Intelligence Amplification and Aggregate Work Capabilities

The CEOs of both Microsoft and Nvidia are pointing out that we are on the verge of a monumental shift, that there are new ways to perform "work".

Basically, it is now very possible to insulate subject matter experts from the technology that they need to do their job without the subject matter experts needing to get their hands dirty with the technology. An example of this can be seen in this short video. (What is going on can be hard to grasp, effectively that software you see understands these theories.)

Subject matter experts (SMEs) that understand an area of knowledge can now utilize technology that is readily available to them in a form that they can easily use that technology.  The technology will do what you tell the technology to do. 

This capability will help subject matter experts automate their own work, amplify their productivity, make them more efficient, and improve the quality of their work output.

By combining the capabilities of humans and the capabilities of tools you achieve an aggregate work capability of the "team" of a human and a machine working together.  In this teaming you achieve a human augmenting the capabilities of a machine and a machine augmenting the capabilities of a human.

Subject matter experts specialize in an area of knowledge.  Other names of area of knowledge include "domain of understanding" or "system of interest" or simply "domain".

Knowledge is a form of familiarity with information from some specific area or corpus. Knowledge is often understood to be awareness of facts, having learned skills, or having gained experience using the things and the state of affairs (situations) within some area of knowledge. 

An area of knowledge is a highly organized socially/community constructed aggregation of shared knowledge for a distinct subject matter.  An area of knowledge has a specialized insider vocabulary (i.e. jargon), underlying assumptions (axioms, theorems, constraints, assertions, restrictions), and perhaps some persistent open questions that have not necessarily been resolved within that area of knowledge (i.e. flexibility is necessary, change occurs).

Accounting is an area of knowledge.  You can explain aspects of the accounting area of knowledge, such as the nature of a financial report, using a logical theory which explains a logical model.  A logical theory can be tested and proven by providing a proof of that logical theory.

Knowledge within an area of knowledge can be represented in human-readable form, in machine-readable form, or in a machine-readable form that can be effectively converted into human-readable form.

You can think about an area of knowledge as being characterized in a spectrum with two extremes:

  • Kind area of knowledge: clear information, clear rules, lots of patterns, lots of rules, repetitive patterns, and typically unchanging tasks.
  • Wicked area of knowledge: obscure data, few or no rules, constantly changing tasks, and abstract ideas.

An area of knowledge can have aspects of both extremes, but tends to lean toward one side of the spectrum or the other.  Financial accounting and reporting tend to lean more toward the “kind” end in many ways, particularly the quantitative aspects of accounting and reporting.  The qualitative aspects may be more in the wicked side of the spectrum.

Knowledge must be managed.  Machine readable knowledge needs to be curated to keep it current.  This curation and management has value of machine readable knowledge is valuable because the machine readable rules are valuable.  This management and curation of rules takes effort.

System stakeholders need to be in agreement as to an undisputed core knowledge of a system. Knowledge within an area of knowledge can be categorized per the Cynefin Framework, into groups.  Those groups are:

  • Best practice (obvious)
  • Good practice (only obvious if you are a subject matter expert and have the right skills and experience)
  • Emergent practice (subject matter experts tend to need to have more skills and experience, then can use principles to group alternatives effectively)
  • Novel practice (tends to be unique, but describable)

Knowledge representation and reasoning (KRR) is about converting information for an area of knowledge into machine understandable form and then enabling a machine such as a computer using software to process that information in a manner that is as good as a human could have performed that task/process or even better than a human could have performed that task/process.

For example, some task or process currently performed by humans that, if measured, would achieve a sigma level of 3 which is a defect rate of 6.7% (about 67,000 defects per million opportunities) would be improved and would achieve a sigma level of 6 which is a defect rate of 0.00034% (about 4 defects per million opportunities). You are hearing me right, defects go from a whopping 67,000 down to 4.

Seem impossible? Lean Six Sigma techniques, philosophies, principles, and practices have been around for years.  They have led to high product quality of manufactured products in many industries.

Traditional tools can be improved by properly combining the capabilities of human systems for performing work and the capabilities of machine systems for performing work.  This results in intelligence amplification and an increase in aggregate work capabilities.  Think of how a calculator augments your abilities. 


Intelligence, as I am defining the term, is the ability to perceive or infer information and reason with respect to that information using a known logic and known reasoning approaches and to retain that information as knowledge to be applied towards achieving specified goals and objectives within a specified environment or context (a.k.a. formal defined system with specified and known boundaries agreed to by a known set of stakeholders).

Intelligence, as I am using the term, is “human-task” performance (a.k.a. work). Intelligence is defined by the “formalism” of a rules-based system or process; the task of set of tasks that must be performed.

A machine (e.g. computer or other such apparatus) is a tool for performing human tasks agreed to by a known group of stakeholders and the system has known goals and objectives.  In essence, this is a formal system with specific boundaries and the system is blind to something not covered by the rules of that specific defined system.

Effectively, a machine can read that knowledge and mimic understanding of that knowledge represented in a knowledge graph and the information available to both a human reader and a machine reader would be the same and therefore the human and machine should reach the same conclusion.

Understandability is the capability of a human and a machine communicating effectively.  Using a machine-understandable logical digital twin represented using a global standard technical syntax such as XBRL or RDF; it is possible to describe well understood and agreed upon quantitative and qualitative associations between things like financial facts or sets/assemblies of financial facts within a report, such as a financial report, more granular information in accounting working papers or audit schedules, or even non-financial information.  Such associations can have any degree of complexity or granularity.  

This information can then be reasoned on using a logic engine such as DATALOG which is an implementation of nearly a complete set of first order logic (i.e. some risky capabilities were removed in order to guarantee that catastrophic logical failures caused by logical paradoxes do not occur and therefore the processing is certain and reliable).

Logic is a tool that is understood by both humans and by machines such as a computer. Logic is a formal set of principles and rules that form a framework for correct reasoning and communications.  There are many different logics and there are many different technical approaches to implementing of logics. First order logic provides an excellent balance between complexity and computability.

Machine readable knowledge representations plus intelligent software agents will change the way work can be performed. Subject matter expertise is going to be a very important when creating or using intelligent software agents.  Inferior products will not help subject matter experts achieve what they need to achieve. Society should explicitly target what it wants from technology. By using curated and audited knowledge representations we get trusted sources of information that intelligent agents can consume and provide useful and provably reliable work output.

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