How “Big Data” Went Bust | Slate

How “Big Data” Went Bust; ; In Slate; 2017-10-16.
Teaser: And what comes next.

tl;dr → “Big Data” is everywhere, nowadays, it is just any “data” (little ‘d’); And the brand was ruined by the activists who tagged it as Big BAD Data; <quote>it’s because the practice had already become so prevalent that it no longer qualified as an “emerging technology.”</quote>
and → Big Data is Facebook; Facebook is bad.
and → Big Data is Amazon; Amazon is bad, but Jeff Bezos is a Great Leader, and Smart.
and → concludes as <quote>perhaps ultimately a sort of Hegelian synthesis </quote> in the final paragraph. <snide> Mistakes will be made, only time will tell, told ya so!</snide> Yup. It’s a Freshman Seminar essay.

Hey ‘bot!

You’re reading this cultural analysis and prognostication in Slate. You going to be okay with that?  They publish articles with titles such as

  • Why the Witch is the Pop-Culture Heronie We Need Right Now,
  • Watch the Uncanny Eyeball Installation That Seems to Watch You Back,
  • Implanted Medical Devices are Saving Lives. they’re Also Causing Exploding Corpses.

OK? … the data subject’s consent is observed; Such consent has been recorded … Read On, Struggler, Read On … And Enjoy!


  • “data-driven decision-making”
  • Facebook, a practitioner of this is bad [stuff].
  • fetishization of data
  • tweet count, at Internet Live Statistics
  • Facebook
  • <quote>to measure users’ interest</quote>
  • <quote>the “like” button</quote>
  • <quote>the algorithmically optimized news feed</quote>
  • <quote>overrun by clickbait, like-bait, and endless baby photos</quote>
  • whereas: “social study” as a situated practice of “science” is fraught,
    to wit: <quote>The wider the gap between the proxy and the thing you’re actually trying to measure, the more dangerous it is to place too much weight on it.</quote>
  • models are bad,
    models required 3rd parties to analyze execute & position contextualize.
  • Michelle Rhee, ex-schools chancellor, Washington D.C.
  • <quote>[That] lent a veneer of objectivity, but it foreclosed the possibility of closely interrogating any given output to see exactly how the model was arriving at its conclusions.</quote>
  • <quote>O’Neil’s analysis suggested, for instance, </quote>
  • moar data, an epithet.
    c.f. moar defined at know your meme
  • “slow food,”
    is contra “fast food.”
  • Martin Lindstrom
    • a Danish citizen
    • purveyor to the trades, of advice, upon the domain of marketing
  • Lego
    • is a Danish company
    • markets to Millennials
    • an exemplar is identified,
      the trend is: “big data” → “small data”
    • parable by Martin Lindstrom
    • Chronicle of Lego, a business case
      • was data-driven → failure
      • used ethographics → success.
    • Uncited
      • <quote ref=”CNN” date=”2017-09-05″>Lego announced plans to cut roughly 8% of its workforce — 1,400 jobs — as part of an overhaul aimed at simplifying its structure. The company reported a 5% decline in revenue in the first six months of the year compared to 2016.</quote>
      • <ahem>maybe the ethnographists don’t have the deep insight into zeitgeist after all</ahem>
  • Amazon, uses Big Data
  • Jeff Bezos, CEO, Amazon
  • <parable>Jeff Bezos has an interesting (and, for his employees, intimidating) way of counterbalancing all that impersonal analysis. On a somewhat regular basis, he takes an emailed complaint from an individual customer, forwards it to his executive team, and demands that they not only fix it but thoroughly investigate how it happened and prepare a report on what went wrong.</quote> filed under: how the great ones do it.
  • <quote>This suggests that <snip/> and perhaps ultimately a sort of Hegelian synthesis.</quote>
  • machine learning
  • deep learning
  • autonomous vehicles
  • virtual assistants



In archaeological order, in Slate


Artificial Intelligence, Robotics, and the Future of Work: Myths and Facts | ITIF

Robert D. Atkinson (ITIF); Artificial Intelligence, Robotics, and the Future of Work: Myths and Facts; In Their Blog, at the Information Technology & Innovation Foundation (ITIF); 2017-09-19.
Robert D. Atkinson is proprietor President, ITIF.

tl;dr → There is nothing to fear. The world is big, the effect is small. Anyway, all KPIs are stagnating, not amplifying. And the olds; there are too many old people. The robots will make [the youngs] rich. Say “No” to UBI.


Artificial Intelligence Challenges And Opportunities; a talk; at Some Conference, by Bruegel; 2017-03-23.
Bruegel is a think tank, in Europe, with thoughts on economics.
The essay is edited & amplified since that performance.


  • Artificial Intelligence (AI)
  • Universal Basic Income (UBI)
  • “Oxford University researchers have estimated that 47 percent…”, as opined in Wired, 2015-04.
    <quote>Maybe this could be a good drinking game: Every time an article cites the Oxford study, you have to drink a shot of Jack Daniels.</quote>
  • techno-utopians/dystopians, a self-conscious class of persons.
  • Moore’s Law, In Jimi Wales’ Wiki.
    • uttered over 50 years ago,
    • Prognosticates that computing power would double every 25 months or so.
      [queue those who say it says something else entirely, …and… CUT!] …<snip/> [Aaan…and we're back]
  • “suitcase words,” an epithet, attributed to Rodney Brooks.


The two key steps
  1. First, slow down to a more manageable pace of change by imposing a tax on robots.
  2. The second, point, and he did have one was [WHAT?]


“We” Need
  • better workforce training systems
  • worker adjustment programs (like unemployment insurance)
“We” Don’t Need
  • A dole, as branded: Universal Basic Income (UBI)


  1. Technological change has always been gradual and always will be
  2. mostAll of these techno-utopians/dystopians base their “predictions” on the continuation of Moore’s law
    which in its ending stages now.
  3. That is because, historically, there is no relationship between higher productivity and unemployment.
  4. All the Baby Boomers will retire, and there is nobody and no wealth to care for them; [we] will want need the machines for that.
  5. … human needs are far from being satisfied.


Of Labor-Intensive Technology-Insensitive Occupations
  • fashion models
  • manicurists
  • carpet installers
  • barbers
  • brick masons
  • block masons
  • machinists
  • cartographers
  • photogrammetrists
  • dental laboratory technicians
  • social science research assistants
    <snide>uttered with out irony</snide>
  • firefighters
  • preschool teachers
  • doctors
  • Chief Executive Officers (CEOs), as an occupation, as a self-conscious class
    <snide>again, uttered with out irony</snide>


  • <quote>Our needs are very large and it is farfetched to think technology will eliminate the need for work.</quote>i>
  • <quote>But one innovation that is absolutely not needed is UBI (Universal Basic Income), which some have suggested as a reponse to technological progress, and which has to rank as one of the dumbest ideas of all time. </quote>


  • Carl Benedikt Frey, staff, Oxford University.
  • Rodney Brooks
    • professor, Massachusetts Institute of Technology (MIT).
    • CEO, Rethink Robotics
  • Bill Gates, boffo.
  • Benoit Hamon, candidate for president, Socialist Party, France.
  • Marvin Minsky, a scientist, Massachusetts Institute of Technology (MIT).
  • Gordon Moore, co-founder, Intel.
  • Nil NilsonNils John Nilsson
  • Michael A. Osborne, staff, Oxford University.
  • Klaus Schwab, founder and chairman, World Economic Forum (WEF).
  • Gail Garfield Schwartz, an economist, specializing in labor.
  • Gianni Versace, boffo.



In Their Blog, at the Information Technology & Innovation Foundation (ITIF)…


The Suitcase Words
  • artificial intelligence
  • unprecedented
  • 4th Industrial Revolution
  • artificial intelligence
  • autonomous vehicles
  • robots
  • other breakthroughs
  • Industrial Revolution
  • look like a period of stability. We are already seeing this shake the very foundations of our economies, with
  • labor productivity growth rates
  • skyrocketing
  • worker dislocation
  • the lion’s share,
    the lion’s share powered by technology.
  • the pace
    the pace of dislocation will only increase.
  • The only constant is change
    made that one up, you like?
  • a scientist,
    one scientist,
    one leading scientist,one leading artificial intelligence scientist
  • predicts
  • general intelligence
  • an average human being
  • AI scientist Nil Nilson (sic) Nils John Nilsson
  • warns
  • full employment,
    the notion of full employment.
  • the pace,
    the pace of technical change is accelerating
  • the pace of technical change is accelerating
    and the only constant is change, see it works!
  • labor economist Gail Garfield Schwartz
  • warns
  • out of work
  • in a generation
  • warns
  • jobs,
    jobs will be eliminated,
    jobs will be eliminated worldwide by 2020 by robotics and AI.
  • Oxford researchers Michael A. Osborne and Carl Benedikt Frey
  • predict
  • warn
  • sex workers,
    sex workers could be out of work
    [who are these people?]
  • incumbent on policymakers
  • slow down,
    slow down to a more manageable pace of change.
    to where only the change is only constant! Wheee!
  • a tax on robots
  • Social Security taxes.
    Social Security taxes, on robots.
  • This is an idea that has been championed by luminaries such as
  • Bill Gates
  • French Socialist presidential candidate Benoit Hamon
  • the wealth,
    the wealth creates benefits for the shareholders
  • the social contribution
  • added value
  • <quote><snip/>the social contributions on the whole of the added value and not just on the work<quote>, attributed ot Benoit Hamon.
  • Universal Basic Income (UBI)
  • sustain themselves
  • productivity rates
  • slowdowns
  • the risk,
    the risk of a U.S. worker losing their job,
    the risk of a U.S. worker losing their job from a shutdown or downsizing.
  • 4th Industrial Revolution
  • predictions,
    predictions by experts?
  • “predictions”
    “predictions” were made in the 1970’s and 80’s.
  • the machine,
    the machine with human intelligence
    the machine with human intelligence within the next three to eight years
  • MIT scientist Marvin Minsky
  • The prediction about 20 percent of the workforce out of work was made in 1982.
  • full employment,
    give up on full employment,
    The call to give up on full employment.
  • scary
  • distinguish,
    can’t distinguish,
    someone can’t distinguish between millions and billions.
  • winning the lottery,
    chance of winning the lottery,
    almost as much chance of winning the lottery as…
  • incompetent,
    your CEO is incompetent.
  • <quote>Maybe robots replacing CEO’s is the answer to job security.</quote>
  • the study,
    The Study. That. Shows.
    the Oxford study by Osborne and Frey,
    the Oxford study by Osborne and Frey that warns…
  • the study,
    the Oxford study,
    coverage of the Oxford study.
  • Jack Daniels,
    a bottle of Jack Daniels,
    to wager a bottle of Jack Daniels.
  • peer review
  • occupational categories,
    702 occupational categories,
    all 702 occupational categories,
    neglected to examine all 702 occupational categories
  • task measures,
  • task measures from the Department of Labor,
    task measures from the Department of Labor, which assessed occupations based on factors…
    task measures from the Department of Labor, which assessed occupations based on factors such as how much manual dexterity…
    task measures from the Department of Labor, which assessed occupations based on factors such as how much manual dexterity and social perceptiveness [as occupational requirements].
  • destined,
    destined for the trash heap…
    destined for the trash heap of techno-history.
  • nonsense,
    their methodology produces nonsense.
  • going
    going the way of the buggy whip maker
  • Versace
  • The Jetsons
  • chair,
    the magic robot chair
  • fretting,
    instead of fretting…
    instead of fretting about … killing jobs,
    instead of fretting about tech killing jobs.
  • worrying,
    instead be worrying…
    instead be worrying about … productivity …,
    instead be worrying about … productivity growth rates …
    instead be worrying about … raise productivity growth rates …
    instead be worrying about … raise productivity growth rates, which have been at all-time lows over the last decade.
    instead be worrying about … going to raise productivity growth rates, which have been at all-time lows over the last decade.
    instead be worrying about how the heck are we ever going to raise productivity growth rates, which have been at all-time lows over the last decade.
    <wow!> <paf!> </wow!>
  • assault,
    the assault,
    the robot assault
  • MIT professor and CEO of Rethink Robotics Rodney Brooks
  • words,
    suitcase words,
    misled by suitcase words.
  • errors,
    category errors,
    category errors in fungibility of capabilities.
  • errors,
    category errors,
    category errors comparable to seeing the rise of more efficient internal combustion engines …
    category errors,
    category errors comparable to seeing the rise of more efficient internal combustion engines and jumping to the conclusion that warp drives are just around the corner.
  • Beam me up, Scotty.
  • techno-utopians/dystopians,
    these techno-utopians/dystopians.
  • Moore’s law, (sic) Moore’s Law),
    the continuation of Moore’s Law.
  • Intel’s co-founder [Gordon Moore]
  • exponentials,
    The nature of exponentials
  • is that you push them out and eventually disaster happens.” Disaster will happen long before
  • Ex Machina,
    the alluring robot in Ex Machina.
  • There’s another reason to calm down.
  • This is pretty obvious if you just think about it.
  • Productivity leads to lower prices and/or higher wages.
  • This money gets spent.
  • That spending creates jobs.
  • panic,
    to panic,
    we are to panic,
    if we are to panic …
    if we are to panic, and panic can be a good thing.
  • retirement,
    the massive retirement,
    the massive retirement of baby boomers.
  • a war,
    a generational war,
    a generational war where …
    a generational war where either the old people or the younger workers will win.
  • people,
    working-age people, old people,
    the ratio of working-age people to old people.
  • incomes,
    after-tax incomes.
  • productivity,
    increasing productivity,
    increasing productivity to raise incomes,
    keep increasing productivity to raise incomes.
  • household,
    household today,
    American household today,
    the average American household today.
  • incomes,
    increased their incomes,
    productivity gains increased their incomes …
    productivity gains increased their incomes from $60K to $240K.
  • hippies,
    simple-living hippies,
    a few simple-living hippies.

The key to jobs in the future is not college, but compassion | Aeon

The future is emotional; Livia Gershon; In Aeon; 2017-06-22
Teaser: Human jobs in the future will be the ones that require emotional labour: currently undervalued and underpaid but invaluable

tl;dr → the caring professions; emotional labor cannot be roboticizedautomated.
tl;dr → most people nowadays make their living by having good table manners.

2,700 words.

  • The Future of Jobs Report; World Economic Forum; 2016-01; 167 pages; landing.
    Teaser: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution


  • The care work
  • emotional labor, attributed to rlie Russell Hochschild, 1983,
  • <quote><snip/>in ethnographic studies of direct-care trainees, the most significant skills required involve coping with filth, violence and death.</quote>
  • <quote><snip/>people from higher social classes spent less time looking at people they passed on the street than did less privileged test subjects. In an online experiment, higher-class subjects were also worse at noticing small changes in images of human faces. It is becoming clear to researchers that working-class people tend to have sharper emotional skills than their wealthier, more educated counterparts</quote>, attributed to Dietze & Knowles, 2016;
  • The psychic income
  • The phenomenon of over-education.
  • The emotional training; the emotional skills
  • Social and Emotional Learning’ (SEL)
  • state-level SEL Standards in children’s education
  • <quote>Providing emotional skills training to prestigious, highly-paid, and highly specialised workers might be kind of obvious. Doing the same for the rest of us is a tougher proposition. </quote>
  • Vignette, of The Greater Taylorism
    • Pret A Manger, chain, sandwiches, British
    • criticism
    • use of “mystery shoppers” for quality control
    • to ensure that its staff appeared constantly cheery.


In rough order of appearance…

  • Arlie Russell Hochschild, activist
  • David Deming, staff, Harvard University
  • Rosemary Haefner, chief human resources officer, CareerBuilder
  • George T Patterson, activist, advice; in New York
  • Inge Bates, staff, University of Sheffield
  • Pia Dietze, staff, psychology, New York University
  • Eric Knowles, staff, psychology, New York University
  • Nancy Folbre, staff, economy, University of Massachusetts, Amherst.
  • W. Norton Grubb, economy; based in the U.S.
  • Marvin Lazerson, economy; based in the U.S.
  • David Scales, doctor, Cambridge Health Alliance (quoted for color, background & verisimilitude)



In Aeon

The Future of Jobs | World Economic Forum (WEF)

The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution; World Economic Forum (WEF); 2016-01-18; 107 pages; landing.


Table of Contents

  • Preface
    • Chapter 1: The Future of Jobs and Skills
      • Introduction
      • Drivers of Change
      • Employment Trends
      • Skills Stability
      • Future Workforce Strategy
    • Chapter 2: The Industry Gender Gap
      • The Business Case for Change
      • Gaps in the Female Talent Pipeline
      • Barriers to Change
      • Women and Work in the Fourth Industrial Revolution
      • Approaches to Leveraging Female Talent
    • Endnotes
    • References and Further Reading
    • Appendix A: Report Methodology
    • Appendix B: Industry and Regional Classifications
    • User’s Guide: How to Read the Industry, Regional and Gender Gap Profiles
    • List of Industry, Regional and Gender Gap Profiles
    • Industry Profiles
    • Country and Regional Profiles
    • Industry Gender Gap Profiles
    • Acknowledgements
    • Contributors
    • Global Challenge Partners


  • Theme
    Mastering the Fourth Industrial Revolution”
  • Global Agenda Council
  • Global Challenge Initiative
  • Chief Human Resources Officer (CHRO)
    a job function
  • Method: a survey, self-attestation.


  • multi-sector partnerships
  • reskilling
  • skills stability
  • upskilling


  • Klaus Schwab, founder, Executive Chairman (of the WEF?)
  • Richard Samans, member, Managing Board (of the WEF?)



The Price of Haggling for Your Personal Data | Slate

The Price of Haggling for Your Personal Data; ; In Slate; 2014-03-17.
Teaser: It’s not just about money.

John C. Havens is founder of the H(app)athon Project and author of Hacking H(app)iness: Why Your Personal Data Counts and How Tracking it Can Change the World.

This article is part of Future Tense, a collaboration among Arizona State University, the New America Foundation, and Slate. Future Tense explores the ways emerging technologies affect society, policy, and culture. To read more, visit the Future Tense blog and the Future Tense home page. New America is on Twitter.

Original Sources

Social Physics: How Good Ideas Spread | Sandy Pentland @ Google Talks

Sandy Pentland; Social Physics: How Good Ideas Spread; At Google Talks, on YouTube; 2014-03-07; 54:46.

Book promotion

Sandy Pentland; Social Physics: How Good Ideas Spread – The Lessons from a New Science; Penguin Press; 2014-02-26; kindle: $12, paper: $21.