Apple Safari Privacy Cookie Features Praised by the EFF | Infosecurity

Apple Safari Privacy Cookie Features Praised; ; In Infosecurity Magazine; 2017-09-23.

Occasion

Andrés Arrieta, Alan Toner (EFF); Apple Does Right by Users, Wrong by Advertisers; In Their Blog at the Electronic Frontier Foundation; 2017-09-20.

Background

The Italian Google-Case: Privacy, Freedom of Speech and Responsibility of Providers for User-Generated Contents | Sartor, Viola

The Italian Google-Case: Privacy, Freedom of Speech and Responsibility of Providers for User-Generated Contents; In International Journal of Law and Information Technology, Volume 18, Issue 4, 1, 2010-12, Pages 356–378. <paywalled>landing, DOI:10.1093/ijlit/eaq010</paywalled>
Giovanni Sartor is
  • Professor of Legal informatics and Legal Theory at the European University Institute (Florence)
  • Professor of Computer and Law at the University of Bologna
Mario Viola is
  • PhD Candidate at the Law Department of the European University Institute (Florence)
  • LLM in Private Law from Rio de Janeiro State University (Brazil)

Never mind. The paper is paywalled.

Algorithmic Accountability: The Big Problems | SAP

Tom Slee (SAP); Algorithmic Accountability: The Big Problems; Their Blog; 2017-10.

tl;dr → You have problems, SAP has expertise in this practice area. Call now.

Original Sources

Yvonne Baur, Brenda Reid, Steve Hunt, Fawn Fitter (SAP); How AI Can End Bias; In Their Other Blog, entitled The D!gitalist; 2017-01-16.
Teaser: Harmful human bias—both intentional and unconscious—can be avoided with the help of artificial intelligence, but only if we teach it to play fair and constantly question the results.

Mentions

  • The Canon is rehearsed.
  • General Data Protection Regulation (GDPR)
    • European
    • “in effect in” 2018 (2018-05-28).

Indictment
Anti-patterns, Negative (Worst) Practices

  • Bad statistics
  • Ill-defined scales
  • Bad Incentives
  • Lack of transparency

Five Axes of Unfairness
Unfairness ↔ Disparate Impact

  1. Target variables
  2. Training data
  3. Feature selection
  4. Proxies
  5. Masking

Remediation

  • Explanation
  • Transparency
  • Audits
  • Fairness

Who

  • Solon Barocas, self [Princeton]
    Trade: theorist.
  • Cynthia Dwork, self [Microsoft]
    Trade: pioneer [theorist]..
  • Seth Flaxman, staff, Oxford University.
    Trade: expert.
  • Bryce Goodman, staff, Oxford University.
    Trade: expert.
  • Cathy O’Neil, self.
    Trade: data scientist statistician who works on a Macintosh Computer and lives in San Francisco.
  • Frank Pasquale, professor, law [Maryland]
    Ttrade: educator.
  • Andrew Selbst, self [U.S. .Court of Appeals]
    Trade: theorist

Referenced

The Coming Tech Backlash | Ross Mayfield

Ross Mayfield; The Coming Tech Backlash; In That Certain Blog at possibly entitled Shift (too much cobranding), sponsored by Newco, but centrally hosted at Medium; 2017-01-03.
Teaser: Tech innovation is killing jobs, not foreign scapegoats, and revolt after Trump will be Luddite
Summary: The tech industry played an influential role in the outcome of the US Presidential election. Not just in providing the medium for Fake News and propaganda. The root cause is job destruction by Automation — that drove a base of dissatisfied rust-belt voters to support Trump. Job destruction is accelerating, and if Tech doesn’t get ahead of this problem, there will be a significant populist backlash against the industry and it’s ability to progress.

Ross Mayfield is CEO & Co-founder, Pingpad; ex-LinkedIn, SlideShare, Socialtext, RateXchange.

tl;dr → The future is foretold. The hipster socialism. A startup is promoted. Robots bad. Need Jobs for the unpublished classes. the lede is buried.  The working classes need Slack addon, Pingpad, that create jobs; a bot-augmented wiki knowledge base.

Lede

<quote>Here’s what Ross Mayfield is doing about the Backlash. Also, for an example of augmentation in action, see our bot-augmented wiki for Slack teams

Mentions

  • “Tech”, The Tech, The Tech Industry
    a.k.a. publishing on the online web, the blogging.
  • McKinsey
  • Oxford
  • <pro-forma><cliché>The future is unevenly distributed</cliché></pro-forma>
  • The Singularity
  • Jobs
    • Truck Driver
    • Bloggist?
  • Basic Income
    • (semi-)Universal Basic Income
    • YCombinator has a “fellows” grant program
  • Pot (a.k.a. Marijuana)
  • Google
    • Google Busses
  • post-ethics era
    • GOP
  • post-truth era
    • Trump
  • Joi Ito, Jeff Howe; Whiplash: How to Survive Our Faster Future; Grand Central Publishing; 2016-12-06; 320 pages; ASIN:1455544590: Kindle: $15, paper: $5+SHT; separately filled.
  • Sack
    • Slack Channel

References

  • Quartz
  • GeekWire
  • Some Study. That. Shows, branded as An Oxford Study
  • Some Study. That. Shows, branded as A McKinsey Study

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

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

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

Occasion

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

Mentions

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

Anti-Nostrum

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

Nostrum

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

Soporific

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

Exemplars

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

Quotes

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

Pantheon

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

Referenced

Previously

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

Argot

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

“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>

Mentions

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.

Who

  • 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.

Pantheon

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

Referenced

Yes, there were papers referrenced.  See notes..

Previously

In archaeological order, in Quanta Magazine

Separately noted..

Video games used to be an escape. Now not even they are safe from ads | The Register

Video games used to be an escape. Now not even they are safe from ads; John Leyden; In The Register; 2017-10-09.
Teaser: Devs seduced by the dark arts of data collection and product placement

tl;dr → Ads: always, everywhere and on every available surface.

Occasion

Chris Boyd (Malwarebytes); Exploring the virtual worlds of advergaming, a talk; performed at the Virus Bulletin conference, in Madrid, Spain; 2017-10-05′ abstract.
tl;dr → The abstract appears as a tutorial on the trade: targeted advertising and on campaign management. No specific companies or practices are named in the abstract.

Mentions

<quote>Mobile apps have embraced advergaming, trending against upfront payments in favour of free games financed through data collection, adverts and in-app purchases. Of the top 30 games on Google Play, 27 apps contained ads and the same number contained in-app purchases. All were free to download and targeted casual gamers.</quote>

Our economic cannibalism | The Washington Post

Our economic cannibalism; Robert J. Samuelson In The Washington Post; 2017-10-08.

tl;dr → <quote>They argue that the economy is riddled with self-serving arrangements, mainly benefiting the rich, that impose excess costs on the poor and middle class and reduce economic growth.</quote>

Book

Brink Lindsey, Steven Teles; The Captured Economy: How the Powerful Enrich Themselves, Slow Down Growth, and Increase Inequality 1st Edition; Oxford University Press; 2017-11-01; “ASIN:019062776X: Kindle: $17, paper: $22+SHT.

Mentions

  • Lots of factoids, sourced from the publiser’s media package.

Who

  • Edward Glaeser, staff [faculty?], Harvard University.
  • Brink Lindsey, staff [staff], Niskanen Center.
  • Steven M. Teles, staff [faculty?], Johns Hopkins University.

It Me: Under the Hood of Web Authentication | Robinson, Zhu

Garrett Robinson, Yan Zhu; It Me: Under the Hood of Web Authentication; At Some Conference; circa 2017-10; N slides.

Message

  1. Do not use (linear) string comparison, ever.
    Avoid: a == b
    Use: PRF(a) == PRF(b)
    where: Pseudo-Random Function PRF with HMACPRF
  2. Use U2F with Web Authentication
  3. 2FA is weakened by the Password Reset Flow
    • Uses SMTP to deliver secrets or capabilities.
    • SMTP is not encrypted.
    • SMTP’s STARTTLS is opportunistic and fails-open (fails to cleartext)

Mentions

Know Thy Futurist | Boston Review

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

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

Models

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

Indictment

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

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

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

Separately noted.