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.
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!
<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>
purveyor to the trades, of advice, upon the domain of marketing
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.
<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>
The Age of Big Data; Staff; Sunday Review, of the The New York Times (NYT) ; 2012-02-12 (five years ago).
Michael Lewis, Moneyball, 2003, ASIN:0393057658
tl;dr → boosterism upon the use of analytics within the business operations of a baseball team.
Shopping Habits; Some Cub Reporter (SCR); In The New York Times (NYT); 2012-02-10.
tl;dr → <perhaps>that story of Charles Duhigg’s about the [Christian?] girl who is pregnant and Target’s algo finds her in her home and serves her advertisements for the happy arrival, but she isn’t married and her father is unamused.</perhaps>
<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>
tl;dr → There is peril to display advertising systems, which are mid-sized linkbaitists and newspapers. Paywalls are indicated.
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.
[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>
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.
<quote>data being neither intrinsically “good” nor “bad,” but rather having “qualities.”<quote>, attributed to Ted McConnell.
behaviors, drive actions.
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>
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>
casual consumer use of VPNs is prevalent [enough to measure]
casual consumer use of Tor is prevalent [enough to measure]
<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>
integrated television and video,
integrated television and video initiatives
strategic elements of advertising campaigns.
growth of mobile.
desire for anonymity.
massive data breaches
Social Security numbers
credit card numbers
technology and services companies
to verb… with large agencies
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
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…
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.
delivering on this vision.
extensive data infrastructure.
deep understanding of
advertising technology ecosystems
marketing technology ecosystems
content technology ecosystems
all the ecosystems,
And across all the ecosystems
consolidation of data
standardization of data
interpretation of data
winners and losers
winners and losers will be decided.
enable massive transformation,
enable massive transformation at <snip/> lower costs.
enable massive transformation at materially lower costs.
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.
“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.
Technological change has always been gradual and always will be
mostAll of these techno-utopians/dystopians base their “predictions” on the continuation of Moore’s law
which in its ending stages now.
That is because, historically, there is no relationship between higher productivity and unemployment.
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.
… human needs are far from being satisfied.
Of Labor-Intensive Technology-Insensitive Occupations
dental laboratory technicians
social science research assistants
<snide>uttered with out irony</snide>
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.
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
a scientist, domain of Artificial Intelligence (AI),
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
out of work
in a generation
jobs will be eliminated,
jobs will be eliminated worldwide by 2020 by robotics and AI.
Oxford researchers Michael A. Osborne and Carl Benedikt Frey
sex workers could be out of work
[who are these people?]
incumbent on policymakers
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
French Socialist presidential candidate Benoit Hamon
the wealth creates benefits for the shareholders
the social contribution
<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)
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 by experts?
“predictions” were made in the 1970’s and 80’s.
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.
give up on full employment,
The call to give up on full employment.
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…
your CEO is incompetent.
<quote>Maybe robots replacing CEO’s is the answer to job security.</quote>
The Study. That. Shows.
the Oxford study by Osborne and Frey,
the Oxford study by Osborne and Frey that warns…
the Oxford study,
coverage of the Oxford study.
a bottle of Jack Daniels,
to wager a bottle of Jack Daniels.
702 occupational categories,
all 702 occupational categories,
neglected to examine all 702 occupational categories
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 for the trash heap…
destined for the trash heap of techno-history.
their methodology produces nonsense.
going the way of the buggy whip maker
the magic robot chair
instead of fretting…
instead of fretting about … killing jobs,
instead of fretting about tech killing jobs.
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!>
the robot assault
MIT professor and CEO of Rethink Robotics Rodney Brooks
misled by suitcase words.
category errors in fungibility of capabilities.
category errors comparable to seeing the rise of more efficient internal combustion engines …
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.
Moore’s law, (sic) Moore’s Law),
the continuation of Moore’s Law.
Intel’s co-founder [Gordon Moore]
The nature of exponentials
is that you push them out and eventually disaster happens.” Disaster will happen long before
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.
we are to panic,
if we are to panic …
if we are to panic, and panic can be a good thing.
the massive retirement,
the massive retirement of baby boomers.
a generational war,
a generational war where …
a generational war where either the old people or the younger workers will win.
working-age people, old people,
the ratio of working-age people to old people.
increasing productivity to raise incomes,
keep increasing productivity to raise incomes.
American household today,
the average American household today.
increased their incomes,
productivity gains increased their incomes …
productivity gains increased their incomes from $60K to $240K.
a few simple-living hippies.
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.
The Latent Model, single-axis [the lede is buried-last]
Men ↔ Women
(bad) ↔ (good)
The Declared Model, orthogonal-axes
Worried ↔ Exuberant
Dystopian ↔ Utopian
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:
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].