5th – Where do we go from here?

Posted on by jmswtlk26

By: John M. Switlik, July 2024; Context: AI, Knowledge Systems, ML, … .

Foreword: Content relates, in part, to this talk:
Geometric/Knowledge Modeling Challenges
at the Fall 2003, COE Conference in Seattle, WA.

We will be looking at two things, at least. The use of qualitative means in AI as evidenced by the work with KBE and the 777 program at Boeing. That will entail reviewing both personal experience plus industry history (principally with a focus on Daussault’s CATIA through several generations). Then, we will look at modeling and analysis, using the demonstrations used for a U.S. Patent as the basis. And, we mean to present another view of algebra and geometry (see Notes: below) that seems to have gotten lost in the shuffle time, ego, and lack of maturity. All of this will be under the auspices of notices of “truth engineering” as core and thereby necessary.

This will be done in blogging form with periodic print files created to establish persistence which is so needed yet hard to create with the way that modern computer systems evolved.

On the other hand, we will point to the legacy basis for the real work, especially of those things that are of the financial infrastructure.

To set the stage, there will be a recap of work prior the the 777 plus some commentary on what happened after that. That covers a timeframe of 40 years which was 4 decades of watching computing evolve through the lens of daily work in the discipline of advanced computing. Bleeding edge, some joked.

Some of the work dealt with the software side of designing computer hardware. Mostly, the viewpoint is software engineering with a maturity focus that went out of style 2 decades ago. Yet, recent accumulations of detritus (took a while to pile up) have caused some type of review where the deficiencies are being discussed.

Hence, this is timely bit of work and recall. Dassault’s CATIA will feature. It started on the IBM mainframe using cards. They worked a deal for the first workstation configuration which was in use for the start of the 777 program. We can look at how that work went through various engineering workstation configuraitons to the PC eventually. KBE with CATIA now is mostly template. That’s not what knowledge requires.

Another system had a smaller focus. Namely, AutoDesk’s little system that uses Lisp. It is mentioned merely to remind people of the old language. What about thinking of that being on the Nvidia chip?

….

With respect to computing and engineering, the following remark pertains one very good example of necessary work (See Maturity, mathematics for an overview.). Several researcher completed a survey of machine learning publications taking note of the mathematical approach that was used. The work represents several key factors. In terms of critical analysis, this is our thoughts which will be explained further.

  • I may as well get it off of my chest. My focus is truth engineering. There are reasons for that. One is that people are involved with the judgments that involve truth. We cannot compute truth. Nor, can we know it outright, in general. Truth is a private experience. Now, then, computing and truth? Let me just mention a few concepts that we will look at further: homogeneity (this is a strong assumption being taken without a basis many times; and ignored that I can see, many times – lots to discuss); equivariance (yes, fiddling, fudging, force fit – I will use the 777 and its success in attaining “fit” as well as meeting form and function – that is, it was the first attempt at complete (not met) digital design; and the metrics accomplishment, it was real – in this paper, the mention of the concept can be found in references and in one area where Lie algebra plays a role); geometry, topology, algebra (there are more subjects to bring to proper attention, such as category theory, dynamics (various sorts), and more. Essentially, as Poincare noted, mathematics is a huge subject. What’s been associated so far with this approach is a small subset, yet it got attention due to the unexpected ability to bring results that got our attention. So, computing got more powerful? We are so far from truth that we have to step back and get more scholarly.
  • Let’s marvel at 2,000 years of work, which accelerated in the last 200.

Context

Boeing Commercial Aircraft

Sperry Univac Knowledge Systems Center

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Notes.

Endnotes.

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