I discussed the knowledge spectrum and reasoning capacity in another blog post several years ago. It is time to have another look.
This graphic from Mills Davis' presentation AI - Externalization of the Mind, slide 66, depicts the relationship between knowledge representationn and reasoning capacity:
(Click image for larger view)
So, there is a little more to achieving the capability of a machine to reason. Yes, you do need an approach to representing knowledge such that a machine, such as a computer, can leverage that knowledge to perform useful work.
- You need a global standard technical format to represent the knowledge. (i.e. XBRL)
- You need standard models that you use to represent the knowledge. (i.e. business report model)
- You need to put knowledge into that format using that model. (i.e. FRF for SMEs; US GAAP)
- You need standard software that can read the knowledge that exists in that technical format and perform the required work. (i.e. Pesseract; XBRL Query; XBRL Cloud)
- You need to prove interoperability between standard software. (i.e. comparison of Pesseract, XBRL Query, XBRL Cloud; conformance suite)
Once you have all five things above, then you simply have a commodity; you have all the pieces of the "mouse trap" (law of irreducible complexity). One should be able to demonstrate why the system works. Here is my demonstration: machine-readable | human readable.
A kluge, a term from the engineering and computer science world, refers to something that is convoluted and messy but gets the job done. Anyone can create something that is complex. But it is hard work to create something that is simple.
Welcome to the Fourth Industrial Revolution! Do you and your organization have the digital maturity necessary to compete? Are you a novice or an expert? What will you create? What role will you play? Do you need to improve your digital maturity?