Mobile 2.0 (Web 3.0) | Benedict Evans

Benedict (Ben) Evans (A16z); Mobile 2.0 (not Web 3.0); In His Blog; 2017-01-31 (one year ago).

tl;dr → A paean. “The mobile” is the Bee’s Knees!
and → The signal is given: a new S-Curve is commencing.
and → The unbunding / rebundling / unbundling / rebundling cycle, a metaphor of growth-cum-renewal.

Mentions

Scope

  • Amazon
    • They sell stuff, which is purchased by “the mobile” (demographic)
    • Alexa
    • Echo
  • Apple
    • AirPods
    • They make The Phones, which are “the mobile” (a totem of the demographic)
  • Facebook
    • is “the browser”
    • Instant Articles
  • Google
    • Accelerated Mobile Pages (AMP)
    • YouTube, is “the video”
  • Snapchat
    • <rly?>
    • Spectacles

Historiography

Web 1.0
1994, Netscape launched
Web 2.0
2004, Tim O’Reilly, broadband, Yahoo, Flickr, “the social”
Mobile 1.0
2007, the iPhone launch.
Web 3.0 Mobile 2.0
“The Web” is dead; the Wintel device & it’s browser is not relevant any more.
Ah Bullion Yoosers, On. Smart. Phones.
WiFi, The ‘Gs, 3 & 4, LTE, High-DPI screens, 100x GPUs, Sensors (cameras). Cameras

Unaccounted

Missing in the Analysis
  • How is it that The Telecoms will allow this?
    Whereas

    • The Phone is sold through their financing apparatus.
    • The Phone operates on spectrum owned by them.
    • The Phone engenders a billable event every month.
  • Are The Telecoms so inept that they cannot (re-)capture the consumer?
    • Constrained by The Regulation?
    • Mired in The Buraucracy?
    • The corporate DNA (as a metaphor for corporate culture) of Old Monopoly Ma Bell disallows new modes of thinking?
    • They are disinterested?
  • Maybe with 5G!

Free Association

  • Computers with eyes
  • Combining the camera with touch
  • Video (the sight, sound & motion)
  • <alert><cliche>Consuming content; Deeper engagement.<cliche></alert>
  • <quote>Video is the new HTML, or the new Flash.<quote>
  • Facebook, is “the browser.”
  • YouTube, is “the video.”
  • sides of the brain.
  • “unbundle” the phone.
  • a cluster of little devices that orbit the (smart) phone.
  • Alexa == Spectacles
    <quote>create a new, independent end-point for the cloud that they can own themselves.</quote>
  • Augmented Reality (AR)
  • multi-touch
  • machine learning
  • computer vision
  • voice recognition
  • WOW!

Parable

Business Decision-Making from Folkloric Reasoning on the The Mythological Paradigms

<quote>Alternatively, you can look at this as part of the way that tech swings from bundling to unbundling:

  • AOL bundled content
  • [T]he [W]eb unbundled AOL
  • Google bundled the web,
  • [A]pps unbundled properties from [T]he [B]rowser,
    but also bundled each site into a single icon,
  • and now these platforms form new bundles.

The pendulum will swing back the other way again, at some point. And in parallel, one could argue that Snapchat itself unbundled not ‘photos’ but fun and self-expression from Facebook.</quote>

Estimation

  • Something about app stores and social distribution.
    Except for China, which is different.
  • App distribution requires owning an app store
  • Content distribution is by the linkbait techniques

 

What Happens When Biases Are Inadvertently Baked Into Algorithms | The Atlantic

When Discrimination Is Baked Into Algorithms; Lauren Kirchner; In The Atlantic; 2015-09-05.
Teaser: As more companies and services use data to target individuals, those analytics could inadvertently amplify bias.

tl;dr → maybe nothing, maybe legal action; lots of activists are looking into it.

Responsive To

The Tiger Mom Tax: Asians Are Nearly Twice as Likely to Get a Higher Price from Princeton Review; Julia Angwin, Surya Mattu, Jeff Larson; In ProPublica; 2015-09-01.
tl;dr → The Princeton Review charges Asians a higher prices for SAT tutoriing, frequently enough for the reporters to measure by the methodology.

Mentions

Quoted

for color, background & verisimilitude

  • Sorelle Friedler
    • postgrad, computer science, Haverford College
    • fellow, Data & Society,

How Social Bias Creeps Into Web Technology | WSJ

How Social Bias Creeps Into Web Technology; Elizabeth Dwoskin; In The Wall Street Journal (WSJ); 2015-08-20.

Mentions

  • Offenses (exemplars)
    • Embarassment over machine-learning categorization failures.
      Photo labeling faliures:

      • African-ancestry person labeled as “apes”
      • African-ancestry person labeled as “gorilla”
      • Concentration camp labeled as “jungle gym”

      Ad targeting

      • Different ads shown to boys and girls.
      • The boys where shown “better” ads.
  • Perpetrators
    • Flickr, Yahoo, 2015-05.
    • Google, 2015-06.
  • Activists
    • Carnegie Mellon University
    • Andrew Selbst
    • Solon Barocas
    • Web Transparency and Accountability Project, Princeton University
  • Solon Barocas (Princeton University), Andrew D. Selbst (U.S. Court of Appeals); Big Data’s Disparate Impact; California Law Review, Vol. 104, 2016 (to appear); 62 pages; SSRN; separately filled.
  • Quoted
    for color, background & verisimilitude
    • Vivienne Ming, CTO, Guild Inc.; expert.
    • Adeyemi Ajao, vice president of technology strategy, Workday.
    • Andrew Selbst, U.S. Court of Appeals Third Circuit.
    • Paul Viola, ex-staff, Massachusetts Institute of Technology (MIT).
    • T.M. Ravi, co-founder and director, Hive (an incubator)
  • Remediations, vignette
    • <quote>Xerox Corp., for example, quit looking at job applicants’ commuting time even though software showed that customer-service employees with the shortest commutes were likely to keep their jobs at Xerox longer. Xerox managers ultimately decided that the information could put applicants from minority neighborhoods at a disadvantage in the hiring process.</quote>, uncited.

Why Images on the Web Are a Data Blindspot and Potential Gold Mine | Advertising Age

Why Images on the Web Are a Data Blindspot and Potential Gold Mine; ; In Advertising Age; 2014-10-28.
Teaser: Tech Firms Look to Unlock Visual Web’s ‘Black Box’ for Brand Advertising

Mentions

  • Category: internet entertainment companies that own image processing technology
    • Facebook
    • Google
    • Yahoo (Flickr)
  • Category: boutiques & specialty shops
  • Category: wish they had image processing capability
  • No Comment
    • Google
    • Pinterest
    • Twitter
  • Quoted
    • Justin Fuisz, CEO of Fuisz Media.
    • Alex James, director of research, Yahoo.
    • Doug Kofoid, president-global solutions, Publicis Groupe’s VivaKi.
    • David Rose., CEO, Ditto Labs.
    • Kate Sirkin, global research lead, Starcom MediaVest Group.
    • Ophir Tanz, CEO of GumGum.
    • Donald Williams, Chief Digital Officer, Horizon Media.

The Fourth Revolution: How the infosphere is reshaping human reality | Luciano Floridi

Luciano Floridi; The Fourth Revolution: How the infosphere is reshaping human reality; Oxford Unniversity Press, 1st edition; 2014-09-01; 272 pages; kindle: $10, paper: $22.

Luciano Floridi is at least

  • Professor of Philosophy and Ethics of Information at the University of Oxford,
  • Senior Research Fellow at the Oxford Internet Institute,
  • Fellow of St Cross College, Oxford.

Promotion

On the usage of Sensor Pattern Noise for Picture-to-Identity linking through social network accounts | Satta, Stirparo

Riccardo Satta, Pasquale Stirparo; On the usage of Sensor Pattern Noise for Picture-to-Identity linking through social network accounts; In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014); 2014; 7 pages; landing; notes.

Abstract

Digital imaging devices have gained an important role in everyone’s life, due to a continuously decreasing price, and of the growing interest on photo sharing through social networks. As a result of the above facts, everyone continuously leaves visual “traces” of his/her presence and life on the Internet, that can constitute precious data for forensic investigators. Digital Image Forensics is the task of analysing such digital images for collecting evidences. In this field, the recent introduction of techniques able to extract a unique ‘fingerprint’ of the source camera of a picture, e.g. based on the Sensor Pattern Noise (SPN), has set the way for a series of useful tools for the forensic investigator. In this paper, we propose a novel usage of SPN, to find social network accounts belonging to a certain person of interest, who has shot a given photo. This task, that we name Picture-to-Identity linking, can be useful in a variety of forensic cases, e.g., finding stolen camera devices, cyber-bullying, or on-line child abuse. We experimentally test a method for Picture-to-Identity linking on a benchmark data set of publicly accessible social network accounts collected from the Internet. We report promising result, which show that such technique has a practical value for forensic practitioners.