Never a dull moment ..;.

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For some, boredom is the great fear. Got to keep moving!

"He had a function, an excuse for activity. For a few hours at least he wouldn’t be bored. ... he drank the coffee, which was still too hot. He reflected that the fear of boredom had driven him the whole of his life."

Ann Cleeves, Novelist

The fear or boredom was a driver ...
Frankly, I know how he feels

Add value
It shouldn't be motion for motion's sake
It should be about the utility of what you are doing
I need an activity plan for every day ... how will this day add value to what I am about?

About utility
Utility is the marginal difference between face value and the value you -- or someone else -- puts on what your are doing or offering. 

If you think about it, almost anyone can offer up face value if they have the skills for that domain, but if you are in constant motion -- avoiding boredom -- then that activity should be directed at more than just face value.

Even if it's just reading a book, the question is: how much better off are you for having engaged in that activity? For me, I read a lot of history because I think there are lessons there to be applied forward that will add value to my endeavors. And, of course, I might avoid a risk I might not otherwise understand.

If you are driven to activity ...
Make it count for something.

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Leonardo's Lament

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"The supreme misfortune is when theory outstrips performance"

Leonardo da Vinci

And then there's this: 

During the technical and political debates in the mid-1930's by the FCC with various engineers, consultants, and business leaders regarding the effect, or not, of sunspots on various frequency bands being considered for the fledgling FM broadcast industry, the FCC's 'sunspot' expert theorized all manner of problems.

But Edwin Armstrong, largely credited with the invention of FM as we know it today, disagreed strongly, citing all manner of empirical and practical experimentation and test operations, to say nothing of calculation errors and erroneous assumptions shown to be in the 'theory' of the FCC's expert.

But, to no avail; the FCC backed its expert.

Ten years later, after myriad sunspot eruptions, there was this exchange: 

Armstrong: "You were wrong?!"

FCC Expert: "Oh certainly. I think that can happen frequently to people who make predictions on the basis of partial information. It happens every day"

++++++++++

Quotations are from the book "The Network"
 

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Out of Sight Activity

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Back in yesteryear, I recall the first time I had a management job big enough that my team was too large for line-of-sight from my desk and location.

Momentary panic: "What are they doing? How will I know if they are doing anything? What if I get asked what are they doing? How will I answer any of these questions?"

Epiphany: What I thought were important metrics now become less important; outcomes rise to the top

• Activity becomes not too important. Where and when they worked could be delegated locally
• Methods are still somewhat important because Quality (in the large sense) is buried in Methods. So, can't let methods be delegated willy nilly
• Outcomes now become the biggie: are we getting results according to expectations?

There's that word: "Expectations"

In any enterprise large enough to not have line-of-sight to everyone, there are going to be lots of 'distant' managers, executives, investors, and customers who have 'expectations'. And, they have the money! So, you don't get a free ride on making up your own expectations (if you ever did)

At the End of the Day

• I had 800 on my team
• 400 of them were in overseas locations
• 400 of them were in multiple US locations
• I had multiple offices
• It all worked out: we made money!

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Do LLMs reason or think?

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In a posting on "Eight to Late", the question is posed: Do large language models think, or are they just a communications tool?

The really short answer from Eight to Late is "no, LLMs don't think". No surprise there. I would imagine everyone has that general opinion.

However, if you want a more cerebral reasoning, here is the concluding paragraph:

Based, as they are, on a representative corpus of human language, LLMs mimic how humans communicate their thinking, not how humans think. Yes, they can do useful things, even amazing things, but my guess is that these will turn out to have explanations other than intelligence and / or reasoning. For example, in this paper, Ben Prystawksi and his colleagues conclude that “we can expect Chain of Thought reasoning to help when a model is tasked with making inferences that span different topics or concepts that do not co-occur often in its training data, but can be connected through topics or concepts that do.” This is very different from human reasoning which is a) embodied, and thus uses data that is tightly coupled – i.e., relevant to the problem at hand and b) uses the power of abstraction (e.g. theoretical models).

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Data rule #1

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The first rule of data:

• Don't ask for data if you don't know what you are going to do with it

Or, said another way (same rule)

• Don't ask for data which you can not use or act upon

 And, your reaction might be: Of course!

But, alas, in the PMO there are too many incidents of reports, data accumulation, measurements, etc which are PMO doctrine, but in reality, there actually is no plan for what to do with it. Sometimes, it's just curiosity; sometimes it's just blind compliance with a data regulation; sometimes it's just to have a justification for an analyst job.

The test:

 If someone says they need data, the first questions are: 

• What are you going to do with the data?
• How does the data add value to what is to be done
• Is the data quality consistent with the intended use or application (**), and 
• Is there a plan to effectuate that value-add (in other words, can you put the data into action)?

And how much data?

Does the data inquisitor have a notion of data limits: What is enough, but not too much, to be statistically significant (*), informative for management decision making, and sufficient to establish control limits?

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Is it alright to guess in statistics?

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Is guessing in statistics like crying in baseball? It's something "big people" don't do.

Or is it alright to guess about statistics? 

The Bayesians among us think so; the frequency guys think not. 

Here's thought experiment: I postulate that there are two probabilities influencing yet a third. To do that, I assumed a probability for "A" and I assumed a probability for "B", both of which jointly influence "C". But, I gave no evidence that either of these assumptions was "calibrated" by prior experience.

I just guessed

What if I just guessed about "A" and "B" without any calibrated evidence to back up my guess? What if my guess was off the mark? What if I was wrong about each of the two probabilities? 

Answer: Being wrong about my guess would throw off all the subsequent analysis for "C".

Guessing is what drives a lot of analysts to apoplexy -- "statisticians don't guess! Statistics are data, not guesses."

Actually, guessing -- wrong or otherwise -- sets up the opportunity to guess again, and be less wrong, or closer to correct.  With the evidence from initial trials that I guessed incorrectly, I can go back and rerun the trials with "A" and "B" using "adjusted" assumptions or better guesses.

Oh, that's Bayes!

Guessing to get started, and then adjusting the "guess" based on evidence so that the analysis or forecast can be run again with better insight is the essence of Bayesian methodology for handling probabilities.

 

And, what should that first guess be?

• If it's a green field -- no experience, no history -- then guess 50/50, 1 chance in 2, a flip of the coin
• Else: use your experience and history to guess other than 1 chance in 2
  

According to conditions

Of course, there's a bit more to Bayes' methodology: the good Dr Bayes -- in the 18th century -- was actually interested in probabilities conditioned on other probable circumstances, context, or events. His insight was: 

• There is "X" and there is "Y", but "X" in the presence of "Y" may influence outcomes differently. 
• In order to get started, one has to make an initial guesses in the form of a hypothesis about not only the probabilistic performance of "X" and "Y", but also about the the influence of "Y" on "X"
  
• Then the hypothesis is tested by observing outcomes, all according to the parameters one guessed, and 
• Finally, follow-up with adjustments until the probabilities better fit the observed variations. 

Always think Bayesian!

• To get off the dime, make an assumption, and test it against observations
• Adjust, correct, and move on!

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Enterprise-quality browser for the PMO

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The trusty internet browser that has been around since the Netscape days of the 1990's has largely been a lay person's user interface to the internet and sundry consumer internet apps accessed via the browser.

Fair enough

Let's stipulate: In the last 30 years that browser user experience has improved dramatically, to be sure.

Something different

But in recent years, and especially accelerating in 2024, the "enterprise-quality" browser had made inroads in the enterprise business world. New browser companies (*)  have formed and are addressing the heightened security needs of the enterprise as well as a myriad of other needs (see below). This opportunity is not lost on the traditional guys from Microsoft, Apple, and Google; they also have their versions of an enterprise browser. (*)

The general requirements set is this:

• The need for an easier and less complicated way to integrate business apps into the browser. 
• More of a "windows" (small 'w') look with multiple app windows in a common display, decidedly different from a row (or column) of tabs.
• Security protections that are more demanding in the enterprise setting.
• Network, IT, and data protection functions built-in 

PMO effects

So in the PMO you may see new browsers and some of your favorite apps, like Office, database engines, scheduling and costing apps, statistical apps, and others that are somewhat "bolt-on" apps to the consumer browsers (Chrome, Edge, and Safari) become a more integrated app set on the enterprise browser. 

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(*) Island and Here, formerly OpenFin, but also "Edge for Business" from Microsoft and "Chrome Enterprise" from Google

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