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 Wired.com; 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.
  • lesswrong.com,
  • 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

Previously filled.

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 Monster.com
    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 cppreference.com
  • 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; ; OAI:oai:doc.utwente.nl:34732; landing, (a photocopy of a paper article) academia.edu, 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.

A Product Management Framework for the Internet of Things | Daniel Elizalde

; A Product Management Framework for the Internet of Things; ; In His Blog; 2016 (circa, per copyright) .


The 5×7 matrix of Technology (Stack layer) × Concern


Technology Stack

  1. Device Hardware
  2. Device Software
  3. Communications
  4. Cloud Platform
  5. Cloud Application

Decision Framework

  • UX
  • Data
  • Business
  • Technology
  • Security
  • Standards
  • Regulations


  • Gap Analysis
  • Lean
  • N-somethning P-something I-something (NPI)
    New Product Introduction (NPI)


  • All steps are order-dependent.
  • The process must be done “just right” or it won’t work.
  • Rinse, repeat.



In His Blog

Overview of the Digital Object Architecture (DOA) | ISOC

Overview of the Digital Object Architecture (DOA); an Information Paper, The Internet Society; 2016-10-25; 8 pages; landing.
contributor credit: Chip Sharp, scrivener


  • Introduction
  • What is the Digital Object Architecture?
  • What is a Handle?
  • Handle Resolution
  • Who Runs the Global Handle System?
  • Examples of systems based on the Digital Object Architecture/Handle System
  • Standards and the Handle System
  • Policy Considerations
  • Trademarks and Service Marks
  • Resourcs


<quote>The Digital Object Architecture (DOA) and associated Handle System® originated at the Corporation for National Research Initiatives (CNRI) in the early 1990’s based on its work on digital libraries under contract for the Defense Advanced Research Projects Agency (DARPA).1 One of the original motivations for its design was the need to identify and retrieve information over long periods of time (on the order of tens or hundreds of years) so persistence was a critical design requirement. At the time it was developed, the Digital Object Architecture was an attempt to shift from a view of the Internet as organized around a set of hosts and the transport to reach them to a view in which the Internet was organized around the discovery and delivery of information in the form of digital objects.<quote>


  • Digital Object Architecture (DOA)
  • associated Handle System®
    Yes, that’s a registration mark, ®.
  • Corporation for National Research Initiatives (CNRI)
  • Defense Advanced Research Projects Agency (DARPA)
  • um, like “digital libraries”
  • DONA Foundation
  • International Telecommunication Union (ITU)
  • Memoranda of Understanding (MOUs)
  • Multi-Primary Administrators (MPAs), manage the partitions of the namespace of the root servers of the top level GHR.
  • Policy Development Process (PDP)


Digital Objects
The records, blobs of bits.
The containers of records objects.
The names are global contextually scoped, universal, persistent.
There is a Handle Protocol, in (at least) version v2.1.
Resolution System and Registries
Like DNS, but different; maps “names” to Handles.

  • Global Handle Registry (GHR), is the root server.
  • Local Handle Services (LHS), are the regional delegates.
The names are, e.g. GUIDs, UUIDs.
  • unique
  • persistent
  • location-agnostic
  • taxonomy-agnostic


Defined as a string:
prefix “/” identifier
prefix is a “like a” reversed FQDN.
identifier is “like a” filename on the FQDN so referenced.
Thus handles are

  • unique
  • persistent
  • location-agnostic
  • taxonomy-agnostic

For the FQDN of the U.S. Library of Congress (LOC)
FQDN: ye-auguste-national-librarye.loc.gov
Handle: gov.loc.ye-auguste-national-librarye


  • Coalition for Handle Services (ETIRI, CDI and CHC)
  • Communications and Information Technology Commission (CITC)
  • Corporation for National Research Initiatives (CNRI)
  • Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG)/ePIC
  • International DOI Foundation (IDF)


DOI® System
  • International DOI Foundation (IDF)
    UK, non-profit, 1998
  • Prefix 10
  • Handle semantics is idiosyncratic, known to themselves.
  • Examples
Persistent Identifier Consortium for eResearch (ePIC)
  • [for the benefit of the] European Research Community
  • Prefix 21


RFC 3650
S. Sun, L. Lannom, B. Boesch. Handle System Overview, RFC 3650, 2003-11.
RFC 3651
S. Sun, Reilly, L. Lannom. Handle System Namespace and Service Definition, RFC 3651, 2003-11.
RFC 3652
S. Sun, S. Reilly, L. Lannom, J. Petrone. Handle System Protocol (ver 2.1) Specification, RFC 3652, 2003-11.
RFC 4452
H. Van de Sompel, T. Hammond, E. Neylon, S. Weibel. The “info” URI Scheme for Information Assets with Identifiers in Public Namespaces, RFC 4452, 2006-04.
ISO 26324:2012
International Organization for Standardization (ISO), “ISO 26324:2012 Information and documentation — Digital object identifier system“, ISO Standard 26324, 2012-06.
ANSI/NISO Z39.84-2005 (R2010)
ANSI/NISO Z39.84-2005 (R2010) Syntax for the Digital Object Identifier. (revised 2010)
ITU-T Recommendation X.1255, Framework for discovery of identity management information, ITU-T, 2014

Trademarks and Service Marks

International DOI Foundation, Inc.
DOI, DOI.ORG, “short DOI” are registered service marks of …
DONA Foundation
DONA, GLOBAL HANDLE REGISTRY, HANDLE SYSTEM are registered service marks of…
Corporation for National Research Initiatives (CNRI)
HANDLE.NET, HDL, HDL.NET, CNRI are registered service marks of …
HDL, HDL.NET are registered trademarks of …
Internet Society
Internet Society is a registered service mark of the …



  • Documents at the DONA Foundation
  • open-stand.org
  • ANSI/NISO Z39.84-2005 (R2010) Syntax for the Digital Object Identifier. (revised 2010)
  • Corporation for National Research Initiatives, Overview of the Digital Object Architecture, July 28, 2012
  • DONA Foundation. DONA Foundation Statutes. Geneva. 2014.
  • International Organization for Standardization (ISO), “ISO 26324:2012 Information and documentation — Digital object identifier system”, ISO Standard 26324, 2012-06.
  • ITU-T Recommendation X.1255, Framework for discovery of identity management information, ITU-T, 2014.
  • R. Kahn, R. Wilensky. “A Framework for Distributed Digital Object Services”, In International Journal of Digital Libraries (2006) 6: 115.
  • Norman, Paskin. The Digital Object Identifier: From Ad Hoc to National to International. In The Critical Component: Standards in the Information Exchange Environment, edited by Todd Carpenter, ALCTS, 2015.
  • Peter J. Denning & Robert E. Kahn, The Long Quest for Universal Information Access. In Communications of the ACM, Vol. 53 No. 12, Pages 34-36.
  • RFC 3650. S. Sun, L. Lannom, B. Boesch. Handle System Overview, IETF, RFC 3650, 2003-11.
  • RFC 3651. S. Sun, S. Reilly, L. Lannom. Handle System Namespace and Service Definition, IETF, RFC 3651, 2003-11.
  • RFC 3652. S. Sun, S. Reilly, L. Lannom. J. Petrone, Handle System Protocol (ver 2.1) Specification, IETF, RFC 3652, 2003-11.
  • RFC 4452. H. Van de Sompel, T. Hammond, E. Neylon, S. Weibel, The “info” URI Scheme for Information Assets with Identifiers in Public Namespaces, IETF, RFC 4452, 2006-04.

Previously filled.

A Tragedy of Manners | Angela Nagle

A Tragedy of Manners; Angela Nagle; In The Baffler; WHEN?
Teaser: Trump and the new age of anti-PC transgression

tl;dr → Manners are a contested space by which actions valorize the hegemonic power valences of the universalist tropes; they are a tussle among the grand ideologues. The author problematizes the domain and limns the transgressive dialogue towards a synthesis which ultimately resulting in the thesis of the conceptual conundrum while at the same time  preserving the original order, thus standing in opposition to itself with both metaphoric as well as rhetorical stances. The lede is buried. To wit:

<quote>The problem in our current, unacknowledged controversy over manners is that while both sides seem to implicitly accept [value] premise, they have directly opposing views of what our system of manners should be doing and what values it should be normalizing.</quote>


Angela Nagle; Kill All Normies: Online Culture Wars From 4Chan And Tumblr To Trump And The Alt-Right; Zero Books; 2017-06-30; 136 pages; Amazon:1785355430: Kindle: $10, paper: $16+SHT; previously filled.


  • seismic shock, means “big”
  • Donald Trump
  • cultural anxiety, means racism, coded racism, encoded racism, latent racism.
  • ping-pong style search
  • British Burkean conservative Peter Hitchens
  • in Buckleyite fashion
  • Hegel, Elements of the Philosophy of Right, 1820.
  • Sittlichkeit
    • a German word (they have words for everythig)
    • an epithet a term of art
    • definition: the ethical life
  • progressives, the good people.
  • the outmoded, prissy-sounding language of manners
  • pride of place
  • the debased rhetoric plotting out
  • metaphorical usage.
  • <quote>the battle over “political correctness”</quote>, a metaporical usage
  • <quote>ongoing war over speech on college campuses</quote>, a metaporical usage
  • <quote>understood through the lens of</quote>, a metaporical usage
  • liberal free speech rights
  • strategic considerations
  • the free speech wars
  • rights under attack from the state
  • <quote>The same basic paradox assails all spheres of political and cultural confrontation</quote>,
    in which “a paradox” does “assail”
  • [They] abjure
    [They] instinctively abjure reckoning
  • The Decivilizing Process
  • gleefully presided over
  • a mass rejection, the mass rejection
  • a liberal sense of
  • political correctness
  • a renegotiation of propriety
  • a pluralist multi-ethnic modern society
  • accommodating
  • admiration
  • transgression
  • straight-talking style.
  • taboo-breaking
  • an unlicensed brand of
  • right-wing cultural subversion, right-wing cultural subversion
  • repressed snobs
  • pearl-clutchers
  • stereotyped view
  • elitist cultural authoritarians—the storm troopers of the liberal language police.
  • renegotiating
  • the very profound question of
  • magnum opus
  • uncomfortable
  • hardy coterie of academic defenders
  • interconnected collective socialization
  • transition into modernity
  • basic lessons
  • collectively negotiated network of self-constraints
  • socialized people into repudiating
  • the governance of public life
  • self-restraint
  • bodily functions
  • the repression of sexual and violent impulses
  • the very fabric of civilization
  • the liberationist ethos of
  • the sixties New Left
  • <quote>the movement spelled a</quote>, a metaphorical usage
  • <quote>a total breakdown of manners and self-restraint in a “permissive society”<quote>, is a hyperbolic usage
  • that critique gained force
    criticism has force, a metaphorical usage, to be sure.
  • wider declensionist narratives
  • neoconservative historian
  • Gertrude Himmelfarb
  • Victorian England
  • to contend that
  • the post-sixties West would be unable to withstand
  • the chaotic force of modernity
  • Western civilization
  • <quote>on the brink of nothing less than total “demoralization,”</quote>, a hyperbolic usage
  • polemicists, Neocon polemicists
  • few dour and cultured leftists, the few
  • Lewis Lapham
  • Gibbon’s Decline and Fall of the Roman Empire
  • the youthful adherents
  • Trumpian, the Trumpian right
  • an allied preoccupation
  • civilizational, civilizational collapse
  • the permissive society
  • quasi-Trumpian supporters
  • the anti-PC resistance
  • Camille Paglia
    • is neo-Freudian
    • Sexual Personae
      , a tome
    • is formidable
      she herself, for her own account
  • most ambivalent and qualified arguments
  • the left-leaning [arguments]
  • celebration of decadent culture
  • exponents, [civilization's] key exponents
  • Oscar Wilde
  • <quote>rescued aesthetic insights in the face of<quote>, a mixed metaphorical usage.
  • largely self-administered cultural collapse
  • a related critical register
  • degeneration theory (Degeneration Theory)
  • Max Nordau
  • Oswald Spengler
  • <quote>shape the tone and content of<quote>, a mixed metaphorical usage.
  • a whole new wave of
  • right-wing alternative media.
  • Part and parcel of
  • declensionist revival (on the right)
  • progress, the idea of progress, the very idea of progress.
  • urgency of [Trump’s] appeal
  • mounting conviction
  • the West
  • rapidly degenerating
  • the rubric of, under the rubric of
  • as administered and championed by
  • cultural liberals
  • Circa 2015
  • 4chan’s /pol/ ‘board
  • a meme, the phrase; the widely-shared meme.
  • the meme, the phrase: “Come on it’s (the current year)”
  • naïve progressives
  • John Oliver
  • questioned, [X] questioned, to question
  • the arbitrary insistence
  • <quote>moving forward in time<quote>, a metaphorical usage
  • superior values.
  • More recently [than circa 2015], which would be the twenty months of 2016 & 2017.
  • the meme, the phrase pair:
    • “$DATE1: $statement1” contra “$DATE2: $statement2”
      where $DATE1 + 30 < $DATE2 && value($statement1) > value($statement2)
    • e.g. “1970: ‘I can’t wait for flying cars/space colonies/a cure for cancer’” contra 2017, an image of a man who identifies as a dog or an adult baby.
  • contemporary identity politics, a representation of contemporary identity politics
  • the political message, the political message is clear
  • claim of dichotomy:
    • either progress itself is a myth
    • all of
      • [we have] stopped progressing
      • [we have] started regressing as a civilization
      • [we are] now intractably sinking into a decivilizing process.
  • Question: You Call That Art?
    Answer: what else could it be? The null hypothesis?
  • An audience
    • looser,
    • right-leaning,
    • online,
  • a meme, the critique-of-progress
    • e.g.Cathedral Gothic Art contra Contemporary Art
    • sarcastically caption: e.g. “progress” or “art then . . . art now.”
  • absurdist
  • <quote>knitting with wool from her vagina</quote>, activities attributed to Casey Jenkins
    which begs the question of how wool got in her vagina; would that be a used tampon?. Juvenile, if true.
  • vastly overrated modern art
  • long been a preoccupation
  • almost a cliché
  • the declension narratives
  • the declension narratives of the right (the third? usage).
  • Francis Schaeffer, How Should We Then Live?, 1976
    honorific: <quote>one of the founding texts of the American religious right<quote>
  • a polemic work, a polemic work of art history.
  • the right-leaning suspicion
  • contemporary art
  • the faux-populist refrain, some variant of the faux-populist refrain
  • “my three-year-old could do that”, an epithet.
  • Roger Scruton, an erudite conservative critic
  • “cult of ugliness”, attributed to Roger Scruton.
  • The young subcultural online right
  • <quote>mourns the death of the ideal of beauty as an extension of its critique of progress</quote>, a mixed metaphorical usage.
  • the hordes of online left-baiters
  • judgments of personal beauty, of women.
  • before-and-after cultural documentation; the transition, the purported transition
    • nice, well-adjusted-looking young women
    • and (or)
      • feminism
      • the ravages of studying the social sciences.
  • exemplar, a hated exemplar: Lena Dunham
  • modern cult of ugliness
  • <quote>channeling the latter avant-garde aesthetic sensibilities of shock and transgression.<quote>ongoing an action attributed to of the [members of the] modern cult of ugliness.
  • confrontationally corpulent nudity, an ongoing action attributed to Lena Dunham.
  • outsider art, contra insider art
  • <quote>[The Nazis] waged war on “degenerate art”<quote>, a metaphorical usage; to wit, National Socialist German Workers’ Party waged actual war as well, such war being one the second most famous policy-based activity for which they are known..
  • Weimar avant-garde, the vibrant Weimar avant-garde
    • a crusade
    • years of reactionary writing
    • modern art beiing
      • ugly
      • Jewish
      • destructive to European traditions
  • …affecting a transition from art to Nazi policy to Donald Trump’s stylistic fluorishes, we see what you did there.
  • Trump’s own famous style
  • fanatically mimicked
  • right-wing culture-jammers
  • a certain avant-gardish notoriety
  • <quote>images so stomach-churning and morally repugnant they “can’t be unseen.”<quote>, an epithet, a passive characterization.
  • The new youthful rightist movements
  • the modern aesthetics of shock and transgression
  • the alternation:
    • horrified critics
    • prolific producers
  • <quote>Trumpians [as a self-conscious class] their leader’s id-driven defiance of the harsh constraints imposed by strict liberal etiquette and sexual mores</quote>
  • [the] coarse “pussy grabbing” comments
  • <quote>the general conditions of cultural decline ushered in by the liberalism of the sixties<quote>
  • Trumpians are not rightist trolls; c.f. <quote>To them and to the rightist trolls</quote>
  • Wherein the shock of throwing X is a pushback against Y
  • <quote>the shock of throwing off liberal etiquette is a pushback against the civilizational decline brought on by those Baby Boomers who threw off their own set of constraints.</quote>
  • Baby Boomers
  • the culture of trolling
  • the culture of style-defining spaces
  • 4chan is
    • a culture of trolling
    • a culture of style-defining spaces
  • [such culture] [is only] a franchise of the far right
  • the fetishization of trolling as
    • “counter-hegemonic”
    • taboo-breaking
  • leftish writers; a characterization, an honorific, an epithet.
  • the sixties view
    • is that systems of personal constraints were the cause of society’s ills rather than the cure.
    • is anti-Freudian.
    • is descended from Rousseau.
  • confused, backswitching narratives of cultural decline
  • <quote>the legacy of Elias sheds an invaluable light</quote>, a mixed metaphorical usage.
  • a body of work about the “decivilizing process”
  • something different than
    • the declension narratives of the right
    • the declension narratives of the left
  • something similar to
    • a communitarian sense of society.
  • the definition [f decline]
    • shorter chains of social interdependence
    • a decrease in
      • in taming of aggressiveness
      • mutual identification
      • the gap between child and adult standards
    • a reliance on external constraints to curb
      • violent impulses
      • unruly impulses
    • an increase in
      • the free expression of aggressiveness
  • Cas Wouters
  • the post-sixties management of manners
  • a less morally constrained time
  • <quote>“a highly controlled decontrolling of emotional controls”<quote>, attributed to Cas Wouters [clearly he too, had no editorial supervision].
  • The Shock Doctrine
    as used here used conflates the argument of Naomi Klein with the critical theoretical implications of public and individual reactions to works of ironic performative criticism as “art.”
  • the memes, the memes of the right
    <quote>the irony-drenched “come on, it’s the current year!” memes of the right</quote>
  • the call to action
    the calls to reject modernity,
    <quote>the merely retrograde calls to reject modernity</quote>
  • Robert Hughes diagnosed
    • an active action
    • claimed: art culture lost 1890→1980
      • Ebullience
      • Idealism
      • Confidence
  • The Shock Doctrine is, and was
    • <quote>the trademark culture-seizing ebullience of modern Western art<quote>
    • the “shock of the new”
    • once heralded the future
  • <quote>[the shock doctrine] was <snip/>a central battleground<quote>, a mixed metaphorical usage.
  • thrashing out the meaning of progress
  • Robert Hughes mourned, an action on his part.
  • the modes, the modes of expression,
    <quote>the nasty, negative, and nihilistic modes of expression that today also paradoxically repulses and characterizes the aesthetic sensibilities of the youthful online right, depending on subtler distinctions of whose rules it is transgressing.</quote>
  • Establishment conservatism, as a self-conscious class.
  • the Trumpians, the Trumpians preside
  • a ghost-dance revival of the very recent past
  • “Make America Great Again”
    • a mission
    • a call to action
  • the legions of the alt-right
  • an imminent nightmarish future
  • <quote>a civilization already dropped off the cliff</quote>, a metaphorical usage.
  • “America is already great!”
    • The centrist insistence
    • The stupendously ineffectual rejoinder to Trump trademarked by the Clinton campaign
    • has an alternative
      • is anemic
      • is uninspiring
      • [is] <quote>a strange kind of end-of-history politics that holds GDP and the gradual liberalization of cultural attitudes as the incontrovertible measure of secular millennialism.</quote>
  • secular millennialism, as measured
    <quote>the incontrovertible measure of secular millennialism</quote>
  • generational living standards
  • the technological affluent future
  • claimed: alt-right memes will have [continued] appeal under the conditions specified.
  • …the rhetorical transition, abruptly, back to the subject of manners. We see what you did there.
  • something about manners
  • an extremely fraught renegotiation
  • …the  rhetorical transition, something about McDonald’s corporation and progress and Nazi Germany and Elias’ thought:
    • The corporate slogan
      of McDonalds, the golden arches of McDonalds
      “A modern and progressive burger company.”
    • Norbert Elias
      • a German
      • a Jew
      • fled Nazi Germany
      • mother died in Auschwitz
  • civilization
  • equals restraint
  • a delicate balance
  • atrophies
  • all is lost
  • <quote>We’re now in the midst of an extremely fraught renegotiation of the values expressed in our system of manners.</quote>
  • the controversy [teach the controversy],
    <quote>our current unacknowledged controversy over manners</quote>


  • Peter Hitchens, a British Burkean conservative.
  • James Burke, a theorist.
  • William Buckley, a theorist.
  • Lena Dunham, a performer; was born, lived in New York, her family members work as artists, work in the arts.
  • Norbert Elias, a scrivener; performed landmark research.
  • Sigmund Freud, a theorist.
  • Gertrude Himmelfarb, historicist, a neoconservative
  • Robert Hughes, a theorist, upon the domain of art
  • Casey Jenkins, a performer; (ahem, is female); has a vagina
  • Lewis Lapham, a leftist, by trade; is dour, is cultured.
  • William Gibbon, a scrivener
  • Max Nordau, a theorist; branded: Degeneration Theory.
  • John Oliver, a performer, of satire; is naive, is progressive (good).
  • Camille Paglia, a theorist; is formidable, she, herself.
  • Jean Jacques Rousseau, a theorist.
  • Francis Schaeffer, a theorist.
  • Roger Scruton, a theorist; is conservative, is erudite.
  • Oswald Spengler, a theorist; branded: Degeneration Theory.
  • Donald J. Trump, boffo, a data subject, the data subject.
  • Slavoj Žižek, a philosoph, a Marxist.
  • Oscar Wilde, a practitioner; is an exponent.
  • Cas Wouters, a theorist; following the theory of Norbert Elias.


  • Georg Wilhelm Friedrich Hegel, Elements of the Philosophy of Right, 1820.
  • Norbert Elias, The Civilizing Process, 1939.
  • Edward Gibbon, The History of the Decline and Fall of the Roman Empire, 1776.
  • Naomi Klein, The Shock Doctrine: The Rise of Disaster Capitalism, 2007.
  • Camile Paglia, Sexual Personae, 1990.
    honorific: a tome.
  • Francis Schaeffer, How Should We Then Live?, 1976.
    honorific: <quote>one of the founding texts of the American religious right<quote>


The suitcase words
  • Avant-garde, The Avant-garde
  • Baby Boomers
  • Burkean
  • Buckleyite
  • Declenscionist Narrative
  • Decivilizing Process, The
  • Descent Theory
  • Freudian, neo-Freudian
  • Left
    • New Left
    • The Left
  • Right
    • Alt-Right
    • The Right
  • Sixties, The Sixties
  • Shock Doctrine, The
  • Trumpian
  • West, The West

Previously filled.