Digital Financial Reporting

Jeff Hawkins, in a 16 minute Ted Talk, How brain science will change computing, explains why a framework is necessary and why a theory is necessary.  He provides this explanation by taking about brains.  I can distill the important message pointed out by this video as it relates to digital financial reporting in this concise statement:

What is conspicuously lacking is a broad framework let alone a theory on how to think about digital financial reports.

Here are some of the important things that Jeff Hawkins points out his video:

• "We don't need more data, we need a good theory."
• "Things look complicated until you understand them."
• "We have an intuitive, strongly held, but incorrect assumption that has prevented us from seeing the answer. There is something that we believe that is obvious, but it is wrong."
• "Intelligence is defined by prediction."
• "We experience the world in a sequence of patterns." 
• "Real intelligence is a sequence of patterns."
• "Successful prediction is understanding."
• "Intelligence is making predictions about novel events."
• "Memory of high-dimensional patterns"
• "Memories are stored and recalled as a sequence of patterns"
• "Must be testable"
• "Must be buildable, if you cannot build it then you don't understand it."

For some reason, I intuitively understood that a theory and framework were necessary.  Rene van Egmond and I created a theory, Financial Report Semantics and Dynamics Theory, back in 2012.  That theory documents a framework.

The fundamental accounting concept relations are predictions.  I have been fiddling around with patterns that I have noticed in financial reports since 2000 or so.  I have used those patterns for many, many things; refining and tuning them along the way.  I have studied systems as part of the work toward my MBA way back in the 1980's and 1990's.

Seems like what Jeff Hawkins is saying is that the artificial intelligence community got it wrong.  (See minute 11 to about 13 in the video.

It seems to me like "memorizing" and "metadata" are the same thing.  Humans can memorize things, but these memories need to be put into machine-readable form for the computer to use.  So, these disclosures and these report frames seem like memories.  Using these memories helps you make predictions, "Because I see assets, because I see liabilities and equity, I predict that this is a balance sheet and not an income statement."

The biggest hurdle that I can see is summarized in this statement made by Jeff Hawkins:

We have an intuitive, strongly held, but incorrect assumption that has prevented us from seeing the answer. There is something that we believe that is obvious, but it is wrong.

[Another useful video, How Intelligent is Artificial Intelligence?]