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!

Mentioned

  • “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

Referenced

Previously

In archaeological order, in Slate

Actualities

Is ‘Smart’ Beta Just Expensive Beta? | Betterment

Staff (Betterment); Is ‘Smart’ Beta Just Expensive Beta?; In Their Blog; 2015-06-15.
Teaser Are ‘smart’ beta funds good for investors? So far, the answer is no.

tl;dr → Betteridge’s Law. No.

From Two Sides Now

Is ‘Smart’ Beta Just Expensive Beta?
Yes.
Are ‘smart’ beta funds good for investors?
No.

Death of NFL inevitable as middle class abandons the game – Chicago Tribune

Death of NFL inevitable as middle class abandons the game; John Kass; In The Chicago Tribune; 2017-09-05.

tl;dr→ brain trauma, injuries.

Mentions

<quote>You really think the NFL is worried about young athletes? If so, they’d have changed the rules years ago, abandoning face masks, enlarging the ball to make it difficult to throw, switching to one platoon football.</quote>

Previously

Understanding Emerging Threats to Online Advertising | Budak, Goel, Rao, Zervas

Ceren Budak (Michigan), Sharad Goel (Stanford), Justin Rao (Microsoft), Georgios Zervas (Boston); Understanding Emerging Threats to Online Advertising; Research Paper No. 2505643, School of Management, Boston University; doi:10.1145/2940716.2940787, ssrn:2505643; 265 pages; 2014-10-06 → 2016-06-29.

tl;dr → There is peril to display advertising systems, which are mid-sized linkbaitists and newspapers. Paywalls are indicated.

Abstract

Two recent disruptions to the online advertising market are the widespread use of ad-blocking software and proposed restrictions on third-party tracking, trends that are driven largely by consumer concerns over privacy. Both primarily impact display advertising (as opposed to search and native social ads), and affect how retailers reach customers and how content producers earn revenue. It is, however, unclear what the consequences of these trends are. We investigate using anonymized web browsing histories of 14 million individuals, focusing on “retail sessions” in which users visit online sites that sell goods and services. We find that only 3% of retail sessions are initiated by display ads, a figure that is robust to permissive attribution rules and consistent across widely varying market segments. We further estimate the full distribution of how retail sessions are initiated, and find that search advertising is three times more important than display advertising to retailers, and search advertising is itself roughly three times less important than organic web search. Moving to content providers, we find that display ads are shown by 12% of websites, accounting for 32% of their page views; this reliance is concentrated in online publishing (e.g., news outlets) where the rate is 91%. While most consumption is either in the long-tail of websites that do not show ads, or sites like Facebook that show native, first-party ads, moderately sized web publishers account for a substantial fraction of consumption, and we argue that they will be most affected by changes in the display advertising market. Finally, we use estimates of ad rates to judge the feasibility of replacing lost ad revenue with a freemium or donation-based model.

How the Frightful Five Put Start-Ups in a Lose-Lose Situation | NYT

How the Frightful Five Put Start-Ups in a Lose-Lose Situation; Farhad Manjoo; In The New York Times (NYT); 2017-10-18.
Teaser: The tech giants are too big. But so what? Hasn’t that always been the case?

tl;dr → Betterid’ge’s Law.  No. ]this time it’s different]
and → Problematizing the space, a jeremiad.
bad → Amazon, Apple, Google, Facebook, Microsoft. branded as “The Frightful Five”

Mentions

  • Frightful Five = Amazon, Apple, Google, Facebook, Microsoft.
    Manjoo’s epithet for the circumscribed scope of these oped pieces ref
  • #sturtups
  • IBM
  • WhatsApp
  • Snapchat
  • Facebppl
  • Snapchat Stproes
  • Instagram
  • IAC
    • Origin
      • Barry Diller [Barry Diller's money]
    • Properties
      • Expedia
      • Match.com
      • Tinder
      • Ask.com
      • Vimeo
      • Angi Homeservices, = Angie’s List + HomeAdvisor.

Who

  • Dara Khosrowshahi, ex-CEO, Expedia.
  • Joey Levin,, chief executive, Uber; ex-chief executive of IAC.
  • Chris Terrill, chief executive, Angi Homeservices.

Pantheon

  • Clayton Christiansen, boffo.
  • Barry Diller, boffo; media tycoon, television.
  • Joseph Shumpeter, boffo.

Referenced

Previously

In archaeological order, in The New York Times (NYT)…

Wireless Carriers Again Busted Collecting, Selling User Data Without Consent Or Opt Out Tools | Techdirt

Karl Bode; Wireless Carriers Again Busted Collecting, Selling User Data Without Consent Or Opt Out Tools; Editor; In His Blog entitled Techdirt; 2017-10-19.

tl;dr → They are doing it; And they are doing it without enough consent.

Original Sources

Philip Neustrom (Shotwell Labs);Want To See Something Crazy Open This Link On Your Phone With Wifi Turned Off; In Their Blog, centrally hosted on Medium for syndication and consequent compensation; WHEN? [recently]
Philip Neustrom is proprietor, Shotwell Labs; with co-founder credit.

Mentions

  • Verizon
  • AT&T
  • Payfone
  • Danal

Claims

  • [Philip Neustrom] also found that the existing opt out mechanisms used by T-Mobile, Verizon, AT&T and other mobile carriers don’t do a damn thing to prevent this data from being monetized:
  • <quote>US telcos [appear to be are absolutely surely certainly; <guideline>look, if you're going to throw allegations, go for for it, all in; none of those waffle words: "maybe" or "suggests" or "appears to be.". Be BOLD!</guideline>] selling direct, non-anonymized, real-time access to consumer telephone data to third party services — not just federal law enforcement officials — who are then selling access to that data.</quote>
  • Opt-Out is a No-Op, to wit:
    <quote>”AT&T’s “consumer choice” opt-out at didn’t appear to do anything to stop this, even after waiting the stated 48 hours. All of the demos were still working for me on the morning of 2017–10–15 after I had opted out on 2017–10–13. Many users on Twitter and elsewhere also report that AT&T’s opt-out process doesn’t do anything here. Verizon’s “opt-out” pages also may not do anything to prevent this, either (A, B).”</quote>

Referenced

  • Choice, a consumer options affordance; AT&T.
  • Some video; On YouTube; circa 2014
    tl;dr → a joint AT&T-Danal presentation from 2014 explaining how this [tracking & availability] technology works; allegedly retracted, “pulled” in the freewheeling street lingo of their millieu.
  • tweet; @dakami.
  • Shrouded URL; at t.co.
  • tweet; @SwiftOnSecurity 2017-10-15.

Previously

In Techdirt

Three Points to Consider before Migrating Away from React Because of Facebook’s ‘BSD+ Patent’ License | Ariel Reinitz

Ariel Reinitz; 3 Points to Consider before Migrating Away from React Because of Facebook’s ‘BSD+ Patent’ License; In His Blog; 2017-08-22T07:11:49.140Z.
Teaser: The grass isn’t always greener…
Ariel Reinitz self-identifies as an attorney with expertise in intellectual proprety; and some professional experience as a developre of software.

Listicle

  1. Alternatives to React may still be vulnerable to Facebook’s React patents
  2. Using React doesn’t mean you ‘can never sue Facebook’
  3. If Facebook uses their Patents ‘offensively,’ using React may shield you from a lawsuit

<quotelike>

Facebook’s React License Patent Clause

tl;dr
  • Facebook (FB) may have patents that cover React.
  • FB won’t use these React patents to stop you from using React code.
  • If you sue FB for patent infringement, FB can respond by using their React patents against you.
  • If FB sues you for patent infringement first, you can counter-sue using your own patents and FB still can’t use their React patents against you.

</quotelike>

PATENTS, the text, it, itself.

Recently

Adam Wolff (Facebook); Relicensing React, Jest, Flow, and Immutable.js; In Their Blog; 2017-09-22; previously filled.

The Evolving Data Landscape: Veracity, Convergence And Anonymity | Ad Exchanger

Ramsey McGrory (Mediaocean); The Evolving Data Landscape: Veracity, Convergence And Anonymity; In AdExchanger; 2017-09-21.
Ramsey McGrory, chief revenue officer at Mediaocean

tl;dr → something about accuracy of imputations in consumer profiles, accuracy of “data.”

Original Sources

Ramsey McGrory (AddThis); The Data Providers One Quadrant Chart To Rule Them All; 2013-02.
tl;dr → it’s a metaphor with four (4) quadrants induced by a 2-axis “system”; later a 3rd access, a Z-axis

  1. online ↔ offline
  2. anonymous → personal
  3. singleton → conglomerate

Mentions

  • <quote>data being neither intrinsically “good” nor “bad,” but rather having “qualities.”<quote>, attributed to Ted McConnell.
  • behaviors, drive actions.
  • Viewability
  • Verification
  • Something allegorical about Viewability and Trust & Safety vending as a separable service of attestation, 2012 → 2017.
  • <quote>Viewability speaks to a broader metadata theme of trust, as well as an underlying theme of data quality and users’ engagement with content delivered against this data.</quote>
  • Hey! That’s not a business, that’s a Business Unit;
    Hey! That’s not a BU, that’s a Product.
    Hey! That’s not a Product, that’s a Feature.
    <quote>Then, these vertical standalone organizations and solutions were horizontally integrated into the operating agencies as capabilities.</quote>

Claimed

  • SafeGraph <quote>works with universities and health organizations to understand movement data and the spread of infectious diseases.</quote>
  • [all] device IDs are persistent
  • <quote>there are growing trends toward people taking control of their anonymization through the use of virtual private networks and Tor</quote>
    • As stated:
      • casual consumer use of VPNs is prevalent [enough to measure]
      • casual consumer use of Tor is prevalent [enough to measure]
    • Contrast with:
      <surely>IPv6 use is prevalent,
      IPv6 use is prevalent enough to warrant dual-stack interfaces on the great centralized ad exchanges.</surely>
  • <quote>mobile, where cookies can’t be used</quote>
  • <quote>that major brands may view agencies as differentiated commodity services, put their media in review with greater frequency and bid them down.</quote>
  • The adtech bubble is ongoing; adtech will be forward-funded on an ongoing basis:<quote><snip/> will continue to be funded with massive capital because the opportunities for innovation and disruption are huge.</quote>

Framework

Three Four V’s of Data
  1. volume
  2. velocity
  3. variety
  4. veracity

Exemplars

Big (conglomerates)
  • Adobe
  • Amazon
  • Google
  • IBM
  • Oracle
  • Salesforce
  • SAP
Cross-Device Fingerprinting
Viewability
  • AdSafe
  • comScore
  • DoubleVerify
  • Moat
Safety
  • Amino
  • White Ops
Data Brokers
  • Experian
  • Acxiom
  • TransUnion
  • Equifax
Trading Desks
  • Xaxis of WPP
  • Nerve Center of VivaKi of Publicis
Data Breached
  • Yahoo
  • Equifax
Salubrious

Hearts & Science
<honorific>won major accounts on a transparent, data-centric and deeply integrated vision.</honorific>

Who

  • Ted McConnell, practitioner.
    <quote>Ted McConnell, an independent consultant in the digital marketing space.</quote>

Referenced

Previously

In Ad Exchanger

Argot

The Suitecase Words
  • ”data truth”
  • “moat for data”
  • “truth of the inference”
  • intenders, as “auto intenders”
  • attitudes
  • demographics
  • measurement
  • cross-device,
    cross-device mapping.
  • deterministic
  • Television
    • connected television
    • addressable television
    • advanced television
    • data-enabled television
    • targeted television
    • integrated television and video,
      integrated television and video initiatives
  • strategic elements
    strategic elements of advertising campaigns.
  • holistic planning
  • anonymous data
  • digital data
  • ad block
  • cookie block
  • mobile,
    growth of mobile.
  • device IDs
  • persistent
  • anonymity,
    desire for anonymity.
  • breaches,
    data breaches,
    massive data breaches

    • Yahoo
    • Equifax
  • sensitive information
    • Social Security numbers
    • birthdates
    • credit card numbers
  • collaboration
  • competition
  • companies,
    services companies,
    technology and services companies
  • space,
    media space.
  • to verb… with large agencies
    • partner
    • coexist
    • compete
  • Z-axis
  • execute,
    acquisitively execute,
    aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute on their strategies,
    continue to aggressively and acquisitively execute on their strategies to deliver on

    • infrastructure
    • data
    • services
  • agencies,
    holding company agencies.
  • solutions,
    data-driven solutions,
    converged, data-driven solutions.
  • vision,
    • transparent vision
    • data-centric vision
    • integrated vision,
      deeply integrated vision.
  • The side,
    • The downside
    • The upside
  • brands,
    major brands.
  • services,
    commodity services,
    differentiated commodity services,
    agencies as differentiated commodity services.
  • themes
    • convergence
    • data activation
    • people
  • change,
    great change,
    in a time of such great change,
    wait for it … wait for it … the only constant is change …thank you, thank you very much, I’ll be here all week.
  • The Bottom Line
  • In a world of…
  • adjectivedata,
    • first-party data
    • third-party data
    • personal data
    • census data
    • anonymous data
    • panel data,
      <mmmmm>…panel data..…</mmmmm>
    • pixel data
  • understanding,
    deeper understanding,
    deeper understanding of consumers’ …
    deeper understanding of consumers’ awareness and interests,
    deeper understanding of consumers’ awareness and interests while enjoying <snip/> profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability of their brands.
  • vision,
    this vision,
    delivering on this vision.
  • infrastructure,
    data infrastructure,
    extensive data infrastructure.
  • understanding,
    deep understanding,
    deep understanding of

    • advertising
    • publishing,
      media publishing
    • ecommerce
  • ecosystems,
    technology ecosystems,

    • advertising technology ecosystems
    • marketing technology ecosystems
    • content technology ecosystems
  • ecosystems,
    the ecosystems,
    all the ecosystems,
    And across all the ecosystems
  • nounof data
    • consolidation of data
    • standardization of data
    • interpretation of data
    • activation
  • winners
    winners and losers
    winners and losers will be decided.
  • transformation,
    massive transformation,
    enable massive transformation,
    enable massive transformation at <snip/> lower costs.
    enable massive transformation at materially lower costs.

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.

Occasion

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.

Mentions

  • 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.

Anti-Nostrum

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?]

Nostrum

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

Soporific

  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.

Exemplars

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>

Quotes

  • <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>

Pantheon

  • 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.

Referenced

Previously

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

Argot

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 …
    errors,
    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.

Know Thy Futurist | Boston Review

Know Thy Futurist; Cathy O’Neil; In Boston Review; 2017-09-25.

tl;dr → Cathy O’Neil, who is not bitter, envies the scholar-gentleman futurists as she aspires to their life of the mind, for which she writes.
and → futurists are scary people; they are serious people; they are never sour or defeated people; they are not silly people.
and → A “four box” model, two axes, four quadrants; named Q1, Q2, Q3, Q4.
and → Facebook is bad.

Models

The Latent Model, single-axis [the lede is buried-last]
  • Men ↔ Women
    (bad) ↔ (good)
The Declared Model, orthogonal-axes
  • Worried ↔ Exuberant
  • Dystopian ↔ Utopian

Indictment

  • data scientists are creating machines
    data scientists are creating machines they do not fully understand.
  • data scientists are creating machines that separates winners from losers,
    data scientists are creating machines that separates winners from losers for reasons that are already very familiar to us
    These reasons are enumerated, by iconic euphemism-cum-epithet as:

    • class
    • race
    • age
    • disability status
    • quality of education
    • and other demographic measures (“other”).
  • [data scientists' activities in the creation of machines] is a threat to the very concept of social mobility.
  • [data scientists' activities in the creation of machines] is the end of the American dream.

Wherein a data scientist is a statistician who lives in San Francisco and performs their work-product on a Macintosh computer [ref].

Separately noted.