Course support for the DIA module "Working in the DIgital Age"

1. ©2013 LHST sarl
The Quantified Self
January 16 2017
Introduction
©2016 L. SCHLENKER
Shots of Awe
Working in the Digital Age
http://Dsign4.biz

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©2014 L. SCHLENKER
Introduction
©2016 L. SCHLENKER

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•Data mediates the experience of
reality.
•Quantimetric self-tracking and
wearable computers
•Quantimetric self-sensing
•Gary Wolf - the Quantified Self
Early prototype of "Quantimetric Self-Sensing"
apparatus, 1996
©2016 L. SCHLENKER
Introduction

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• Data on Inputs, perceptions, outputs
• Self –knowledge – what am I like?
• Cognitive, affective, executive self
• Physical, social and physiological
worlds
• Impact on how experience is encoded
“Organize the world's information and make it
universally accessible and useful”
Introduction
©2016 L. SCHLENKER

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The author suggests that the "Quantified Self"
movement is about self knowledge. What does
he want to know about himself?
Describe one of the applications described in the
article (audience, data sources, interface, use
scenarios, observations).
The article points out the fallacy of "magical
thinking". What does this mean and how does
this this apply to the Quantified Self?
The article concludes that the goal isn't to do
more work, but to do better work. What does
this mean to you?
Richard J. Anderson
©2014 L. SCHLENKER
Spying on Myself
Introduction
©2016 L. SCHLENKER

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• Pythagoras of Samos - number is the key to reality
• Immanuel Kant - reality is comprehensible through
categories of significance (schemata)
• Michel Foucault: - technologies of the Self
• Martin Heidegger - care of the self before care of others
• Timothy Leary – «turn on, tune in, drop out”
• Steve Mann: - Souveillance vs. Surveillance
Introduction
©2016 L. SCHLENKER

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• Management is about taking
decisions
• We don’t need more data – we
need better decisions
• All decisions can be qualtified
and then quantified
• “Complexity" is largely an illusion
caused by poor decision-making
Decision
Making
©2016 L. SCHLENKER

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Why do we take poor decisions?
• The object of measurement (i.e., the
thing being measured) is not
understood.
• The concept or the meaning of
measurement is not understood.
• The methods of measurement are
not well understood
Decision
Making
©2016 L. SCHLENKER

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• What does productivity mean (faster, more
impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
Lewis Mumford, Technics and Civilization
Decision
Making
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• Is sociology an art or a science ?
• Mesaurement is the reduction of
uncertainty through putting a
number on it
• In science , engineering, actuarial
science, economics, - we talk of
putting a number on it
www.google.com/dashboard
©2014 L. SCHLENKER
"Although this may seem a paradox,
all exact science is based on the idea
of approximation” Bertrand Russel
Decision
Making
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• Reducing the number of
potential outcomes is the key
to better decision-making
• Develop unambiguous
definitions and measurement
• What data do I have, Choose
the appropriate measure
• Understand how people react
to the data
www.google.com/dashboard
©2016 L. SCHLENKER
Ask Examples Resources
Is it possible that this may
already have been
researched?
The average cost of IT
training for given type of
user
Go to the
library
(Internet)
Could it be projected from
past experience?
Growth in product demand Research the
market
Does it leave a trail of
some kind?
Current level of customer
retention
Look for the
data
Could it be observed in
real-time?
The amount of time an
equipment operator spends
filling out forms
Unsupervised
learning
Can it be tested? The effect of a new system
on the productivity of a
sales clerk
Supervised
learning
Decision
Making

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• Mobile devices and embedded
sensors can track heart rate, blood
sugar, caloric intake, sleep quality…. .
• Capters can beam data to cloud
databases, which send advice to
consumers
• There is a real need in health-care to
cut down the number of unnecessary
medical visits
• Google has funded 23andMe Inc.,
Fitbit has drawn $43 million from
investment firms
©2014 L. SCHLENKER
Health and Well Being
Application
Areas
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• 8.4% of Americans, or over 25 million
people, suffer from asthma.
• Third-leading cause of death in the US
with $50 billion associated annual
healthcare costs
• Helps researchers pinpoint
environmental triggers and monitor the
population of asthma suffers
• Attaches to an asthma inhaler and logs
the time and geographic location each
time its used
• Uncontrolled asthma declines by 50
percent
http://youtu.be/6CH1IxzmwUs
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Propeller Health Application
Areas
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• People have been keeping checklists and
to do’s for decades
• Ask the right question and then find the
right mix between curiosity and
measurable data
• RescueTime led writer Gina Trapani to
switch to a standing desk and WordPress
creator Matt Mullenweg do impose new
email rules.
•Mint for tracking where every Euro and
cent goes.
•MoodPanda for noting on a simple 1-10
scale how you’re feeling
•PlaceMe, for automated location tracking
system
Personal and Group
Productivity
Application
Areas
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• Uses fashion and IT to create
responsive clothes offering therapeutic
value
•Scents as tools to improve mental and
physical wellbeing
• A localized ‘scent cloud’ is released to
fit specific moods
• Goal is unlock emotional memories and
to complement mood monitoring tools for
the ‘Quantified Self’
Dr Jenny Tillotson
©2014 L. SCHLENKER
Sensory Fashion
Application
Areas
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• Track what you read – when, what, where you
stop, what you highlight, what you annotate
• Track what you write - how many words, how
many pages, what and when you write….
• Track how you learn - who you listen to , what
you say, how you search….
• Technology can enable real-time feedback
•Santa Monica College’s Glass Classroom,
Stanford’s Multimodal Learning Analytics
• Do something with what you discover
http://glassclassroom.blogspot.fr/2012/12/the-glass-
classroom-big-data.html
©2014 L. SCHLENKER
Education Application
Areas

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• Tennis is stats heavy : serve
percentages, forehand winners, aces,
unforced errors
• Give the amateur some way of
assessing his or her game
• Hitting the sweet spot 100 % of the
time doesn’t mean you’ll win
• Data is often misleading, but winning
is often about fractions.
• It does have the potential to change
the way we think about coaching
Babolat Application
Areas
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• Using data for personal meaning
challenge our ideas about human
connection
• Social networks like Facebook and
Twitter transform our social
interactions into quantifiable data
streams
• Social Graph - interactions
between people in a social network
• Is it possible to track emotions,
passions and memories?
• Could QS help us live together in a
sustainable way?
Will our communities be looking after us,
taking care, encouraging us, as well as discipline us?
Joerg Blumtritt
©2016 L. SCHLENKER
Social Interaction Application
Areas

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• Examples
•Walmart : 1 million transactions/hr
•BBC: 7 PB video served/month
• Big Data definition: data sets on social
interactions that are too complex for traditional
DBMS (volume, velocity, variety)
• Little Data : data sets on individual rather
collective behavior
• Structured and unstructured data
Source: Mary Meeker, Internet Trends,
©2014 L. SCHLENKER
Big Data, Little Data Technologies

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• Computing as service rather than a
product
• Focuses on maximizing shared resources
• Public, private or hybrid
• Infrastructure as a service (IaaS)
• Platform as a service (PaaS)
• Software as a service (SaaS)
©2014 L. SCHLENKER
Technologies

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• The idea that certain data should
be freely available to everyone to
use
• Facts cannot legally be
copyrighted, but aggregated data
can be privately owned.
• Journal publication is an implicit
release of the data to the
Commons
• Midata, the UK government’s
initiative to give consumers
access to data about them that is
held by brands
Anja Jentzsch
©2014 L. SCHLENKER
Open Data Technologies

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•The Internet of things: Physical
objects linked by the Internet
that interact through web
services
•Usual gadgetry (e.g.;
smartphones, tablets) and now
everyday objects: cars, food,
clothing, appliances, materials,
parts, buildings, roads
•Embedded microprocessors in
5% human-constructed objects
(2012)1
1Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012.
http://singularitysummit.com/scheduleMelanie Swan
The Internet of Things
Technologies
©2016 L. SCHLENKER

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• Study of abstract data to
improve human cognition
• Lévi-Strauss – the world has
become so complex that we
must “simplify it” to understand
it
•Goal of data visualization is to
communicate information
clearly and efficiently
• Visualization is today a critical
component in scientific
research, data mining, finance,
and market studies
©2014 L. SCHLENKER
Visualisation Technologies

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• Study Richard Anderson’s Spying
On Myself
• Produce one blog post for Monday
on your personal views of:
- What is self knowledge?
- In this vision, who are you?
- What keeps you from taking better
decisions?
- How does “magical thinking” apply
to you?
- In what domain does the
quantified/qualified self make sense?
• Post your blog post and read your
colleagues’