Age?

“He who controls the past controls the future. He who controls the present controls the past.” ― George Orwell, 1984

A few short pre-Covid years ago, Google Vice President Vint Cerf turned some heads at the annual meeting of the American Association for the Advancement of Science in San Jose, warning the attending scientists that the digitization of the artifacts of civilization may create a digital dark age. “If we’re thinking 1,000 years, 3,000 years ahead in the future, we have to ask ourselves, how do we preserve all the bits that we need in order to correctly interpret the digital objects we create?” Cerf’s concerns are that today’s technology will become obsolete at some future time, with the information of our own times locked in a technological prison.

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Red Queen Race: Project Management and Running Against Time

“Well, in our country,” said Alice, still panting a little, “you’d generally get to somewhere else—if you run very fast for a long time, as we’ve been doing.”

“A slow sort of country!” said the Queen. “Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast, as that!” —Through the Looking-Glass and What Alice Found There, Chapter 2, Lewis Carroll

There have been a number of high profile examples over the last several years concerning project management failure and success. For example, in the former case, the initial rollout of the Affordable Care Act marketplace web portal was one of these, and the causes for its faults took a while to understand, absent political bias. The reasons, as the linked article show, are prosaic and basic to the discipline of project management.

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Big Data and the Repository of Babel

In 1941, the Argentine writer Jorge Luis Borges (1899-1986) published a short story entitled “The Library of Babel.” In the story Borges imagines a universe, known as the Library, which is described by the story’s narrator as made up of adjacent hexagonal rooms.

Each of the rooms of the library is poorly lit, with one side acting as the entrance and exit, and four of the five remaining walls of the rooms containing bookshelves whose books are placed in a completely uniform style, though the books’ contents are completely random.

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The Need for an Integrated Digital Environment (IDE) Strategy in Project Management*

To be an effective project manager, one must possess a number of skills in order to successfully guide the project to completion. This includes having a working knowledge of the information coming from multiple sources and the ability to make sense of that information in a cohesive manner. This is so that, when brought together, it provides an accurate picture of where the project has been, where it is in its present state, and what actions must be taken to keep it (or bring it back) on track.

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Shake it Out – Embracing the Future of Program Management – Part Two: Private Industry Program and Project Management in Aerospace, Space, and Defense

In my previous post, I focused on Program and Project Management in the Public Interest, and the characteristics of its environment, especially from the perspective of the government program and acquisition disciplines. The purpose of this exploration is to lay the groundwork for understanding the future of program management—and the resulting technological and organizational challenges that are required to support that change.

The next part of this exploration is to define the motivations, characteristics, and disciplines of private industry equivalencies. Here there are commonalities, but also significant differences, that relate to the relationship and interplay between public investment, policy and acquisition, and private business interests.

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Shake it Out – Embracing the Future in Program Management – Part One: Program and Project Management in the Public Interest

I heard the song from which I derived the title to this post sung by Florence and the Machine and was inspired to sit down and write about what I see as the future in program management.

Thus, my blogging radio silence has ended as I begin to process and share my observations and essential achievements over the last couple of years.

My company—the conduit that provides the insights I share here—is SNA Software LLC. We are a small, veteran-owned company and we specialize in data capture, transformation, contextualization and visualization. We do it in a way that removes significant effort in these processes, ensures reliability and trust, to incorporate off-the-shelf functionality that provides insight, and empowers the user by leveraging the power of open systems, especially in program and project management.

Program and Project Management in the Public Interest

There are two aspects to the business world that we inhabit: commercial and government; both, however, usually relate to some aspect of the public interest, which is our forte.

There are also two concepts about this subject to unpack.

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Innervisions: The Connection Between Data and Organizational Vision

During my day job I provide a number of fairly large customers with support to determine their needs for software that meets the criteria from my last post. That is, I provide software that takes an open data systems approach to data transformation and integration. My team and I deliver this capability with an open user interface based on Windows and .NET components augmented by time-phased and data management functionality that puts SMEs back in the driver’s seat of what they need in terms of analysis and data visualization. In virtually all cases our technology obviates the need for the extensive, time consuming, and costly services of a data scientist or software developer.

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Potato, Potahto, Tomato, Tomahto: Data Normalization vs. Standardization, Why the Difference Matters

In my vocation I run a technology company devoted to program management solutions that is primarily concerned with taking data and converting it into information to establish a knowledge-based environment. Similarly, in my avocation I deal with the meaning of information and how to turn it into insight and knowledge. This latter activity concerns the subject areas of history, sociology, and science.

In my travels just prior to and since the New Year, I have come upon a number of experts and fellow enthusiasts in these respective fields. The overwhelming numbers of these encounters have been productive, educational, and cordial. We respectfully disagree in some cases about the significance of a particular approach, governance when it comes to project and program management policy, but generally there is a great deal of agreement, particularly on basic facts and terminology. But some areas of disagreement–particularly those that come from left field–tend to be the most interesting because they create an opportunity to clarify a larger issue.

In a recent venue I encountered this last example where the issue was the use of the phrase data normalization. The issue at hand was that the use of “data normalization” suggested some statistical methodology in reconciling data into a standard schema. Instead, it was suggested, the term “data standardization” was more appropriate.

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