Working Up

by Dwayne Phillips

Recent fueling tests at NASA once again show us the peril of shortcuts.

NASA recently completed a fueling test for its Space Launch System. Good for them. Test completed.

The story in the link is a good one as it describes several things that went wrong while fueling. One of the problems meant they had to repair a valve. WHAT? Repair a valve? Was that valve tested beforehand? Were they ready to run the big test after all the little tests were completed?

Or perhaps they took a few shortcuts (like not testing all the parts before testing the system).

Then there was the minor issue of a fire in a grassy area next to the rocket. They felt that was not a problem, so they changed the software so that the fueling would continue despite the little fire.

I hope nothing terrible happens because of that shortcut. What is a little fire anyways? What could possibly go wrong? Why was there logic in the software that stopped fueling when a fire occurred? Someone at sometime thought that could be bad (or really really bad), so they put that logic in the software.

This is a great example of folks taking shortcuts. They see a problem and decide that skipping something will gain them time. As written earlier, I hope nothing terrible happens. After all, they will one day put human beings on top of that great big rocket and have a—hopefully controlled—great big explosion that hurls them to the moon.

by Dwayne Phillips

We have more data today. Really? I don’t think so. We do have more places to put data, so we need to put something there. Right?

I don’t know how many times I have read about all the new data we have today. The amount of data we have is BIG. The amount of data we will have in five years will be a thousand (or is it a million) times more than we have today. We are collecting zillions of zillions of bytes every day.

I disagree. Those are opinions, and we can all have our own opinions.

Fact: we have more places to put data than ever before and the places to put data is rapidly growing.

This week I read about 30TeraByte disk drives coming next year. Put a dozen of these 30TeraByte disks under my table at home and … and I don’t know what to do with them. I can put a few dozen sensors in my backyard that record the weather a thousand times a second and store that data on all those 30TeraByte disks. I can put a dozen 120FramesPerSecond cameras in and around my house and put all that data on all those 30TeraByte disks. Wow! Data. Data storage.

And what would I have? Data that has almost no meaning and almost certainly has no audience of persons who want any of it.

My opinion is that when we have a place to put data, we will find data to put there. See? We did grow our data by leaps and bounds. Our predictions were true. Right?

We have the ability to collect more digital data. Wonderful analog-to-digital converters made that possible. We have the ability to store more digital data. Wonderful new magnetic-media technologies made that possible.

More data? I guess so. More data that anyone wants? I doubt it.

by Dwayne Phillips

A part of the Great Resignation and Great Embarrassment is the Great Transition. We are in this period of time now. We think we know the end state, but things sure are chaotic now with many unforeseen and unwanted events.

We had the Great Resignation. That is still in progress. The pandemic accelerated that.

We had the Great Embarrassment. Again, still in progress and also accelerated by the pandemic. We learned that we have been grossly inefficient in much of what we have done for the last 20 years.

The great reaction is what we really have. Since work from home is now okay, tech companies can recruit nationwide instead of local only. And where are tech companies recruiting? Other tech hubs. That is because that is where the tech talent is. Silicon Valley is hiring from Austin and Washington D.C. and Boston and…

Wait a minute. That’s not how it’s supposed to be. Tech companies are supposed to be recruiting from Kansas and North Dakota. Recruiting from other tech hubs, however, is the way it is. We are in the great transition.

One day, big tech will hire from Kansas and North Dakota. There are colleges in those places and computer science and computer engineering graduates there. But we are not at “one day.” We are here today.
This is the chaos of transition. “In time…” Well, we aren’t at that place, yet.

by Dwayne Phillips

I see a trend in people using the word “modern” to describe technology. I find that unfortunate.

Just this morning, I read:

• modern data stack
• modern programming practice
• modern machine learning
• modern artificial intelligence
• modern compilers

And then I quit.

I guess I am one of “those people” who expect others to say what they mean and mean what they say or some other cliche’ that “those people” use. I am old enough to have gone through the debates about modern and post modern this and that. The use of modern as an adjective was rejected and tossed aside. The word was redefined. At least I thought it was.

Every day now I read about “modern” this and that technology. I guess the engineers and scientists didn’t go through the post-modern or post-post-modern discussions.

Modern artificial intelligence (AI) means AI practices that are less than ten years old, or is it five? Sometimes I am confused on these numbers. Then again, modern this-or-that technology usually means, “I learned this technology. I have yet to learn any approaches to problems that are older.”

That definition of “modern” is disdained. It causes us to admit that we don’t know as much as we think we do. The person realizes that, “Oh, there are other ways to solve this problem and I don’t know any of those other ways. Perhaps I should study more before opening my mouth or clicking the keys on my blog.”

Here comes the concluding part of this blog post from an old person who tends to conclude these things:

Use the adjective “recent” before stating a noun or the phrase “this is what I have learned in the last six months” around a noun. For example:

• recent machine learning techniques
• these are the machine learning techniques I have learned in the last six months

Others may understand you better as you are saying what you mean and meaning what you say. Come on folks, let’s do better.

by Dwayne Phillips

We move forward to the 1980s when the lawyers prevented artificial intelligence (AI) from helping us do our jobs.

Note: This post is about legal maneuvers that prevent helpful AI systems. It is not about AI systems that were built poorly and mimic some human tendencies to discriminate against persons illegally. Those poorly made systems should be banned until fixed.

In the 80s, AI technology produced “expert systems.” These systems held the rules of thumb that experts used to do their jobs. The expert systems never had bad days and didn’t forget things. The expert systems performed better than humans and would have been great helpers in many fields (think lessening deaths in hospitals).

One problems was that the users (e.g., hospital administrators) would be deploying systems that they knew were not 100%. They would be liable to the lawyers, so they canned them and stayed with people only. Note, the people were correct about 80% of the time. The expert systems were correct about 90% of the time. Why? People have bad days. Lack of sleep or indigestion or other slings and arrows of the day cause otherwise knowledgeable persons to forget this or that on that or this day. The expert systems didn’t have bad days.

We are back. The lawyers have come around to understanding the recent generation of AI and how it works and what it does and that it too is not correct 100% of the time. Hence, users would be deploying systems that they knew were not 100%. They would be liable to the lawyers, so users are discarding them and staying with people only.

For some reason, we don’t deploy AI as “advisors” instead of “deciders.” Just like the expert systems of 1980s, we can deploy the most recent generation of AI as advisors. The final decision is left to humans. The AI can nudge the human with a, “Hey, think about this. Remember that?”

Well, let’s see what happens this time around.

by Dwayne Phillips

Please, choose words wisely so that actions will match the words.

Yesterday, I was hit with a couple of examples of the Aesop’s fable. The trouble was, these were real life, and a boy wasn’t crying—it was adults jabbering nonsense. I won’t repeat the examples. There is something about these crying-wolf examples that when repeated a day or two later they are so ridiculous that they are not believable.

Sometimes people exaggerate to make a point. “The hole in the roof is thiiiiiiiiiisssssss big,” we exclaim with arms stretched wide to stress that a repair is needed right now. No. The hole was not that big, but we all understand, right?

Sometimes the exaggeration proclaims something else. The speaker exaggerates to the point of lying, and once a person lies they become like the boy crying wolf in the fable; no one trusts a liar.

If exaggerating to make a point, preface the exaggeration with, “I am exaggerating to make a point. I need your attention right now.” That works better. Not great, but better.

Words that don’t match action or needed action tend to be heard as lies. Let’s be careful and do better, especially if you need people to work unpaid overtime over the weekend because fill-in-the-blank-with-an-untrue-catastrophe.

By Dwayne Phillips

Time is a marvelous tool. Allow time between an action or event and the reaction or the feedback for the event.

I have spoken at large gatherings many times in my career (500 or 600 persons attending, most of them staying awake). I enjoy it. When I am finished speaking, I am full of adrenaline or the mental equivalent of that. I have been backstage when performers finish performances. Same thing with them. They are excited and more excited.

One thing usually happens when coming offstage or finishing a talk. All that excitement clogs the ears. I cannot hear anything people are telling me. Of course my ears are working. Of course I hear their words, but ask me one minute later what someone told me, and, well, I don’t know. I have observed the same with professional performers. For a few moments after the performance, they don’t take anything in. The input is blocked.

There needs to be some time between the act and the react. There needs to be some time for the person to return to a normal state of mind so that their input is open and they can hear words and comprehend them.

When a performer or speaker steps off the stage and asks, ”How did I do?” Answer, ”Let’s talk later.” Otherwise, the reaction is lost.

And now we bring this blog post to the real world of work. Someone walks in with a draft of a report. ”Just finished this. Tell me what you think,” they say with sparkles of excitement gushing from their eyes. Answer, ”Let’s talk later.”

A group of persons just deploys a new version of software. Same words, ”Tell us what you think.” Same answer, ”Let’s talk later.”

This is why a project retrospective should be conducted a while after the project ends. This is why a book review should be delivered a while after the book is published. When the reaction quickly follows the action, the words fall to the floor.

What do you think of this blog post? Let’s talk later.

by Dwayne Phillips

Americans are coming out of isolation from our reaction to the virus. One result is a growing trend to form unions.

There is a growing trend in America to form unions for workers in coffee shops, restaurants, warehouses, etc. The obvious explanation is that these places have become rich and have not shared those riches with the workers who enabled the riches. It is time to redistribute the riches!

Here is another explanation borne of old age and perspective: this is a result of our reaction to the virus. Everyone—well, most everyone—stayed home and stayed away from other people. We were isolated. We didn’t belong to anything.

One of the things that a worker’s union does is provide a place to belong. People gather in a room and vote on a union. “Most of us are in favor of forming a group of us. We will all belong to us!” WOW! This is exciting! We all now belong to all of us!

Enough exclamation points, but this is how it is. People stand and shout and cheer and hug. And, well, we haven’t hugged anyone in two years, so this is great fun!

And all this union forming shows a great failure of other institutions. The obvious choice is a failure of the managers to create a workplace that everyone enjoys. You don’t turn a workplace upside down if you like it, right?

Here is another explanation: this is a failure of institutions where they used to belong. Those places practically closed due to our reaction to the virus. People lost a belonging place. They want a belonging place. None there? Form one. In walks a union organizer. Hey! This is a belonging place.

Most folks joining these new unions know nothing of Flint, Michigan and a sit down strike or anything else about the history of organized labor. They just know that they want to belong to something and all their old belonging places ceased to exist.

Sometimes we choose a reaction to an event. Most of the time, we have little thought behind our choices. Odd things happen afterwards. I have yet to find an essay that said the virus would bring back labor unions. As usual, the experts were wrong.

by Dwayne Phillips

It helps to have someone standing next to you. This is a simple, old, and often overlooked aid to working better.

A buddy is someone standing next to you whom you assume knows what they are doing. Often, a few moments later, you both realize that you both assumed the other is the one who knows what they are doing. In fact, neither knows what they are doing.

Despite have two people standing together when neither knows what they are doing, the situation works to a good conclusion. And how can that be? Zero plus zero equals zero. Sometimes, however, in the state of human endeavors the zero sum becomes something far greater than zero.

There is something about us folks that having a buddy next to us changes everything. Logic does not apply. Math does not apply. The buddy overrules all else.

It is at this point in the blog post where the writer offers insight into how this actually works. Sorry. Not today. Not on this blog post.

I have no clue as to how the buddy changes the outcome. I only relate this as I have seen it work well countless times in my life.

by Dwayne Phillips

The basic yes-no question is quite easy to answer. It is either “yes” or “no.” Recognizing the question is a bit more difficult, but not impossible.

There is a type of question for which the answer is either “yes” or “no.” It is quite easy to answer as there are only two answers with one alternative. Okay, right?

There seems to be a difficulty here as many question answer-ers don’t recognize the yes-no question. They want to answer with an explanation containing many words (many “if,” “and,” and “but”).

The yes-now question centers around the English form of the “to be” verb. Examples include:

• Do you have …
• Did you …
• Is this a …
• Can you …
• Are you …

Once we hear those types of words, we only have to say “yes” or “no.”

Often there is another question following the yes-no question. Continue with the discussion, but only after answering the first question first.

• For example, “Did you analyze this?”
• Answer “yes” or “no.”
• If “yes,” the next question could be, “Did you write a report on the analysis?”
• If “yes,” the next question could be, “May I have a copy of the report?”

Pretty simple, huh? If a “no” answer arises, we may take the conversation in one of many different directions.

The answer to, “Did you analyze this?” is not “Well, I could if you want me to.”

The answer to, “Did you write a report on the analysis?” is not “Not yet.”

Let’s keep it simple.

A big part of this simplicity is trust. Both persons trust one another to be honest. Both persons trust one another to have good intentions in the questions and answers.

Once again, we encounter the adage that if you don’t have trust among colleagues, nothing else matters.