Truth, what is it?

TL;DR -- Knowledge systems were (and are to be considered) a primal step in the advancement of computational intelligence. Recent issues notwithstanding, we know how to handle the slop. Part of the ways and means will deal with experience. 

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So, if one gripes, one ought to have a solution. In fact, the glories of progress, if things are done right, rests with those who can show the way. But, slop as mentioned in the last post? 

Walk and not talk. The trouble with AIn't is that it talks. Very well. Do? Not so much. So, the kids of the valley went after "agents" as if they offer miracles for the following: you cannot train out the slop after the fact. Good people know that quality is built in during the process. 

At Linkedin: KBE (and MBSE).

Want to fly a plane that was done by one of these current buckets'o'bits? Seriously. 

This post leads to an article that I am writing on Linkedin. The topics will go back to the '80s which is when there had been a two-decade effort at AI after the Dartmouth meeting in the '50s. And, it deals with the Lisp of John McCarthy which had been packaged into a workstation (Lisp Machine). Coming forward, the world followed DoD (now DoW) into the "quant'" world of numerics. 

Lisp was ported to Unix. Well, the SUN networking workstations were great. But, they were not the qualitative expert as we got with the Lisp Card (to be discussed). Anyway, after a solid grounding in the use of the Lisp Machine where we did prototypes in all sorts of disciplines that worked phenomenally well, I went to Boeing and worked on the 777 digital definition project. 

This is introductory. But, the engineering support work for a major design effort was at the time that solid modeling was getting mature plus the accumulation of mathematical routines (all around) was making the computer more of a tool to replace the slide rule (which was a remarkable little thing). 

That led to "truth engineering" as a pursuit of the middle-out truth that bridges the Suites (uppers, C, G, ...) and the trenches where matter hits the road. 

Where is the "truth" of the matter? Well, it'll take some time to discuss. But, demonstration via computational examples is a good way to go. After all, we have not yet learned what is necessary to be adept generally with complex systems. 

Remarks: Modified: 04/06/2026

04/06/2026 -- Refreshed image representing progress made in 1995 and forgotten which need some attention now with regard to the future. The whole of the "knowledge" realm outside of the usual focus points, such as academia, needs to be brought to bear now with respect to AI/ML. There is some indication of that happening.