philosophy of big data

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


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


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

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Personal Data

Group Data

Tension: Individual vs Institution

Sense of data belonging to a group

Open 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


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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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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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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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


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

1212


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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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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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


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.


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

18Source: 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

191Source: 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

20Source: http://en.wikipedia.org/wiki/Big_data, http://wikibon.org/blog/big-data-statistics


Basis for Networked Sensing Protocols

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Inorganic, Organic, Hybrid, Evolved, Autonomic, Automatic

Biomimicry, Synthetic BiologyFish, Hive, Swarm

Turbulence, Chaos, Perturbation


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


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


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.


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.


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.


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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Methods: Map represents Territory?

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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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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


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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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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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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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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Big Data

What other kinds of things is Big Data like?

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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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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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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


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

http://www.youtube.com/watch?v=dD36IajCz6A


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?


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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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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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


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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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


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?


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?



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


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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]


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