quantified self ideology: personal data becomes big data
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A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.TRANSCRIPT
7 February 2014Université Paris Descartes, Paris FranceSlides: http://slideshare.net/LaBlogga
Melanie [email protected]
Quantified Self Ideology: Personal Data becomes Big Data
7 February 2014QS Big Data 2
About Melanie Swan Founder DIYgenomics, science and technology
innovator and philosopher Singularity University Instructor, IEET Affiliate
Scholar, EDGE Contributor Education: MBA Finance, Wharton; BA
French/Economics, Georgetown Univ Work experience: Fidelity, JP Morgan, iPass,
RHK/Ovum, Arthur Andersen Sample publications:
Source: http://melanieswan.com/publications.htm
Kido T, Kawashima M, Nishino S, Swan M, Kamatani N, Butte AJ. Systematic Evaluation of Personal Genome Services for Japanese Individuals. Nature: Journal of Human Genetics 2013, 58, 734-741.
Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
Swan, M. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012, 1(3), 217-253. Swan, M. Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen. J Pers Med 2012, 2(3), 93-118.
Swan, M. Steady advance of stem cell therapies. Rejuvenation Res 2011, Dec;14(6):699-704. Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet Med 2010,
May;12(5):279-88.
7 February 2014QS Big Data
Progress of TechnoHuman Evolution
3
7 February 2014QS Big Data 4
Data
Big Data!
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Inspired by: Average is Over, Tyler Cowen, 2013: Decline of knowledge worker jobs due to machine intelligence more efficiently performing 75% of tasks; optimal mix is 75% machine + 5% human
Human’s Role in the World is Changing
7 February 2014QS Big Data
Conceptualizing Big Data Categories
6
Personal Data
Group Data
Tension: Individual vs Institution
Sense of data belonging to a group
Open Data
7 February 2014QS Big Data
Agenda Personal Data
Quantified Self Quantified Self and Big Data Advanced QS Concepts
Group Data Urban Data
Conclusion
7
7 February 2014QS Big Data
What is the Quantified Self?
8
Individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information
Data acquisition through technology: wearable sensors, mobile apps, software interfaces, and online communities
Proactive stance: obtain and act on information
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
7 February 2014QS Big Data
Smartring (ElectricFoxy), Electronic tattoos (mc10), $1 blood API (Sano Intelligence), Continuous Monitors (Medtronic)
9
Smartphone, Fitbit, Smartwatch (Pebble), Electronic T-shirt (Carre), Scanaflo Urinalysis1
QS Sensor Mania! Wearable Electronics
Source: Swan, M. Sensor Mania! J Sens Actuator Netw 2012.1Glucose, protein, leukocytes, nitrates, blood, bilirubin, urobilinogen, specific gravity, and pH urinalysis
Increasingly continuous and automated data collection
7 February 2014QS Big Data
Wearables: a Platform and an Ecosystem
10
Smart Gadgetry Creates Continuous Personal Information Climate
PC/Tablet/Cloud
SmartphoneNew Wearable Platforms: Smartwatch, AR/Glass, Contacts
AR = Augmented Reality
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Miniaturization: BioSensor Electronic Tattoos
11Source: http://www.jacobsschool.ucsd.edu/pulse/winter2013/page3.shtml#tattoos
Electrochemical Sensors
Tactile Intelligence:Haptic Data Glove
Chemical SensorsDisposable Electronics
Wearable Electronics: Detect External BioChemical Threats and Track Internal Vital Signs
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Quantified Self Worldwide Community Goal: personalized knowledge through
quantified self-tracking ‘Show n tell’ meetups
What did you do? How did you do it? What did you learn?
12Source: Swan, M. Overview of Crowdsourced Health Research Studies. 2012.
Videos, Conferences, Meetup Groups
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Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/
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Quantified Self Project Examples
Low-cost home-administered blood, urine, saliva tests
OrSense continuous non-invasive glucose monitoring
Cholestech LDX home cholesterol test
ZRT Labs dried blood spot tests
Food consumption (1 yr)1 and the Butter Mind study2
Study
1Source: http://flowingdata.com/2011/06/29/a-year-of-food-consumption-visualized2Source: http://quantifiedself.com/2011/01/results-of-the-buttermind-experiment
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Quantified Self Measurements…
151METs = Metabolic equivalents Source: http://measuredme.com/2012/10/building-that-perfect-quantified-self-app-notes-to-developers-and-qs-community-html/
Physical Activities Miles, steps, calories, repetitions, sets, METs1
Diet and Nutrition Calories consumed, carbs, fat, protein, specific ingredients, glycemic index,
satiety, portions, supplement doses, tastiness, cost, location Psychological, Mental, and Cognitive States and Traits
Mood, happiness, irritation, emotion, anxiety, esteem, depression, confidence IQ, alertness, focus, selective/sustained/divided attention, reaction, memory,
verbal fluency, patience, creativity, reasoning, psychomotor vigilance Environmental Variables
Location, architecture, weather, noise, pollution, clutter, light, season Situational Variables
Context, situation, gratification of situation, time of day, day of week Social Variables
Influence, trust, charisma, karma, current role/status in the group or social network
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The Quantified Self is Mainstream
16
Self-tracking statistics (Pew Research Center) 60% US adults track weight, diet, or exercise 33% US adults monitor blood sugar, blood pressure,
headaches, or sleep patterns 9% receive text message health alerts 40,000 smartphone health applications
QS thought leadership Press : BBC, Forbes, and Vanity Fair Electronics show focus at CES 2013 Health 2.0: “500+ companies making self-management tools; VC funding up 20%”
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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QS Experimentation Motivation and Features
17Source: DIYgenomics Knowledge Generation through Self-Experimentation Study http://genomera.com/studies/knowledge-generation-through-self-experimentation
DIYgenomics QS Study (n=37)
Desired outcome: optimality and improvement (vs pathology resolution) Personalized intervention for depression,
low energy, sleep quality, productivity, and cognitive alertness
Rapid experimental iteration through solutions and kinds of solutions
Resolution point found within weeks Pragmatic problem-solving focus, little
introspection
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Source: http://www.DIYgenomics.orghttp://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking
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History of the Quantified Self
19
Sanctorius of Padua 16th c: energy expenditure in living systems; 30 years of QS weight/food data
QS Philosophers Epicureans, Heidegger, Foucault): ‘care
of the self’ ‘Self’: recent concept of modernity
QS: contemporary formalization using measurement, science, and technology to bring order and control to the natural world, including the human body
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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Sensor Mania! QS Gadgetry Trend
20Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
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Wireless Internet-of-Things (IOT)
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
Image credit: Cisco
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6 bn Current IOT devices to double by 2016
22Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T
3 year doubling cycle
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IOT World of Smart Matter IOT Definition: digital networks of
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
231Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule
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IOT Contributing to Explosion of Big Data Big Data definition: data sets too
large and complex to process with on-hand database management tools (volume, velocity, variety)
Examples Walmart : 1 million transactions/hr
transmitted to 3 PB database BBC: 7 PB video served/month from
100 PB physical disk space Structured and unstructured data Big data is not smart data
Discarded, irretrievable
24Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
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Networked Sensing – New Topology
25
Machine:MachineVL Sensor Networks
Internet of Things6LoWPANS
Human:HumanTelephone System
(POTS)
Human:Machine Machine:MachineInternet ProtocolPacket Switching
Unprecedented Scale Requires New Communications Protocols
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Basis for Networked Sensing Protocols
26
Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic
Biomimicry, Synthetic BiologyFish, Hive, Swarm
Turbulence, Chaos, Perturbation
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Annual data creation in zettabytes (10007 bytes) 90% of the world’s data created in the last 2 years Sectors: personal, corporate, government, scientific
Defining Trend of Current Era: Big Data
Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trendshttp://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf
2 year doubling cycle
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Typical Big Data Problems
Perform sentiment analysis on 12 terabytes of daily Tweets
Predict power consumption from 350 billion annual meter readings
Identify potential fraud in a business’s 5 million daily transactions
28http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
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QS is inherently a Big Data problem
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Data collection, processing, analysis Cloud computing for consumer processing
Local computing tools are not available to store, query, and manipulate QS data sets
Cloud-based analysis: Predictive modeling, natural-language processing, machine learning algorithms over very-large data sets of heterogeneous data
Rapid growth in QS data sets Manually-tracked ‘small data’ is now
automatically-collected ‘big data’ Excel -> Hadoop Macros -> MapReduce/Mahout
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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QS Big Data ChallengePredictive Cardiac Risk Monitoring
30Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
Heart rate monitor sampling 250 times per second 9 gigabytes of data per person per month
Cardiac events can be predicted two weeks ahead of time
Phase I: Collect, store, process, analyze data Compression and search algorithms Identify event triggers
Phase II Predict and intervene with low false-positives
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QS Big Data: Personal Health ‘Omics’
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DNA: SNP mutations
Microbiomics
Proteomics
RNA expression profiling
Epigenetics
Health 2.0:Personal Health
InformaticsDNA: Structural
variation
Metabolomics
Source: Academic papers re: integrated health data streams: Auffray C, et al. Looking back at genomic medicine in 2011. Genome Med. 2012 Jan 30;4(1):9. Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012 Mar 16;148(6):1293-307.
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QS Big Data: Personal Information Streams
Genome: SNP mutations
Structural variationEpigenetics
Microbiome
Transcriptome
Environmentome
Metabolome
Diseasome
Proteome
Personal and Family Health
History
Prescription History
Lab Tests: History and Current
Demographic Data
Self-reported data: health, exercise,
food, mood journals, etc.
Biosensor Data Objective Metrics
Quantified Self Device Data
Mobile App Data
Quantified SelfTraditional‘Omics’
Standardized Questionnaires
Legend: Consumer-available
32
Personal Robotics
Smart Car
Smart Home
Environmental Sensors
Internet-of-Things
Community Data
32Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature: Journal of Human Genetics (2013) 58, 734–741.
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APIs and Multi-QS Data Stream Integration
33
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Fluxstream Unified QS Dashboard
34Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/
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Sen.se Integrated QS Dashboard
35Source: http://blog.sen.se/post/19174708614/mashups-turning-your-data-into-something-useable-and
‘Mulitviz’ display: investigate correlation between coffee consumption, social interaction, and mood
7 February 2014QS Big Data
Wholly different concept and relation to data Formerly everything signal, now 99% noise Medium of big data opens up new methods: Exception, characterization, variability, pattern recognition,
correlation, prediction, early warnings Big Data causality is ‘quantum mechanical’ Allows attitudinal shift to active from reactive Two-way communication: biometric variability in the
translates to to real-time recommendations Example: degradation in sleep quality and hemoglobin A1C
levels predict diabetes onset by 10 years1
361Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
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Big Data opens up new Methods Google: large corpora and simple algorithms Foundational characterization (previously unavailable)
Longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly, and emergent phenomena
New kinds of Pattern Recognition (different structures) Analyze data in multiple paradigms: time, frequency, episode, cycle,
and systemic variables (transaction, experience, behavior) New trends, cyclicality, episodic triggers, and other elements that
are not clear in traditional time-linear data Multi-disciplinarity
Turbulence, topology, chaos, complexity, etc. models
37Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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Opportunity: QS Data Commons Common repository for personal informatics
data streams Fitbit, Jawbone UP, Nike, Withings, myZeo,
23andMe, Glass, Pebble, Basis, BodyMedia Architecting consumer-friendly models
Open-access databases, developer APIs, front-end web services and mobile apps
(Precedent: public genotype/phenotype data) Accommodate multi-tier privacy standards Ecosystem value propositions: service providers,
research community, biometric data-owners Role of public and private service providers
38Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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Github: de facto QS Data Commons
39Source: https://github.com/beaugunderson/genome
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QS Frontier: Mental Performance Optimization
40
‘Siri 2.0’ Personal Virtual Coach from DIYgenomics
Sources: http://cbits.northwestern.edu and http://quantifiedself.com/2009/03/a-few-weeks-ago-i
Source: DIYgenomics Social Intelligence Studyhttp://diygenomics.pbworks.com/w/page/48946791/social_intelligence
PTSD App Mood Management Apps from
Mobilyze and M. Morris
Source: http://www.ptsd.va.gov/public/pages/ptsdcoach.asp
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Next-gen QS Services: Quality of Life
41
QS Aspiration Apps: Happiness, Emotive State (personal and group), Well-being, Goal Achievement
Category and Name Website URLHappiness Tracking Track Your Happiness http://www.trackyourhappiness.org/Mappiness http://www.mappiness.org.uk/The H(app)athon Project http://www.happathon.com/MoodPanda http://moodpanda.com/TechurSelf http://www.techurself.com/urwellEmotion Tracking and SharingGotta Feeling http://gottafeeling.com/Emotish http://emotish.com/Feelytics http://feelytics.me/Expereal http://expereal.com/Population-level Emotion BarometersWe Feel Fine http://wefeelfine.org/moodmap http://themoodmap.co.uk/Pulse of the Nation http://www.ccs.neu.edu/home/amislove/twittermood/Twitter Mood Map
http://www.newscientist.com/blogs/onepercent/2011/09/twitter-reveals-the-worlds-emo-1.html
Wisdom 2.0 http://wisdom2summit.com/Personal Wellbeing PlatformsGravityEight http://www.gravityeight.com/MindBloom https://www.mindbloom.com/Get Some Headspace http://www.getsomeheadspace.com/Curious http://wearecurio.us/uGooder http://www.ugooder.com/Goal Achievement PlatformsuMotif http://www.uMotif.com/DidThis http://blog.didthis.com/Schemer https://www.schemer.com/ (personalized recommendations)Pledge/Incentive-Based Goal Achievement PlatformsGymPact http://www.gym-pact.com/Stick http://www.stickk.com/ Beeminder https://www.beeminder.com/
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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Next-gen QS Services: Behavior Change
42Source: http://askmeevery.com/
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Next-gen QS Services: Behavior Change Shikake: Sensors embedded
in physical objects to trigger a physical or psychological behavior change
Examples: Transparent trash cans Trash cans playing an
appreciative sound to encourage litter to be deposited
Stairs light up on approach Appreciative ping/noise from
QS gadgetry
43Source: http://mtmr.jp/en/papers/taai2013v2.pdf
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Next-gen QS Services: 3D Quantification
44
BodyMetrics and Poikos: Fitness and Clothing Customization Apps
OMsignal: Smart Apparel 24/7 Biometric Monitoring
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Sense of ourselves as information generators in constant dialogue with the pervasive information climate
Subject and environment co-create (Baudelaire’s detached flâneur observing the
modern city); now data is the co-producing environment
Subjectivation: The TechnoBioCitizen
Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
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Magnetic Sense: Finger and Arm Magnets
North Paw Haptic Compass Anklet and Heart Sparkhttp://www.youtube.com/watch?v=D4shfNufqSg
http://sensebridge.net/projects/heart-spark
Extending our senses in new ways to perceive data as sensation
Serendipitous Joy: Smile-triggered EMG muscle sensor with an LED headband display
Building Exosenses for the Qualified Self
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
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Exosenses: Quantified Intermediates Networked quantified intermediates for
human senses: smarter, visible, sharable through big data processing
Vague sense of heart rate variability, blood pressure; haptically-available exosenses make the data explicit
Haptics, audio, visual, taste, olfactory mechanisms to make metrics explicit: heart rate variability, blood pressure, galvanic skin response, stress level
Skill as exosense: technology as memory, self-experimentation as a form of exosense
47
Gut-on-a-chip
Lung-on-a-chip
Source: web.mit.edu/newsoffice/2012/human-body-on-a-chip-research-funding-0724.html
Nose-on-a-chip
Chip-on-a-Ring
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QS Big Data Frontier: Neural Tracking
24/7 Consumer EEG, Eye-tracking, Emotion-Mapping, Augmented Reality Glasses
48
Consumer EEG Rigs
1.0
2.0
Augmented Reality Glasses
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
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QS Big Data Frontier: DIYneuroscience
49http://www.diygenomics.org/files/DIYneuroscience.pdfhttps://www.facebook.com/DIYneuroscience
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QS Big Data: Biocitizen is Locus of Action
Individual
2. Peer collaboration and health advisors
Health social networks, crowdsourced studies, health advisors, wellness coaches, preventive care plans,
boutique physicians, genetics coaches, aestheticians, medical tourism
3. Public health systemDeep expertise of traditional health system
for disease and trauma treatment
1. Continuous health information climate Automated digital health monitoring, self-tracking devices, and mobile apps providing personalized recommendations
Source: Extended from Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009, 2, 492-525.
7 February 2014QS Big Data
Conceptualizing Big Data Categories
51
Personal Data
Group Data
Tension: Individual vs Institution
Sense of data belonging to a group
Open Data
7 February 2014QS Big Data
Agenda Personal Data
Quantified Self Quantified Self and Big Data Advanced QS Concepts
Group Data Urban Data
Conclusion
52
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Group Data: Smart City, Future City
Image: http://www.sydmead.com
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Global Population: Growing and Aging
54Source: UN Habitat – 2010http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html
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3 billion new Internet users by 2020
55Source: Peter Diamandis Singularity University
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Over 50% worldwide population in 2008 5 billion in 2030 (estimated) Megacity: (>10 million and possibly 2,000/km2)
Human Urbanization: Living in Cities
56
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Megacity Growth Rates
Source: Wikipedia
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Big Urban Data: Killer Apps
58Source: Copenhagen Pollution Levels, MIT Senseable City Lab
Public transit, traffic management, eTolls, parking, adaptive lighting, smart waste, pest control, hygiene management, asset tracking, smart power grid
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Data Signature of Humanity
59Source: http://senseable.mit.edu/signature-of-humanity/
MIT SENSEable City Lab – the Real-Time City
Flexible services responding predictively to individual and community-level demand (ex: pedestrian load)
7 February 2014QS Big Data
Urban Data: 3D Buildings + Population Density
60Source: ViziCities
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3D Tweet Landscape, ODI Chips
61Source: http://vimeo.com/67872925http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl
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3D Urban Data Viz: Decision-making Tool
62Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/
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Group Data: Office Building Community
63Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010
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Himalayas Water Tower
Big Data 3D Printed Dwellings of the Future
Living Treehouses – Mitchell Joachim
Masdar, Abu Dhabi – Energy City of the Future
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Agricultural Innovation: Vertical Farms, Tissue-Engineered Meat
65
San Diego, California (planned)
Singapore (existing)
Modern Meadow (existing)1
1Source: http://www.popsci.com/article/science/can-artificial-meat-save-world
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Reconfiguration of Space: Seasteading
66
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Transportation Revolution
67
Solar Power: Tesla + Solar City
Self-Driving CarPersonalized Pod Transport
Source: Google's Self-Driving Cars Complete 300K Miles Without Accident, Deemed Ready for Commutinghttp://techcrunch.com/2012/08/07/google-cars-300000-miles-without-accident/
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Another Pervasive Trend: Crowdsourcing
68Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw
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Crowd Models Extend to all Sectors Crowdsourcing: coordination of large numbers of
individuals (the crowd) through an open call on the Internet in the conduct of some sort of activity Economics: crowdsourced labor marketplaces, crowdfunding,
grouppurchasing, data competition (Kaggle) Politics: flashmobs, organizing, opinion-shifting, data-mining Social: blogs, social networks, meetup, online dating Art & Entertainment: virtual reality, multiplayer games Education: MOOCs (massively open online courses) Health: health social networks, digital health experimentation
communities, quantified self Digital public goods: Wikipedia, online health databanks, data
commons resources, crowdscience competitions
69
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Agenda Personal Data
Quantified Self Quantified Self and Big Data Advanced QS Concepts
Group Data Urban Data
Conclusion
70
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But wait…Limitations and Risks Transition to access not ownership models Data rights and responsibilities
Personal data and group data Regulatory and policy tensions
Surveillance (top-down) vs souveillance (bottom-up) Multi-tier privacy and sharing preferences Digital divide accessibility, non-discrimination
Precedent: Uninformed consumer with lack of access (e.g.; health data, genomics, credit scoring)
Consumer non-adoption, ease-of-use, social acceptance, value propositions, financial incentive
71
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Increasingly a Foucauldian surveillance society Downside: NSA surveillance of citizens sans recourse Upside: continual biomonitoring for preventive medicine
Mindset shifts and societal maturation Honesty about true desires (Deleuze’s desiring production) Reduce shame: needs tend to be singular not individual Wikipedia (1% open participation, 99% benefits) Radical openness
Evolving Shape of #1 Concern: Privacy
72
Privacy
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Proliferation of New QS Big Data Flows
QS Device Data Biometric data (HRM), personal genomic data Personal medical and health data QS neural-tracking, eye-tracking, affect data
Personal IOT Data Cell phone, wearable electronics data Smartphone digital identity & payment
Personal Urban Data Smart home, smart car Smart city data (e.g.; transportation)
Personal Robotics Data
73
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Top 10 QS Big Data Trends
Internet-of-Things (IOT)Sensor Networks
3 billion New People Online
3D Information Visualization
Megacity Growth
Smart CityFuture City
QS Device Ecosystem
Crowdsourcing
Self-Empowerment DIY Attitude
74
Wearable Electronics
Urban DataBiocitizen
Personal Data
Group Data
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QS Big Data Summary Next-gen QS services
Wearable Electronics as the QS platform Improve quality of life, facilitate behavior change
IOT continuous personal information climates QS Big Data
Wholly different relation to data: 99% noise Rights and responsibilities model of data access
Group Data Megacity growth, urban data flow, 3 bn coming online
Personal Data Technology-enabled biocitizen self-produces in the data
environment and takes action
75
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The Philosophy of Big DataCentrally about our relation to technology:
Our attunement to technology as an enabling background helps us see the
possibilities for the true meaningfulness of our being - Heidegger
Source: Heidegger, M. The Question Concerning Technology, 1954; Derrida, J. Paper Machine, 2005
The thinking of the event (organic, singular) is joined to the thinking of the machine (inorganic,
repetition), where the new logic is the virtualization of the event by the machine, a virtuality that
extends the classical opposition of the possible and the impossible - Derrida
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Apply philosophical principles to modern technology
Technology Futures Institute
77
http://melanieswan.com/TFI.html
OntologyExistence
Subjectivation
Ethics
AestheticsValorization
Meaning-making
Reality
Language
Big Data
Wearables
Surveillance Society
Synthetic Biology
Bioart
Biohacking
NanoCognition
Cognitive Enhancement
3D Printing
http://melanieswan.com/TFI.html
7 February 2014QS Big Data
Technology Futures Institute Mission: use philosophy to improve the rigor of our thinking
about science and technology Sample Projects
Ethics of Perception in Nanocognition – Perception is a feature (Glass, electronic contacts, nanorobotic cognitive aids), not an evolutionary given, therefore how do we want to perceive
Neural Data Privacy Rights – Rethinking ethics for neuro-sensing Digital Art and Philosophy – Integration of science/technology,
aesthetics, and meaning-making in complex human endeavor A Critical Theory of BioArt – How artists appropriating biological
materials and practices to create art is or is not art Conceptualizing Big Data – How big data is remaking our world Live Philosophy Workshop – Hands on concept generation
Services Strategic Collaborations, Research Papers, Articles Speaking engagements, Workshops, Classes, Conferences Philosophy Studies: Epistemology1, Subjective Experience2
781http://genomera.com/studies/knowledge-generation-through-self-experimentation2http://genomera.com/studies/subjective-experience-citizen-qualia-study
http://melanieswan.com/TFI.html
7 February 2014Université Paris Descartes, Paris FranceSlides: http://slideshare.net/LaBlogga
Melanie [email protected]
Merci!Questions?
Quantified Self Ideology: Personal Data becomes Big Data