Shoebox and its uses

Tl;DR -- Shoebox and its uses? We start with a 1992 book. 

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At one manufacturing environment, there was a tool kit that was flat and cushy with outlines of tools needd for a job. If assembly, it would also have the parts to be put together. The machinist/assembler would take the kit(s) to the machine/workstation which included everything needed. At one time, one would go to a tool crib when a need cropped up. 

So, optimization and savings were the goal for the change. 

Now, we're into the digital age and having the pains, growing type and other. What does it all mean? A book from the early 1990s had an interesting take on the matter. This list identifies the book and the author and points to other material. 

•  Mirror Worlds: or the Day Software Puts the Universe in a Shoebox... - fortunately, Google has somewhat of a preview.
• David Gelernter - the author was son of Herbert Gelernter, early AIer.  
• Wikipedia: Mirror world (on the book); Mirror Worlds (a company following the idea). 

Why is this important? Several reasons. "shoebox" itself has been used several ways. We'll trace down those related to AI, in particular.

 

Remarks: Modified: 09/07/2024

09/07/2024 --  Add image. 

von Neumann, Hopper

TL:DR -- von Neumann influenceed us all over a long period of time. Hopper was there, in the beginning, contributing to what we have now. Times have changed. Lessons learned need to be re-evaluated. Options, in the future, will have greater potenttial to be wise or not. Our choice. 

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Last year, I started to work with an associate at Sperry Univac on looking at the history of computing, from the perspective of the people involved. Of late, I have been working on a project with respect to the future of computing which took time away from that effort. 

Our notes are at the TGS site: https://tgsoc.org/papers/. Larry Walker was the Director of Sperry's Knowledge Systems Center where I worked prior to going to Boeing Computer Services, in Wichita KS. With the advent of GenAI in 2022, we both agreed that this untimely event was like referenced by Alan Kay after his intitial review: a train wreck waiting to happen. 

Larry was involved with the development of the early operating systems (Control Data, Sperry) and played a leadership role through several decades. He has written of his work at Sperry's KSC and later. 

Well, lots of topics can be discussed. This blog has many posts on the subject: machine learning; science of information; mathematics. For now, we'll be posting reading material. 

Today, we see more evidence of mature presentations coming to fore. ACM's Communications has had several articles, recently. Today, we look at the Myth of the Coder. The authors start during the time of von Neumann who was a prominent mathematican and who was influential in several fields. In computing, he left us with the model still being used. 

His thought was that there were four stages (levels) involved with computing. 1st - the mathematician was involved with the problem and solution being written in a format that emphasized the formality of the situation. In other words, the algorithm then was a real thing to behold. Heuristics and other formats were for the less formal presentation. 2nd - this level took the mathematical view and converted it into a flowchart. We all need to witness that and how it worked. 3rd - this is where the actual coding was done using the language of the computer. We'll skip over details until a later date, but every manufacture had their own notions ited to the underlying hardware (circuitry). Finally, 4th - worried about the specifics of computing (binary representation) and rescource allocations (memory, et al). 

Stopping for a moment, those who deal with C or C++ (of course, there are others at this level) is familiar with those types of choices. Coming forward to the realm of Python, lots of this type of considerations is not usually on the plate. In between, we find other approaches. And, today, a whole new realm exists at the HTML/CSS level. 

Reminder? We will emphasize domains which is where knowledge is accumulated and utilized. von Neumann was dealing a lot with Physics which has a close tie with numeric mathematics which is in vogue (way to much, as John will explain). Mathematics has many other limbs and roots, and we will look at these from one perspective to note for the future: category theory. 

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Wait, the article steps forward. By the time John got his start, FORTRAN was the focus in the type of engineering computing that he did. But, COBOL was prime in business. It wasn't long, though, that various types of offshoots appeared. Lisp, for example, which we will use as the paragon modeling. There were methods to parametrically specify requirements. Some approaches were tabular which could handle fairly sophisticated situations. 

Think of the modern spreadsheet whose presence is everywhere, it seems. 

Grace Hoppe and COBOL saw 2 levels - analytst, programmer. Those lower steps which were specific to the machine (say, IBM or Sperry or ...) were handled by the compiler and its after proceesses (a huge one was the linkedit step that created the executable. 

Now, with Python or JavaScript, that happens almost automatically. But, reminder, Lisp was doing that type of interpretative approach way back. 

There are other varieties of roles over the years. One key fact is that this article does not consider the domain requirements which will be more importantly handled now with the fiasco of GenAI. 

Considering the historical distinction
between coder and programmer.

Remarks: Modified: 09/04/2024

09/04/2024 --  

Hugging Face

TL;DR -- Old company, IBM, has entered the AI world with a code-sharing effort. This is an example of recent technology changes. 

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IBM got wise and decided to open up machine learning with a code focus. We have mentioned GitHub a time or two. This post is meant to bring this effort to attention, as we will be paying attention. They have tens of thousands of companies signed up. 

So, let's start with an image an a link. 

IBM's new User Camp
related to the Granite approach

This is for code sharing which we may get into. Basically, our focus is analytic, for now. 

Remarks: Modified: 08/30/2024

08/30/2024 --  

Geometric Science of Information

TL;DR -- Two basic pieces of mathematics are algebra and geometry. There are others. But, the geometry folks have met over the years and are presenting what they know in a manner that needs attention. Hence, we'll look at that before going on to general modeling. 

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We write of technology. But, we have to look at mathematics, as well. From several senses, as it is the basis for parts of STEM and has operational use as well as being involved with the more esoteric notions.

Of late, I have been reading of the Geometry of Information. In terms of knowledge, one thinks about logic or machine learning which has many aspects. But, core to our being are two facilities that we use of many others. I stress these two due to the topics related to things going awry when the understanding is insufficient to prevent misuse or creation of lures that lead to disaster. 

For instance, when ChatGPT was looked at by an elderly AI person, he said: train wreck waiting to happen. We have written of GenAI (the overview concep) a lot this past year, including examples of using the systems. And, we talked about our leaning toward Gemini. 

But, we are going to be more specific about mathematics as we look at knowledge systems (from the '80s) as being of importance for trying to balance the potential harm of machine learning with human considerations about their future. Our theme will be (like we read in a DOD paper) that humans (and their intuition) are the focus, and the artificial is a tool to be applied by humans. Of course, that is hard to see given the configuration that provides us a bully (another one - we have had those forever) that is a know-it-all or wants to be (wannabe). 

Okay, we will do this on a regular basis. Let's leave with an image from the GSI folks. 

Remarks: Modified: 08/29/2024

08/29/2024 --  

Technology - basis

TL;DR -- Trust? Computers? Mathematics? Somewhere and someone is worthy? Or not? We can tackle that in our focus on technology theme. 

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So, we asked, Trust whom? Since the question is not easily answered, let's discuss a few things about the situation in order to shed some light on alternative choices and how to decide between then. 

To do this, we will mention a source that might be unexpected to some: Emily Noether. Ms Noether came to the US due to WWII and taught mathematics at Bryn Mawr. We will look further at her family and at the contributions of one of her students: Bartel Leendert van der Waerden. After WWII, Mr van der Waerden taught at Johns Hopkins in the U.S. but went back to Amsterdam. 

The theme of the post is "Technology - basis" which goes with our new focus. Mr. van Der Waerden wrote a definitive book on Modern Algebra. In particular, our interest is Volume II (available via archive[.org]) which deals with some of the mathematics being used in computing, especially the advanced methods associated with machine learning.   

Our intent is to cover artificial intelligence from stem to stern and from top to bottom. We started that effort a few years ag and now accelerate due to the recent infusion of AI into the culture everywhere. Despite arguments that deny this statement, we can know what's behind the covers of computing and explain it so as to make it availabe to public consumption. 

And, as we see with the University of Cambridge, AI is everywhere (as this search shows). Now, we picked Cambridge since logs of the early folks in New England were of that institution. Too, Ms Noether figures since the mathematics goes back to the early 1900s. Hence, the studies of her students and their students are apropos for attention. 

On a broader scope, modern physics makes use of the insightts of Ms Noether. This paper by Prof Baez of the University of California, Riverside shows: Getting to the Bottom of Noether’s Theorem. 

Remarks: Modified: 08/25/2024

08/25/2024 --  

Trust whom?

TL;DR -- After doing an extensive sweep of the internet (information universe), especially with respect to technical details, one can ask, whom to trust? GenAI, over time, will make the problem worse. So,our technology focus fits within that scheme of things. Where do we go from here? 

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There has not been a post since early August as busy with an assessment of technolgy with respect to computing and the GenAI that has been becoming more prominent on the landscape throughout the year. 

My activity has been reviewing the past two decades which saw the internet bloom and had machine learning come back with a vengeance once Job's gift got out and was replicated by other players. 

In particular, I have been concenttrating on database management changes plus the newer modes of accomplishing functionality.  

A post on Quora somewhat summarizes the current viewpoint (worldview, educated opinion) that I have which stresses that technology is now suitable for a regular focus as it runs throughout our lives introducting changes that many times have not been beneficial.

This is the question/answer: As Oracle's CTO, what emerging AI and machine learning advancements are poised to revolutionize enterprise data management in the next decade? The questions was asked by Larry Ellison in his role as CTO of Oracle. 

I created this post for the answer which starts a series of stick diagrams that will be improved later. 

Another bit of activity involves a project that will experiment with a new technique for handling economic resources thereby removing some of the downsides of the ca-pital-sino which will be sandboxed, eventuall.  

 Remarks: Modified: 08/22/2024

08/22/2024 --    

Memory - present, past

TL;DR -- The cloud and such? What's the use? Science was an early motivation for the research and engineering. We're at a crossroad, somewhat. So, posts on technology themes will be a regular occurrence. 

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Technology is the theme as it has given us a universal memory machine, somewhat. That is, computing and its cloud and the related trackers provide a seemingly endless memory. That digital ocean was one of the things used to train GenAI (ChatGPT, &c). Back in 2015, we had a post (Web/cloud presence) that brought up the choice for us all, ought what we do be forgotten? 

At that time, the cloud was 10 years old (we date it from GSA's decision that the US Government could use the emerging resource). We found out the other day, that Microsoft's cloud is used in the work of 85% of the US Departments. What comes to mind? Single point of failure, for one thing. 

Technology brings changes to which we have to adapt. That neverending affair can be a little offsetting. We do have choices in the matter which will be discussed on a regular basis. 

Today, let's look at keeping something that has value. 

The context is Physics and its rewards. The technology is a blog that quit adding new material a few years ago: Lady Science. There were two contributors from 2014 (see Archive - of their Monthly Issue). The first of these was titled "Delivery Room Drama" (17 Oct 2014). The last issue of a post was 8 Oct 2021. On browsing the site, we thought that it's good that this material is still around dealing with the topics of women, history and science. Some of the earlier posts were oriented toward art. One of the writers went back to school to learn the history of science. 

And, the blog got attention. Fortunately, it's archived at the Library of Congress. After a pause to look at our interest, we will discuss how we found this blog. 

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So, our technology focus will make use of this material. Too, we like to do some comparisons related to time. In 2014 (see posts from that year), we were working in this blog plus our website which was hosted by a site using Linux as the OS. We started with blogger in 2010 which was picked up by Google. Having started our site with Microsoft, we moved when they went to Office365. After researching content vs configuration, we went with custom development. We have blogged about the technical aspects on the necessary choices and will organize a better summary review at some point. As, right now,we're looking at the future of software from several angles. We can all see the edges fraying. How did this happen? Can we recover without too much pain? Lots off questions. 

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Also, we have paid attention to women and science in posts. See post on the daughter (Geertruida de Haas-Lorentz) of Hendrik Lorentz. We discovered her through the experiment of Einstein that she gave to a museum which represented the facility of small experiments. 

Today, in LinkedIn, we saw a post of The Nobel Prize group about the 1912 marriage of (Margrethe Nørland Bohr) of Neils Bohr. She was actively involved in his research and writing, but that work is not mentioned by the group. A comment provided this post: Crafting Quantum Theory: Margrethe Bohr and the Labor of Theoretical Physics. Their son got a Nobel Prize for his work on the nucleus. 

This blog represents several things. It's content will be apropos to the continuing discussions of STEM and its uses. Too, though, the structure of the content and the approach to the subjects are remarkable. As well, we like how it was designed for several reasons. Which of the various subjects to be addressed will be first is unknown. We're bringing the blog to awareness, in general. 

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Our new project deals with a redux on AI along several fronts which deals with the timely problems that will emerge soon enough. 

Remarks: Modified: 08/05/2024

08/05/2024 --