Thoughts on Attaining Literacy
This blog post is a response to a LinkedIn post by Paul Wilkinson effectively calling for a literacy project. The literacy project relates to the notion of "XBRL for all" or "AI-powered personal finance reporting using XBRL — the same structured reporting language used by Fortune 500 companies, scaled for every individual."
I agree with both things Paul is saying. I agree that personal finance can benefit from the same things Fortune 500 companies can benefit from. And I also agree that what is needed to make that a reality is a literacy project, as opposed to a technology project.
What is going on in the world seems to be misunderstood by many, if not most, people. Two key aspects of what is going on is the move to "digital" and harnessing the power of "artificial intelligence".
Very specifically, "digital" and "artificial intelligence" as applied to the following as stated by Paul:
"Today, GAAP and IFRS are languages spoken by specialists. But giving every person the tools to use them, starting with their own household finances, could empower billions to allocate scarce resources with greater fluency, more precision, and better results."
Or, restating Paul's statement slightly to maximize the scope; I would say, "How do we take advantage of the move to 'digital' and the new tool 'artificial intelligence' to make accounting, reporting, auditing, and analysis better, faster, and/or cheaper for Fortune 500 companies, for small businesses, for not-for-profit organizations, for state and local governments, and for individual personal finance?"
We do this at scale and we enable the creation of industrial processes using these new capabilities.
So, literacy. What exactly is literacy? As I understand it, literacy is part of a progression. Literacy is "baseline comprehension" of something. Proficiency is "competent performance" related to something. Fluency is "smooth, effortless execution" of something. Mastery is "deep command" of something.
Our objective here is the first step in the progression, literacy. That gets one moving down the right path. Once on the right path, then proficiency, fluency, and mastery are then possible.
This is my best take on how to achieve literacy when it comes to this change to "digital" and leveraging this new tool "artificial intelligence".
The Map is Not the Territory
The first thing to understand is that what is happening is a paradigm shift. Our mental model of the world (the map) and the world itself (the territory) need to match. What is happening is that the world is changing and we need to adjust our mental map to the new territory. We need a new map. Old maps will no longer work.
The Kuhn Cycle explains this dynamic. To briefly summarize this dynamic; a "paradigm shift" or "paradigm change" step occurs in the Kuhn Cycle when some new paradigm or new model is taught to newcomers to the field, as well as to those already in the field. When the new paradigm becomes the generally accepted guide (i.e. becomes best practices), a paradigm shift or change occurs in the field.
The first step to literacy is to understand that the territory has changed; so you need a new map to understand this new territory.
Digital Works Differently
This paradigm shift is caused by the difference between how "realspace" (the real world, analog) and "cyberspace" (the internet, digital) operate. While many things will stay the same; it is also the case that entirely new business models and products are possible.
Understanding what is and what is not possible in this new world is part of building your digital proficiency.
Computers are Dumb Beasts
It is important to understand the strengths/capabilities of computers and the obstacles that get in the way of harnessing the power of computers.
The following are the fundamental strengths or capabilities of computers:
- store information reliably and efficiently
- retrieve information reliably and efficiently
- process stored information reliably and efficiently, mechanically repeating the same process over and over
- instantly accessible information, made available to individuals and more importantly other machine-based processes anytime and anywhere on the planet in real time
The following are the major obstacles which must be overcome to harnessing the power of computers:
- business professional idiosyncrasies; different business professionals use different terminologies to refer to exactly the same thing
- information technology idiosyncrasies; information technology professionals use different technology options, techniques, and formats to encode and store, retrieve, and process exactly the same information
- inconsistent domain understanding of and technology's limitations in expressing interconnections within an area of knowledge
- computers are dumb beasts; computers don't understand themselves, the programs they run, or the information that they store, retrieve, process, or provide access to
Complexity and Computability
Understanding the definitions of "complexity" and "computability" are critically important to understanding what a computer can and cannot do.
Every system has a level of complexity. There are two groups of complexity: complex and non-complex. Non-complex systems are computable by definition, they have computability. Complex systems are not computable by definition. And so, a system can be simple (i.e. computable), complicated (i.e. computable), or complex (i.e. not totally computable, but some tasks may be computable). This outline distinguishes and explains the possible alternatives:
- Non-complex (computable)
- Simple system: The system is "non-complex" and therefore computable; clear and obvious for a non-subject matter expert to understand, and the set of elements, categories, and interaction patterns are fully understood. Control techniques can be used to eliminate all risk from the system.
- Complicated system: The system is "non-complex" and therefore computable; clear and obvious for a subject matter expert in the area of knowledge to which the system relates to understand, and the set of elements, categories, and interaction patterns are fully understood. Control techniques can be used to eliminate all risk from the system.
- Complex (not computable)
- Complex system: The system is "complex" and therefore NOT computable; tend to lack clear boundaries, tend to be constantly changing and evolving, there tend to be large numbers of elements, categories, and interaction patterns which are not completely understood, the system seems to contradict itself on occasion, and the number of forces impacting the system tends to be large and the dynamics are not well understood. Control techniques cannot be used to fully eliminate all risk from the system.
- Complex systems with non-complex subsystems: A complex system with some simple or complicated subsystems which can be separated and some aspects made computable.
- Complex systems which can be simplified to simulate non-complex systems: A complex system which can be "dumbed down" to a degree to enable the system to be computable but also adequately meet the goals and objectives of system stakeholders.
Intelligence
Intelligence is an idea that philosophers and scientists have been debating for thousands of years. There is no one formal agreed upon definition of intelligence. Human intelligence and machine intelligence are different; confusing the two leads to misunderstandings of what a machine is truly capable of doing. Machines don't understand, machines only interpret. Humans can interpret and also understand.
This is my working definition of intelligence: Intelligence is the ability to acquire and apply skills and knowledge within a specific area. Skills are recognized patterns of actions and behaviors. Knowledge is facts and information. Crystalized intelligence is memory. Fluid intelligence is about processes and mechanisms for working with intelligence.
Artificial Intelligence
There are two primary categories or subfields of artificial intelligence. Each category of artificial intelligence has a basket of capabilities and it is critically important to use the right tool for the job. The two primary categories of artificial intelligence are:
- Rules-based or symbolic artificial intelligence: Rules-based artificial intelligence is based on explicitly created hand-crafted knowledge and rules provided to a machine by humans. Rules-based artificial intelligence can be guaranteed to be 100% reliable to the extent of the hand-crafted knowledge and rules provided.
- Probability-based or machine-learning: Probability-based artificial intelligence works by enabling machines to learn by analyzing data and identifying patterns and clustering those patterns into groups. It then uses what it has learned to make decisions or predictions. Because this type of artificial intelligence is based on probability and statistics and known patterns it cannot be relied on to be correct 100% of the time.
Epistemic Risk
- Bricks, 2 by 4s, cement, rebar; that is the level of "data". The raw materials used to build a house. "Unprocessed".
- Walls, doors, windows, roof, floor; that is the level of "information". Information is like the bricks, 2 by 4s, and cement arranged into the structures that make up the house. "Processed bricks, 2 by 4s, and cement."
- House, a structure made up of walls, doors, windows, roof, and floor that you can live in. The house is made of walls, doors, windows, a roof, and a floor which makes it something you might be able to live in. "Completed structure."
- Blueprints, inspection process, building code; that is the epistemology. Epistemology is the blueprint, the engineering, and the inspection process which is used to answer questions like: Is this house solid? Was the building code followed when the house was constructed? Can we trust the house? Should we take the risk and live in the house? Epistemology is less about what you know, and more about how you know what you know, and whether you should trust it. "Assessment."
Governance
Governance is about the framework for decision making and collaborative action which formally specifies how a group of stakeholders organizes themselves and makes decisions and otherwise collaborates within some specific system or context to get things done and/or to achieve some common goal or objective. Governance is the system of rules, roles, processes, and relationships that guide how the group of stakeholders operate. Governance is the meta‑layer that, at it's core, ensures:
- Accountability: Who is responsible. It establishes who decides what. Performance.
- Procedures: How are decisions made. It establishes protocols. Controls. Process.
- Authority: Who is allowed. It ensures people are following the rules. Permissions.
- Alignment: Are we following our defined purpose. It checks to determine whether the group is on track. Predictability. Safety. Transparency. Openness. Integrity. Effectiveness. Collaboration.
- Assurance: Are we doing what we said we would do. Auditability. Traceability.
Governance is the control framework that keeps a system coherent. Governance is how a group makes sure things are done properly.
Meaning
Meaning is always mediated, situated, and carried by human collectives. Meaning is not something inherent in just words or objects. Meaning is consciously and deliberately produced through shared human systems, shaped by context, and sustained by communities of stakeholders.
The triangle of meaning is about precision in communication. It helps to explain the difference between meaning, representation of meaning, and real world things that exist in the world.
Meaning is the "interpretable function" that makes information actionable; knowledge is the justifiable, actionable result of the meaning creation process. Meaning arises when information is interpreted within a shared conceptual frame, the "context" or the process of being "situated". Communication is the effective exchange of meaning.
Just because a machine can read something, that does not mean the machine understands anything or that a machine can interpret what it is reading and perform useful work.
Machines interpret information based on predefined rules. Humans understand information based on context, skills, experience, research, observation, and reasoning. You cannot automate understanding.
Understanding is when something makes sense to you well enough that you can use it, explain it, or see how it fits into the context with or connects to other things you know.
Meaning is the content. Understanding is the competence (skill and experience).
Meaning is static. Understanding is dynamic.
Meaning is an encoding; Understanding is an active process: interpreting, integrating, contextualizing, applying, reasoning.
Meaning is the map. Understanding is the ability to navigate the terrain.
Meaning is intersubjective. Understanding is individual.
Literacy when it comes to "Digital" and "Artificial Intelligence"
A computer is a tool. There are times when a group of stakeholders with common objectives and goals can specify those objectives and goals enough to reach an agreement and create a useful system for that group of stakeholders for that area of knowledge. If done with the right governance to properly "orchestrate" the system, and if the system is complete; then a virtuous cycle or "feedback loop" or "causal chain" can result.
"Digital" is not about software or about technology. Digital is a mindset. Digital is about a new paradigm where humans and machines need to coexist and work together. If this can be created, then humans can benefit.
One example of what is possible can be seen when you look at Sarbanes Oxley. The consultancy Gartner points out that the typical Fortune 1000 company uses 800 electronic spreadsheets to prepare its regulatory compliance financial report. Sarbanes Oxley addresses the symptom of a problem. What Sarbanes Oxley does not do is address the conditions which result in those symptoms. This is explained in the blog post, Introducing the Global Open Industry Standard Digital Closing Book.
The same industrial processes that can serve Fortune 1000 companies can also serve small and medium sized businesses and even individuals who desire to manage their personal finances, file their taxes, apply for a mortgage loan, or run their business.
Just like the Universal Product Code (UPC) and the ISO Standard shipping container benefited society; so to can standards like XBRL, the Open Information Model (OIM) and the Standard Business Report Model (SBRM).
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