Erik on Software

Helpful Votes: Part 10

Two weeks ago, I reached a milestone of 850 reviews at Amazon, and I noticed for the first time that reviewer rankings have disappeared.

About 6 months ago, I had commented that my ranking had been hovering around #3000 for quite some time, and that my ranking had continued to be relatively stable since that time period, so I'm not sure what to make of this development. Some light research in the community seems to indicate that rankings have disappeared in the past, albeit seemingly for specific individuals, but the Amazon ranking web page is no longer available, and I came across a Reddit post which seems to confirm that Amazon did away with their ranking system about 5 or 6 weeks ago.

Since the milestone when my reviews reached 3500 helpful votes about 6 months ago, Amazon merchants have provided a variety of free products that I've requested from them, but apart from an elliptical machine which I ordered and then canceled (due to target family member loss of interest) and an incredible 55-inch 4k smart TV from Samsung, most items I've ordered have been extremely practical consumables: as I mentioned in my last post, this largely consists of varied nootropics and whole food vitamins.

Last year was the first in which I permitted myself to surpass the $600 threshold of free product which requires income tax reporting, keeping in mind that the vast majority of consumables don't even contribute to total product amount received. Last year was an experiment to determine the impact of surpassing the $600 threshold, and because it was negligible I decided to permit myself to do so again this year, but only for products which are either practical or borderline practical. For example, the TV replaced a 32-inch TV that I purchased 11 years ago, but I mainly ordered it for family members since I don't really watch TV, and the old TV is good enough for my movie watching while running on my treadmill.

My top review, which resulted in my shooting up to a ranking of #1967 back in May 2012, was for Madeleine Albright's new memoir at the time entitled "Prague Winter: A Personal Story of Remembrance and War, 1937-1948". At the time of that review, Amazon was still permitting downvoting in addition to upvoting, as well as commenting on reviews.

Community Post: Part 2 ("Colleague" Meets DALL-E)

Gfesser via DALL-E: "Hieronymus Bosch painting of male and female business colleagues respectfully disagreeing with one another while running together along the Chicago lakefront."

A well-received community post I recently made:
https://www.linkedin.com/posts/erikgfesser_career-wordorigin-respect-activity-6998107676408770560-dHt_

Early in my #career, I called individuals with whom I worked "co-workers", and later, "teammmates", but got into the habit of calling them "colleagues" quite some time ago. I've been reading a great book called "The Ideal Executive: Why You Cannot Be One and What To Do About It", by Ichak K. Adizes, which in chapter 7 discusses the (surprising) #wordorigin of "colleague".

"What I'm going to say now may sound very simple and obvious, but it took twenty years and a lot of personal pain to discover it. I wish it *were* obvious. A colleague is not someone who agrees with you. A colleague is someone who *disagrees* with you but for whom you have #respect. Why? Because you don't learn from those who agree with you. You learn from those who *disagree*, in the course of the debate that evolves out of the conflict between you. Learning from differences is painful, but we also enrich ourselves through being different. The root of the word 'colleague', in fact, comes from the Latin word 'Collegum', which means 'to arrive together'. In other words, we started with different points of view, but through interacting we have arrived at the same point. And in Hebrew, the words Amit (colleague) and Imut (conflict) derive from the same root. Words that share the same root are interrelated; in this case, the connection is that there are no collegial relationships without conflict. And the reverse can happen: Good conflict can make people to become colleagues, if they learn from each other because and not in spite of the #conflict. Used appropriately, it can help legitimize and unite our differences. Like a spouse, who is ezer keneged (helpful against), colleagues help each other cross-fertilize ideas and expand horizons - through conflict. Without conflict, then, you don't have a #colleague. For good decision-making, team members who both respect and disagree with one another - colleagues - are essential."

After deciding to post this, I thought this might be a good occasion to experiment with the DALL-E #deeplearning image synthesis model for the first time. After generating dozens of images, I ended up choosing the attached image, which was generated by the following description I wrote: "Hieronymus Bosch painting of male and female business colleagues respectfully disagreeing with one another while running together along the Chicago lakefront." More on this in the comments below. :-)

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Gfesser: So about the description I input into DALL-E: I ended up needing to play with the wording, and choose a variation that met my needs. My original wording simply stated "business colleagues", but all the images kept generating seemingly male images, so I explicitly added "male and female". Another interesting aspect of the generated images was that almost every single one depicted people running into each other as though they were going to collide, rather than running alongside each other, and the few which depicted people running in the same direction also had them facing forward rather than toward each other. All of this said, I'm liking DALL-E and will continue to experiment with it in the future.

Associate Professor of Computer Science and Engineering: Erik, as we know now so well, many words assume meaning with usage over time or as a matter of current convention/jargon. Most org cultures might only grudgingly accept frequent disagreements, but frequent conflicts would seem dysfunction to them. Be as that may, I think, parties need to be sure that they have facts right and POVs well-grounded. They place it respectfully as their individual submissions and do not push it beyond their powers (pay grade etc.). Some of this subject would have to do with personalities and people unwilling to take feedback/advice; [Psychologist] is a great expert here.

Gfesser: True, terms can assume different meanings over time, and as we know from the technology space, organizations can't even agree on how to define commonly used terms in the present. Adizes discusses colleagues from the perspective of the same team and same level, and argues that while leadership requires a variety of strengths, a given individual by nature cannot typically exhibit all of these, so executives should specifically look to hire those who fill these gaps: hiring individuals who repeatedly bring the same strengths to the table leads to redundancy. Per my explanation of the reason why I chose the image I posted, most of the generated images demonstrated unhealthy conflict by showing people about to collide with one another, possibly due to analysis paralysis (for lack of a better term) or different goals.

Associate Professor of Computer Science and Engineering: Erik Honestly, I've kinda firmed up away from a strengths-based approach to one that strives to match techno-project requirements of a job/assignment with my preparedness (e.g. skills, knowledge, domain etc), self-awareness & that X-factor, which definitely has to be courage, but could be grit/risk-taking/persistence as well. For example, there might not be much scientific evidence for the strengths-based approach.

Regarding the peripheral topic of etymology, off-hand I can say, the Hebrew root (Afro-asiatic family) "mit" or "mut"--which seems to mean view or thinking--have the same form "mat" & almost the same meaning in Indo-European family members--Punjabi & Hindi. So, for example, in both Hindi & Punjabi, ek-mat means unanimity, sam-mat (seh-mat in Urdu) means consensus, lok-mat means people's say, etc. Off-hand, I can't say, whether the root is Proto-Indo-European (PIE) or came across via Persian/Arabic from the Afro-asiatic family. Or the other way around--went from these Indo-European members via Persian/Arabic to Hebrew. Of interest here is that another Indo-European root sounds similar if not the same--English mate, mit (Punjabi), meet (Hindi or Urdu), mitr (Hindi/Sanskrit) all mean the same.

Gfesser: Tracing natural language lineage is always harder than doing so for computer languages, isn't it. As far as the workplace is concerned, I would agree with respect to "hard" skills, and this topic can be an especially opinionated subject due to the variety of the human experience: one-size-fits-all solutions are rare.

Associate Professor of Computer Science and Engineering: Erik True. Genomic studies seem to settle some issues there. For example, they seem to be lend credence the old steppes hypothesis that the origin of PIE is the steppes north of Black Sea & Caspian Sea :)

Personal Reading List: 4Q2022

My personal reading list for October, November, and December 2022.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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AWS Certified Database - Specialty (DBS-C01) Certification, by Kate Gawron, Packt Publishing, 2022. Copy provided by Packt publicist Nivedita Singh.

The Ideal Executive: Why You Cannot Be One and What to Do About It, by Ichak Kalderon, The Adizes Institute, 2004.

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 At Bat (Langsam)

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Machine Learning Engineering in Action, by Ben Wilson, Manning, 2022. Copy provided by author Ben Wilson.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change, by Camille Fournier, O'Reilly, 2017.

The Staff Engineer's Path: A Guide for Individual Contributors Navigating Growth and Change, by Tanya Reilly, O'Reilly, 2022.

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In the Hole

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97 Things Every Data Engineer Should Know, edited by Tobias Macey, O'Reilly, 2021. Copy provided by Promethium.

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Second Edition), by Christoph Molnar, Independently Published, 2022.