Working Up

by Dwayne Phillips

Stop worrying about the next technology. Do something simple that has all the functions you need.

The database. Sigh. Gosh, what is next? What is the next database technology that will solve all my problems?

Requirements: Enter information. Find entered information. Read entered information. Change entered information.

Solution: The ASCII text file.

The text file meets all the requirements. What is more, it met them 30 years ago and will still work 30 years from now.

Use Notepad (Windows), TextEdit (Mac), emacs or Vim (Unix and those other two as well). I think these things also run on iPhones and Android phones and all the tablets.

There must be a catch. No, there isn’t. I can put all my recipes in this text file. I can list the titles and authors of all the books I have ever owned or read. I can find these things later. I can alter them and copy them and morph them into something else and do all sorts of things that I want to do.

Simple. Do something simple that moves you in the desired direction.

Buy something? Analyze something? If that is what you want to do, do it. If you want a database that works, just do a text file. If you want to do something, do something simple in the right direction.

by Dwayne Phillips

Each generation laments how no one recognizes how special they are. Well, sort of. Times change, ya’ know?

I just saw another one of these things online. It was a black and white photo of kids on their bicycles in the street. We were the last generation to ride bikes in the neighborhood. We used a clothes pin to attach a playing card to the bike. The playing card flapped in the spokes of the wheel. It made the bike sound like a motorcycle for at least two minutes, then it tore itself to bits.

We were the last generation to eat Kool-Aid from the package and turn our tongues purple or red or green or something.

Alas, time. It moves. Of course we were the last generation to…

I am writing this on Saturday, September 3rd, 2022. This day is unique in the history of mankind. Those alive today will be the only persons in human history to be alive today. We are special. If you don’t believe me, well, you just are worth believing anything obvious or some put down that I can state about you who don’t think I am really special and so are all the others in my arbitrary “generation.”

Alas, time. It moves. Today is unique; tomorrow will be unique, and so will the day after. I will be the last person alive to write this blog post. See, I’m unique and special. Well, there are probably a million other folks out there writing blog posts today. Maybe I’m not so unique.

Alas, time. It moves. Enjoy today. Watch some kids doing whatever it is those unique kids in their unique generation are doing on this unique day. Smile. No one else will ever experience today again.

by Dwayne Phillips

We need more explainers. These are persons who answer the question, “Whatcha’ doin’?”

• Whatcha’ doin’?
• Hows’it work?
• How ya’ doin’?

These are simple questions. These are the basic questions we ask others all the time. Often, we repeat the question six or seven times in different forms and still don’t receive an answer. Why? Because we don’t have enough explainers.

An explainer has the ability to answer a basic question. An explainer can understand a question and answer that question instead of answering 531 other questions.

The explainer gives the who, what, where, when, why, and how.

Somehow, the explainer keeps track of those things, sticks to those things, and doesn’t wander off into the weeks.

The explainer understands how much detail the person asking the question wants.

For some reason, we don’t have many explainers hanging around. Those of us who are explainers are quite irritated by those who either cannot or do not want to explain.

by Dwayne Phillips

There is nothing wrong with mimicry. Let’s stop kidding ourselves; much of today’s machine learning is simple mimicry.

mimicry: noun, the action or art of imitating someone or something, typically in order to entertain or ridicule.

Let’s be candid here. Much of Machine Learning (ML)—a currently popular branch of Artificial Intelligence (AI)—is mimicry. Point to a million pictures and say, “These are giraffes, these are penguins, and these are snakes.” The system will be able to mimic you and correctly say, “These are giraffes, these are penguins, and these are snakes.”

That is a simple case of ML. There are more complicated ones, but those are more complicated types of the same thing. “I sat on a fill-in-the-blank.” What will I put in that blank? Probably not “nail” or “bumble bee.” Probably “chair” or “couch.” Simple mimicry.

Is that intelligence? Again, let’s be candid. The answer is, “No.” Is that useful? Well, in many situations it is quite useful and in many situations it enables a machine to do a rather mundane job. That is also useful.

The current ML branch of AI can be quite useful and profitable. Let’s stop there and not delve into calling it intelligent or sentient.

by Dwayne Phillips

“Everyone” wants flexibility in their job (work from home when I want). How many of us want “meaning” in our job?

“I can do this job from anywhere at almost anytime of day or night.”

That statement is immediately followed by the question, “So why do you want me here from 9 to 5 Monday through Friday?”

If the statement is true, the question is an excellent one that probably does not have an excellent answer. If I can do the tasks from anywhere at anytime (as long as I meet the deadline), what is this office building attendance?

Okay, let’s back up a bit. If I can do the job anywhere anytime, how many other people can do the job? If someone has a basic mastery of the English language, they can do the job as well as me. If they live in a relatively low-cost-of-living place on earth, they can do my job at a much lower salary than me. They have the job. I am unemployed.

Wait a minute, how did we go from, “I want flexibility” to “I am unemployed?” The answer is, “simply and quickly.”

Okay, let’s back up a bit. Flexible jobs bring a high risk of unemployment. Sorry folks.

Let’s move to another type of job; a job that is full of meaning. Meaningful jobs may not be so flexible. They may require more brains than I alone have. That means several people in the same place at the same time to combine brains and solve problems that are too difficult for me. The result is much more meaningful than the jobs that can be done anywhere, anytime, (by just about anyone).

ZoomerTeamz meeting? They are sometimes inefficient. I don’t have a home computer with a 99-inch touch screen that links to the 99-inch touch screens of the other persons on the team. We are not all drinking the same coffee, breathing the same air, feeling the same carpet under our feet, sitting in the same chairs, and so on. We are a fake team; not a real team. We are probably not doing meaningful work on ZoomerTeamz.

Sigh. Flexibility or meaning? Why do we have to answer these questions?

by Dwayne Phillips

When assuming that another person already knows something, ask, “And why would they know that?”

It happened again the other day at work, someone interrupted a presentation, pointed to something mentioned but not shown, and asked, “But what is that thing? What does it do?”

The presenter, unable to hide the aghast expression lurking under the surface, answered, “That’s the universal do-hicky that universally underlies the entire system and allows it to universally do everything in the universe. Everyone knows that. Surely you know that, huh?”

The presenter and, to spread the blame, the entire presentation team never asked, “And why would they know that?”

They are working on this project. Everyone on this project knows that!

And when did we provide them with that information?

I might blame this on the agile development world. We value working software or systems more than documentation. Sometimes this becomes, we value working software or systems and disdain and neglect documentation. If a project lasts more than six weeks, it is probably worth the time and money to document things well and in a manner that makes it easy for new persons to enter the project and know what everyone else knows.

Then again, back in the days before we were all agile we didn’t have good documentation. New persons were chastised for not knowing what they should know. No one asked, “And why would they know that?”

Those who knew such things had job security and, more importantly, a feeling of superiority over the unknowing who were just cannon fodder for the next round of job layoffs.

Sometimes we should ask, “What are we doing here?” If we are doing something worthwhile that will last past the weekend, perhaps we should provide the information others need so that they will know that just like we know it. This isn’t easy. Don’t want to do it? Hire someone to do it. Pay them well to do the task well. It pays for itself later.

by Dwayne Phillips

The number crunchers now rule the world. How did that happen?

Many years ago I was a number cruncher. I did then what people still call “digital signal processing.” We took analog signals, magically made them numbers in computers via gadgets called analog-to-digital converters or A/D converters, and happily applied digital approximations of mathematics. Wow. That was great fun. And, to a large extent, it all worked.

We used supercomputers (which were far advanced but are now puny compared to an iPhone), programmed in FORTRAN (another story in technology management for another day), and it all worked well. We wanted x87 co-processors in our PCs. These were special number-crunching processors that boosted the performance of the x86 CPUs. They were the predecessors of today’s GPUs, which perform the same function.

Then there were “the other guys.” “They” processed text. They considered how to teach a computer to understand subject-verb-object. They created lists of unique words. They did “stemming” and other things that linguists and English majors understood. Those things might be interesting to some persons, but really? We were crunching numbers.

What would real programmers want to do, CRUNCH or parse?

One day I woke to find that the text processors were crunching numbers. Let’s apply machine learning to text processing. Machine learning was the term the newspapers used for pattern recognition and machine intelligence (PAMI) and all types of supervised and unsupervised learning and long equations that were digital approximations of mathematics—wait, we are using the same terms used earlier. These machine learning algorithms turned text into digital approximations of linear algebra. Matrix multiply. “DO LOOPS” (an ancient term sometimes quoted by old FORTRAN programmers) and all that. The levels of the loops were deep, but they were just loops and IF and THEN and ELSE. The same thing us old “number crunchers” did.

All of a sudden, those linguists and English majors wanted to talk to “us” not “them.” And, some of “them” wanted to become “us.”

Time and technology march on. Now we are all crunching numbers to a greater extent than ever before. MegaFLOPS has become some greek-prefix-FLOPS that I’ve never heard before. Enough ranting for an old man.

Now, where can I find one of those new Nvidia GPUs that has an air conditioner built in to keep the computer from bursting into flames so I can pop it into my mega PC and crunch some numbers?

by Dwayne Phillips

If someone asks a question in English, please answer in English. Please avoid some other private language.

In recent conversations, I asked, “Your research, how far ahead in time are you working? Your work may become reality in 1, 5, 10 years?” The answer was, “TRL 4.” (see this for a translation) Deep sigh.

I asked in English. Please reply in English. Oh, we don’t talk in those terms. We talk in our own private language that only we understand. If you don’t understand us, you are not worthy of us.

Perhaps I react too harshly. These are smart, caring people who mean well. They are using a language that is near and dear to them. It is not, however, the language that I used to start the conversation. Sadly, this occurs in many fields of endeavor.

It is not easy to discuss matters that are unique to a field in plain English. Each field has its own vocabulary. Sometimes that is writ of habeas corpus, electrocardiogram, IMSI and TMSI, or domain name service. There are English-language explanations for these. Let’s try to use them. Please.

by Dwayne Phillips

Back in 1990, I was part of this cloud computing and remote work—in a manner of speaking.

Cloud computing has enabled remote work—as we know it today. I guess the last five words are the key—as we know it today. Some of us have done this type of work for decades.

Back in 1990, I wrote magazine articles for several publications. The most frequent magazine was C/C++ Users Journal. This was so long ago that people talked about the C programming language. It wasn’t until later that we tossed in “C++.”

Yes, in 1990, there was a form of electronic communication. The ARPANET had led to email and bulletin board services for some of us. I was not one of us at the time. I wrote proposals on the computer, printed them, and mailed them to magazine publishers via the US Mail using envelopes and stamps and all that. The magazine publishers replied via the same US Mail.

After a few articles were published, a publisher might call me on the telephone. We would talk about future articles and series of articles. I would write the articles and mail the paper and the floppy disks to the publishers.

I was working remotely with people I never met. Our relationship was in the cloud of uncertainty and suspense. It worked. I met a few of the publishers with whom I worked at conferences. That was a great joy. It was rare. Perhaps the rarity increased the joy.

Around 1995, Windows 95 arrived. It was the first Microsoft Windows that really worked for everyone. Also arriving was America OnLine (AOL) with its free CDs that gave free but limited accounts. AOL had email and such. Magazine publishers created websites with editorial guidelines, publication schedules, and requests for articles. We communicated via a cloud of networks that I really didn’t understand. It worked. We were remotely working with one another. Gone were the envelopes and stamps.

I guess times have changed with advances in technology. We don’t use external cameras, microphones, and speakers. I have half-a-dozen external plastic microphones in a cardboard box at the bottom of a pile in a closet somewhere. Everything is built in and “just works” in a manner of speaking.

Still, let’s not forget that this is old stuff for many of us.

by Dwayne Phillips

Perhaps this is the complaint of an old man, but all the Zoomer Teams meetings just aren’t getting it done.

The title of this post is an old expression from an old time. We used to talk with people on the telephone. That worked to a point, but we couldn’t “picture” the person. We needed to see them.

Okay, technology fixed that. We have computers and communications. We have $5 video cameras and $5 microphones and speakers. We “see” one another. We can put a face to a name.

Well, that works a bit. It is “better than nothing.” Let’s be candid, it doesn’t work.

These distance everything everythings we are doing don’t work. We don’t have the time to notice the little things and build pictures in our minds that bring us closer to reality. Time and physical proximity allow us to ask questions and discuss matters that seemingly don’t matter.

• What is that pen you always carry?
• Why do you put a pencil behind your ear?
• You roll your foot around while typing, does it hurt?
• You bring your lunch in paper wrappers instead of Tupperware, is there a reason?

Stupid little notices and questions and conversations. These help us put a face to a name. These help us understand why someone turns red when I use a phrase from my childhood. These help us work through the misunderstandings that are common in working daily with others. These add to a pleasant and productive workplace. These help us sleep at night.

Once I know someone pretty well, I can have Zoomer Teams meetings and “get by.” New people? No, I can’t put a face to a name. Perhaps I am too old for this. Perhaps I have something in all this meandering.