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Redwood Shores CA, March 31, 2015
Slides: http://slideshare.net/LaBlogga
Melanie Swan
m@melanieswan.com
Philosophy of Big Data
March 31, 2015
Philosophy of Big Data 2
About Melanie Swan
 Philosopher of Information Technology
 Singularity University Instructor, IEET
Affiliate Scholar, EDGE Contributor
 Education: MBA Finance, Wharton; BA
French/Economics, Georgetown Univ, MA
Candidate Philosophy, Kingston University
 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.
March 31, 2015
Philosophy of Big Data
Gartner Hype Cycle: Maturation of Big Data
3
Source: http://www.gartner.com/newsroom/id/2819918
March 31, 2015
Philosophy of Big Data
Big Data: Heaven or Hell?
“Hi! I'm a Googlebot! I'm indexing your home”
Source: http://www.ftrain.com/robot_exclusion_protocol.html 4
March 31, 2015
Philosophy of Big Data 5
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
March 31, 2015
Philosophy of Big Data
Conceptualizing Big Data Categories
6
Personal Data
Group Data
Tension: Individual vs Institution
Sense of data belonging to a group
Open Data
March 31, 2015
Philosophy of Big Data
Definition
7
The Philosophy of Big Data is the branch of
philosophy concerned with the foundations,
methods, and implications of big data;
the definitions, meaning, conceptualization,
knowledge possibilities, truth standards, and
practices in situations involving very-large data
sets that are big in
volume, velocity, variety, veracity, and variability
March 31, 2015
Philosophy of Big Data
Philosophy of Big Data at Two Levels
 Industry Practice: internal to
the field as a generalized
articulation of the concepts,
theory, and systems that
comprise the overall conduct
of big data
 Social Impact: external to
the field, considering the
impact of big data more
broadly on individuals,
society, and the world
8
March 31, 2015
Philosophy of Big Data
What is Data?
• Data is facts and statistics collected
together for reference or analysis;
underlying facts and statistics
• Information is facts provided or
learned about something or
someone; knowledge gleaned from
these facts and statistics
• Both may be used as a basis for
reasoning or calculation
• Formerly distinct, now synonymous
9
March 31, 2015
Philosophy of Big Data
What is Big Data?
 Big data is high-volume, high-
velocity and high-variety
information assets that demand
cost-effective, innovative forms of
information processing for
enhanced insight and decision
making
 Assessed per 5 “V” parameters:
volume, velocity, variety, veracity,
and variability
10
March 31, 2015
Philosophy of Big Data
What is Information? (advanced)
11
Information Theory Underlying Mechanism Class of
Theory
Shannon Information Probability Quantitative
Fisher Information Probability Quantitative
Kolmogorov
Complexity
Computation Quantitative
Quantum Information Quantum Mechanics Quantitative
Semantic Information Truth, Accuracy Qualitative
Information as a
State of an Agent
True Beliefs (propositions
that need not be true, but
are believed to be true)
Qualitative
Like energy (kinetic, potential, electrical, chemical, and nuclear)
quantitative formulations of content, entropy, probability, and
updating
March 31, 2015
Philosophy of Big Data
 Annual data creation in zettabytes (10007
bytes)
 90% of the world’s data created in the last 2 years
Defining Trend of Current Era: Big Data
Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends
http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf
2 year doubling cycle
12
12
March 31, 2015
Philosophy of Big Data
Big Data Composition
 Massive amounts of data generated daily which
cannot be processed with conventional data
analysis tools (volume, velocity, variety)
 Impossible to store all generated data, 90% real-time
surgical video feeds discarded
 Scientific, governmental, corporate, and personal
 Each generating exabytes/year
 1990s data management challenge solution: low-cost
storage, massively parallel processing, data warehouses
13
http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
13
March 31, 2015
Philosophy of Big Data
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
14
http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
14
March 31, 2015
Philosophy of Big Data 15
Diversity of Big Data-producing Entities
Autonomous
Car
Smart Contract
DAOs/DACs
Enhanced Human
IOT/M2M
Smartnetworks
Whole Brain
Emulations
Hybrid
Classic Human
Source: http://futurememes.blogspot.com/2015/01/blockchain-thinking-transition-to.html
Neocortical
Column Arrays
Deep-Learning
Clusters
Machine Learning
Algorithms
Simulated
Minds
High-frequency
Trading Networks
Real-time Bidding
Arrays
Brain-computer
Interfaces
Digital Mindfile
Uploads
Artificial Life
Synthetic BiologyDesigned
Life
Cellular
Automata
Supercomputers AI
Agents
Expert Systems
Autonomic
Computing
Natural Language
Processors
Brain Scans
Animals
Personal
Robotics
Smarthome
Networks
March 31, 2015
Philosophy of Big Data
Sensor Mania! Wearables, IOT, M2M
16
Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the
Quantified Self 2.0. J Sens Actuator Netw 2012.
March 31, 2015
Philosophy of Big Data 17
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
March 31, 2015
Philosophy of Big Data
6 bn Current IOT devices to double by 2016
18
Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T
3 year doubling cycle
March 31, 2015
Philosophy of Big Data
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
19
1
Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012.
http://singularitysummit.com/schedule
March 31, 2015
Philosophy of Big Data
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
20
Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
March 31, 2015
Philosophy of Big Data
Basis for Networked Sensing Protocols
21
Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic
Biomimicry, Synthetic Biology
Fish, Hive, Swarm
Turbulence, Chaos, Perturbation
March 31, 2015
Philosophy of Big Data
Networked Sensing – New Topology
22
Machine:Machine
VL Sensor Networks
Internet of Things
6LoWPANS
Human:Human
Telephone System
(POTS)
Human:Machine
Machine:Machine
Internet Protocol
Packet Switching
Unprecedented Scale Requires New Communications Protocols
March 31, 2015
Philosophy of Big Data
Sen.se Integrated Dashboard
23
Source: 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
March 31, 2015
Philosophy of 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 real-time recommendations
 Example: degradation in sleep quality and hemoglobin A1C
levels predict diabetes onset by 10 years1
24
1
Source: Heianza et al. High normal HbA(1c) levels were associated with
impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
March 31, 2015
Philosophy of Big Data
A New World of Futurity
 Shifting from focus on the past (known) and the
present (measurable) to the future (predictable)
 Increasing importance of math and heuristics
 Statistics: mode, mean, variance, outliers
 Probability: quantum mechanics, semiconductors,
nanomaterials, financial markets, disease risk, preventive
medicine
 Systemic, dynamic, episodic, chaotic worldviews
 Collaboration especially drawing upon
crowdsourced communities
25
Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature:
Journal of Human Genetics (2013) 58, 734–741.
March 31, 2015
Philosophy of Big Data
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
26
Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data
June 2013, 1(2): 85-99.
March 31, 2015
Philosophy of Big Data
Philosophy considers Methods
 Definition, terminology, approaches,
classification, information organization,
question-asking, proof and evidence
standards, adequation, map-territory, and
explanandum-explanans linkage
 Explanandum-explanans linkage
 Adequation, degree and type of connection between
that which needs to be explained (explanandum)
and that which contains the explanation (explanans)
 Question set-up
 Are the most important questions are being asked,
how questions are formulated, what kinds of
answers are sought
27
March 31, 2015
Philosophy of Big Data
Methods: Map represents Territory?
28
March 31, 2015
Philosophy of Big Data
Traditional Scientific Method
 What is the role of the scientific method?
 Has the scientific method has been superseded by big data
methods?
 Required, relevant, valid, usable, complementary?
 Is novel discovery available through big data
methods?
 New kinds of knowledge are now available through big data
conceptualizations and practices?
29
March 31, 2015
Philosophy of Big Data
Hypothesis, Complexity, and Capability
30
“Scientific evidence that confirms or
disconfirms a hypothesis is with traditional
conceptions of science.
Instead, the new way is to consider the
capacity of organic molecules to act
differently in different situations, individually
and together” – B. Vincent-Bensaude
The focus is on the persistent and ongoing
capacity of phenomena, not their behavior in
one fixed situation
March 31, 2015
Philosophy of Big Data
Complexity
 Industrial age: defined laws of thermodynamics
 Contemporary age: define laws of complexity
 Task of big data: identify the underlying principles
that transcend the diversity, historical contingency,
and interconnectivity of phenomena like financial
markets, populations, ecosystems, war, pandemics,
and cancer
 Obtain an overarching predictive, mathematical
framework for complex systems would, in principle,
incorporate the dynamics and organization of any
complex system in a quantitative, computable (e.g.;
big data) framework
31
March 31, 2015
Philosophy of Big Data
Hypothesis
 Hypothesis no longer needed when
numerous experimental linkages can be
determined at any later moment instead
of inquiry having to be pre-specified
 Science could become ‘theory-free’
without hypotheses leading inquiry
 PRO: more objective approach to truth, but on other
might be too open, ephemeral, and unguided
 CON: theoretical assumptions persist and guide
inquiry even if explicitly-specified hypotheses are not
present
32
March 31, 2015
Philosophy of Big Data
Fallacies: Big Data is not Smart Data
 Data is big, therefore it must be
important – NO!
 ‘More’ data must be better – NO!
 Complicated data must be better –
NO!
 False tendency to accord big data
undue importance, prominence, and
status by being in awe of its sheer
size, quantity, and reach
33
March 31, 2015
Philosophy of Big Data
What are Big Data Scientists Saying?
 Jim Harris, Data Science Consultant: beware of
big data fundamentalism; need for data
philosophers
 Evelyn Rupert, Goldsmith’s London, Economies
and Ecologies of Big Data: (dangerous)
normative relation to data ; no reality, just
representation; data is performative
 Grady Booch, IBM Chief Scientist: human and
ethical aspects, tremendous social benefits, full
life-cycle of data, ineffective legal controls
 James Kobielus, IBM Big Data Evangelist: no
‘single version of the truth’; be critical of
beautiful data visualizations and data-driven
narrative stories
34
March 31, 2015
Philosophy of Big Data
Big Data
What other kinds of
things is Big Data like?
35
March 31, 2015
Philosophy of Big Data
Big Data: Profound Unknown
 Profound, overwhelming, intangible
unknown
 Approaches: how do we deal with
something that is unknown?
 Other vast unknowns
 Exploring the ‘new’ world
 Space
 God/spiritual realm
 Disease cure
 National debt
 Large-project completion
36
March 31, 2015
Philosophy of Big Data
Responses to the Big Data Unknown
 Analogy
• Representation, visualization, map (issue of repticity
(representational accuracy))
 Story, narrative, myth
 Understand through opposition
 Borders, limits
 Autoimmunity, Antifragility
 Quantitative approaches
 Data quality
 Statistics
37
March 31, 2015
Philosophy of Big Data
Sublime vs. Uncanny
 Sublime: loftiness, excellence, inspiration; sublime
is the name given to what is absolutely great
(Critique of Judgment (Kant, 1790))
 Uncanny: beyond normal/expected; plays on fears
(The Uncanny (Sigmund Freud, 1919))
38
Source: Lessons on the Analytic of the Sublime (Jean-Francois Lyotard, 1991)
The sublime is a crisis where we realize the
inadequacy of the imagination and reason to
each other (the differend); we are straining the
mind at the edges of itself and its conceptuality
March 31, 2015
Philosophy of Big Data
Big Data: Sublime or Uncanny?
39
Listening Post : Real-Time Data Responsive Environment
(Mark Hansen and Ben Rubin, 2001)
http://www.youtube.com/watch?v=dD36IajCz6A
Source: The Sublime in Interactive Digital Installation by Tegan Bristow
March 31, 2015
Philosophy of Big Data
Is Big Data Different?
40
Is big data part of the natural ongoing
process of making our world more intelligible
and manageable (collect and exploit
information)?
Is there something about big data which is
fundamentally different than animal breeding,
the plow, eyeglasses, the airplane,
computing, and the Internet?
March 31, 2015
Philosophy of Big Data
Understand through Opposites
 Opposites (big data vs. small data)
 Possible to have a just world without a notion (and
experience?) of injustice? A world of equality without
inequality?
 Radical forgiveness of even the most unforgivable
(Derrida)
 Interrelations and Dynamism
 Being with one another vs. alterity (Heidegger)
 Fúsis: rising out of itself, taking back into itself
(Heraclitus 500 BCE)
 Plasticity (giving form, taking in form, exploding
form) (Malabou 2012)
41
March 31, 2015
Philosophy of Big Data
Border, Boundaries, Flexibility
 Autoimmunity (Derrida)
 Autoimmunity: porous borders, possibility of
self-suicide, identity cannot be completely
closed
 Absolute immunity: nothing would ever
happen
 Antifragilility (Taleb)
 Antifragility: systems that are open to mistakes
and learn quickly; resilient and vibrant
 Fragility: over-controlled systems that aim for
stability and avoid change; brittle, weak, and
breakable
42
March 31, 2015
Philosophy of Big Data
Different Self-definition per Big Data
 Data and subject co-produce each other
 Example: Biocitzen concept is a shift,
humans interacting with personalized big
data is fundamentally changing our view of
what it is to be human in the world
 Having your own genomic data to look up
your status as new research is published
 Brain neuroplasticity
 Alzheimer disease
 Happiness gene
43
http://www.contempaesthetics.org/newvolume/pages/article.php?articleID=244
Baudelaire, The Painter of Modern Life and Other Essays, 1863
March 31, 2015
Philosophy of Big Data
Relation of Individual and Society
 Theme: government surveillance
and diminution of liberty (NSA 2.0)
 Scary/not-scary threshold, Brin:
souveillance (crowd) response to
surveillance (government)
 Foucault: biopower (top-down) vs.
(the more pernicious) self-
disciplinary power (bottom-up)
 Deleuze: rid ourselves of self-
imposed microfascisms
44
March 31, 2015
Philosophy of Big Data
 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
45
Privacy
March 31, 2015
Philosophy of Big Data 46
Is this image of “real”?
What kind of real?
Real life? Artificial Life?
Synthetic Biology?
Computer-generated
image?
We are in a world that is fundamentally changing,
Proliferation in reality categories
What is Real?
March 31, 2015
Philosophy of Big Data
Society for the Philosophy of Information
Workshop Questions (http://socphilinfo.org)
47
Concept Philosophical Questions
Causality How should we find causes in the era of ‘data-driven
science’? Do we need a new conception of causality to fit
with new practices?
Quality How should we ensure that data are good enough quality
for the purposes for which we use them? What should we
make of the open access movement? What kind of new
technologies might be needed?
Security How can we adequately secure data, while making it
accessible to those who need it?
Big Data What defines big data as a new scientific method? What
is it and what are the challenges?
Uncertainty Can big data help with uncertainty, or does it merely
generate new uncertainties? What technologies are
essential to reduce uncertainty elements in data-driven
sciences?
March 31, 2015
Philosophy of Big Data
Philosophy of Big Data
 The branch of philosophy concerned
with the foundations, methods, and
implications of big data
 Industry practice
 Social impact
 3 classes of philosophical concerns
 Ontology (existence, reality): What is it?
What does it mean?
 Epistemology (knowledge): What is
knowledge here? Proof standard?
 Valorization (ethics, aesthetics): What is
noticed, overlooked? What is ethical
practice? What is beauty, elegance?
48
Sources: http://www.melanieswan.com/documents/Philosophy_of_Big_Data_SWAN.pdf
March 31, 2015
Philosophy of Big Data
49
Conclusions: Philosophy of Big Data
Source: Heidegger, M. The Question Concerning Technology, 1954
 Centrally concerns our relation to
technology: we want the ‘right’
relation to technology, one that is
enabling, not enslaving (Heidegger)
 Everything is being questioned:
scientific method, hypothesis, what is
knowledge, representation, proof
 Crucial importance of questioning
and explaining in big data: ‘what it is’
and ‘what it means’
Redwood Shores CA, March 31, 2015
Slides: http://slideshare.net/LaBlogga
Melanie Swan
m@melanieswan.com
Philosophy of Big Data
Thank you!
Questions?

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Philosophy of Big Data

  • 1. Redwood Shores CA, March 31, 2015 Slides: http://slideshare.net/LaBlogga Melanie Swan m@melanieswan.com Philosophy of Big Data
  • 2. March 31, 2015 Philosophy of Big Data 2 About Melanie Swan  Philosopher of Information Technology  Singularity University Instructor, IEET Affiliate Scholar, EDGE Contributor  Education: MBA Finance, Wharton; BA French/Economics, Georgetown Univ, MA Candidate Philosophy, Kingston University  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.
  • 3. March 31, 2015 Philosophy of Big Data Gartner Hype Cycle: Maturation of Big Data 3 Source: http://www.gartner.com/newsroom/id/2819918
  • 4. March 31, 2015 Philosophy of Big Data Big Data: Heaven or Hell? “Hi! I'm a Googlebot! I'm indexing your home” Source: http://www.ftrain.com/robot_exclusion_protocol.html 4
  • 5. March 31, 2015 Philosophy of Big Data 5 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
  • 6. March 31, 2015 Philosophy of 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. March 31, 2015 Philosophy of Big Data Definition 7 The Philosophy of Big Data is the branch of philosophy concerned with the foundations, methods, and implications of big data; the definitions, meaning, conceptualization, knowledge possibilities, truth standards, and practices in situations involving very-large data sets that are big in volume, velocity, variety, veracity, and variability
  • 8. March 31, 2015 Philosophy of Big Data Philosophy of Big Data at Two Levels  Industry Practice: internal to the field as a generalized articulation of the concepts, theory, and systems that comprise the overall conduct of big data  Social Impact: external to the field, considering the impact of big data more broadly on individuals, society, and the world 8
  • 9. March 31, 2015 Philosophy of Big Data What is Data? • Data is facts and statistics collected together for reference or analysis; underlying facts and statistics • Information is facts provided or learned about something or someone; knowledge gleaned from these facts and statistics • Both may be used as a basis for reasoning or calculation • Formerly distinct, now synonymous 9
  • 10. March 31, 2015 Philosophy of Big Data What is Big Data?  Big data is high-volume, high- velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making  Assessed per 5 “V” parameters: volume, velocity, variety, veracity, and variability 10
  • 11. March 31, 2015 Philosophy of Big Data What is Information? (advanced) 11 Information Theory Underlying Mechanism Class of Theory Shannon Information Probability Quantitative Fisher Information Probability Quantitative Kolmogorov Complexity Computation Quantitative Quantum Information Quantum Mechanics Quantitative Semantic Information Truth, Accuracy Qualitative Information as a State of an Agent True Beliefs (propositions that need not be true, but are believed to be true) Qualitative Like energy (kinetic, potential, electrical, chemical, and nuclear) quantitative formulations of content, entropy, probability, and updating
  • 12. March 31, 2015 Philosophy of Big Data  Annual data creation in zettabytes (10007 bytes)  90% of the world’s data created in the last 2 years Defining Trend of Current Era: Big Data Source: Mary Meeker, Internet Trends, http://www.kpcb.com/insights/2013-internet-trends http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/healthcare-leveraging-big-data-paper.pdf 2 year doubling cycle 12 12
  • 13. March 31, 2015 Philosophy of Big Data Big Data Composition  Massive amounts of data generated daily which cannot be processed with conventional data analysis tools (volume, velocity, variety)  Impossible to store all generated data, 90% real-time surgical video feeds discarded  Scientific, governmental, corporate, and personal  Each generating exabytes/year  1990s data management challenge solution: low-cost storage, massively parallel processing, data warehouses 13 http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx 13
  • 14. March 31, 2015 Philosophy of Big Data 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 14 http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx 14
  • 15. March 31, 2015 Philosophy of Big Data 15 Diversity of Big Data-producing Entities Autonomous Car Smart Contract DAOs/DACs Enhanced Human IOT/M2M Smartnetworks Whole Brain Emulations Hybrid Classic Human Source: http://futurememes.blogspot.com/2015/01/blockchain-thinking-transition-to.html Neocortical Column Arrays Deep-Learning Clusters Machine Learning Algorithms Simulated Minds High-frequency Trading Networks Real-time Bidding Arrays Brain-computer Interfaces Digital Mindfile Uploads Artificial Life Synthetic BiologyDesigned Life Cellular Automata Supercomputers AI Agents Expert Systems Autonomic Computing Natural Language Processors Brain Scans Animals Personal Robotics Smarthome Networks
  • 16. March 31, 2015 Philosophy of Big Data Sensor Mania! Wearables, IOT, M2M 16 Source: Swan, M. Sensor Mania! The Internet of Things, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012.
  • 17. March 31, 2015 Philosophy of Big Data 17 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
  • 18. March 31, 2015 Philosophy of Big Data 6 bn Current IOT devices to double by 2016 18 Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T 3 year doubling cycle
  • 19. March 31, 2015 Philosophy of Big Data 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 19 1 Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule
  • 20. March 31, 2015 Philosophy of Big Data 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 20 Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
  • 21. March 31, 2015 Philosophy of Big Data Basis for Networked Sensing Protocols 21 Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic Biomimicry, Synthetic Biology Fish, Hive, Swarm Turbulence, Chaos, Perturbation
  • 22. March 31, 2015 Philosophy of Big Data Networked Sensing – New Topology 22 Machine:Machine VL Sensor Networks Internet of Things 6LoWPANS Human:Human Telephone System (POTS) Human:Machine Machine:Machine Internet Protocol Packet Switching Unprecedented Scale Requires New Communications Protocols
  • 23. March 31, 2015 Philosophy of Big Data Sen.se Integrated Dashboard 23 Source: 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
  • 24. March 31, 2015 Philosophy of 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 real-time recommendations  Example: degradation in sleep quality and hemoglobin A1C levels predict diabetes onset by 10 years1 24 1 Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
  • 25. March 31, 2015 Philosophy of Big Data A New World of Futurity  Shifting from focus on the past (known) and the present (measurable) to the future (predictable)  Increasing importance of math and heuristics  Statistics: mode, mean, variance, outliers  Probability: quantum mechanics, semiconductors, nanomaterials, financial markets, disease risk, preventive medicine  Systemic, dynamic, episodic, chaotic worldviews  Collaboration especially drawing upon crowdsourced communities 25 Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature: Journal of Human Genetics (2013) 58, 734–741.
  • 26. March 31, 2015 Philosophy of Big Data 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 26 Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
  • 27. March 31, 2015 Philosophy of Big Data Philosophy considers Methods  Definition, terminology, approaches, classification, information organization, question-asking, proof and evidence standards, adequation, map-territory, and explanandum-explanans linkage  Explanandum-explanans linkage  Adequation, degree and type of connection between that which needs to be explained (explanandum) and that which contains the explanation (explanans)  Question set-up  Are the most important questions are being asked, how questions are formulated, what kinds of answers are sought 27
  • 28. March 31, 2015 Philosophy of Big Data Methods: Map represents Territory? 28
  • 29. March 31, 2015 Philosophy of Big Data Traditional Scientific Method  What is the role of the scientific method?  Has the scientific method has been superseded by big data methods?  Required, relevant, valid, usable, complementary?  Is novel discovery available through big data methods?  New kinds of knowledge are now available through big data conceptualizations and practices? 29
  • 30. March 31, 2015 Philosophy of Big Data Hypothesis, Complexity, and Capability 30 “Scientific evidence that confirms or disconfirms a hypothesis is with traditional conceptions of science. Instead, the new way is to consider the capacity of organic molecules to act differently in different situations, individually and together” – B. Vincent-Bensaude The focus is on the persistent and ongoing capacity of phenomena, not their behavior in one fixed situation
  • 31. March 31, 2015 Philosophy of Big Data Complexity  Industrial age: defined laws of thermodynamics  Contemporary age: define laws of complexity  Task of big data: identify the underlying principles that transcend the diversity, historical contingency, and interconnectivity of phenomena like financial markets, populations, ecosystems, war, pandemics, and cancer  Obtain an overarching predictive, mathematical framework for complex systems would, in principle, incorporate the dynamics and organization of any complex system in a quantitative, computable (e.g.; big data) framework 31
  • 32. March 31, 2015 Philosophy of Big Data Hypothesis  Hypothesis no longer needed when numerous experimental linkages can be determined at any later moment instead of inquiry having to be pre-specified  Science could become ‘theory-free’ without hypotheses leading inquiry  PRO: more objective approach to truth, but on other might be too open, ephemeral, and unguided  CON: theoretical assumptions persist and guide inquiry even if explicitly-specified hypotheses are not present 32
  • 33. March 31, 2015 Philosophy of Big Data Fallacies: Big Data is not Smart Data  Data is big, therefore it must be important – NO!  ‘More’ data must be better – NO!  Complicated data must be better – NO!  False tendency to accord big data undue importance, prominence, and status by being in awe of its sheer size, quantity, and reach 33
  • 34. March 31, 2015 Philosophy of Big Data What are Big Data Scientists Saying?  Jim Harris, Data Science Consultant: beware of big data fundamentalism; need for data philosophers  Evelyn Rupert, Goldsmith’s London, Economies and Ecologies of Big Data: (dangerous) normative relation to data ; no reality, just representation; data is performative  Grady Booch, IBM Chief Scientist: human and ethical aspects, tremendous social benefits, full life-cycle of data, ineffective legal controls  James Kobielus, IBM Big Data Evangelist: no ‘single version of the truth’; be critical of beautiful data visualizations and data-driven narrative stories 34
  • 35. March 31, 2015 Philosophy of Big Data Big Data What other kinds of things is Big Data like? 35
  • 36. March 31, 2015 Philosophy of Big Data Big Data: Profound Unknown  Profound, overwhelming, intangible unknown  Approaches: how do we deal with something that is unknown?  Other vast unknowns  Exploring the ‘new’ world  Space  God/spiritual realm  Disease cure  National debt  Large-project completion 36
  • 37. March 31, 2015 Philosophy of Big Data Responses to the Big Data Unknown  Analogy • Representation, visualization, map (issue of repticity (representational accuracy))  Story, narrative, myth  Understand through opposition  Borders, limits  Autoimmunity, Antifragility  Quantitative approaches  Data quality  Statistics 37
  • 38. March 31, 2015 Philosophy of Big Data Sublime vs. Uncanny  Sublime: loftiness, excellence, inspiration; sublime is the name given to what is absolutely great (Critique of Judgment (Kant, 1790))  Uncanny: beyond normal/expected; plays on fears (The Uncanny (Sigmund Freud, 1919)) 38 Source: Lessons on the Analytic of the Sublime (Jean-Francois Lyotard, 1991) The sublime is a crisis where we realize the inadequacy of the imagination and reason to each other (the differend); we are straining the mind at the edges of itself and its conceptuality
  • 39. March 31, 2015 Philosophy of Big Data Big Data: Sublime or Uncanny? 39 Listening Post : Real-Time Data Responsive Environment (Mark Hansen and Ben Rubin, 2001) http://www.youtube.com/watch?v=dD36IajCz6A Source: The Sublime in Interactive Digital Installation by Tegan Bristow
  • 40. March 31, 2015 Philosophy of Big Data Is Big Data Different? 40 Is big data part of the natural ongoing process of making our world more intelligible and manageable (collect and exploit information)? Is there something about big data which is fundamentally different than animal breeding, the plow, eyeglasses, the airplane, computing, and the Internet?
  • 41. March 31, 2015 Philosophy of Big Data Understand through Opposites  Opposites (big data vs. small data)  Possible to have a just world without a notion (and experience?) of injustice? A world of equality without inequality?  Radical forgiveness of even the most unforgivable (Derrida)  Interrelations and Dynamism  Being with one another vs. alterity (Heidegger)  Fúsis: rising out of itself, taking back into itself (Heraclitus 500 BCE)  Plasticity (giving form, taking in form, exploding form) (Malabou 2012) 41
  • 42. March 31, 2015 Philosophy of Big Data Border, Boundaries, Flexibility  Autoimmunity (Derrida)  Autoimmunity: porous borders, possibility of self-suicide, identity cannot be completely closed  Absolute immunity: nothing would ever happen  Antifragilility (Taleb)  Antifragility: systems that are open to mistakes and learn quickly; resilient and vibrant  Fragility: over-controlled systems that aim for stability and avoid change; brittle, weak, and breakable 42
  • 43. March 31, 2015 Philosophy of Big Data Different Self-definition per Big Data  Data and subject co-produce each other  Example: Biocitzen concept is a shift, humans interacting with personalized big data is fundamentally changing our view of what it is to be human in the world  Having your own genomic data to look up your status as new research is published  Brain neuroplasticity  Alzheimer disease  Happiness gene 43 http://www.contempaesthetics.org/newvolume/pages/article.php?articleID=244 Baudelaire, The Painter of Modern Life and Other Essays, 1863
  • 44. March 31, 2015 Philosophy of Big Data Relation of Individual and Society  Theme: government surveillance and diminution of liberty (NSA 2.0)  Scary/not-scary threshold, Brin: souveillance (crowd) response to surveillance (government)  Foucault: biopower (top-down) vs. (the more pernicious) self- disciplinary power (bottom-up)  Deleuze: rid ourselves of self- imposed microfascisms 44
  • 45. March 31, 2015 Philosophy of Big Data  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 45 Privacy
  • 46. March 31, 2015 Philosophy of Big Data 46 Is this image of “real”? What kind of real? Real life? Artificial Life? Synthetic Biology? Computer-generated image? We are in a world that is fundamentally changing, Proliferation in reality categories What is Real?
  • 47. March 31, 2015 Philosophy of Big Data Society for the Philosophy of Information Workshop Questions (http://socphilinfo.org) 47 Concept Philosophical Questions Causality How should we find causes in the era of ‘data-driven science’? Do we need a new conception of causality to fit with new practices? Quality How should we ensure that data are good enough quality for the purposes for which we use them? What should we make of the open access movement? What kind of new technologies might be needed? Security How can we adequately secure data, while making it accessible to those who need it? Big Data What defines big data as a new scientific method? What is it and what are the challenges? Uncertainty Can big data help with uncertainty, or does it merely generate new uncertainties? What technologies are essential to reduce uncertainty elements in data-driven sciences?
  • 48. March 31, 2015 Philosophy of Big Data Philosophy of Big Data  The branch of philosophy concerned with the foundations, methods, and implications of big data  Industry practice  Social impact  3 classes of philosophical concerns  Ontology (existence, reality): What is it? What does it mean?  Epistemology (knowledge): What is knowledge here? Proof standard?  Valorization (ethics, aesthetics): What is noticed, overlooked? What is ethical practice? What is beauty, elegance? 48 Sources: http://www.melanieswan.com/documents/Philosophy_of_Big_Data_SWAN.pdf
  • 49. March 31, 2015 Philosophy of Big Data 49 Conclusions: Philosophy of Big Data Source: Heidegger, M. The Question Concerning Technology, 1954  Centrally concerns our relation to technology: we want the ‘right’ relation to technology, one that is enabling, not enslaving (Heidegger)  Everything is being questioned: scientific method, hypothesis, what is knowledge, representation, proof  Crucial importance of questioning and explaining in big data: ‘what it is’ and ‘what it means’
  • 50. Redwood Shores CA, March 31, 2015 Slides: http://slideshare.net/LaBlogga Melanie Swan m@melanieswan.com Philosophy of Big Data Thank you! Questions?