Google Allegedly Ostracized

January 18, 2020

I worked in the San Francisco area once affectionately known as Plastic Fantastic. My recollection is that most of the people with whom I worked and socialized were flexible. There was the occassional throwback who longed for the rigidity of the Midwestern farm life. But overall, chill was the word. The outfit who paid me to do whatever it was they thought I was my skill was an easy going money machine. Most of the high technology outfits were just starting to get a sense of the power and impact afforded those who were comfortable with online technologies, nifty must have gadgets, and a realization that members of the high school science club could call the shots.

Imagine my surprise when I read the allegedly accurate “San Francisco Pride Members Pass Resolution to Ban Google, YouTube from Future Parades.” The write up states:

Members of the LGBTQ+ organization say they passed an amendment to ban Google, YouTube and Alphabet, as well as the Alameda County Sheriff’s Office, from future celebrations after a vote at their monthly membership meeting Wednesday night. In a statement released to SFGATE on Thursday, SF Pride members and former Google engineers Laurence Berland and Tyler Breisacher said they are now urging the board of directors to formally approve the motion at their upcoming meeting on Feb. 5.

Remarkable if true. The Google HR and marketing departments will have to step up their efforts. Recruitment may become more difficult. The PR vibes are doing the Hopf fibration thing. (This is a nice way of saying, “Difficult to understand.”)

Stephen E Arnold, January 18, 2020

Are Catalogs Made from Dead Trees Rising from the Ashes of Retail?

January 6, 2020

DarkCyber spotted an interesting write up about dead trees. The article is about printed catalogs. Paper. You remember the stuff, don’t you? “Catalog Retailers See Reason for Optimism after Declines” contains a somewhat surprising statement; to wit:

New companies are mailing catalogs. And even died-in-the-wool online retailers like Amazon and Bonobos are getting into the act. “They’re tapping out on what they’re able to do digitally,” said Tim Curtis, president of CohereOne, a direct marketing agency in California. “They’ve got to find some new way to drive traffic to their websites.”

Does this assertion translate into a certain exhaustion of the possibilities of online advertising. Maybe pop up or Google fatigue is affecting people looking for information.

Consider online information services. I encountered a situation with the Daily Mail, a British newspaper. The site would not display. There were ads loading, questions to answer, and pop ups to dismiss. I solved the problem by navigating to another site. Too much hassle.

A paper catalog can be viewed and maybe used to buy something without the annoyance.

“Driving traffic to a Web site” may be less important than a catalog’s ability to deliver information without digital annoyances, creepy tracking cookies, and ads for products one just purchased.

Stephen E Arnold, January 6, 2020

Why Black Boxes in Smart Software?

January 5, 2020

I read “Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition.” The source is HDSR, which appears to be hooked up to MIT. Didn’t MIT find an alleged human trafficker an ideal source of contributions and worthy of a bit of “black boxing”? (See “Jeffrey Epstein’s money bought a cover-up at the MIT Media Lab.”) The answer seems obvious: Keep prying eyes out. Prevent people from recognizing how mundane flashy stuff actually is.

The write up from HDSR states:

The belief that accuracy must be sacrificed for interpretability is inaccurate. It has allowed companies to market and sell proprietary or complicated black box models for high-stakes decisions when very simple interpretable models exist for the same tasks.

The write up moves with less purpose that Jeffrey Epstein.

I noted this statement as well:

Let us insist that we do not use black box machine learning models for high-stakes decisions unless no interpretable model can be constructed that achieves the same level of accuracy. It is possible that an interpretable model can always be constructed—we just have not been trying. Perhaps if we did, we would never use black boxes for these high-stakes decisions at all.

I love the privileged tone of the passage.

Here’s my take:

Years ago I prepared for a European country’s intelligence service an analysis of the algorithms used in smart software. I thought this was an impossible job. But after making some calls, talking to wizards, and doing a bit of reading about what’s taught in computer science classes, my team and I unearthed several interesting factoids:

  1. The black box became the marketing hot button in the mid 1990s. The outfit adding oomph to mystery and secrecy was Autonomy. If you are not familiar with the company, think Bayesian maths. Keep the neuro linguistic programming mechanism under wraps differentiated Autonomy from its competition.
  2. Computer science and advanced mathematics courses around the world incorporated into their courses of study some useful and mostly reliable methods; for example, k means. There were another nine computational touchstones we identified. Did we miss a few? Probably, but my team concluded that most of the fancy math outfits were using a handful of procedures and fiddling with thresholds, training data, and workflows to deliver their solutions. Why reveal to anyone that under the hood most of the fancy stuff for NLP, text analytics, machine learning, and the other buzzwords which seem so 2020 were the same.
  3. My team also identified that each of the widely used, what we called “good enough” methods, could be manipulated. Change a threshold here, modify training data there, create a feedback loop and rules there—the system output results that appeared quite accurate, even useful. Putting the methods in a black box disguised for decades the simple methods used by Cambridge Analytica to skew outputs and probably elections. Differentiation comes not from the underlying methods; uniqueness is a result of the little bitty tweaks. Otherwise, most systems are just lik the competitions’ systems.

Net net: Will transparent methods prevail? Unlikely. Making something clear reduces its perceived value. Just think how linking Jeffrey Epstein to MIT alters the outputs about good judgment.

Black boxes? Very useful indeed. Secrets? Selective revelation of facts? Millennial marketing? All useful

Stephen E Arnold, January 5, 2020

When Millennials Market, Craziness Ensues

January 3, 2020

How this for logic?

Assertion: What you read in blog posts stopped working months ago.

Logic problem: This assertion appears in a blog post “5 Marketing Trends for 2020 by a Grumpy Martech CEO.”

Observation: The statement is contradicted by its appearance in a blog post.

Your view may be more generous than mine. That’s what makes life interesting.

What else does the write up reveal in its logical flourish?

  1. Don’t do cold calls. Observation: Yeah, but that’s what conventions are based on. Walking up to a person and introducing oneself and engaging in a conversation. If you do this, your are making a big mistake. Alternative: Telepathy maybe?
  2. Marketing tools do the same thing. Yeah, that’s a bold statement. But isn’t there a difference between relying on a networking event armed with a — horror of horrors — a printed brochure and buying Google AdWords? Same thing? No, gentle reader. No, no, no.
  3. Words don’t work. Got it. Videos with music. There you go.
  4. Use a new channel like a — wait for it — a directory. Example: Capterra? What? Isn’t that a hollow shell with a bit of fluff added for impact.


Stephen E Arnold, January 3, 2019

Intellisophic: Protected Content

December 28, 2019

Curious about Intellisophic? If you navigate to, you get this page. If you know that Intellisophic operates from, you get a live Web site that looks like this:


No links, and there is no indication who operates this page.

You persevere and locate a link to the “real” Intellisophic. You spot the About page and click it. What renders?


Yep, protected information.

Even companies providing specialized services to governments with “interesting” investors and solutions, provides a tiny bit of information; for example, check out

DarkCyber finds it interesting that a company in the information business, does not provide any information about itself.

Stephen E Arnold, December 28, 2019

Emoto Marketing: Is This a Trendlet Aborning

December 26, 2019

I read, quite by accident, a write up mentioned by a young executive at a holiday party. The essay manifests what I call “emoto marketing”. This is shorthand for an emotional, sensitive, I-want-to-help approach to selling consulting services.

You can read this interesting sales pitch at Leowid, which is the author’s shorthand for himself. The essay is “I Coached 101 CEOs, Founders, VCs and Other Executives in 2019: These Are the Biggest Takeaways.” Be aware that there is a pop up enjoining the reader of the essay to “join me for regular adventures into the unknown.”

Now if there is one thing that, in my experience, makes high performers nervous is the unknown. Plus, there’s the risk of failure, which today includes allegations of improper behavior, missteps memorialized in pix from a college party, and plain old human failings like alcohol, synthetic opioids, and friendly Uber drivers.

Straight away, I translate the 101 into one therapy session every three days or a couple of conferences with 50 and a half shattered attendees. Either way, the learnings from these emoto interactions could be indicative of why software able to figure out the emotional payload of an email will thrive in 2020. Doesn’t everyone one a semantically, context aware daemon buzzing in one’s mobile device?

Let’s look at three of the findings; read the essay for the other insights. Be sure to sit down, however. The revelations may knock the wind out of the sails of your 75 foot sailboat.

  1. People are “bags of emotion.” I sort of knew this after I learned a person unhappy with holiday gifts, pulled out a weapon and began taking pot shots at the gift givers.
  2. Manage focus, not time. I understand that paying attention and listening are important. I watch LivePD and see how the inattentive find themselves in uncomfortable situations.
  3. Boundaries create connections. The social graph is important to the emoto marketer.

To sum up, the essay combines pseudo science, self help, and MBA speak with unabashed emotional appeals. If facts won’t work, go for emotion.

DarkCyber will focus, not just listen, in order to discern other examples of this approach to selling services. Imagine an emoto marketing campaign from McKinsey & Co or a government agency.

The author trained as a trauma therapist and lives in Vienna, Austria (a very flexible and emotional city I believe). Oh, the author lives near a forest.


Stephen E Arnold, December 26, 2019

Content Marketing: HBR and Adobe

December 26, 2019

I spotted an interesting write up in CIO Magazine, one of those editorial Gibralters branded IDG. Why was the story an attention grabber? It was an ad.

How the Harvard Business Review Used Personalization and Automation to Enhance Success” explains that bulk email is a super business method. Not spam, enhancement of success. Yes.

I learned that the Harvard Business Review has energized its business by using Adobe marketing technology. The HBR brand, its magazine, its executive centric podcasts, and its pride of place in “business” were not enough. Energize, not spamming with email. Please, note that.

The write up explains that Adobe (once the beloved arts and crafts software outfit) has marketing technology that delivers. Here’s the proof:

Using Adobe Campaign, HBR sent out 4.5 million triggered emails that had an average open rate of 28% and a click-through rate of 5%. These are impressive results that surpassed previous efforts. Adobe Campaign also allows HBR to drive more targeted campaigns and expand volumes to reach a wider range of audiences.

Yep, email in the spirit of America Online’s free CDs. No physical disc, just email.

The other interesting facet of the write up is that the email blasts are presented as an objective story.

What’s that say about the underpinnings of a Harvard MBA and the business precepts outlined in those HBR podcasts and articles?

MBA schools and money raising programs need marketing too. Which company is the winner with this PR story in CIO? You will need to attend a Harvard Executive Program to formulate the “right” answer.

Stephen E Arnold, December 26, 2019

How to Be Numero Uno in AI Even Though the List Has a Math Error and Is Incomplete

December 24, 2019

DarkCyber spotted an interesting college ranking. Unlike some of the US college guides which rank institutions of higher learning, the league table published by Yicai Global takes a big data approach. (Please, keep in mind that US college rankings are not entirely objective. There are niceties like inclusions, researcher bias, and tradition which exert a tiny bit of magnetic pull on these scoreboards.)

According to “Six Chinese Colleges Place in CSRankings’ Top Ten AI List”, the US and other non-Chinese institutions are simply not competitive. Note that “six” in the headline.

How were these interesting findings determined? The researchers counted the number of journal articles published by faculty at the institutions in the sample. DarkCyber noted this statement about the method:

CSRankings is an authoritative global ranking of computer science higher educational institutions compiled by the AMiner team at Tsinghua. Its grading rests entirely on the number of scholarly articles faculty members publish.

The more papers—whether good, accurate, or science fiction—was the sole factor. There you go. Rock solid research.

But let’s look at the rankings:

  1. Top AI institution in the world: Tsinghua University.
  2. Not listed. Maybe Carnegie Mellon University
  3. Peking University
  4. University of the Chinese Academy of Sciences
  5. Not listed. Maybe MIT?
  6. Nanyang Technological University
  7. Not listed. Maybe Stanford, the University of Washington, or UCal Berkeley?
  8. Shanghai Jiao Tong University
  9. Not listed. Maybe Cambridge University
  10. Not listed. DarkCyber would plug in École nationale supérieure des Mines de Saint-Étienne whose graduates generally stick together or maybe the University of Michigan located in the knowledge wonderland that is Ann Arbor?

Notice that there are five Chinese institutions in the Top 10 list. Yeah, I know the source document said “six.” But, hey, this is human intelligence, not artificial intelligence at work.

Who’s in the Top 10. Apparently Carnegie Mellon and MIT were in the list, but that’s fuzzy. The write up references another study which ranked “all area” schools. Does MIT teach literature or maybe ethics?

To sum up: Interesting source, wonky method, and incomplete listing. Plus, there that weird six but just five thing.

CSRankings’ Liao Shumin may want to fluff her or his calligraphy brush for the next go round; otherwise, an opportunity to do some holiday coal mining in Haerwusu may present itself. “Holiday greetings from Inner Mongolia” may next year’s follow up story.

Stephen E Arnold, December 24, 2019

Do Four Peas Make a Useful Digital Pod?

December 24, 2019

The Four P’s of Information

This has the problem with data since at least the turn of this century—Forbes posts a “Reality Check: Still Spending More Time Gathering Instead of Analyzing.” Writer and Keeeb CTO Sid Probstein reminds us:

“Numerous studies of ‘knowledge worker’ productivity have shown that we spend too much time gathering information instead of analyzing it. In 2001, IDC published its venerable white paper, ‘The High Cost of Not Finding Information,’ noting that knowledge workers were spending two and a half hours a day searching for information. Since then, we have seen the rise of the cloud, ubiquitous computing, connectivity and everything else that was science fiction when we were kids becoming a reality — including the imminent emergence of AI. Yet in 2012, a decade after the IDC report, a study conducted by McKinsey found that knowledge workers still spend 19% of their time searching for and gathering information, and a 2018 IDC study found that ‘data professionals are losing 50% of their time every week’ — 30% searching for, governing and preparing data plus 20% duplicating work. Clearly, all the technology advances have not flipped the productivity paradigm; it seems like we still spend more time searching for information that exists rather than analyzing and creating new knowledge.”

Probstein believes much of the problem lies in data silos. There are four subsets of the data silo issue, we’re told, but most proposed solutions fail to address all of them. They are the “four P’s” of information: Public Data (info that is searchable across the World Wide Web), Private Data (information behind login pages or firewalls), Paid Data (like industry research, datasets, and professional information), and Personal Data (our own notes, bookmarks, and saved references). See the article for more about each of these areas. Bridging these silos remains a challenge for knowledge workers, but it seems businesses may be taking the issue more seriously. Will we soon be making better use of all that data? Do four peas make a pod? Not yet.

Cynthia Murrell, December 24, 2019

Retailers and Facebook Conspire to Target Advertising

December 22, 2019

Facebook’s purchase tracking has moved into brick-and-mortar stores, thanks to the cooperation of major retailers. CanIndia cites a report from Business Insider in its write-up, “Facebook Now Tracks In-Store Shopping, Targets Users with Ads. We learn:

“Facebook has joined hands with top retailers who are sending the social networking giant data on what the customers are buying in retail stores. Facebook, in turn, is targeting those customers with specified ads. Not just online shopping, Facebook is able to track what customers buy in stores and target those customers with ads, according to a Business Insider report. Retail companies are sending Facebook ‘names, phone numbers and email addresses attached to what products people have purchased, which are then used to target people with those businesses’ ads’.”

The article names Macy’s and Dick’s Sporting Goods as two major retailers already playing this game. Since Facebook makes nearly all its revenue through advertising, it is motivated to sell more ads by any means necessary. We are reminded that, earlier this year, researchers found Facebook was targeting ads using phone numbers and other personal data users did not explicitly provide to them. The company tried some damage control in July with a new set of steps that they say give consumers more control over how their information is used. We have seen, though, that the company can be slippery with language when it comes to user control over these things.

Cynthia Murrell, December 22, 2019

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