Code Dependent: Pros and Cons of the Algorithm Age | Pew Research

, ; Code Dependent: Pros and Cons of the Algorithm Age; 2017-02-08; 87 pages; landing.
Teaser: Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.

tl;dr → there be dragons; this is an important area; the future is at stake; the alarum has been sounded; there are seers who can show us the way. In their own words.

Series

Future of the Internet, of Pew Research & Elon University.

Table of Contents

  • Overview
  • Themes illuminating concerns and challenges
  • Key experts’ thinking about the future impacts of algorithms
  • About this canvassing of experts
  • Theme 1: Algorithms will continue to spread everywhere
  • Theme 2: Good things lie ahead
  • Theme 3: Humanity and human judgment are lost when data and predictive modeling become paramount
  • Theme 4: Biases exist in algorithmically-organized systems
  • Theme 5: Algorithmic categorizations deepen divides
  • Theme 6: Unemployment will rise
  • Theme 7: The need grows for algorithmic literacy, transparency and oversight
  • Acknowledgments

Promotion

Code-Dependent: Pros and Cons of the Algorithm Age; , (Pew Research Center); In Their Blog; 2017-02-08.

Teaser: Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment/

Mentions

  • Pew Research Center of the Pew Charitable Trusts
  • Imagining the Internet Center at Elon Univesity
  • <ahem>the Singularity enthusiasts … .</ahem>

Themes

  1. Algorithms will continue to spread everywhere
  2. Good things lie ahead
  3. Humanity adn human judgement are lost wwhen data nad predictive modeling become paramount
  4. Biases exist in algorithymically-organized systems
  5. algorithmic categorizations deepen divides
  6. Unemployment will rise
  7. The need grows for algorithmic literacy, transparency and oversight.

Argot

  • <snicker>Artificial Intelligence (AI)</snicker>
  • algocratic governance
  • surveillance capitalism
  • information capitalism
  • topsight
  • black-box nature [of]
  • digital scientism
  • obedience score

Quoted

  • Aneesh Aneesh, Stanford University.
  • Peter Diamandis, CEO, XPrize Foundation.
  • Shoshana Zuboff, Harvard.
  • Jim Warren, activist.
  • Terry Langendoen, expert, U.S. National Science Foundation.
  • Patrick Tucker technology editor at Defense One,.
  • Paul Jones, clinical professor at the University of North Carolina-Chapel Hill and director of ibiblio.org.
  • David Krieger, director of the Institute for Communication & Leadership IKF,.
  • Galen Hunt, partner research manager at Microsoft Research NExT,.
  • Alf Rehn, professor and chair of management and organization at Åbo Akademi University in Finland,.
  • Andrew Nachison, founder at We Media,.
  • Luis Lach, president of the Sociedad Mexicana de Computación en la Educación, A.C.
  • Frank Pasquale, professor of law, University of Maryland.
  • Jeff Jarvis, reporter.
  • Cindy Cohn, executive director at the Electronic Frontier Foundation,.
  • Bernardo A. Huberman, senior fellow and director of the Mechanisms and Design Lab at HPE Labs, Hewlett Packard Enterprise.
  • Marcel bullinga, expert.
  • Michael Rogers, principal, Practical Futurist.
  • Brian Christian, Tom Griffiths.
  • David Gelertner.
  • Deloitte Global (anonymous contributors).
  • Barry Chudakov, founder and principal at Sertain Research and StreamFuzion Corp.
  • Stephen Downes, staff, National Research Council of Canada,.
  • Bart Knijnenburg, assistant professor in human-centered computing at Clemson University.
  • Justin Reich, executive director at the MIT Teaching Systems Lab.
  • Dudley Irish, tradesman (a coder).
  • Ryan Hayes, owner of Fit to Tweet,.
  • Adam Gismondi, a visiting scholar at Boston College.
  • Susan Etlinger, staff, Altimeter Group.
  • Chris Kutarna, fellow, Oxford Martin School.
  • Vintno Cert, Internet Hall of Fame, vice president and chief internet evangelist at Google:.
  • Cory Doctorow, writer, computer science activist-in-residence at MIT Media Lab and co-owner of Boing Boing.
  • Jonathan Grudin, Microsoft.
  • Doc Searls, director, Project VRM, Berkman Center, Harvard University,.
  • Marc Rotenberg, executive director of the Electronic Privacy Information Center.
  • Richard Stallman, Internet Hall of Fame, president of the Free Software Foundation.
  • David Clark, Internet Hall of Fame, senior research scientist at MIT,.
  • Baratunde Thurston, Director’s Fellow at MIT Media Lab, ex-digital director of The Onion.
  • Anil Dash, pundit.
  • John Markoff, New York Times.
  • Danah Boyd (“danah boyd”), founder, Data & Society, an advocacy group.
  • Henning Schulzrinne, Internet Hall of Fame, professor at Columbia University,.
  • Amy Webb, futurist and CEO at the Future Today Institute.
  • Jamais Cascio, distinguished fellow at the Institute for the Future.
  • Mike Liebhold, senior researcher and distinguished fellow at the Institute for the Future,.
  • Ben Shneiderman, professor of computer science at the University of Maryland,.
  • David Weinberger, senior researcher at the Harvard Berkman Klein Center for Internet & Society.

Referenced

Previously filled.

Stanford 2025, the purpose of the elite university, Java, JavaScript

Context

Stanford 2025, about.

Consideration

A nice counterpoint to Lowen’s history in Creating the Cold War University [below]. In reading the About page, understanding who funded this and why they might have done that, I’m struck by the lifelong learning aspect and the conceptual abandonment of the “alumni” concept. That’s probably the biggest suspension of disbelief that one must have. Second to that though is that there is an argument to be made about whether autodictatism (generally the Unschooling Movement) is appropriate and to which domains of expertise it applies.  Rather than argue that, I’ll spend the time here to highlight a generation-scale ongoing experiment and debate that has been occurring at Stanford Computer Science for around twenty years.

The story runs like this: “back in the day” (of the ’90s), the discipline of Computer Science had a certain rite of passage at Stanford, Cal and probably everywhere wherein after the first intro course in a teaching language like WATFIV or Pascal, the student was immediately expected to undertake the data structures, compiler or operating systems course with mastery of the <satire>One True Language</satire>: C of Unix.  Many did not make that transition, which probably was the point of arranging the course sequence that way. Same pattern in Chem, Physics, and the B-school sequences.

In the era in question here, pre-Bubble I, Prof. Eric Roberts at Stanford, chose to migrate the introductory course to Java for pedagogical and practical reasons. Not the least was that there was demand for Java-centric knowledge in industry. Among the debates of the day, was whether an elite school like Stanford was supposed to be in the business of teaching “job skills in support of the IT trades” or whether the time and money being spent at the institution was better used to teach general principles, provoke the critical thinking and develop of timeless deep understanding.  MIT taught intro via Scheme in this era. Whereas nowadays the industry, and especially Google via the legal reminding system [cited below], understands that Java is a licensed product offering of Oracle Corporation with structured community availability and user feedback machinery patterned after the “open source” cultures. The argument was made at the time that Java, with it’s lububrious OO frameworks, “no pointer” memory model, garbage collection and “cannot crash” runtime engine was both better for teaching and the right set-point for the career path into industry.

I sketch this now because here, twenty years later, the debate is substantially the same: is the purpose of The University and the 4-year degree system about inculcating a desire for incremental lifelong learning as a “sense of self improvement” program [c.f. Parker, below], is it in support of career skills production of knowledge workers in the global economy, or regionally is it the training venue to the trades (crudely, is Stanford no different than DeVry [c.f. the Thompson & Smiley  pieces below]) or is there more to the brand, the venue, the institution, the traditions of the big schools & liberal arts themselves and their Enlightenment extensions into areas of practice?

I’m reminded of this debate both from the pointer to the Stanford 2025 outreach site and also because of some recent signal-type events which caused some notice in-industry. Stanford’s transition from Java to JavaScript for 2017-Spring.

Disclosures
  • I and my cohort learned it “old school.”
  • Today, many IT shop hire for Java and JavaScript skills, which are tested for in the interviews: can the prospect drive the compiler, show the code produced.
  • The transition occurred because [we] “couldn’t hire” C++ people, who where elsewhere in more specialized areas, and because of the effects of the Greater Taylorism in the industry: [we] didn’t need to any more.  JavaScript is good enough for “light programming” and Java for the “heavy coding.”
Editorializing

One can follow the Taylorism on into the future tense as the Function-as-a-Service devops-as-business models.  The lifelong learning, pay-as-you-go tutorials, continuous degree programs and micro-certification are just another aspect of Taylorism.  Why pay for a generalist C++ skill set when one can buy Java skills to suit the purpose? Why buy Java skills when one can get MOOC-certified JavaScript? Why buy programming expertise at all when Excel light skills will suit the purpose?  Why buy Excel when Google Sheets is “free” and in your browser right now? There are answers to these conundrums, but organizations do develop differently depending upon how they view the questions and evolve in path dependence from the answers they choose.

Referenced

in archaeological order…

 

Stanford PDV 91 — How to Think Like a Futurist: Improve Your Powers of Imagination, Invention, and Capacity for Change

Signup

Syllabus

References

  • Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, ISBN:978-0143110378, paperback: 2017-06-06.
    Required.
  • Jane McGonigal, Reality is Broken, ISBN:978-0143120612,
    Recommended.
  • Rebecca Solnit, Hope in the Dark: Untold Histories, Wild Possibilities, ISBN 1608465764,
    Recommended.

First Assignment

<quote>A favorite saying of futurists is: “Get there early.” As futurists, we think about things long before they start to happen. Since our first class meeting is still in the future, this is the perfect opportunity for you to start getting there early.

Before our first class, please read the following two essays:

You’ll notice that our syllabus includes quotes throughout for inspiration and provocation. After you’ve read these two essays, please send me an email with the one sentence from each essay that stood out to you. (That is, please send Prof. McGonigal your favorite quote from each essay.) Prof. McGonigal will collect and share these on the course website. The email address is on The Internet.</quote>

Previously filled.

Tiger Cub Strikes Back: Memoirs of an Ex-Child Prodigy About Legal Education and Parenting | Peter H. Huang

Peter H. Huang (University of Colorado Law School); Tiger Cub Strikes Back: Memoirs of an Ex-Child Prodigy About Legal Education and Parenting; In 1 British Journal of American Legal Studies 297 (2012); 2011-11-11; 51 pages; ssrn:1958366.

tl;dr → starts with a diversity theme, moves on to cultural misunderstandings of the immigrant experience and then it’s just straight out growing up and coming-of-age and launch into adult life.  Wholly within the isolated world of academics & educators.

Abstract

Available at SSRN

I am a Chinese American who at 14 enrolled at Princeton and at 17 began my applied mathematics Ph.D. at Harvard. I was a first-year law student at the University of Chicago before transferring to Stanford, preferring the latter’s pedagogical culture. This Article offers a complementary account to Amy Chua’s parenting memoir. The Article discusses how mainstream legal education and tiger parenting are similar and how they can be improved by fostering life-long learning about character strengths, emotions, and ethics.

From the paper

I am a Chinese American who at 14 enrolled at Princeton and at 17 began my applied mathematics Ph.D. at Harvard. I was a first-year law student at the University of Chicago before transferring to Stanford, preferring the latter’s pedagogical culture. This Article offers a complementary account to Amy Chua’s parenting memoir. The Article discusses how mainstream legal education and tiger parenting are similar and how they can be improved by fostering life-long learning about character strengths, emotions, and ethics. I also recount how a senior professor at the University of Pennsylvania law school claimed to have gamed the U.S. News & World Report law school rankings.

Responsive to

Amy Chua; Battle Hymn of the Tiger Mother; Penguin Books; 2011; 258 pages; kindle: $10, paper, $0.01+SHT.

Mentions

  • Amy Chua
  • tiger mom
  • Madeline Levine
  • Martin Seligman
    • founded positive psychology
    • defined flourishing
      requires five items (PERMA)

      1. Positive Emotion
      2. Engagement
      3. Positive Relationships
      4. Meaning
      5. Accomplishment
  • Judgement & Decision-Making (JDM)
  • cognitive intelligence
  • Scholastic Aptitude Test (SAT)
  • Law School Admissions Test (LSAT)
  • Assertions (assumptions of the article, the thesis of the article)
    1. JDM is required for success.
    2. JDM is [the set of] skills of emotion, emotional intelligence.
    3. <quote>education concerning and life-long practice of cultivating one’s character strengths, ethics, and professionalism are crucial to achieving happiess and satisfaction in school, work, and life.</quote>
  • Multi-state Professional Responsibility Examination (MPRE)
  • American Bar Association (ABA), Model Code of Judicial Conduct.
  • Science, Technology, Engineering, Mathematics (STEM)
  • Cites Star Trek, page 22.
    • Star Trek; the original series, Season 1; 1966.
    • Star Trek: The Devil in the Dark; NBC; originally broadcast 1967-03-09.
  • lots of personal antecdotes

References

Selected.

  • Paper Tigers; Wesley Yang; In New York Magazine; 2011-05-08.
    Teaser: What happens to all the Asian-American overachievers when the test-taking ends?
  • Mark R. Lepper & David Greene, Undermining Children’s Intrinsic Interest with Extrinsic Reward: A Test of the “Overjustification” Hypothesis, 28 J. PERSONALITY & SOC. PSYCHOL. 129 (1973).
  • Mark R. Lepper, David Greene editors., THE HIDDEN COSTS OF REWARD: NEW PERSPECTIVES ON THE PSYCHOLOGY OF HUMAN MOTIVATION, 1978
  • Edward L. Deci, Richard M. Ryan, The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior, 11 PSYCHOL. INQUIRY 227 (2000)
  • Ben Dean, Learning about Learning; In Some Blog entitled AUTHENTIC HAPPINESS; circa 2004,
    tl;dr → defining love of learning, explaining its benefits, and how to develop and nourish it.
  •  Race to Nowhere, a movie

Via: backfill.

Diffusion of Innovations | Everett M. Rogers

Everett M. Rogers; Diffusion of Innovations, 5th Edition Paperback; Free Press; 5th Edition; 2003-08-16; 576 pages; kindle: $25, paper: $13+SHT; earlier editions kindle: $24, paper: $0.01+SHT.

Table of Contents

  1. Elements of diffusion.
  2. A history of diffusion research.
  3. The generation of innovations.
  4. The Innovation-decision process.
  5. Attributes of innovations.
  6. Innovativeness and adapter categories.
  7. Diffusion networks.
  8. The change agent.
  9. Innovation in organizations.
  10. Consequences of innovations.

Mentions

Individual Decision Life Cycle Model

  1. Knowledge
    … of the innovation.
  2. Persuasion
    i.e. forming a favorable or unfavorable attitude toward it.
  3. Decision
    to accept or reject.
  4. Implementation
    … of the innovation.
  5. Confirmation
    i.e. seeking reinforcement of the decision from others.

Mass Adoption Life Cycle Model

  1. innovators,
  2. early adopters,
  3. early majority,
  4. late majority,
  5. laggards.

Review

Via: backfill