Dell UP3218K | An “8K” monitor at 32″ via 2x DisplayPort

Promotional Page

Does it work with Linux (Wayland Weston etc.): UNKNOWN.
There is no clear positive statement that it does… or does not.

The headline specs are all those fun “little lies” that commerce gets to flourish itself with …wherein “8K” equals 7600, and “32 inches” equals 28.4″ etc.


  • “8K” = 7680 x 4320
  • 32″ = 31.5″ =28.4″ × 8.5″ × 24.3″
  • 17 Kg = 37.47 Lbs.
  • 60 Hz
  • 2x DisplayPort 1.4+
    Something about HDMI 2.0 maybe.


From the Drivers & Manuals subpage

  • May or may not be Windows Only
    Some commentariat that MacOS is “not supported.”
    Claimed <quote>All NVIDIA 10xx cards, and their TITAN line, support 7680×4320@60Hz natively.</quote>
  • Requires both cables to provide the “8K” mode.
  • Which graphics cards are supported?
  • AMD video cards may or may not be supported.
  • Intel HD Graphics may or may not be supported.

Via: backfill.

IAB Transparency & Consent Framework

Transparency & Consent Framework, Interactive Advertising Bureau (IAB)


  • Accessing a device allow storing or accessing information on a user’s device.
  • Advertising personalisation allow processing of a user’s data to provide and inform personalised advertising (including delivery, measurement, and reporting) based on a user’s preferences or interests known or inferred from data collected across multiple sites, apps, or devices; and/or accessing or storing information on devices for that purpose
  • Analytics allow processing of a user’s data to deliver content or advertisements and measure the delivery of such content or advertisements, extract insights and generate reports to understand service usage; and/or accessing or storing information on devices for that purpose.
  • Content personalisation allow processing of a user’s data to provide and inform personalised content (including delivery, measurement, and reporting) based on a user’s preferences or interests known or inferred from data collected across multiple sites, apps, or devices; and/or accessing or storing information on devices for that purpose.


  • Matching data to offline sources combining data from offline sources that were initially collected in other contexts.
  • Linking devices allow processing of a user’s data to connect such user across multiple devices.
    Precise geographic location data allow processing of a user’s precisegeographic location data in support of a purpose for which that certain third party has consent.

Purpose versus Feature

  • Purpose is a data use that drives a specific business model and produces specific outcomes for consumers and businesses. Purposes must be itemised at the point of collection, either individually or combined.
  • Feature is a method of data use or data sourcing that overlaps across multiple purposes. Features must be disclosed at the point of collection, but can be itemised separately to cover multiple purposes.


GCC C++ Modules TS Branch in Git from the original Subversion service


GCC for Modules TS is mastered in Subversion. It would be fun to have it copied and available in Git. Because that’s what the cool kids use nowadays.


  • svn://



cd /…/vault/git/svn/org.gnu.gcc
mkdir c++-modules
svnadmin create c++-modules
cat > c++-modules/hooks/pre-revprop-change <<EOF
exit 0;
chmod +x c++-modules/hooks/pre-revprop-change

svnsync init file:///…/vault/git/svn/org.gnu.gcc/c++-modules svn://
# one line of output
# …quick…

svnsync sync file:///…/vault/git/svn/org.gnu.gcc/c++-modules
# …lots and lots of output…
# …long time passing…
# …think "five days" as 1 rev/sec is common and you need r25500…

cd /…/vault/git/clones
git svn clone file:///…/vault/git/svn/org.gnu.gcc/c++-modules -T trunk -b branches -t tags

Also, -s is the same as -T trunk -b branches -t tags


Can We Foresee the Future? Explaining and Predicting Cultural Change | SPSP (Varnum & Grossman)

Igor Grossmann, Michael E. W. Varnum in their roles as; editor of the blog of Society for Personality and Social Psychology) Can We Foresee the Future? Explaining and Predicting Cultural Change; In That Certain Blog; 2017-10-17.

tl;dr → Yes. Betteridge’s Law fails.
ahem → No. Betteridge’s Law holds. Surely no one can know the future, and anyone who says they can is either high or a fool, perhaps both. One can problematize quibble on the epistemology sense of the word “to know,” if you think you have time for that sort of thing.


Michael E. W. Varnum, Igor Grossmann. (2017). Cultural change: The how and the why. In Perspectives on Psychological Science. DOI:10.1177/1745691617699971


The promotional build running up to the release of that certain sequel (2017) to the movie Blade Runner (1982) which is in turn based on a short novel by Philip K. Dick entitled Do Androids Dream of Electric Sheep? (Doubleday 1968) [Answer: No (whereas Androids, after the Ice Cream Sandwich release, are functionally people too, being as they feel pain and love, as eloquently and forcefully testified by Rutger Hauer in a monologue performed so memorably on that dark & rainy night), again, Betteridge's Law holds, c.f. Jimi Wales' Wiki, Jimi Wales' Wiki].


A means & method for producing new predictions, which is better.

  • Uniqueness.
  • Rigorous
    • Theory-Driven [not Theory-Laden].
    • Testable [falsifiable]
  • Empirical.
    • Documentation
      Whereas sociology is either slow journalism [documentation] or activism [promotion] in service to personal ideals.
    • Repeatable
      Replicatability is not claimed. It’s a best practice for high fidelity journalism.

<quote>What is unique is a rigorous theory-driven attempt to not only document but to test explanations for patterns of societal change empirically </quote>

The enumerated [cultural] changes are features of the ecology [our ecologies].
<quote>This emerging work suggests <snide>asserts</snide> that among the most powerful contributors to cultural changes in areas like individualism, gender equality, and happiness are shifts in essential features of our ecologies.</quote>
This schema was shown in animal behavior; now it is replicated with people [our people].
<quote>The idea that variations in ecological dimensions and cues like scarcity or population density might be linked to behavioral adaptations has been widely explored in animal kingdom, and recently started to gain prominence as a way to explain variations in human behavior.</quote>

  • Ellis, Bianchi, Griskevicius, & Frankenhuis, 2017.
  • Sng, Neuberg, Varnum, & Kenrick, 2017.


  • It’s an “implications” paper:
    <quote>but also has fundamental implications for psychometric assumptions and replicability in psychological science.</quote>
  • <quote>Neither experts nor lay people do much better than chance
    as “proven” in: Tetlock, 2006; Tetlock & Gardner, 2016.</quote>
  • <quote>psychological phenomena unfold within a temporal context,</quote> → <fancier>events occur over spans of time; therefor psychological events occur over spans of time<fancier>,
    the insight is attributed to Kurt Lewin and Lev Vygotsky; unnamed “other theorists.”
  • ngrams, as mentioned in Google Books.
  • cross-lagged statistical models
  • cross-correlation functions
  • tests of Granger causality
  • SES (Socio-Economic Status; i.e. Marx-archetype class.1
  • The Misery Index, of [NAME] Okun.
  • ecological framework
  • big data
  • econometric tools
  • insights from machine learning
  • predictive science of cultural change.
  • emerging science of cultural change
  • predictive psychological science (Yarkoni & Westfall, 2017)


Individualism ↔ Collectivism
A focus on uniqueness and independence and emphasis of self-expression (or not.
Gender Equality
Obvious: equality between the [two] genders, which are named as: Male and Female (Female and Male).
Obvious: that buddhist thing; as evidenced in self-attestation surveys.
The WEIRD Population
The white American middle-class college students.

  • Western,
  • Educated,
  • Industrialized,
  • Rich,
  • Democratic.

References (at least):

  • Joseph Nenrich, Steven J. Heine, Ara Norenzayan; The Weirdest People in the World; In Some Journal, Surely; 2009-03-05; 58 pages (23,703 words).
    Cited herein: Henrich, Heine, & Norenzayan, 2010.
    Teaser: How representative are experimental findings from American university students? What do we really know about human psychology?


  • Isaac Asimov, boffo.
    Honorific: <quote>the seminal science fiction author — inventor of the fictional discipline of psycho-history.</quote>
  • Gerd Hofstede, documentarian.
  • Kurt Lewin, theorist.
  • Nostradamus; boffo.
    Opus: Quatrains, many years ago.
  • Lev Vygotsky, theorist.



  • individualism,
  • gender equality,
  • happiness.


  • model cultural change
    on a large scale.
  • using data,
    using cross-temporal data
  • using theory or theories,
    using theories derived from behavioral ecology.
  • “can usher in” [what?]
  • a new era in research,
    a new era in research social psychological and personality research.

unclear… if this means more better hard Sci-Fi or more sooth can be said:

  • more voluminous,
  • more accurate,
  • more relevant,
  • more pithy,
  • more cogent,
  • more better prognostications.


Method of Prognostication
  • ecological framework,
  • big data,
  • econometric tools.


far future: 2047 → 2117.


Obtain the Salubrious Result.

  • society,
  • the economy,
  • politics.

Events in the areas of…

  • scientists,
    specifically: behavioral scientists,
  • policy makers,
    specifically: [hired] regulators and [elected] politicians.
  • anyone,
    as such: the laity, the general public.
Charlatans, Experts
  • pundits,
  • economists,
  • intelligence analysts,
    generally, any and all analysts.
Drift, across time, same place
Results in social science are idiosyncratic and perishable. To wit:
<quote>There is no guarantee that the structure of psychological constructs (and their relationship to each other) remains consistent over time – a critical insight for anybody studying individual differences or the interaction of the social context and personality.</quote>
Drift, across time, different places
Results in social science are idiosyncratic to the place and perishable. To wit:
Second, in behavioral and management sciences that focus on cross-cultural comparisons, we need to ensure that our measurements are made contemporaneously.</quote>
Untestable, uninferrable
Documentation practices produces records as evidence; such cannot be used to as inputs to a reasoning process. To wit:
<quote><snip/> for those interested in the ways socio-cultural context impacts human minds, the new field of cultural change enables better tests of theories regarding the origin and evolution of cross-cultural variations than the cross-sectional approaches that are currently standard in the field. Time series data permit stronger inferences regarding the causes of cultural variation than is possible from datasets where putative causes and outcomes are measured only once and at the same time.</quote>
Implications, there are implications; this is important work.
<quote><snip/> have some implications for debates about replicability.
This is not to say that cultural change is likely the explanation for many or most failures to replicate previous findings, but when there is a large temporal remove between the original studies and replication attempts, it may be wise to consider this when interpreting any discrepancies or changes in effect sizes.

  • Greenfield, 2017; Varnum & Grossmann, 2017.
Drift, invalid population sampling
Whereas psychology “research” is done on The WEIRD Population, the results are incorrect.
<quote>Most samples we collect are “WEIRD,” consisting largely of white American middle-class college students who it turns out are not psychologically representative of humanity. But perhaps more importantly emerging insights from the cross-temporal study of psychological processes suggest <snide>assert<snide> that as psychologists, whether we are aware of it or not, we are studying a moving target.


  • Changes in baby naming practices in the US from the 1880’s to the 2010s and predictions for future trends through 2030.
    from Grossmann and Varnum (2015).
  • Voter turnout
  • Twenge & Campbell
  • …others…


Self-esteem, narcissism, and intelligence have increased in Western societies since 1980.
<quote>over the past several decades<quote>

  • Twenge & Campbell, 2001.
  • Twenge, Konrath, Foster, Campbell, & Bushman, 2008.
  • Flynn, 1984.
  • Trahan, Stuebing, Fletcher & Hiscock, 2014.
Social capital has declined since [sometime]
…as evidenced in e.g. involvement in civic organizations and voter turn-out.

  • Putnam, 1995.
  • Putnam, 2000.
Gender equality has risen, in “The West,” since 1950.
<quote>over the past 60-70 years.<quote>

  • Varnum & Grossmann, 2016.
Individualist attitudes, practices, and relational patterns have increased in 60+ countries
  • Grossmann & Varnum, 2015.
  • Santos, Varnum & Grossmann, 2017.
Changes in The Environmemt, generalized, cause changes in Behavior, generalized;
This occurs in individuals and composes into groups.
><quote>The idea that variations in ecological dimensions and cues like scarcity or population density might be linked to behavioral adaptations has been widely explored in animal kingdom, and recently started to gain prominence as a way to explain variations in human behavior.</quote>

  • Ellis, Bianchi, Griskevicius, & Frankenhuis, 2017.
  • Sng, Neuberg, Varnum, & Kenrick, 2017.
White-collar employment causes individualism.
White-collar employment correlates with individualism.
<quote><snip/>a shift toward greater affluence and white- (vs. blue) collar occupations was the most robust ecological predictor of levels of individualism over time, further shifts in levels of SES consistently preceded changes in levels of individualism in America – a finding that has since been extended and cross-validated by our team in a study examining the rise of individualism around the globe.</quote>

  • Grossmann & Varnum, 2015.
  • Santos, Varnum, & Grossmann, 2017.
Disease causes sexism.
The disease level cause the sexism level.
Infectious disease level decline causes the gender equaltiy increase.
<quote>It turned out that a decline in levels of infectious disease was the most robust factor predictor of rising gender equality, a finding we were able to replicate in the UK, and in both societies we found evidence that changes in pathogen levels preceded shifts in gender equality</quote>

  • Varnum & Grossmann, 2016.
Happiness has decreased in the United States since 1800.
<quote>Research examining affect in books and newspaper articles over a 200-year span shows a long-term decline in American happiness.</quote>

  • Iliev, Hoover, Dehghani, & Axelrod, 2016.
Misery causes inverse happiness
Whereas well-being is functionally the same as happiness, the Misery Index measures inverse happiness.
<quote>Levels of well-being in [these] studies appeared linked to Okun’s Misery Index, an economic indicator that combines unemployment and inflation rates, consistent with the idea that scarcity or abundance of resources matters for happiness.</quote>

  • Iliev et al., 2016.
Only the level of envy matters.
Whereas well-being is functionally the same as happiness,
and envy being a manifestation of differential happiness,
and happiness decreases as inequality increases;
thus absolute levels of happiness do not matter,
the differences between the happiness levels matters,
the level of envy matters.
<quote>Another study exploring the cause of changes in levels of well-being over time in the US found strong links to levels of economic inequality, suggesting <snide>asserting without proof</snide> that happiness decreases as inequality increases, suggesting<snide>asserting</snide> that not only absolute levels of resources but their distribution in an environment (what behavioral ecologists call “resource patchiness”) help to explain changes in well-being over time.</quote>

  • Oishi, Kesebir, & Diener, 2011.


  • Ellis, B. J., Bianchi, J., Griskevicius, V., & Frankenhuis, W. E. (2017). Beyond risk and protective factors: An adaptation-based approach to resilience. Perspectives on Psychological Science, 12(4), 561–587. DOI:10.1177/1745691617693054
  • Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171 – 191. DOI:10.1037/0033-2909.101.2.171.
  • Greenfield, P. M. (2017). Cultural change over time: Why replicability should not be the gold standard in psychological science. Perspectives on Psychological Science, 12(5), 762-771. DOI:10.1177/1745691617707314
  • Grossmann, I. & Varnum, M. E. W. (2015). Social structure, infectious diseases, disasters, secularism, and cultural change in America. Psychological Science, 26(3) 311-324. DOI:10.1177/0956797614563765
  • Henrich, J., Heine, S.J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 62–135. doi:10.1017/S0140525X0999152X
  • Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (revised and expanded). New York, NY: McGraw-Hill I
  • liev, R., Hoover, J., Dehghani, M., & Axelrod, R. (2016). Linguistic positivity in historical texts reflects dynamic environmental and psychological factors. Proceedings of the National Academy of Sciencesof the U.S.A, 113(49), 7871-7879. DOI:10.1073/pnas.1612058113
  • Oishi, S., Kesebir, S., & Diener, E. (2011). Income inequality and happiness. Psychological science, 22(9), 1095-1100. DOI:10.1177/0956797611417262
  • Putnam, R. D. (1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6(1), 65-78.
  • Putnam, R. D. (2000). Bowling alone: America’s declining social capital. In Culture and politics (pp. 223-234). Palgrave Macmillan US.
  • Santos, H. C., Varnum, M. E. W., Grossmann, I. (2017). Global increases in individualism. Psychological Science. DOI:10.1177/0956797617700622
  • Sng, O., Neuberg, S. L., Varnum, M. E., & Kenrick, D. T. (2017). The crowded life is a slow life: Population density and life history strategy. Journal of Personality and Social Psychology, 112(5), 736 754. DOI:10.1037/pspi0000086
  • Tetlock, P. E. (2006). Expert Political Judgment. How Good Is It? How Can We Know? Princeton, NJ: Princeton University Press.
  • Tetlock, P. E., & Gardner, D. Superforecasting: The art and science of prediction. Broadway Books.
  • Trahan, L. H., Stuebing, K. K., Fletcher, J. M., & Hiscock, M. (2014). The Flynn effect: A meta-analysis. Psychological Bulletin, 140(5), 1332 – 1360. DOI:10.1037/a0037173
  • Twenge, J. M., & Campbell, W. K. (2001). Age and birth cohort differences in self-esteem: A cross-temporal meta-analysis. Personality and Social Psychology Review, 5(4), 321-344. DOI:10.1207/S15327957PSPR0504_3
  • Twenge, J. M., Konrath, S., Foster, J. D., Keith Campbell, W., & Bushman, B. J. (2008). Egos inflating over time: A cross-temporal meta-analysis of the Narcissistic Personality Inventory. Journal of Personality, 76(4), 875-902. DOI:10.1111/j.1467-6494.2008.00507.x
  • Varnum, M. E. W. & Grossmann, I. (2017). Cultural change: The how and the why. Perspectives on Psychological Science. DOI:10.1177/1745691617699971
  • Varnum, M. E. W. & Grossmann, I. (2016). Pathogen prevalence is associated with cultural changes in gender equality. Nature Human Behaviour, 1(0006). doi:10.1038/s41562-016-0003
  • Yarkoni, T., & Westfall, J. A. (2017). Choosing prediction over explanation in psychology: lessons from machine learning. Perspectives on Psychological Science. DOI:10.1177/1745691617693393

Previously filled.

Attack of the Zombie Web Sites, owned by 301 Network, Monkey Frog, Market 57, Orange Box, Arceneaux, Becks, AdSupply, Focus Marketing, Lepton Labs, Willis, Corson, VivaGlam, RecipeGreen, Van Derham | BuzzFeed

Attack of the Zombie Websites; Craig Silverman; In BuzzFeed; 2017-10-17.
Teaser: <snip>actual reporting, by an actual reporter</snip> how seemingly-credible players in the ad supply chain can play an active role in — and profit from — fraud.


Whereas the article buries the lede way way down under the fold…
  • 301network Media, allied “dbas”; Matt Arceneaux, Andrew Becks.
    Monkey Frog Media, Market 57, Orange Box Media
  • AdSupply, allied “dbas”; Eric Willis, Chris Corson.
    Focus Marketing, Lepton Labs
  • KVD Brand Inc.; Katarina Van Derham.

Original Sources

  • Social Puncher, an research boutique, operated as
  • Pixelate, opined; claims independent discovery.
  • Protected Media, opined, as commissioned, from BuzzFeed.
  • Integral Ad Sciences (IAS), opined, as commissioned, from BuzzFeed..


  • “self-driven”
  • “session hijacking”
  • “friend or foe” system
  • “ad hell”
  • <quote>It was the digital equivalent of skimming from a casino.</quote>
  • “Clawbacks”
  • “In-human traffic,” “non-human traffic”
    because nobody in the trade wants to say “robot.”


The Offenses
  • “Approximately” 40 websites.
  • “Over” 100 brands [what's a brand?]
  • “roughly” 50 brands “appeared multiple times.” [what does that mean?]
The Tease
  • <quote>the CEO of an ad platform and digital marketing agency is an owner of 12 websites that earned revenue from the fraudulent views, and his company provided the ad platform used by sites in the scheme.</quote>
  • <quote>That company is owned by a model and online entrepreneur who played Bob Saget’s girlfriend on the HBO show Entourage.</quote>
  • <quote><snip/>a former employee of a large ad network who runs a group of eight sites that were part of the fraud, and who consults for a company with another eight sites in it.</quote>
  • <quote>A site in the scheme is owned by the cofounder of one of the 20 largest ad networks in the United States</quote>.


  • 301network, a marketplace (“an ad platform”) and allied “dbas”;
    Matt Arceneaux, Andrew Becks.
  • AdSupply, various “dbas”;
    Eric Willis, Chris Corson.
  • KVD Brand Inc.;
    Katarina Van Derham.
  • Matt Arceneaux, CEO, partner, 301 Digital Media.
  • Andrew Becks, COO, partner, 301 Digital Media
  • Eric Willis, vice president, OMG LLC
    is a man,
    ex-staff AdSupply,
  • Chris Corson, founder of AdSupply,
    is part owner of an [unnamed] LLC that operates
  • Katarina Van Derham,
    • is a publisher,
      is an online publisher,
    • lives in Los Angeles,
    • has performed as a model
    • has fame,
      has fame from playing Bob Saget’s girlfriend on the HBO show Entourage.
    • owns KVD Brand Inc.
301 Digital Media
  • a marketing agency
  • Nashville, TN
  • LinkedIn page [existed]

    • Scripps
    • Pfizer
  • gold-level sponsor, Digital Marketing Conference, New York, 2017-11.


  • Integral Ad Sciences (IAS) → $20 million in 2017.
  • Pixelate → $2 million per year.”
    Which is it?

The Validation

Re-checking the work of the Social Puncher staff
  • Integral Ad Science (IaS)


  • Ford
  • Hershey’s
  • Johnson & Johnson
  • MGM Resorts International
  • Proctor & Gamble (P&G)
  • Unilever
  • Charmin
  • Olay
  • Oral-B
  • Orgullosa
    [is that really a brand? yes. Spanish, translation proud
    <quote>Orgullosa is for women who don't settle for walking the same path, but instead make a new one every day.</quote> <quote ref="presser">P&G’s Orgullosa Launches the Nueva Latina Campaign to Celebrate and Showcase the Unique Experience of the Bicultural, Modern Latina </quote>]
  • Secret


  • Matt Arceneaux, CEO, 301 Digital Media
    listed as a perpetrator.


For color, background & verisimilitude…
  • Amin Bandeali, the CTO of Pixalate
  • Shailin Dhar, (now) founder, Method Media Intelligence.
    Method Media Intelligence is a research boutique
  • Mary Hynes, director of corporate communication, MGM International
  • Kristin Lemkau, chief marketing officer, JPMorgan Chase.
  • Jalal Nasir, CEO, Pixalate.
  • Maria Pousa, chief marketing officer, Integral Ad Sciences (IAS).
  • Marc Pritchard, chief brand officer, Proctor & Gamble (P&G)
    honorific: the consumer products giant
  • Vlad Shevtsov, director of investigations, Social Puncher
  • David Taylor, CEO, Proctor & Gamble (P&G).
  • Mike Zaneis, CEO, Trustworthy Accountability Group


The location of the fraud
    well, there’s your problem… the TLD online just feels sketchy, doesn’t it?
    • uses automated [robot] content generation scheme
      “100% Fully Automated Videos – You won’t have to worry about new content. Comes with a custom plugin with your own license,” via blurb at Flippa,
    • 2016-12, purchased by Katarina Van Derham, for $59 in an auction
    • 2017-01 → 2017-08, was “showered” with traffic, then none.
    • branded Viva Glam,
    • operated by Katarina Van Derham since 2012,
    • not purchased from Pakistan or elsewhere.


  • Monkey Frog Media LLC.
    • is a shell company [a holding company]
    • exposed for fraud “at seven sites”, by Pixelate [WHEN?]
    • Owned by Matt Arceneaux
    • d.b.a. Happy Planet Media
    • Has five more web sites
      whose domains are registered as being owned by 301 Digital Media, which is [owned?] by Matt Arceneaux
    • earlier [WHEN?] Matt Arcenaux’s home address for registration.
    • since 2015; as evidenced by 2015-12-11, Matt Arceneaux signs a contract as the “manager” of Monkey Frog Media.
  • Market 57 LLC
    • which had five sites
    • Same asddress as 301 Media
    • failing
    •, uses 301 Media’s Amazon affiliate code
    • earlier [WHEN?] Matt Arcenaux’s home address for registration.
  • Orange Box Media LLC
    • owns five sites
    • filing
    • uses Matt Arcenaux’s home address.
    • Observed by the Social Puncher staff: at circa 2017-09-08T12:00 EDT, all sites were unavailable simultaneously
  • Something about Facebook.
    Facebook is bad.
  • AppNexus was trading 301 Network Media’s media.
  • Online Media Group LLC (OMG LLC)
    • A shell company [a holding company]
    • owns seven sites
    • ran session hijacking code
    • Eric Willis, vice president, OMG LLC
      is a man.
  • AdSupply, seemed clean, maybe;

    • Chris Corson, cofounder, executive vice president, AdSupply.
    • Chris Corson, is part owner of an LLC that operates, a site that contained [the] session hijacking code.
      • is longstanding
      • produces some original content
      • has a YouTube channel, “close to” 2 million subscribers.
    • Focus Marketing. LLC,
      Chris Corson is the part owner.
    • Lepton Labs LLC,
      • purveyors of AllDaySlim, a weight-loss elixr.
    • Chris Corson is the part owner.
  • KVD Brand Inc.
    • eight sites
    • performed in the session hijacking scheme.
    • owned by Katarina Van Derham
    • bought the sites & their business from “someone in Pakistan.”
      • uses automated [robot] content generation scheme
        “100% Fully Automated Videos – You won’t have to worry about new content. Comes with a custom plugin with your own license,” via blurb at Flippa,
      • 2016-12, purchased by Katarina Van Derham, for $59 in an auction
      • 2017-01 → 2017-08, was “showered” with traffic, then none.


Hosted at

  • Something, of
  • Something, maybe an article, from
  • Something, maybe a “website,” of Focus Marketing. LLC
  • Something, maybe a “website,” of OMG LLC (Online Media Group, LLC)
  • Something, maybe a “product page,” for AllDaySlim, a weight-loss elixr.

Hosted at

  • Media Kit of, as archived circa 2015-02-17T06:33:42.

Hosted at

Hosted on

Hosted on

Previously filled.

Partnership on AI

Partnership on AI
Uses Responsive Web Design (RWD) so it only “works” on a handset form factor is “mobile first” [scrape-scroll down, which is non-obvious in the officework environment]

Statement of Purpose

<quote>Established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.</quote>


Tier 1
  • Amazon
  • Apple
  • DeepMind, of Google
  • Google, of Alphabet (GOOG)
  • Facebook
  • IBM
  • Microsoft
Tier 2
Generalizing, they comprise NGOs, Centers, Centres and industry booster clubs.


As, tenets, creed, doctrine, belief, theses; enumerated as eight fourteen (Item Six has seven sub-parts)…

  • Goals to be attained. the <bizpeak>BHAG</bizspeak>.
    as indicated by a directional sense. of the effort-to-be-expended. (EtbE).
  • Values to be held, preferring privileging one value over another.
    as measured in effort-to-be-expended (EtbE).
  • Belief to be held.
  1. [Goal] The greatest good for the greatest number.
    [EtbE] ensure an outcome, like a guarantee.
  2. [Goal] Educate the seekers of the knowledge..
    [EtbE] a state of being; being bound over to, tasked unto, being committed to.
  3. [Goal] Outreach as dialog and participation.
    [EtbE] a state of being; being bound over to, tasked unto, being committed to.
  4. [Belief] Something about a broad range of stakeholders.
    [EtbE] a state of being, that such belief is so held.
  5. [Goal] Something about representation in the business community.
    [EtbE] something about “engage with” and a participation metric.
  6. [Concern] Privacy of individuals
    [EtbE] work towards.
  7. [Concern] Security of individuals
    [EtbE] work towards.
  8. [Concern] understanding and respect; a.k.a. “to serve and protect”
    [EtbE] strive.
  9. [Goal] Responsibility to [the data controllers].
    [EtbE] work towards.
  10. [Goal] Control these dangerous and powerful [and important and really really cool] technologies.
    [EtbE]: ensure an outcome, similar to a guarantee.
  11. [Goal] Violate no international laws (“conventions”); violate no human rights.
    [EtbE] oppose, wherein such an opinion is so held.
  12. [Goal[ Do no harm.
    [EtbE] promote, wherein such an opinion is so held.
  13. [Goal] Provenance tracing for system supervision.
    [EtbE] a state of being, that the belief is so held.
    <ahem>This is a system architecture requirement; it does not require a belief system or an attestation to any specific belief.</ahem>
  14. [Goal] Cooperation within the Professions so enumerated as: Scientist, Engineer.
    [EtbE]: Strive.


Dimensions of concern are metaphorically themed as pillars, evoking an image of a Greek temple, whence knowledge came

  1. Safety
  2. Supervision
    enumerated as Fairness, Transparency, Accountability
  3. HCI (Human-Computer Interface))
  4. Labor (the anti-Luddism)
  5. Society (LE, Policy, Regulation, etc.)
  6. Charity
  7. Other


  • Blog cadence as press releases is “about every four months.”
  • They don’t seem to have a position paper [yet].

Previously filled.


Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society | Pasquale

Frank A. Pasquale III; Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society; Ohio State Law Journal, Vol. 78, 2017, U of Maryland Legal Studies Research Paper No. 2017-21; 2017-07-14; 13 pages; ssrn:3002546.

tl;dr → A comment for Balkin. To wit:
  1. Balkin should have supplied more context; such correction is supplied herewith.
  2. More expansive supervision is indicated; such expansion is supplied herewith.
  3. Another law is warranted; not a trinity, but perfection plus one more.
Fourth Law

A [machine] must always indicate the identity of its creator, controller, or owner.
<ahem>Like… say… a license, to operate, to practice; a permit; as manifest in a license plate, certificate of operation, certificate of board, a driver license; contractor license, a Bar Association Number, a VIN number, a tail number, a hull number.</ahem>

Three Laws, previous:

  1. machine operators are always responsible for their machines.
  2. businesses are always responsible for their operators.
  3. machines must not pollute.

So it is just like planes, trains & automobiles.


<quote>Balkin’s lecture is a tour de force distillation of principles of algorithmic accountability, and a bold vison for entrenching them in regulatory principles. <snip>…etc…</snip></quote>


  • Regulators
  • non-functional requirements
    the branded “By Design” theories

    • responsibility-by-design,
    • security-by-design,
    • privacy-by-design,
    • attribution-by-design [traceability-by-design].
  • Audit logs.
  • A Licentiate, the licentia ad practicandum
  • Supervisory Control.


Jack Balkin makes several important contributions to legal theory and ethics in his lecture, “The Three Laws of Robotics in the Age of Big Data.” He proposes “laws of robotics” for an “algorithmic society” characterized by “social and economic decision making by algorithms, robots, and AI agents.” These laws both elegantly encapsulate, and add new principles to, a growing movement for accountable design and deployment of algorithms. [This] comment aims to

  1. contextualize his proposal as a kind of “regulation of regulation,” familiar from the perspective of administrative law,
  2. expand the range of methodological perspectives capable of identifying “algorithmic nuisance,” a key concept in Balkin’s lecture, and
  3. propose a fourth law of robotics to ensure the viability of Balkin’s three laws.


  • Jack Balkin, Knight Professor of Constitutional Law and the First Amendment, Law School, Yale University; via Jimi Wales’ Wiki.


[in case it wasn't otherwise clear]

<quote>Balkin’s lecture is a tour de force distillation of principles of algorithmic accountability, and a bold vison for entrenching them in regulatory principles. As he observes, “algorithms

  1. construct identity and reputation through
  2. classification and risk assessment, creating the opportunity for
  3. discrimination, normalization, and manipulation, without
  4. adequate transparency, monitoring, or due process.

[endquote]” They are, therefore, critically important features of our information society which demand immediately attention from regulators. High level officials around the world need to put the development of a cogent and forceful response to these developments at the top of their agendas. Balkin’s “Laws of Robotics” is an ideal place to start, both to structure that discussion at a high level and to ground it in deeply rooted legal principles.

It is rare to see a legal scholar not only work at the deepest levels of policy (in the sense of all those normative considerations that should inform legal decisions outside of the law governing the case), but also recommend in clear and precise language a coherent set of concrete recommendations that both exemplify principles of critical and social theory, and stand some chance of being adopted by current government officials. That is Balkin’s achievement in The Three Laws of Robotics in the Age of Big Data. It is work to cite, celebrate, and rally around, and an auspicious launch for Ohio State’s program in Big Data & Law.</quote>


Jack M. Balkin  (Yale); The Three Laws of Robotics in the Age of Big Data; Ohio State Law Journal, Vol. 78, (2017), Forthcoming (real soon now, RSN), Yale Law School, Public Law Research Paper No. 592; 2016-12-29 → 2017-09-10; 45 pages; ssrn:2890965; previously filled, separately noted.


The Suitcase Words
  • Big Data,
    Age of Big Data
  • laws of robotics
    Three Laws of Robotics
  • algorithmic society
  • social decision-making,
    social decision-making by algorithms
  • economic decision-making,
    economic decision-making by algorithms
  • algorithms
  • robots
  • Artificial Intelligence (AI)
  • AI Agents
  • encapsulate
  • principles
  • accountable design
  • deployment of algorithms
  • contextualize
  • regulation of regulation
  • perspective of administrative law
  • methodological perspectives,
    range of methodological perspectives
  • algorithmic nuisance
  • fourth law of robotics
  • viability, to ensure the viability of
  • three laws

Previously filled.

Exploring ADINT: Using Ad Targeting for Surveillance on a Budget — or — How Alice Can Buy Ads to Track Bob | Vines, Roesner, Kohno

Paul Vines, Franziska Roesner, Tadayoshi Kohno; Exploring ADINT: Using Ad Targeting for Surveillance on a Budget — or — How Alice Can Buy Ads to Track Bob; In Proceedings of the 16th ACM Workshop on Privacy in the Electronic Society (WPES 2017); 2017-10-30; 11 pages; outreach.

tl;dr → Tadayoshi et al. are virtuosos at these performance art happenings. Catchy hook, cool marketing name (ADINT) and press outreach frontrunning the actual conference venue. For the wuffie and the lulz. Nice demo tho.
and → They bought geofence campaigns in a grid. They used close-the-loop analytics to identify the sojourn trail of the target.
and → dont’ use Grindr.


The online advertising ecosystem is built upon the ability of advertising networks to know properties about users (e.g., their interests or physical locations) and deliver targeted ads based on those properties. Much of the privacy debate around online advertising has focused on the harvesting of these properties by the advertising networks. In this work, we explore the following question: can third-parties use the purchasing of ads to extract private information about individuals? We find that the answer is yes. For example, in a case study with an archetypal advertising network, we find that — for $1000 USD — we can track the location of individuals who are using apps served by that advertising network, as well as infer whether they are using potentially sensitive applications (e.g., certain religious or sexuality-related apps). We also conduct a broad survey of other ad networks and assess their risks to similar attacks. We then step back and explore the implications of our findings.


  • Markets
    They chose

    • Facebooik
    • not Google
    • etc.
    • not to fight with big DSPs;
      the picked the weaker ones to highlight.
  • Apps
    They chose

    • lower-quality apps.
    • adult apps
      few “family oriented” [none?] apps.
    • <ahem>Adult Diapering Diary</ahem>
      <ahem>Adult Diapering Diary</ahem>


  • DSPs sell 8m CEP (precision) location.

Spooky Cool Military Lingo


Targeting Dimensions

  • Demographics
  • Interests
  • Personally-Identifying Information (PII)
  • Domain (a usage taxonomy)
  • Location
  • Identifiers
    • Cookie Identifier
    • Mobile Ad Identifier (e.g. IDFA, GPSAID)
  • Technographics
    • Device (Make Model OS)
    • Network (Carrier)
  • Search

Media Types

Supply-Side Platforms (SSPs)

  • Adbund
  • InnerActive
  • MobFox
  • Smaato
  • Xapas

Supply (the adware itself, The Applications, The Apps)

  • Adult Diapering Diary
  • BitTorrent
  • FrostWire
  • Grindr
  • Hide My Texts
  • Hide Pictures vault
  • Hornet
  • iFunny
  • Imgur
  • Jack’D
  • Meet24
  • MeetMe
  • Moco
  • My Mixtapez Music
  • Pregnant Mommy’s Maternity
  • Psiphon
  • Quran Reciters
  • Romeo
  • Tagged
  • Talkatone
  • TextFree
  • TextMe
  • TextPlus
  • The Chive
  • uTorrent
  • Wapa
  • Words with Friends

Demand-Side Platforms (DSPs)

  • Ademedo
  • AddRoll
  • AdWords
  • Bing
  • Bonadza
  • BluAgile
  • Centro
  • Choozle
  • Criteo
  • ExactDrive
  • Facebook
  • GetIntent
  • Go2Mobi
  • LiquidM
  • MediaMath
  • MightyHive
  • Simpli.Fi
  • SiteScout
  • Splicky
  • Tapad



  • Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, Claudia Diaz. 2014. The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. In Proceedings of the ACM Conference on Computer and Communications Security.
  • Rebecca Balebako, Pedro Leon, Richard Shay, Blase Ur, Yang Wang, L Cranor. 2012. Measuring the effectiveness of privacy tools for limiting behavioral advertising. In Web 2.0 Security and Privacy.
  • Hal Berghel. 2001. Caustic Cookies. In His Blog.
  • Interactive Advertising Bureau. 2015. IAB Tech Lab Content Taxonomy.
  • Interactive Advertising Bureau. 2017. IAB Interactive Advertising Wiki.
  • Giuseppe Cattaneo, Giancarlo De Maio, Pompeo Faruolo, Umberto Ferraro Petrillo. 2013. A review of security attacks on the GSM standard. In Information and Communication Technology-EurAsia Conference. Springer, pages 507–512.
  • Robert M Clark. 2013. Perspectives on Intelligence Collection. In The intelligencer, a Journal of US Intelligence Studies 20, 2, pages 47–53.
  • David Cole. 2014. We kill people based on metadata. In The New York Review of Books
  • Jonathan Crussell, Ryan Stevens, Hao Chen. 2014. Madfraud: Investigating ad fraud in android applications. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services. ACM, pages 123–134.
  • Doug DePerry, Tom Ritter, Andrew Rahimi. 2013. Cloning with a Compromised CDMA Femtocell.
  • Google Developers. 2017. Google Ads.
  • Steven Englehardt and Arvind Narayanan. 2016. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, pages 1388–1401.
  • Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan Mayer, Arvind Narayanan, Edward W Felten. 2015. Cookies that give you away: The surveillance implications of web tracking. In Proceedings of the 24th International Conference on World Wide Web. ACM, pages 289–299.
  • Go2mobi. 2017.
  • Aleksandra Korolova. 2010. Privacy violations using microtargeted ads: A case study. In Proceedings of the 2010 IEEE International Conference on IEEE Data Mining Workshops (ICDMW), pages 474–482.
  • Zhou Li, Kehuan Zhang, Yinglian Xie, Fang Yu, XiaoFeng Wang. 2012. Knowing your enemy: understanding and detecting malicious web advertising. In Proceedings of the 2012 ACM conference on Computer and Communications Security. ACM, pages 674–686.
  • Nicolas Lidzborski. 2014. Staying at the forefront of email security and reliability: HTTPS-only and 99.978 percent availability.; In Their Blog. Google.
  • Steve Mansfield-Devine. 2015. When advertising turns nasty. In Network Security 11, pages 5–8.
  • Jeffrey Meisner. 2014. Advancing our encryption and transparency efforts. In Their Blog, Microsoft.
  • Rick Noack. 2014. Could using gay dating app Grindr get you arrested in Egypt?. In The Washington Post.
  • Franziska Roesner, Tadayoshi Kohno, David Wetherall. 2012. Detecting and Defending Against Third-Party Tracking on the Web. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI).
  • Sooel Son, Daehyeok Kim, Vitaly Shmatikov. 2016. What mobile ads know about mobile users. In Proceedings of the 23rd Annual Network and Distributed System Security Symposium (NDSS).
  • Mark Joseph Stern. 2016. This Daily Beast Grindr Stunt Is Sleazy, Dangerous, and Wildly Unethical. In Slate, 2016.
  • Ryan Stevens, Clint Gibler, Jon Crussell, Jeremy Erickson, Hao Chen. 2012. Investigating user privacy in android ad libraries. In Proceedings of the Workshop on Mobile Security Technologies<e/m> (MoST).
  • Ratko Vidakovic. 2013. The Mechanics Of Real-Time Bidding. In Marketingland.
  • Craig E. Wills and Can Tatar. 2012. Understanding what they do with what they know. In Proceedings of the ACM Workshop on Privacy in the Electronic Society (WPES).
  • Tom Yeh, Tsung-Hsiang Chang, Robert C Miller. 2009. Sikuli: using GUI screenshots for search and automation. In Proceedings of the 22nd annual ACM Symposium on User Interface Software and Technology. ACM, pages 183–192.
  • Apostolis Zarras, Alexandros Kapravelos, Gianluca Stringhini, Thorsten Holz, Christopher Kruegel, Giovanni Vigna. 2014. The dark alleys of madison avenue: Understanding malicious advertisements. In Proceedings of the 2014 Conference on Internet Measurement Conference
  • Tiliang Zhang, Hua Zhang, Fei Gao. 2013. A Malicious Advertising Detection Scheme Based on the Depth of URL Strategy. In Proceedings of the 2013 Sixth International Symposium on Computational Intelligence and Design (ISCID), Vol. 2. IEEE, pages 57–60.
  • Peter Thomas Zimmerman. 2015. Measuring privacy, security, and censorship through the utilization of online advertising exchanges. Technical Report. Tech. rep., Princeton University.


The Suitcase Words

  • Mobile Advertising ID (MAID)
  • Demand-Side Platform (DSP)
  • Supply-Side Platform (SSP)
  • Global Positioning System (GPS)
  • Google Play Store (GPS)
  • geofencing
  • cookie tracking
  • Google Advertising Identifier (GAID)
    Google Play Services Advertising Identifier (GAID)
  • Facebook
  • Snowden
  • WiFi

Previously filled.

The Three Laws of Robotics in the Age of Big Data | Balkin

Jack M. Balkin  (Yale); The Three Laws of Robotics in the Age of Big Data; Ohio State Law Journal, Vol. 78, (2017), Forthcoming (real soon now, RSN), Yale Law School, Public Law Research Paper No. 592; 2016-12-29 → 2017-09-10; 45 pages; ssrn:2890965.

tl;dr → administrative laws [should be] directed at human beings and human organizations, not at [machines].


  1. machine operators are responsible
    [for the operations of their machines, always & everywhere]
  2. businesses are responsible
    [for the operation of their machines, always & everywhere]
  3. machines must not pollute
    [in a sense to be defined later: e.g. by a "tussle"]

None of this requires new legal theory; c.f. licensing for planes, trains & automobiles; and on to nuclear plants, steel unto any intellectual business operation of any kind (ahem, medical, architecture, legal services; and anything at all under the Commerce Clause, no?)


  • Isaac Asimov, the stories of
    …and the whole point of the stories was the problematic nature of The Three Laws, They seemed fun and clear but they were problematized and the don’t work as a supervisory apparatus. Maybe they don’t work at all. Is the same true here? Not shown.
  • Laws of Robotics,
    Three Laws of Robotics.
  • [redefined] the “laws of robotics” are the legal and policy principles that govern [non-persons, unnatural-persons].

Concepts Principles (HF/SE/IF/AN)

  1. homunculus, a fallacy
  2. substitution, an effect
  3. information fiduciaries, a role
  4. algorithmic nuisance, an ideal (an anti-pattern


A matrix, the he cross product, of twelve (12) combinations:

Requirement of (TAdP)
  1. Transparency
  2. Accountability
  3. due Process
Principles of (HF/SE/IF/AN)
  • [the] homunculus fallacy
  • [a] substitution effect
  • information fiduciaries
  • algorithmic nuisance


The Suitcase Words
  • Isaac Asimov.
  • three law of robotics.
  • programmed,
    programmed into every robot.
  • govern.
  • robots.
  • algorithms.
  • artificial intelligence agents..
  • legal principles,
    basic legal principles.
  • the homunculus fallacy.
  • he substitution effect.
  • information fiduciaries.
  • algorithmic nuisance.
  • homunculus fallacy.
  • attribution.
  • human intention.
  • human agency.
  • robots.
  • belief,
    false belief.
  • person
    little person.
  • robot.
  • program.
  • intentions,
    good intentions.
  • substitution effect.
  • social power.
  • social relations.
  • robots.
  • Artificial Intelligence (AI).
  • AI agents.
  • algorithms.
  • substitute,
    algorithmssubstitute for human beings.
  • operate,
    algorithms operate as special-purpose people..
  • mediated
    ,mediated through new technologies.
  • three laws of robotics
    Three Laws of Robotics.
  • Algorithmic Society.
  • robots.
  • artificial intelligence agents.
  • algorithms.
  • governments.
  • businesses.
  • staffed.
  • Algorithmic Society.
  • asymmetries,
    asymmetries of information,
    asymmetries of monitoring capacity,
    asymmetries computational power.
  • Algorithmic Society:.
  • operators,
    operators of robots,
    operators of algorithms
    operators of artificial intelligence agents.
  • information fiduciaries.
  • special duties,
    special duties of good faith,
    special duties fair dealing.
  • end-users, clients and customersdata subjects.
  • businesses,
    privately owned businesses.
  • the public,
    the general public..
  • duty,
    central public duty.
  • algorithmic nuisances.
  • leverage utilize use.
  • asymmetries of information,
    asymmetries of monitoring capacity,
    asymmetries of computational power.
  • externalize,
    externalize the costs,
    externalize the costs of their activities.
  • algorithmic nuisance.
  • harms
    harms of algorithmic decision making.
  • discrimination
    intentional discrimination.
  • pollution,
    unjustified pollution
    socially unjustified pollution
    contra (socially-)justified pollution.
  • power
    computational power.
  • obligations,
    obligations of transparency,<br/ obligations of due process,
    obligations of accountability.
  • obligations flow.
  • requirements,
    substantive requirements,
    three substantive requirements.
  • transparency.
  • accountability.
  • due process.
  • obligation,
    an obligation of.
  • fiduciary relations.
  • public duties.
  • measure,
    a measure,
    a prophylactic measure.
  • externalization,
    unjustified externalization
    unjustified externalization of harms.
  • remedy,
    remedy for harm.

Previously filled.

Wall Street Firms to Move Trillions to Blockchains in 2018 | IEEE Spectrum

Wall Street Firms to Move Trillions to Blockchains in 2018; Amy Nordrum; In IEEE Spectrum; 2017-09-29.
Teaser: The finance industry is eagerly adopting the blockchain, a technology that early fans hoped would obliterate the finance industry

tl;dr → Depository Trust and Clearing Corporation (DTCC) will trial something with a blockchain in the title.



The Old Money Managers


The New Money Managers



  • Consensus 2017, the booster conference
  • Hype Cycle, Gartner Group.
    The metaphor of the Trough of Disillusionment of underdamped an control system, comprehending the social process of the diffusion of innovation.


The Boosterists

The Products


  • The canon is recited
  • Depository Trust and Clearing Corporation (DTCC)
  • permissioned blockchain
  • Hyperledger
  • J.P. Morgan
  • Axoni
  • Axcore
  • Consensus, a conference
  • Bloomberg
  • Thompson Reuters
  • Chain
  • IBM
  • Microsoft
  • Goldman Sachs
  • DApps
  • Go, a programming language
  • Hyperledger Fabric
  • Ethereum
  • public chain
  • Citibank
  • Proof of Concept (PoC)
    Proof of Work (PoW)
    Proof of Stake (PoS)
  • Enterprise Ethereum Alliance
  • World Economic Forum, (WEF)
  • Guernsey
  • Unigestion
  • Goldman Sachs
  • Northern Trust
  • R3
  • Corda


“Satoshi Nakamoto,” The Prophet.
An archetype figure: a Santa Claus or Moses or even a Jesus-type figure. “He” came, gave us a gift (and behold! it was perfect in every way!); upon the Redemption, he was Assumed and thus disappeared. No one is sure who “he” was or if “he” really existed. Whether “he” existed at all is not important to those of The Faith. “He” has no childhood friends or contemporaries who knew “him.” All we have are “his” writings, enshrined in the Wayback Machine and conspiracy theory discussion forums. Maybe “he” really was from The Future; maybe “he” really was sent by our descendants to prevent a Greater Evil, as was foretold in multi-part Hollywood hit movie, The Terminator. Maybe The Blockchain is itself “The Skynet” as was prophesied. No one knows. But, HURRY, INVEST NOW!


For color, background & verisimilitude…



In IEEE Spectrum

Previously filled.

“Information Bottleneck” Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine

New Theory Cracks Open the Black Box of Deep Learning; Natalie Wolchover; In Quanta Magazine, also syndicated out to copied onto; 2017-10-09; pdf.
Teaser: A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

tl;dr → the “information bottleneck,” an explainer; as the metaphor.
and → <quote><snip/> that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts.</quote>


  • Deep Neural Networks (DNN)
  • “deep-learning” algorithms
  • <buzzzz>the architecture of the brain</buzzzz>
  • architectures of networks
  • Information is about…
    • semantics, information is about semantics.
    • relevance → information is about relevance.
  • “deep belief net”
  • renormalization
  • “critical point”
  • “stochastic gradient descent”
  • “back-propagated”
  • Whereas
    • “certain” very large deep neural networks don’t seem to need a drawn-out long compression phase in order to generalize well.
    • use: early stopping in memorization
  • Naftali Tishby et al. contra Andrew Saxe et al. disagree on approaches, classifications & capabiliteis of DNN algorithms; e.g., the applicability of early stopping.
  • The two-phase learning model of “fitting & compression” is not similar to “the way” that children learn, attri uted to Brenden Lake.

Phases of Deep Learning

“fitting” or “memorization”
Is shorter (than the longer phase).The network learns labels for training data.
“compression” or “forgetting”
Is longer (than the shorter phase).
The network observes new data, to generalize against it. The network
optimizes (“becomes good at”) generalization, as measured differential with the (new) test data.


  • 330,000-connection-deep neural networks to recognize handwritten digits in that certain 60,000-image corpus.
    Modified NIST database (National Institute of Standards and Technology)
  • adult [human] brains → “several hundred trillion” connections among circa 86 billion neurons.

Not Amenable [to DNNs or ML at all]

  • Classifiability
  • Discrete problems
  • Cryptographic problems


  • Alex Alemi, Staff, Google.
    …quoted for color, background & verisimilitude; a booster.
  • William Bialek, Princeton University.
  • Kyle Cranmer, physics, New York University.
    …quoted for color, background & verisimilitude; a skeptic.
  • Geoffrey Hinton,…quoted for color, background & verisimilitude; is non-committal, “It’s extremely interesting.”
    • Staff, Google
    • Faculty, University of Toronto
  • Brenden Lake, assistant professor, psychology & data science statistics, New York University.
    In which a data scientist is a statistician who performs statistics on a Macintosh computer in San Francisco; and Prof. Lake’s employer is the university system of the State of New York.
  • Pankaj Mehta
  • Ilya Nemenman, faculty, biophysics, Emory University.
  • Fernando Pereira, staff, Google.
  • David Schwab
  • Andrew Saxe, staff, Harvard University.
    Expertise: Artificial Intelligence, The Theory of The Science of The Study of The Neuron; a.k.a. neuroscience.
  • Ravid Shwartz-Ziv, graduate student, Hebrew University, Jerusalem, IL.
    Advisor: Naftali Tishby
  • Naftali Tishby, Hebrew University, Jerusalem, IL.
  • Noga Zaslavsky, graduate student, Emory Univerity.
    Advisor: Ilya Nemenman.


  • Stuart Russell, éminence grise.
  • Claude Shannon, theorist.


  • (perhaps) Naftali Tishby; Some Talk; Some Conference, in Berlin; On YouTube
  • Naftali Tishby, Fernando C. Pereira, William Bialek; The Information Bottleneck Method; 1999 (2000-04-24); 18 pages; arXiv:physics/0004057, pdf.
    <quote>first described [the “information bottleneck”] in purely theoretical terms </quote>
  • Ravid Shwartz-Ziv, Naftali Tishby; Opening the Black Box of Deep Neural Networks via Information; 2017-03-02 → 2017-04-29; 19 pages, arXiv:1703.00810
    tl;dr → application of methods are reported.
  • Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy; Deep Variational Information Bottleneck; In Proceedings of Some Conference with the Acronym ICLR (ICLR); 2017; 19 pages; arXiv:1612.00410, pdf
    tl;dr → approximation methods are described.
  • Pankaj Mehta, David J. Schwab; An exact mapping between the Variational Renormalization Group and Deep Learning; 2014-10-14; 9 pages; arXiv:1410.3831.
    tl;dr → <quote>surprising paper</quote>, per Natalie Wolchover.
  • Naftali Tishby, Noga Zaslavsky; Deep Learning and the Information Bottleneck Principle; In Proceedings of the IEEE Information Theory Workshop (ITW); 2015-03-09; 9 pages; arXiv:1503.02406.
  • Modified National Institute of Standards and Technology (MNIST), a database.
  • Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum; Human-level concept learning through probabilistic program induction; In Science (Magazine); 2015.
    tl;dr → suggests asserts without proof that the [human] brain may does deconstruct the handwritten letters into a series of previously-known hand strokes.


In archaeological order, in Quanta Magazine

.Previously filled.

Know Thy Futurist | Cathy O’Neil (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.


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:

    • 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.


Nicole Aschoff; The New Prophets of Capital; Verso; 2015-03-31; 150 pages; ASIN:1781688109: Kindle: $10, paper: $4+SHT; review (2015-03-31, O’Neil likes it).
Nicole Aschoff is an editor at Jacobin magazine; she produces content for The Guardian, The Nation, Al Jazeera, and Dissent.


  • A complaint, and she does have one, but presented with scattered thinking; and not a lot of clarity on the problem at hand or proposals towards their remediation.
  • Always easier to criticize than to create. Imagine what someone with such an expansive viewpoint onto The Forseeable could accomplish towards remediation of the now-problematized span if the energies were dedicated towards practice instead of petulant dissent on theory.
  • Oddly, for someone who is pitching a graphical model with Cartesian-styled orthogonal axes, a.k.a. the “four box model of B-school decision theory, she (or her editors acting in her name and the name of the venue), did not see fit to publish a diagram along with the prose.
  • Wherein a data scientist is a statistician who lives in San Francisco and performs their work-product on a Macintosh computer.


  • Singularity University
    motto: “Be Exponential.”
  • Cathy O’Neil self-identifies as a futurist.
    <quote>And I am myself a futurist. </quote>
  • Effective Altruism
    A theory of Peter Singer
  • Future of Humanity Institute
  • Something about Artificial Intelligence (AI) contra algorithms.
    <quote>[Yann LeCun] was careful to distinguish between AI and algorithms.</quote>
    The deciderata being [this is a very old definition, not due to LeCun]

    • An Artificial Intelligence (domain)
      is that which cannot (now) be done with computers.
    • An Algorithm (an algorithmic domain)
      is what can be done (nowadays) using computers.


  • <quote>A futurist is a person who spends a serious amount of time—either paid or unpaid—forming theories about society’s future.</quote>
  • <quote>[Because] at the heart of the futurism movement lies money, influence, political power, and access to the algorithms that increasingly rule our private, political, and professional lives.</quote>
  • Singularity, The Singularity (definition); is “The Rapture” from Biblical lore. <quote><snip/>a singularity is a moment where technology gets so much better, at such an exponentially increasing rate, that it achieves a fundamental and meaningful technological shift of existence, transcending its original purpose and even nature.</quote>
  • <quote>The kinds of technologies these two groups consider are nearly disjoint, and even where they do intersect, the futurists’ takes are diametrically opposed.</quote>
  • <quote>Futurists are ready to install hardware in their brains because, as young or middle-age white men, they have never been oppressed.</quote>
  • <quote>These futurists are ready and willing to install hardware in their brains because, as they are mostly young or middle-age white men, they have never been oppressed. </quote> (second utterance).
  • <sneer><quote>(If this sounds like a science fiction fantasy for sex-starved teenagers, don’t be surprised.</quote></sneer>
  • <quote>the concept of effectiveness is limited by the fact that suffering, like community good, is hard to quantify.</quote>
  • <quote>As a group these futurists are fundamentally sympathetic figures but woefully simplistic regarding current human problems.</quote>
  • <sneer><quote>[Technoutopianists] latch on to the latest idea—e.g., will Bitcoin solve the world’s problems?—and turn it into a paid speech.</quote></sneer>
  • <quote>Most futurists are talking about sci-fi fantasies.<quote>
  • “positive futures”
    <snide><quote>It is not entirely clear what that means, but I doubt it means free credit for everyone.</quote></snide>
  • <snide><quote>This is the slick and ingratiating sales force for the futurism movement.<quote></snide>
  • <quote>In the end [her] taxonomy (as amusing as [she] finds it) doesn’t really matter to the average person.</quote>


  • Nicole Aschoff, theorist.
  • Sergey Brin, boffo.
  • Nick Bostrom, booster..
  • Alida Draudt, practice, Capital One; lesbian (“who techs”)
  • Daniel Drezner, theorist.
  • Robert Heinlein, theorist.
  • Steve Jobs, prophet.
  • Ray Kurzweil, a theorist; ex-practitioner: inventor credit, author credit.
  • Yann LeCun, practitioner; [a, the?] director of Artificial Intelligence (AI), Facebook.
  • Gordon Moore, practitioner; co-founder credit, Intel Corp.
  • Elon Musk, boffo.
  • Larry Page, boffo.
  • Ayn Rand, theorist.
  • Peter Singer, theorist.
  • Eliezer Yudkowsky, expert, artificial intelligence.




The Suitcase Words
  • artificial intelligence,
    omnipotent artificial intelligence.
  • consciousness,
    machines gain consciousness,
  • transcend,
    transcend to another plane of existence.
  • clones
  • futurism
  • American dream (American Dream)
  • status quo (pedestrian Latin as status quo)
  • without,
    without unions, public education, and social safety nets.
  • outcomes
  • mock,
    mock them,
    mock them for their silly sounding and overtly religious predictions
  • Google,
  • IBM
  • Ford
  • Department of Defense
  • My hope is that by better understanding the motivations and backgrounds of the people involved—however unscientifically—we can better prepare ourselves for the
  • struggle,
    political struggle,
    upcoming political struggle
  • narrative,
    whose narrative,
    whose narrative of the future
  • oligarchs,
    tech oligarchs
  • flying cars
  • live forever
  • workers,
    gig economy workers
  • health care,
    affordable health care
  • singularity
    The Singularity
  • singularity myths
  • computer,
    the computer
  • self-aware,
    self-aware and intelligent
  • vindictive
  • believe,
    believe fervently,
    futurists believe fervently,
    some futurists believe fervently in a singularity.
  • worried
  • theorize
  • excited
  • scared
  • cautious
  • jubilant
  • Utopianists
  • Dystopianists
  • libertarians
  • seasteaders (movement)
  • Moore’s Law
  • transistor
  • Singularity University
  • hardware,
    install hardware,
    install hardware in their brains,
    Futurists are ready to install hardware in their brains because <snip/> they have never been oppressed.
    Futurists are ready to install hardware in their brains because, as young or middle-age white men, they have never been oppressed.
  • hobbyists,
    these futurists are hobbyists..
  • theories
  • wealth
  • top 0.1 percent.
  • wealthier,
    become even wealthier,
    They think of the future in large part as a way to invest their money and become even wealthier.
  • worked,
    once worked at
  • own,
    own Silicon Valley companies,
    still own Silicon Valley companies, venture capital firms, or hedge funds.
  • think,
    think of themselves,
    think of themselves as deeply clever,
    think of themselves as deeply clever—possibly even wise.
  • meritocracy
  • wine,
    expensive wine
  • drug,
    drug of choice
  • riches,
    enormous riches,
    enormous riches and very few worldly concerns
  • death and disease.
  • augmenting,
    augmenting intelligence,
    augmenting intelligence through robotic assistance
  • quality,
    better quality of life,
    better quality of life through medical breakthroughs
  • cryogenics
  • Sergey Brin
  • Larry Page
  • people,
    young people,
    blood of young people.
  • worst-case scenario
  • uploaded,
    uploaded software in the cloud.
  • graphics,
    virtual reality graphics,
    excellent virtual reality graphics,
    control the excellent virtual reality graphics,
    they can control the excellent virtual reality graphics
    a place where they can control the excellent virtual reality graphics.
  • ideas
  • teenagers,
    sex-starved teenagers
  • Robert Heinlein
  • Ayn Rand
  • blind spot,
    “I win” blind spot
  • racism
  • sexism
  • classism
  • politics
  • technology,
    solved by technology
  • government,
    the next government,
    program the next government.
  • proprietary
  • hoi polloi,
    the hoi polloi
  • the system,
    gaming the system.
  • existence,
    the nature of existence,
    the nature of existence in the super-rich bubble
  • something,
    something distinctly modern,
    something distinctly modern and computer-oriented
  • futurism,
    futurism of this flavor,
    futurism of this flavor is inherently elitist, genius-obsessed, and dismissive of larger society.
  • men,
    the men,
    the men—majority men
  • women
  • science fiction,
    dystopian science fiction,
    read dystopian science fiction,
    read dystopian science fiction in their youth,
    read dystopian science fiction in their youth and think about all the things that could go wrong once the machines become self-aware,
    read dystopian science fiction in their youth and think about all the things that could go wrong once the machines become self-aware, which has a small (but positive!) probability of happening.
  • Eliezer Yudkowsky
  • biases
  • philosophies,
    practical philosophies
  • Bayes’ Theorem
  • Roko’s basilisk
  • thought experiment
  • AI,
    an AI,
    a powerful AI,
    a superintelligent and powerful AI,
    a future superintelligent and powerful AI.
  • vindictive
  • hypothetical
  • Roko,
    Roko’s basilisk
  • AI,
    Friendly AI
  • singularity,
    a positive singularity
  • Effective Altruism,
    Effective Altruism movement
  • Peter Singer
  • Effective Altruists
  • suffering
  • responsibility,
    personal responsibility,
    personal responsibility for optimizing our money to improve the world.
  • parody
  • suffering
  • factions,
    factions believe
  • “existential risks”
  • events,
    futuristic events,
    unlikely futuristic events,
    unlikely futuristic events that are characterized by computations,
    unlikely futuristic events that are characterized by computations besieged by powers of ten,
    unlikely futuristic events that are characterized by computations besieged by powers of ten and could thus cause enormous suffering.
  • Nick Bostrom
  • Future of Humanity Institute
  • Elon Musk,
    shove Elon Musk,
    I will shove Elon Musk,
    I will shove Elon Musk into this Q2 group,
    I will shove Elon Musk into this Q2 group, even though he is not a perfect fit.
  • entrepreneur,
    an entrepreneur,
    a powerful entrepreneur,
    rich and powerful entrepreneur,
    an enormously rich and powerful entrepreneur,
    being an enormously rich and powerful entrepreneur, he probably belongs in the first group,
    being an enormously rich and powerful entrepreneur, he probably belongs in the first group, but he sometimes shows up at Effective Altruism events,
    being an enormously rich and powerful entrepreneur, he probably belongs in the first group, but he sometimes shows up at Effective Altruism events, and he has made noise recently about the computers getting mean,
    being an enormously rich and powerful entrepreneur, he probably belongs in the first group, but he sometimes shows up at Effective Altruism events, and he has made noise recently about the computers getting mean and launching us into World War III. The Guardian
  • cynics,
    The cynics,
    The cynics among us
  • Mars
  • technoutopianists.
  • Bitcoin
  • They are not super wealthy, but they aspire to be wealthier and more famous.
  • Follow the money here and you will find that they are what
  • “thought leaders,”
    single-idea merchants,
    single-idea merchants paid by oligarchs,
    single-idea merchants paid by oligarchs to feel special at TED or TED-like conferences.
  • The New Prophets of Capital
  • Nicole Aschoff
  • they,
    they will peddle,
    they will peddle whatever depoliticized fad captures the attention of the super rich at a given time.
  • Steve Jobs,
    Steve Jobs as their patron saint,
    Steve Jobs as their patron saint, they represent the American dream,
    Steve Jobs as their patron saint, they represent the American dream on overdrive
  • Steve Jobs,
    Steve Jobs as their patron saint,
    Steve Jobs as their patron saint, they represent the American dream,
    Steve Jobs as their patron saint, they represent the American dream on overdrive; They represent a disdain for the status quo,
    Steve Jobs as their patron saint, they represent the American dream on overdrive; They represent a disdain for the status quo and the notion that we can solve it all,
    Steve Jobs as their patron saint, they represent the American dream on overdrive; They represent a disdain for the status quo and the notion that we can solve it all without the old, outdated trappings of unions, public education, and social safety nets.
  • time,
    no time,
    they have no time,
    they have no time for taking on difficult questions,
    they have no time for taking on difficult questions of structural inequality,
    they have no time for taking on difficult questions of structural inequality that do not fade away with the wave of a magical wand.
  • selling,
    selling something,
    most obviously selling something,
    they are the type of futurist that is most obviously selling something,
    Far from actually fixing problems, they are the type of futurist that is most obviously selling something,
    Far from actually fixing problems, they are the type of futurist that is most obviously selling something: a corporate vision, blind faith in the titans of industry, and the sense of well-deserved success.
  • Alida Draudt
  • apital One
  • Lesbian Who Tech, a conference
  • “positive futures”
  • free,
    free credit,
    free credit for everyone.
  • women,
    more women,
    more women still,
    more women still in this group,
    There are more women still in this group …
  • control,
    control the conversation,
    their aim is to control the conversation,
    their aim is to control the conversation and,
    their aim is to control the conversation and, <snip/> to cause that future,
    their aim is to control the conversation and, <snip/> to cause that future, to become a fixed, normalized idea,
    their aim is to control the conversation and, <snip/> to cause that future, to become a fixed, normalized idea,
    their aim is to control the conversation and, <snip/> to cause that future, to become a fixed, normalized idea in our collective imagination,
    their aim is to control the conversation and, <snip/> to cause that future, to become a fixed, normalized idea in our collective imagination—even if that means a surveillance state,
    their aim is to control the conversation and, <snip/> to cause that future, to become a fixed, normalized idea in our collective imagination—even if that means a surveillance state with good shopping,
    their aim is to control the conversation and, in repeating predictions about the future often enough, to cause that future, to become a fixed, normalized idea in our collective imagination—even if that means a surveillance state with good shopping.
  • people
  • singularities
  • worried
  • women
  • group,
    my group
  • women,
    majority women,
    majority women, gay men,
    majority women, gay men, and people of color.
  • underrepresented,
    underrepresented at the data science institutes
    underrepresented at the data science institutes popping up all over the country
    underrepresented at the data science institutes popping up all over the country because the commercial goals of such places are inconsistent with our inconvenient cries of concern.
  • concerned,
    I am concerned,
    And I am concerned,
    And I am concerned.  Because <reasons>enumerated</reasons>.
  • personality tests
  • filter out
  • applicants,
    job applicants,
    qualified job applicants
  • algorithms,
    risk algorithms
    crime risk algorithms,
    crime risk algorithms that convince judges,
    crime risk algorithms that convince judges to issue longer sentences.
  • algorithms,
    automated algorithms
  • processes,
    decision making processes,
    human decision making processes,
    important human decision making processes,
    most important human decision making processes,
    our most important human decision making processes,
    replacing our most important human decision making processes,
    already replacing our most important human decision making processes.
  • future,
    hypothetical future,
    hypothetical future of human suffering.
  • class
  • race
  • age
  • disability
  • eduation
  • measures,
    demographic measures,
    other demographic measures.
  • futurists
  • fantasies,
    sci-fi fantasies.
  • futurism,
    the heart of futurism,
    the heart of futurism lies money, influence, political power,
    the heart of futurism lies money, influence, political power, and access to the algorithms,
    the heart of futurism lies money, influence, political power, and access to the algorithms that increasingly rule our private, political, and professional lives.
  • Yann LeCun
  • Facebook
  • Go,
    the game Go,
    the study of the game Go
  • algorithm,
    a machine-learning algorithm
  • algorithm,
    the Facebook algorithm,
    the Facebook algorithm is already sufficiently powerful to manipulate our democracy.
  • the Q1 technologists
  • the Q3 technoutopianists
  • chess
  • Go
  • future,
    the future,
    picture of the future,
    pretty picture of the future,
    their pretty picture of the future,
    painting their pretty picture of the future.
  • success,
    what success looks like
  • clarity of purpose
  • model of success
  • world,
    hypothetical world,
    In a hypothetical world where…
    In a hypothetical world where people could live forever,
    In a hypothetical world where people could live forever—gobbling up resources indefinitely,
    In a hypothetical world where people could live forever—gobbling up resources indefinitely and exerting political influence,
    In a hypothetical world where people could live forever—gobbling up resources indefinitely and exerting political influence with outdated political frameworks,
    In a hypothetical world where people could live forever—gobbling up resources indefinitely and exerting political influence with outdated political frameworks—should we allow them to?
  • person,
    average person.
  • decision,
    automated decision.
  • Starbucks Scheduling System
  • algorithms,
    the algorithms,
    the algorithms that already charge people with low FICO scores more for insurance.
  • algorithms,
    the algorithms,
    the algorithms that already send black people to prison for longer.
  • algorithms,
    the algorithms,
    the algorithms that send more police to already over-policed neighborhoods.
  • algorithms,
    the algorithms,
    the algorithms with facial recognition cameras at every corner.
  • power,
    old fashioned power,
    look like old fashioned power,
    all of these look like old fashioned power,
    all of these look like old fashioned power to the person who is being judged.
  • power
  • influence
  • scenario,
    worst-case scenario
  • AI,
    vindictive AI,
    a vindictive AI
  • Sergey Brin
  • birthday,
    two-hundredth birthday.
  • scenario,
    worst-case scenario
  • capitalism,
  • elite,
    member of the elite,
    skeptical member of the elite,
    not a skeptical member of the elite in sight.

Previously filled.

Incompatible: The GDPR in the Age of Big Data | Tal Zarsky

Tal Zarsky (Haifa); Incompatible: The GDPR in the Age of Big Data; Seton Hall Law Review, Vol. 47, No. 4(2), 2017; 2017-08-22; 26 pages; ssrn:3022646.
Tal Z. Zarsky is Vice Dean and Professor, Haifa University, IL.

tl;dr → the opposition is elucidated and juxtaposed; the domain is problematized.
and → “Big Data,” by definition, is opportunistic and unsupervisable; it collects everything and identifies something later in the backend.  Else it is not “Big Data” (it is “little data,” which is known, familiar, boring, and of course has settled law surrounding its operational envelope).


After years of drafting and negotiations, the EU finally passed the General Data Protection Regulation (GDPR). The GDPR’s impact will, most likely, be profound. Among the challenges data protection law faces in the digital age, the emergence of Big Data is perhaps the greatest. Indeed, Big Data analysis carries both hope and potential harm to the individuals whose data is analyzed, as well as other individuals indirectly affected by such analyses. These novel developments call for both conceptual and practical changes in the current legal setting.

Unfortunately, the GDPR fails to properly address the surge in Big Data practices. The GDPR’s provisions are — to borrow a key term used throughout EU data protection regulation — incompatible with the data environment that the availability of Big Data generates. Such incompatibility is destined to render many of the GDPR’s provisions quickly irrelevant. Alternatively, the GDPR’s enactment could substantially alter the way Big Data analysis is conducted, transferring it to one that is suboptimal and inefficient. It will do so while stalling innovation in Europe and limiting utility to European citizens, while not necessarily providing such citizens with greater privacy protection.

After a brief introduction (Part I), Part II quickly defines Big Data and its relevance to EU data protection law. Part III addresses four central concepts of EU data protection law as manifested in the GDPR: Purpose Specification, Data Minimization, Automated Decisions and Special Categories. It thereafter proceeds to demonstrate that the treatment of every one of these concepts in the GDPR is lacking and in fact incompatible with the prospects of Big Data analysis. Part IV concludes by discussing the aggregated effect of such incompatibilities on regulated entities, the EU, and society in general.


<snide><irresponsible>Apparently this was not known before the activists captured the legislature and affected their ends with the force of law. Now we know. Yet we all must obey the law, as it stands and as it is written. And why was this not published in an EU-located law journal, perhaps one located in … Brussels?</irresponsible></snide>



    1. Purpose Limitation
    2. Data Minimization
    3. Special Categories
    4. Automated Decisions


  • Big Data (contra “little data”)
  • personal data
  • Big Data Revolution
  • evolution not revolution
    no really, revolution not evolution
  • The GDPR is a regulation “on the protection of natural persons,”
  • EU General Data Protection Regulation (GDPR)
  • EU Data Protection Directive (DPD)
  • IS GDPR different than DPD?  Maybe not.  Why? c.f. page 10.
  • Various attempts at intuiting bright-line tests around the laws are recited.
    It is a law, but nobody knows how it is interpreted or how it might be enforced.
  • statistical purpose
  • analytical purpose
  • data minimization
  • pseudonymization
  • reidentification
  • specific individuals
  • <quote>n the DPD, article 8(1) prohibited the processing of data “revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-union membership, and the processing of data concerning health or sex life,” while providing narrow exceptions.85 This distinction was embraced by the GDPR.</quote>
  • Article 29 Working Party
  • on (special) category contagion
    “we feel that all data is credit data, we just don’t know how to use it yet.”
    c.f. page 19; attributed to Dr. Douglas Merrill, then-founder, ZestFinance, ex-CTO, Google.
  • data subjects
  • automated decisions
  • right to “contest the decision”
  • obtain human intervention
  • trade secrets contra decision transparency
    by precedent, in EU (DE), corporate rights trump decision subject’s rights.
  • [a decision process] must be interpretable
  • right to due process [when facing a machine]


Big Data is…

  • …wait for it… so very very big
    …thank you, thank you very much. I will be here all week. Please tip your waitron.
  • The Four Five “Vs”
The Four Five “Vs”
  1. The Volume of data collected,
  2. The Variety of the sources,
  3. The Velocity,
    <quote>with which the analysis of the data can unfold,</quote>,
  4. The Veracity,
    <quote>of the data which could (arguably) be achieved through the analytical process.</quote>,
  5. The Value, yup, that’s five.
    … <quote>yet this factor seems rather speculative and is thus best omitted.</quote>,

The Brussels Effect

  • What goes on in EU goes global,
  • “Europeanization”
  • Law in EU is applied world-wide because corporate operations are universal.


  • purpose limitation,
  • data minimization,
  • special categories,
  • automated decisions.


There are 123 references, across 26 pages of prose, made manifest as footnotes in the legal style. Here, simplified and deduplicated.

Previously filled.

Payment Request API | W3C

Payment Request API; W3C; 2017-09-21.

  • Adrian Bateman, Microsoft Corporation
  • Zach Koch, Google
  • Roy McElmurry, Facebook
  • Domenic Denicola, Google
  • Marcos Cáceres, Mozilla


Web of Things (WoT), Architecture, Thing Description, Scripting API | W3C


Web of Things (WoT) Architecture, 2017-09-14.

  1. WoT Thing Description
  2. WoT Scripting API
  3. WoT Binding Templates.

Web of Things (WoT) Thing Description, 2017-09-14.


Describes the metadata and interfaces of Things.

Web of Things (WoT) Scripting API, 2017-09-14.


Operates on Things characterized by Properties, Actions and Events.


Web of Things (WoT) Architecture


The W3C Web of Things (WoT) is intended to enable interoperability across IoT Platforms and application domains. Primarily, it provides mechanisms to formally describe IoT interfaces to allow IoT devices and services to communicate with each other, independent of their underlying implementation, and across multiple networking protocols. Secondarily, it provides a standardized way to define and program IoT behavior.

This document describes the abstract architecture for the W3C Web of Things. It is derived from a set of use cases and can be mapped onto a variety of concrete deployment scenarios, several examples of which are given. This document is focused on the standardization scope of W3C WoT, which consists of three initial building blocks that are briefly introduced and their interplay explained.

The WoT Thing Description (TD) provides a formal mechanism to describe the network interface provided by IoT devices and services, independent of their implementation. Provision of a TD is the primary requirement for a device to participate in the Web of Things. In fact, defining a Thing Description for an existing device allows that device to participate in the Web of Things without having to make any modifications to the device itself. WoT Binding Templates define how a WoT device communicates using a concrete protocol. The WoT Scripting API—whose use is not mandatory—provides a convenient mechanism to discover, consume, and expose Things based on the WoT Thing Description.

Other non-normative architectural blocks and conditions underlying the Web of Things are also described in the context of deployment scenarios. In particular, recommendations for security and privacy are included, while the goal is to preserve and support existing device mechanisms and properties. In general, W3C WoT is designed to describe what exists rather than to prescribe what to implement.

Web of Things (WoT) Thing Description


This document describes a formal model and common representation for a Web of Things (WoT) Thing Description. A Thing Description describes the metadata and interfaces of Things, where a Thing is an abstraction of a physical entity that provides interactions to and participates in the Web of Things. Thing Descriptions provide a narrow-waist set of interactions based on a small vocabulary that makes it possible both to integrate diverse devices and to allow diverse applications to interoperate. Thing Descriptions, by default, are encoded in JSON-LD. JSON-LD provides both a powerful foundation to represent knowledge about Things and simplicity, since it allows processing as a JSON document. In addition to physical entities, Things can also represent virtual entities. A Thing Description instance can be hosted by the Thing itself or hosted externally due to Thing’s resource restrictions (e.g. limited memory space) or when a Web of Things-compatible legacy device is retrofitted with a Thing Description.

Web of Things (WoT) Scripting API


The Web of Things (WoT) provides layered interoperability between Things by using the WoT Interfaces.

This specification describes a programming interface representing the WoT Interface that allows scripts run on a Thing to discover and consume (retrieve) other Things and to expose Things characterized by properties, Actions and Events.

Scripting is an optional “convenience” building block in WoT and it is typically used in gateways that are able to run a WoT Runtime and script management, providing a convenient way to extend WoT support to new types of endpoints and implement WoT applications like Thing Directory.




The Suitcase Words
  • W3C Web of Things (WoT)
  • IoT Platforms
  • interfaces
  • devices
  • services
  • implementation
  • multiple networking protocols
  • standardized
  • behavior
  • abstract architecture
  • use cases
  • mapped
  • scenarios,
    deployment scenarios,
    concrete deployment scenarios
  • standardization scope
  • WoT Thing Description (TD),
    Thing Description (TD)
  • formal mechanism
  • network interface
  • independent of implementation
  • participate
  • WoT Binding Templates,
    Binding Templates. [no acronym]
  • WoT Scripting API,
    Scripting API.
  • blocks,
    blocks and conditions,
    architectural blocks and conditions,
    non-normative architectural blocks and conditions.
  • scenarios,
    deployment scenarios,
    the context of deployment scenarios,
    in the context of deployment scenarios.
  • Web of Things (WoT)
  • Thing Description
  • narrow-waist
    a narrow-waist set,
    a narrow-waist set of interactions,
    a narrow-waist set of interactions based on a small vocabulary,
    a narrow-waist set of interactions based on a small vocabulary that makes it possible both,
    a narrow-waist set of interactions based on a small vocabulary that makes it possible both to integrate diverse devices and to allow diverse applications to interoperate.
  • foundation … knowledge
  • a Web of Things-compatible legacy device
  • layered interoperability
  • Things
  • WoT Interface
  • Actions
  • Events
  • gateways
  • WoT Runtime
  • script management
  • endpoints
  • Thing Directory

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As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing? | Ad Week

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing?; ; In Ad Week (Advertising Week); 2017-09-24.
Teaser: Acquisition of The Weather Company fuels a new division

tl;dr → Watson (a service bureau, AI-as-a-Service) is open for business.


The Weather Company

  • lines of business
    • location-based targeted audiences, delivered to the trade.
    • weather indica, delivered to consumers.
  • 2.2 billion locations/15 minutes
  • Dates
    • WHEN?, Acquisition by IBM
    • 2016-01, new business strategy,
      “AI” as a service (AIaaS)
  • Artificial Intelligence (AI)
  • Cloud Computing
  • Products
    • WeatherFx
    • JourneyFx
  • The Weather Company is a <quote>legacy business<quote> (deprecated).
  • AIaaS is a <quote>cutting-edge advertising powerhouse</quote> (house of power).

Watson Advertising

  • Cognitive Advertising
    • contra Computational Advertising, circa the ‘oughties (2005)
    • something about
      • <buzzzz>transform every aspect of marketing from </buzzz>
      • something about image and voice recognition to big data analysis and custom content.
  • What is it? (What is Watson-as-a-Service?)
    • Count: <quote>dozens</quote>
    • Interfaces
      • API
      • Projects <quote>studio-like</quote>
    • Pricing: <quote>millions of dollars</quote>
    • Structure: four (4) sub-units
  • “<snip/>It’s not been designed to target consumers the same way that Alexa or Siri have been,” attributed to Cameron Clayton.


The 4 pillars of Watson Advertising.
  1. Targeting, Audience construction & activation
  2. Optimization, Bidding & buying
  3. Advertising, Synthesis of copy and creative
  4. Planning, media planning, the buy plan, the execution plan

Audience Targeting

  • the flagship service
  • neural networks
  • scoring users, propensity scoring <quote>based on how likely they are to take an action</quote>
  • towards CPA or CCPV or CPVisit or <more!>
  • Performable on the Weather Company O&O
    • <quote>but on TV, print, radio and other platforms. <quote>
    • Partnerships
      • Cognitiv
      • Equals 3


Bidding Optimization
  • Is too boring for details early in the article.
  • Optimize against brand-specific KPIs.
  • Uses <buzzz>deep learning and neural networks</buzzz>
  • Optimize Cost Per Action (CPA).


  • Badged as Watson Ads and Watson Advertising
  • Services
    • content creation
    • content copywriting
  • Launched: 2016-06.
  • Is merely: nterest-Based Advertising (IBA)
    which in turn is a but regulatory term of art, that covers a wide range of in-trade practices.
  • Sectors, aspirational
    • <fancy>aviation</fancy> (airline ticket booking?)
    • insurance
    • energy
    • finance
  • Cognitive Media Council,
    • a focus group.
    • a user group, “friends & family” of the business.
    • a group of important customers representatives
      <quote>senior-level executives from agencies and brands</quote>
Reference Customers
  • Mirai
  • Prius Prime
  • Benefits
    Attributed to Eunice Kim, Toyota (TMNA), something about…

    • <buzzz>create a one-to-one conversational engagement</buzzz>
    • <buzzz>garner insights about the consumer thought process that could potentially inform our communication strategies elsewhere”</buzzz>
  • the Soup people
  • Something about creative synthesis
    themed as: recipe generation with flu symptoms with location
H&R Block
  • Something about creative synthesis
    themed as: automated robot tax expert, suggest tax deductions.
UM [You and Em]
  • An agency. Off shore? They have a “U.S. CEO” Maybe one of those English Invasion thingies.
  • Refused to name their client.
  • Something about auto dealerships.
  • <quote>meshing Watson data with client stats to analyze metrics across a large number of car dealerships in a way that optimizes ad spend while also checking local inventory to see whether or not it should personalize an ad to someone in that market.<quote>
  • <quote>combination of weather data, Google searches and pollen counts to trigger when media should be bought in various markets.</quote>


  • <quote>AI-powered planning</quote>


Something about a partnership for understanding marketing texts.
Jeremy Fain, CEO and co-founder
Equals 3
Lucy, a product-service-platform.
Something about <quote>to uncover extra insights and research.<quote>

Fairness & Balance


Ogilvy & Mather
  • Honorific <quote>longtime agency<quote> [fof record for IBM].
[Television] campaign, with Bob Dylan.
Synthesis of the trailier for Morgan (a move; genre: science fiction)
Performance, an “analysis” of the stylings of Antoni Gaudi, <quote>inspire an art installation </quote> (what does that mean?)
The “art installation” was exhibited at the Mobile World Congress in Barcelona.

…is quoted
the future is boosted.

  • “AI services”
  • “Big Data services”


  • The people are “afraid” of AI.
  • The people need to be groomed to accept AI.

Ensmoothen & enpitchen the Artificial Intelligence (AI) as…

  • humble
  • friendly
  • ”I’m here to help’ type personality”

Attributed to Lou Aversano, Ogilvy.


James Kisner, Jeffries

Via: James Kisner, A Report, Jeffries, 2017-07.
Jeffries is an opinion vendor in support of an M&A banking operation.
tl;dr → Watson is a failing product-service. <quote>IBM is being “outgunned” in the race…</quote> (yup, he mixed the metaphor).

  • as evidenced in measured job listings at
    Apple had more listings booked thereon than IBM.
  • Customers were interviewed.
    Watson’s performance/price ratio was low (the rate card is very high).
    2016-10, IBM reduced the rate card for API access <quote>by 70 percent</quote>
  • Lots of press
  • Not a lot of monetary results, as evidenced in the quarterly & annual reports.
Joe Stanhope, Gartner

Via: an interview, perhaps;
Gartner Group is an opinion vendor.

  • Too much hype, can be forgiven
  • Gartner runs the Hype Cycle brand
  • Claims: <quote>IBM does seem to be all-in with Watson.<quote> (be nice to hear that from IBM, not as a “hot-take” from a newshour pundit
DemandBase, Wakefield Research

A Report; attributed to “staff”; DemandBase and Wakefield Research

  • A survey,
    • “how do you feel?”
    • Do you “have plans-to …” in the next N months.
  • There are a lot of uncertainties


Training Data
  • Just isn’t there.
  • And … computers can only give answers, it can’t give [pose] questions.
Does it [even] Work?
  • No one knows.
  • Many are nervous.
  • No one wants to be first to fail
    (& be fired for outsourcing their job function to The AI).


  • Einstein, of Salesforce(.com)
  • Sensei, of Adobe
  • Buying operations, Xaxis of WPP
    the “AI” is a “co-pilot” to the trading desk operator; optimization recommendations towards CPM and viewability; North American operations only.
  • others?
    Surely everyone nowadays has some initiative that does “co-pilot”-level decision support to adops.
Research Efforts
  • Amazon
  • Facebook
  • Google
Venture Capital
  • Albert
  • Amenity Analytics
  • LiftIgniter
  • Persado
Amenity Analytics

An exemplar of the smaller-nimbler-smarter clones of the Watson genre.

  • A Watson-type experience, but cheaper
  • Does text mining of press releases
  • Reference customers:
  • A spin-out from some hedge fund, <quote>origins in the hedge fund world</quote>
  • Nathaniel Storch, CEO, Amenity Analytics.
  • <zing!>“Think of it as ‘moneyball’ for media companies,”<zing!>, attributed to Nathaniel Storch.


  • Siri, of Apple
  • Cortana, of Microsoft
  • Now, of Google


  • Lou Aversano, U.S. CEO, Ogilvy & Mather (Ogilvy, O&M).
  • Jordan Bitterman, CMO, Watson (Business Unit), IBM.
    attributed in quoted material aso “earlier this year” (2017?); c.f. Michael Mendenhall
  • Kasha Cacy, U.S. CEO, UM
    UM is an agency.
  • Cameron Clayton,
    • General Manager, Content and IoT Platform, Watson (Business Unit), IBM..
    • ex-CEO, The Weather Company
  • Jacob Colker, “entrepreneur in residence,” The Allen Institute
    …quoted for color, background & verisimilitude.The Allen Institute is a tank for thinkers.
  • Jeremy Fain, CEO and co-founder, Cognitiv.
  • Chris Jacob, director of product marketing, Marketing Cloud, Salesforce(.com).
  • Eunice Kim, media planner, Toyota Motor North America (TMNA).
    …quoted for color, background & verisimilitude.
  • James Kisner, staff, Jeffries.
    …quoted for color, background & verisimilitude.
    Jeffries is an advice shop, like Gartner, but different.
  • Francesco Marconi,
    …quoted for color, background & verisimilitude.

    • strategy manager and AI co-lead, Associated Press
    • visitor, MIT Media Lab
  • Michael Mendenhall, CMO, Watson (BU), IBM.
    announced as CMO in prior press [Ad Week, Marty Swant, 2017-07-07].
  • Sara Robertson, VP of Product Engineering, Xaxis of WPP.
  • Joe Stanhope, staff, Forrester
    …quoted for color, background & verisimilitude.
  • Nathaniel Storch, CEO, Amenity Analytics.
  • Marty Wetherall, director of innovation, FallonFallon is the agency that certain campaign booked on Watson for H&R Block


  • Antoni Gaudi, architect (per civil engineering), citizen of Spain.


In archaeological order, within Advertising Week

Previously filled.

N4626 – Working Draft, C++ Extensions for Networking (2017)

N4626Working Draft, C++ Extensions for Networking, a.k.a. Networking Technical Specification, Networking TS, Jonathan Wakely, 2017-03-17.


  • at
  • Section 4.2 <quote>The design of this specification is based, in part, on the Asio library written by Christopher Kohlhoff.</quote>
  • N4480C++ Extensions for Library Fundamentals, Version 2, 2015-11-25.



#include <experimental/net>

Buys everything.

In Phases && Slices
#include <experimental/netfwd>
#include <experimental/buffer>
#include <experimental/executor>
#include <experimental/internet>
#include <experimental/io_context>
#include <experimental/socket>
#include <experimental/timer>

Incorporates subcomponentry in stages.


Finally, once standardized, someday; after the year “202a.”
(inlined) std::experimental::net::v1
Currently, under draft, during trials, maybe now; prior to the year “202a.”
a.k.a. std::experimental::net::v1::ip.
The Internet Protocol Subsystem


As elaborated in <net>, a.k.a. <experimental/net>.
Using the "post-standardized" naming conventions:




  • Contents (this list)
  • List of Tables
  1. Scope
  2. Normative references
  3. Terms and definitions
  4. General Principles
    1. Conformance
    2. Acknowledgments
  5. Namespaces and headers
  6. Future plans (Informative)
  7. Feature test macros (Informative)
  8. Method of description (Informative)
    1. Structure of each clause
    2. Other conventions
  9. Error reporting
    1. Synchronous operations
    2. Asynchronous operations
    3. Error conditions
    4. Suppression of signals
  10. Library summary
  11. Convenience header
    • Header <experimental/net> synopsis
  12. Forward declarations
    • Header <experimental/netfwd> synopsis
  13. Asynchronous model
    • Header <experimental/executor> synopsis
    • Requirements
    • Class template async_result
    • Class template async_completion
    • Class template associated_allocator
    • Function get_associated_allocator
    • Class execution_context
    • Class execution_context::service
    • Class template is_executor
    • Executor argument tag
    • uses_executor
    • Class template associated_executor
    • Function get_associated_executor
    • Class template executor_binder
    • Function bind_executor
    • Class template executor_work_guard
    • Function make_work_guard
    • Class system_executor
    • Class system_context
    • Class bad_executor
    • Class executor
    • Function dispatch
    • Function post
    • Function defer
    • Class template strand
    • Class template use_future_t
    • Partial specialization of async_result for packaged_task
  14. I/O services
    • Header <experimental/io_context> synopsis
    • Class io_context
    • Class io_context::executor_type
  15. Timers
    • Header <experimental/timer> synopsis
    • Requirements
    • Class template wait_traits
    • Class template basic_waitable_timer
  16. Buffers
    • Header <experimental/buffer> synopsis
    • Requirements
    • Error codes
    • Class mutable_buffer
    • Class const_buffer
    • Buffer type traits
    • Buffer sequence access
    • Function buffer_size
    • Function buffer_copy
    • Buffer arithmetic
    • Buffer creation functions
    • Class template dynamic_vector_buffer
    • Class template dynamic_string_buffer
    • Dynamic buffer creation functions
  17. Buffer-oriented streams
    • Requirements
    • Class transfer_all
    • Class transfer_at_least
    • Class transfer_exactly
    • Synchronous read operations
    • Asynchronous read operations
    • Synchronous write operations
    • Asynchronous write operations
    • Synchronous delimited read operations
    • Asynchronous delimited read operations
  18. Sockets
    • Header <experimental/socket> synopsis
    • Requirements
    • Error codes
    • Class socket_base
    • Socket options
    • Class template basic_socket
    • Class template basic_datagram_socket
    • Class template basic_stream_socket
    • Class template basic_socket_acceptor
  19. Socket iostreams
    • Class template basic_socket_streambuf
    • Class template basic_socket_iostream
  20. Socket algorithms
    • Synchronous connect operations
    • Asynchronous connect operations
  21. Internet protocol
    • Header <experimental/internet> synopsis
    • Requirements
    • Error codes
    • Class ip::address
    • Class ip::address_v4
    • Class ip::address_v6
    • Class ip::bad_address_cast
    • Hash support
    • Class template ip::basic_address_iterator specializations
    • Class template ip::basic_address_range specializations
    • Class template ip::network_v4
    • Class template ip::network_v6
    • Class template ip::basic_endpoint
    • Class template ip::basic_resolver_entry
    • Class template ip::basic_resolver_results
    • Class ip::resolver_base
    • Class template ip::basic_resolver
    • Host name functions
    • Class ip::tcp
    • Class ip::udp
    • Internet socket options
  • Index
  • Index of library names
  • Index of implementation-defined behavior

Previously filled.

Resources for Getting Started with Distributed Systems | Caitie McCaffrey

Caitie McCaffrey (Microsoft); Resources for Getting Started with Distributed Systems; In Her Blog; 2017-09-07.

tl;dr → Distributed Sagas, within the .NET culture of Microsoft.


  • Distributed SAGA
  • Simple API for Grid Applications (SAGA); In Jimi Wales’ Wiki.
  • Tao
  • Espresso
  • Transaction Processing Performance Council (TPPC, TPC)
  • Pre-materialized aggregates, a technique.

The Canon (A Canon)

Exemplars (Bloggists)

Post Mortems (After Action Reports)

Exemplars (NoSQL)

  • Bigtable, Google
  • Cassandra
  • CouchDB
  • Dynamo, Amazon
  • HBase of Apache
  • MongoDB
  • Neo4J
  • Redis
  • Riak
  • SimpleDB, Amazon

Exemplars (Full SQL)

  • MySQL
  • Oracle
  • … and so on.




The Suitcase Words
  • 2-Phase Commit (2PC)
  • Available Continuous Impressive Dancing (ACID)
    Atomic, Consistent, Isolated, Durable (ACID)
  • Basically-Available, Slow Soft State, Eventually-Consistent (BASE, BASSEC)
    BASE (i.e., not ACID)
  • BLOOM, a programming language, the CALM programming language
  • Consistency As Logical Monotonicity (CALM)
  • Conflict-free Replicated Data Type (CRDT)
  • Consistency, Availability, Partition-Tolerance (CAP), (Folk-) Theorem
  • Fisher, Lynch, Patterson (FLP) Theorem
  • Liveness
  • Lots of Labor (LOL)
  • Safety
  • Serializability
  • Single System Image (SSI)
  • Read Atomic Multi-Partition (RAMP) Transactions

Previously filled.

On Constructed Culture and Technological Determinism as Self-Fulfillling Prophecies

Harro van Lente, Arie Rip; Expectations in Technological Developments: An Example of Prospective Structures to be Filled in by Agency; 28 pages; ;; landing, (a photocopy of a paper article), landing as Chapter 7; In Cornelis Disco, Barend vander Meulen, Getting New Technologies Together: Studies in Making Sociotechnical Order; Walter de Gruyter; 1998; An earlier version of this paper was prepared, submitted, presented at the XXIth (21st?) World Congress of Sociology, ISA, Bielefield, DE, 1994-07-18; separately filled.

Mads Borup, Nik Brown, Kornelia Konrad, Harro Van Lente; The Sociology of Expectations in Science and Technology; an editorial; In Technology Analysis & Strategic Management, Volume 18, Numbers 3/4, 285 –298, July – September, 2006-07; 14 pages; DOI:10.1080/09537320600777002; paywall; copy; separately noted.

Leonardo Bursztyn, Georgy Egorov, Stefano Fiorin; From Extreme to Mainstream: How Social Norms Unravel; Working Paper No. 23415; National Bureau of Economic Research (NBER); 2017-05; paywall; separately noted.
tl;dr →something about needing “just the right” amount of correlational clustering to allow ideas to spread appropriately.

Rand Waltzman; The Weaponization of Information; CT-473; Rand Corporation; 2017-04-27; 10 pages; landing.
Teaser: The Need for Cognitive Security

Testimony presented before the Senate Armed Services Committee, Subcommittee on Cybersecurity on 2017-04-27; separately filled..

Christopher Paul, Miriam Matthews; The Russian “Firehose of Falsehood” Propaganda Model; PE-108-OSD; Rand Corporation; 2016; 16 pages (landscape, like slideware); landing; separately noted.
Teaser: Why It Might Work and Options to Counter It


Syllabus for Solon Barocas @ Cornell | INFO 4270: Ethics and Policy in Data Science

INFO 4270 – Ethics and Policy in Data Science
Instructor: Solon Barocas
Venue: Cornell University


Solon Barocas


A Canon, The Canon

In order of appearance in the syllabus, without the course cadence markers…

  • Danah Boyd and Kate Crawford, Critical Questions for Big Data; In <paywalled>Information, Communication & Society,Volume 15, Issue 5 (A decade in Internet time: the dynamics of the Internet and society); 2012; DOI:10.1080/1369118X.2012.678878</paywalled>
    Subtitle: Provocations for a cultural, technological, and scholarly phenomenon
  • Tal Zarsky, The Trouble with Algorithmic Decisions; In Science, Technology & Human Values, Vol 41, Issue 1, 2016 (2015-10-14); ResearchGate.
    Subtitle: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making
  • Cathy O’Neil, Weapons of Math Destruction; Broadway Books; 2016-09-06; 290 pages, ASIN:B019B6VCLO: Kindle: $12, paper: 10+SHT.
  • Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information; Harvard University Press; 2016-08-29; 320 pages; ASIN:0674970845: Kindle: $10, paper: $13+SHT.
  • Executive Office of the President, President Barack Obama, Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights; The White House Office of Science and Technology Policy (OSTP); 2016-05; 29 pages; archives.
  • Lisa Gitelman (editor), “Raw Data” is an Oxymoron; Series: Infrastructures; The MIT Press; 2013-01-25; 192 pages; ASIN:B00HCW7H0A: Kindle: $20, paper: $18+SHT.
    Lisa Gitelman, Virginia Jackson; Introduction (6 pages)
  • Agre, “Surveillance and Capture: Two Models of Privacy”
  • Bowker and Star, Sorting Things Out
  • Auerbach, “The Stupidity of Computers”
  • Moor, “What is Computer Ethics?”
  • Hand, “Deconstructing Statistical Questions”
  • O’Neil, On Being a Data Skeptic
  • Domingos, “A Few Useful Things to Know About Machine Learning”
  • Luca, Kleinberg, and Mullainathan, “Algorithms Need Managers, Too”
  • Friedman and Nissenbaum, “Bias in Computer Systems”
  • Lerman, “Big Data and Its Exclusions”
  • Hand, “Classifier Technology and the Illusion of Progress” [Sections 3 and 4]
  • Pager and Shepherd, “The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets”
  • Goodman, “Economic Models of (Algorithmic) Discrimination”
  • Hardt, “How Big Data Is Unfair”
  • Barocas and Selbst, “Big Data’s Disparate Impact” [Parts I and II]
  • Gandy, “It’s Discrimination, Stupid”
  • Dwork and Mulligan, “It’s Not Privacy, and It’s Not Fair”
  • Sandvig, Hamilton, Karahalios, and Langbort, “Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms”
  • Diakopoulos, “Algorithmic Accountability: Journalistic Investigation of Computational Power Structures”
  • Lavergne and Mullainathan, “Are Emily and Greg more Employable than Lakisha and Jamal?”
  • Sweeney, “Discrimination in Online Ad Delivery”
  • Datta, Tschantz, and Datta, “Automated Experiments on Ad Privacy Settings”
  • Dwork, Hardt, Pitassi, Reingold, and Zemel, “Fairness Through Awareness”
  • Feldman, Friedler, Moeller, Scheidegger, and Venkatasubramanian, “Certifying and Removing Disparate Impact”
  • Žliobaitė and Custers, “Using Sensitive Personal Data May Be Necessary for Avoiding Discrimination in Data-Driven Decision Models”
  • Angwin, Larson, Mattu, and Kirchner, “Machine Bias”
  • Kleinberg, Mullainathan, and Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores”
  • Northpointe, COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity
  • Chouldechova, “Fair Prediction with Disparate Impact”
  • Berk, Heidari, Jabbari, Kearns, and Roth, “Fairness in Criminal Justice Risk Assessments: The State of the Art”
  • Hardt, Price, and Srebro, “Equality of Opportunity in Supervised Learning”
  • Wattenberg, Viégas, and Hardt, “Attacking Discrimination with Smarter Machine Learning”
  • Friedler, Scheidegger, and Venkatasubramanian, “On the (Im)possibility of Fairness”
  • Tene and Polonetsky, “Taming the Golem: Challenges of Ethical Algorithmic Decision Making”
  • Lum and Isaac, “To Predict and Serve?”
  • Joseph, Kearns, Morgenstern, and Roth, “Fairness in Learning: Classic and Contextual Bandits”
  • Barocas, “Data Mining and the Discourse on Discrimination”
  • Grgić-Hlača, Zafar, Gummadi, and Weller, “The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making”
  • Vedder, “KDD: The Challenge to Individualism”
  • Lippert-Rasmussen, “‘We Are All Different’: Statistical Discrimination and the Right to Be Treated as an Individual”
  • Schauer, Profiles, Probabilities, And Stereotypes
  • Caliskan, Bryson, and Narayanan, “Semantics Derived Automatically from Language Corpora Contain Human-like Biases”
  • Zhao, Wang, Yatskar, Ordonez, and Chang, “Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints”
  • Bolukbasi, Chang, Zou, Saligrama, and Kalai, “Man Is to Computer Programmer as Woman Is to Homemaker?”
  • Citron and Pasquale, “The Scored Society: Due Process for Automated Predictions”
  • Ananny and Crawford, “Seeing without Knowing”
  • de Vries, “Privacy, Due Process and the Computational Turn”
  • Zarsky, “Transparent Predictions”
  • Crawford and Schultz, “Big Data and Due Process”
  • Kroll, Huey, Barocas, Felten, Reidenberg, Robinson, and Yu, “Accountable Algorithms”
  • Bornstein, “Is Artificial Intelligence Permanently Inscrutable?”
  • Burrell, “How the Machine ‘Thinks’”
  • Lipton, “The Mythos of Model Interpretability”
  • Doshi-Velez and Kim, “Towards a Rigorous Science of Interpretable Machine Learning”
  • Hall, Phan, and Ambati, “Ideas on Interpreting Machine Learning”
  • Grimmelmann and Westreich, “Incomprehensible Discrimination”
  • Selbst and Barocas, “Regulating Inscrutable Systems”
  • Jones, “The Right to a Human in the Loop”
  • Edwards and Veale, “Slave to the Algorithm? Why a ‘Right to Explanation’ is Probably Not the Remedy You are Looking for”
  • Duhigg, “How Companies Learn Your Secrets”
  • Kosinski, Stillwell, and Graepel, “Private Traits and Attributes Are Predictable from Digital Records of Human Behavior”
  • Barocas and Nissenbaum, “Big Data’s End Run around Procedural Privacy Protections”
  • Chen, Fraiberger, Moakler, and Provost, “Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals”
  • Robinson and Yu, Knowing the Score
  • Hurley and Adebayo, “Credit Scoring in the Era of Big Data”
  • Valentino-Devries, Singer-Vine, and Soltani, “Websites Vary Prices, Deals Based on Users’ Information”
  • The Council of Economic Advisers, Big Data and Differential Pricing
  • Hannak, Soeller, Lazer, Mislove, and Wilson, “Measuring Price Discrimination and Steering on E-commerce Web Sites”
  • Kochelek, “Data Mining and Antitrust”
  • Helveston, “Consumer Protection in the Age of Big Data”
  • Kolata, “New Gene Tests Pose a Threat to Insurers”
  • Swedloff, “Risk Classification’s Big Data (R)evolution”
  • Cooper, “Separation, Pooling, and Big Data”
  • Simon, “The Ideological Effects of Actuarial Practices”
  • Tufekci, “Engineering the Public”
  • Calo, “Digital Market Manipulation”
  • Kaptein and Eckles, “Selecting Effective Means to Any End”
  • Pariser, “Beware Online ‘Filter Bubbles’”
  • Gillespie, “The Relevance of Algorithms”
  • Buolamwini, “Algorithms Aren’t Racist. Your Skin Is just too Dark”
  • Hassein, “Against Black Inclusion in Facial Recognition”
  • Agüera y Arcas, Mitchell, and Todorov, “Physiognomy’s New Clothes”
  • Garvie, Bedoya, and Frankle, The Perpetual Line-Up
  • Wu and Zhang, “Automated Inference on Criminality using Face Images”
  • Haggerty, “Methodology as a Knife Fight”
    <snide>A metaphorical usage. Let hyperbole be your guide</snide>

Previously filled.