Redwood Shores CA, March 31, 2015
Slides: http://slideshare.net/LaBlogga
Melanie [email protected]
Philosophy of Big Data
March 31, 2015Philosophy 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, 2015Philosophy of Big Data
Gartner Hype Cycle: Maturation of Big Data
3Source: http://www.gartner.com/newsroom/id/2819918
March 31, 2015Philosophy 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
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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
March 31, 2015Philosophy of Big Data
Conceptualizing Big Data Categories
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Personal Data
Group Data
Tension: Individual vs Institution
Sense of data belonging to a group
Open Data
March 31, 2015Philosophy of Big Data
Definition
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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, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy of Big Data
What is Information? (advanced)
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Information Theory Underlying Mechanism Class of Theory
Shannon Information Probability Quantitative
Fisher Information Probability QuantitativeKolmogorov 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, 2015Philosophy 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-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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March 31, 2015Philosophy 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
13http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
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March 31, 2015Philosophy 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
14http://www.dbta.com/Editorial/Think-About-It/What-is-Big-Data-A-Market-Overview-82509.aspx
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March 31, 2015Philosophy 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 SystemsAutonomic Computing
Natural Language Processors
Brain Scans
AnimalsPersonal Robotics
Smarthome Networks
March 31, 2015Philosophy of Big Data
Sensor Mania! Wearables, IOT, M2M
16Source: 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
March 31, 2015Philosophy of Big Data
6 bn Current IOT devices to double by 2016
18Source: http://www.businessinsider.com/growth-in-the-internet-of-things-2013-10?IR=T
3 year doubling cycle
March 31, 2015Philosophy 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
191Source: Vinge, V. Who’s Afraid of First Movers? The Singularity Summit 2012. http://singularitysummit.com/schedule
March 31, 2015Philosophy 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
20Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics
March 31, 2015Philosophy of Big Data
Basis for Networked Sensing Protocols
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Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic
Biomimicry, Synthetic BiologyFish, Hive, Swarm
Turbulence, Chaos, Perturbation
March 31, 2015Philosophy of Big Data
Networked Sensing – New Topology
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Machine:MachineVL Sensor Networks
Internet of Things6LoWPANS
Human:HumanTelephone System
(POTS)
Human:Machine Machine:MachineInternet ProtocolPacket Switching
Unprecedented Scale Requires New Communications Protocols
March 31, 2015Philosophy of Big Data
Sen.se Integrated Dashboard
23Source: 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, 2015Philosophy 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
241Source: Heianza et al. High normal HbA(1c) levels were associated with impaired insulin secretion. Diabet Med 2012. 29:1285-1290.
March 31, 2015Philosophy 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
25Source: Kido, Swan, et al. Systematic evaluation of personal genome services. Nature: Journal of Human Genetics (2013) 58, 734–741.
March 31, 2015Philosophy 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
26Source: Swan, M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data June 2013, 1(2): 85-99.
March 31, 2015Philosophy 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
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March 31, 2015Philosophy of Big Data
Methods: Map represents Territory?
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March 31, 2015Philosophy 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?
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March 31, 2015Philosophy of Big Data
Hypothesis, Complexity, and Capability
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“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, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy of Big Data
Big Data
What other kinds of things is Big Data like?
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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))
38Source: 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, 2015Philosophy of Big Data
Big Data: Sublime or Uncanny?
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Listening Post : Real-Time Data Responsive Environment (Mark Hansen and Ben Rubin, 2001)
http://www.youtube.com/watch?v=dD36IajCz6ASource: The Sublime in Interactive Digital Installation by Tegan Bristow
March 31, 2015Philosophy of Big Data
Is Big Data Different?
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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, 2015Philosophy 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)
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March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
43http://www.contempaesthetics.org/newvolume/pages/article.php?articleID=244
Baudelaire, The Painter of Modern Life and Other Essays, 1863
March 31, 2015Philosophy 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
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March 31, 2015Philosophy 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
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Privacy
March 31, 2015Philosophy 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, 2015Philosophy of Big Data
Society for the Philosophy of Information Workshop Questions (http://socphilinfo.org)
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Concept Philosophical QuestionsCausality 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, 2015Philosophy 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?
48Sources: http://www.melanieswan.com/documents/Philosophy_of_Big_Data_SWAN.pdf
March 31, 2015Philosophy of Big Data
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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 [email protected]
Philosophy of Big Data
Thank you!Questions?