Mind the Gap

To understand artificial intelligence appropriately, one needs to understand the gap between where we are now and where we are going to end up in the future.  To get over a gap you need a bridge. Structure is the ultimate bridge between the status quo and the future if you are looking for reliable, dependable, repeatable industrial processes.  And there are no short cuts, but there are some clever tricks.

So, where are we now?  To explain where we are now, I want to go backwards one step further and explain where we were when I began my career in accounting at Price Waterhouse.  When I began my work as an auditor for Price Waterhouse in 1982, audit working papers or "audit bundles" were 100% paper documents.  "Closing books" or "closing binders" were literally a three ring binder or a set of files in a desk drawer.

By about 2003 audit bundles, closing books, and financial statements were beginning to be all "e-paper".  By e-paper or electronic paper I mean electronic spreadsheets, word processor documents, PDF files generated from word processor documents and report writers, maybe some HTML, probably some PDF images of documents scanned.

What was the gap that was crossed between 1982 and a world of all paper accounting and audit working papers and reports and 2003ish with a new world of  electronic paper documents? Here is how that gap was crossed:
  • Software: Easy to use software such as Lotus 1-2-3, Microsoft Excel, Word Perfect, Microsoft Word, Adobe Acrobat and other such software enabled accountants to process words and numbers electronically in what amounts to electronic documents or "e-paper" instead of having to write things on paper on physical paper documents.
  • Hardware: The software needed to run on something; first we got the desktop personal computer, then the luggable computer, and ultimately the laptop computer.  We got a lot of other software gadgets like hard drives and other stuff also.
  • Internet: In the 1980s and early 1990s, that software and hardware was useful and we could transfer information from one computer to another using floppy disks; but the usability really took off when it became easier to network computers together into a "work group" and then things got even better when the Internet took off and everything was easy to connect.
  • Interoperability: A decent level of interoperability existed in that you could get information out of one software application and get that information into some other software application; not optimal but you could make it work. Lots of rekeying and copy/paste.
And so, where did we end up?  Where are we now?  We have very reliable instant access to "e-paper" documents that costs us pennies (i.e. really inexpensive).  The electronic spreadsheet, a type of document, is a de facto global standard, and a very useful tool for creating electronic documents of all sorts.  The Internet has taken off and we have been through versions Web 1.0, Web 2.0, and are now at Web 3.0. Artificial intelligence is still being sorted out; we cannot differentiate what the real possibilities might be and what is hype.

The bottom line is that there are two unknowns. Unknown #1 is where exactly are we going to end up.  Unknown #2 is how exactly are we going to get to where we end up?  There seems to be different "camps" or "tribes" or "groups" or "patterns of thinking".

What is absolutely certain is that we will not end up at the status quo.  I can tell you that definitively.  Change is inevitable.  In fact, change is appearing pretty immanent.

Here is what I am thinking and why I am thinking it.

First, computers are not going to rule the world. A computer is a tool.  A computer is a machine.  The biggest risk related to artificial intelligence is that humans will rely on artificial intelligence in ways that it should not and that certain humans will abuse artificial intelligence.

Second, computers are dumb beasts. Computers have four capabilities they can perform reliably: (1) store information, (2) retrieve information, (3) process information, (4) make information instantly accessible.  In order to effectively use those four capabilities; there are obstacles that MUST be overcome. (A) different business professionals use different terminologies to refer to exactly the same thing, (B) information technology professionals use different technology options, techniques, and formats to do the same thing, (C) inconsistent domain understanding of and technology's limitations in expressing interconnections within a domain, (D) computers are dumb beasts that don't understand themselves, the programs they run, or the information that they store/retrieve/process/provide access to.

Third, while computers are dumb beats; the obstacles can be overcome and you can get computers to perform useful work if you have the right skills and experience.  However, you must use the right tool(s) for the task.

And so, here is the camp that I am in.  Step number one is that you have to give computers a chance to actually succeed.  A very significant problem is that a lot of information is stored in the form of documents that serve as "costumes" to present that information to humans. These document oriented representations are very inconsistent and they are very challenging for a computer to sort out.  Documents, including spreadsheets, are ambiguous at scale. There are exactly three approaches to overcome this obstacle and each approach has a specific level of success that the approach can achieve:
  1. Have a computer figure out the information.  Currently, the best information that I have shows that a computer can get things about 74% to 87% correct and you can't tell which is correct and which is incorrect.  This probability based approach is OK for certain use cases, but it is not OK when accuracy and precision matter (i.e. where there is no room for failure).
  2. Have a human figure out the information. It has been well understood that if you give a machine rules that it can effectively interpret; reliable, repeatable processes can be created.  But the problem is that having humans do all this work is expensive.
  3. Have a human and computer team up to figure out the information. A hybrid approach which combines #1 and #2 is possible and this human-machine teaming which is referred to as neuro symbolic artificial intelligence can be quite effective.
So the graphic below which was generated by artificial intelligence attempts to help you understand what I am saying.  If you take properly structured information from some machine interpretable format and you give that information to a machine; that machine can generate a proper human readable format that a human can use to interpret that same information. The "path" shown by the green arrow can be guaranteed reliable and repeatable, industrial strength processes can be created.  However, this is not the case for the red arrow.  If you take documents that humans created which tend to be too inconsistent for a computer to reliably and accurately interpret; you cannot (I repeat, cannot) create reliable, repeatable processes that never make mistakes.  This simply can never work.  Full stop.

But a human and machine team structuring information is not enough.  Global open industry standards are necessary to scale systems.  No way around it.

Complexity must be managed.  Complexity can be managed.  Business professionals must be provided appropriate tools where they can effectively manage and operate within their community of practice and not deal with technical complexity.

My approach is to create a flexible, modular, reliable, global open industry based, semantic oriented mechanism which is artificial intelligence enabled which I refer to as the "digital information organism".   Using that lower level "Lego-like" framework, structure, and process; higher level accounting, reporting, audit, and analysis artifacts can be created including ledgers, journals, trial balances, lead schedules, reconciliations, statements, policies, disclosures or an entire closing book, an entire audit bundle, and entire financial statement, entire analysis model, or an entire reporting framework. All of these different types of artifacts are all based on the one global open industry standards based, model driven, semantic oriented, digital information organism core pattern which follows the design pattern of a holon.

This framework, structure, and process is proven to work effectively, here is an example financial statement holon which has been implemented using the XBRL technical syntax. Other technical formats could also be used.  A financial statement is a well understood formal semantic structure.

While all my work,  my garden as I call it, relates to financial accounting, reporting, audit, and analysis; I contend that this framework, structure, and process will work equally as well for general business reporting.

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