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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