Quantified Self Ideology: Personal Data becomes Big Data

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


Data

Big Data!


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


Conceptualizing Big Data Categories

6

Personal Data

Group Data

Tension: Individual vs Institution

Sense of data belonging to a group

Open Data


Agenda Personal Data

Quantified Self Quantified Self and Big Data Advanced QS Concepts

Group Data Urban Data

Conclusion

7


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.


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


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


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


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


Source: http://www.meetup.com/QSParis/, http://www.meetup.com/ParisGlassUG/


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


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


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%”



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


Source: http://www.DIYgenomics.orghttp://genomera.com/studies/dopamine-genes-and-rapid-reality-adaptation-in-thinking


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



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.


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


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


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


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


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


Basis for Networked Sensing Protocols

26

Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic

Biomimicry, Synthetic BiologyFish, Hive, Swarm

Turbulence, Chaos, Perturbation


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


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


QS is inherently a Big Data problem

29

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



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


QS Big Data: Personal Health ‘Omics’

31

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.


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.


APIs and Multi-QS Data Stream Integration

33


Fluxstream Unified QS Dashboard

34Source: http://johnfass.wordpress.com/2012/09/06/bodytrackfluxtream/


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


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.


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



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



Github: de facto QS Data Commons

39Source: https://github.com/beaugunderson/genome


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


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/



Next-gen QS Services: Behavior Change

42Source: http://askmeevery.com/


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


Next-gen QS Services: 3D Quantification

44

BodyMetrics and Poikos: Fitness and Clothing Customization Apps

OMsignal: Smart Apparel 24/7 Biometric Monitoring


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.


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



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


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



QS Big Data Frontier: DIYneuroscience

49http://www.diygenomics.org/files/DIYneuroscience.pdfhttps://www.facebook.com/DIYneuroscience


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.


Conceptualizing Big Data Categories

51

Personal Data

Group Data

Tension: Individual vs Institution

Sense of data belonging to a group

Open Data





Conclusion

52


Group Data: Smart City, Future City

Image: http://www.sydmead.com


Global Population: Growing and Aging

54Source: UN Habitat – 2010http://avondaleassetmanagement.blogspot.com/2012/05/japan-aging-population.html


3 billion new Internet users by 2020

55Source: Peter Diamandis Singularity University


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


Megacity Growth Rates

Source: Wikipedia


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


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)


Urban Data: 3D Buildings + Population Density

60Source: ViziCities


3D Tweet Landscape, ODI Chips

61Source: http://vimeo.com/67872925http://www.slideshare.net/robhawkes/bringing-cities-to-life-using-big-data-webgl


3D Urban Data Viz: Decision-making Tool

62Source: http://www.wired.com/autopia/2013/08/london-underground-3d-map/


Group Data: Office Building Community

63Source: http://www.siembieda.com/burg.html, BURG, San Jose CA 2010


Himalayas Water Tower

Big Data 3D Printed Dwellings of the Future

Living Treehouses – Mitchell Joachim

Masdar, Abu Dhabi – Energy City of the Future


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


Reconfiguration of Space: Seasteading

66


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/


Another Pervasive Trend: Crowdsourcing

68Source: Eric Whitacre's Virtual Choir 3, 'Water Night' (2012), http://www.youtube.com/watch?v=V3rRaL-Czxw


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





Conclusion

70


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


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


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


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


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


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


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



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


7 February 2014Université Paris Descartes, Paris FranceSlides: http://slideshare.net/LaBlogga

Melanie [email protected]

Merci!Questions?

Quantified Self Ideology: Personal Data becomes Big Data

Quantified Self Ideology: Personal Data becomes Big Data

Technology

Transcript of Quantified Self Ideology: Personal Data becomes Big Data