Data quotes
From Motley Bytes Wiki
Contents
- 1 David_Auerbach
- 2 Arthur_Bloch
- 3 Fred_Brooks
- 4 David_Deutsch
- 5 David_Epstein
- 6 Richard_Hamming
- 7 Yuval_Noah_Harari
- 8 Sabine_Hossenfelder
- 9 Edwin_Hubble
- 10 Samuel_Karlin
- 11 Ipek_Ozkaya
- 12 Joel_Pel
- 13 Steven_Pinker
- 14 Robert_M_Pirsig
- 15 Todd_Rose
- 16 Herbert_Simon
- 17 John_Tukey
- 18 Shannon_Vallor
- 19 Waldo_and_Boussard
- 20 Eugene_Wigner
- 21 Shoshana_Zuboff
David_Auerbach
Model Ontologies
“It is the irony of the data age that computers, with little to no understanding of the models they are employing, are increasingly acting as primary arbiters of the ontologies employed by humans.”
- Source: David Auerbach, in the article “Bitwise: A Life in Code” published in 2019 in Communications of the ACM, Volume 62, Issue 9 (September). DOI: 10.1145/3347864.
Safe Accurate Data
“The specific problem computer science faces in these ‘data network’ scenarios is that of making the data ‘safe’ and ‘accurate’ for the networks.”
- Source: David Auerbach, in the article “Bitwise: A Life in Code” published in 2019 in Communications of the ACM, Volume 62, Issue 9 (September). DOI: 10.1145/3347864.
Arthur_Bloch
Finagle's Third Law
“In any collection of data, the figure most obviously correct, beyond all need of checking, is the mistake.
Corollaries:
1. No one whom you ask for help will see it.
2. Everyone who stops by with unsought advice will see it immediately.”
- Source: Attributed to a fictional character named Finagle, reported in the book "Murphy's Law and Other Reasons Why Things Go Wrong" written by Arthur Bloch, first edition published in 1977 by Price/Stern/Sloan, Los Angeles, California. ISBN-10: 0843104287. ISBN-13: 978-0843104288. Chapter Murphology, page 15.
- Notes: Often attributed to a fictional character named Finagle, its true origin remains uncertain and likely derived from multiple sources. See https://www.devx.com/terms/finagles-law/.
Finagle's Rules
1. “To study a subject best, understand it thoroughly before you start.” 2. “Always keep a record of data — it indicates you've been working.” 3. “Always draw your curves, then plot your reading.” 4. “In case of doubt, make it sound convincing.” 5. “Experiments should be reproducible — they should all fail in the same way.” 6. “Do no believe in miracles — rely on them.”
- Source: Attributed to a fictional character named Finagle, reported in the book "Murphy's Law and Other Reasons Why Things Go Wrong" written by Arthur Bloch, first edition publish in 1977 by Price/Stern/Sloan, Los Angeles, California. ISBN-10: 0843104287. ISBN-13: 978-0843104288. Chapter Murphology, page 16.
- Notes: Often attributed to a fictional character named Finagle, its true origin remains uncertain and likely derived from multiple sources. See https://www.devx.com/terms/finagles-law/.
- Keywords: Murphy(topic)_quotes Law_quotes Study_quotes Subject_quotes Best_quotes Understand_quotes Before_quotes Start_quotes Always_quotes Keep_quotes Record_quotes Data_quotes Indicate_quotes Work_quotes Draw_quotes Curve_quotes Plot_quotes Read_quotes Case_quotes Doubt_quotes Make_quotes Sound_quotes Convince_quotes Experiment_quotes Reproducible_quotes Fail_quotes Same_quotes Way_quotes Believe_quotes Miricle_quotes Rely_quotes
Fred_Brooks
Tables versus Flowcharts
“Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowcharts; they'll be obvious.”
- Source: Brooks Jr., F. P. (1995). The Mythical Man-Month - Essays on Software Engineering (Anniversary Edition) | 1975/1995 (Anniv.). Boston. MA: Addison-Wesley. ISBN: 0201835959. Chapter 9, page 102.
David_Deutsch
Amending Data
“Amending the ‘data’, or rejecting some as erroneous, is a frequent concomitant of scientific discovery, and the crucial ‘data’ cannot even be obtained until theory tells us what to look for and how and why.”
- Source: David Deutsch, The Beginning of Infinity: Explanations That Transform the World (2011).
Data Interpretation
“We never know any data before interpreting it through theories. All observations are, as Popper put it, theory-laden, and hence fallible, as all our theories are.”
- Source: David Deutsch, The Beginning of Infinity: Explanations That Transform the World (2011).
- Keywords: Data_quotes Interpretation_quotes Theory_quotes Observation_quotes Fallibility_quotes Bias_quotes
Error Correction
“Without error-correction all information processing, and hence all knowledge-creation, is necessarily bounded. Error-correction is the beginning of infinity.”
- Source: David Deutsch, The Beginning of Infinity: Explanations That Transform the World (2011).
David_Epstein
Researcher Freedom
“The problem of researcher freedom leading to false conclusions is now known to be pervasive in science. The age of big data and big-database software makes it easier than ever to mix and match statistics in pursuit of positive results that are likely just artifacts of chance.”
- Source: David Epstein in the book “Inside the Box: How Constraints Make Us Better”. Published 2026 by Riverhead Books. Page 38. ISBN 978-0593715734 (Ebook).
- Keywords: Problem_quotes Research_quotes Freedom_quotes Lead_quotes False_quotes Conclusion_quotes Know_quotes Now_quotes Pervasive_quotes Science_quotes Age_quotes Big_quotes Data_quotes Database_quotes Software_quotes Make_quotes Easy_quotes Mix_quotes Match_quotes Statistics_quotes Pursuit_quotes Positive_quotes Result_quotes Likely_quotes Artifact_quotes Chance_quotes
Richard_Hamming
Scientific Laws
“Science is composed of laws which were originally based on a small, carefully selected set of observations, often not very accurately measured originally; but the laws have later been found to apply over much wider ranges of observations and much more accurately than the original data justified.”
- Source: Richard Hamming, 'The Unreasonable Effectiveness of Mathematics', The American Mathematical Monthly (Feb 1980), 87 No.2.
Yuval_Noah_Harari
Role of AI
“AI is taking up the role of both bureaucrats and mythmakers. AI systems know how to find and process data better than flesh-and-blood bureaucrats, and AI is also acquiring the ability to compose stories better than most humans.”
- Source: Yuval Noah Harari writing in his book ‘Nexus: A Brief History of Information Networks from the Stone Age to AI’. Published in 2024 by Random House. Kindle edition. Chapter 3 page 68. ISBN 978-0593734247.
- Keywords: Artificial_Intelligence_quotes AI_quotes Artificial_quotes Intelligence_quotes Role_quotes Bureaucrat_quotes Mythmaker_quotes System_quotes Knowledge_quotes Find_quotes Process_quotes Data_quotes Better_quotes Flesh_quotes Blood_quotes Acquire_quotes Ability_quotes Compose_quotes Story_quotes Human_quotes
Sabine_Hossenfelder
Closer Reality
“The foundations of physics give us a closer look at reality, but the closer we look at reality, the more slippery it becomes. Our heavy use of mathematics is a major reason. The more the fundamental descriptions of nature have become divorced from our everyday experience, the more we must rely on mathematical rigor. This reliance has consequences. Using math to describe reality means that the same observations can be equivalently explained in many different ways. That’s just because there are many sets of mathematical axioms that will give the exact same predictions for all available data. Thus, if you want to assign 'reality' to one of your explanations, you won’t know which.”
- Source: Hossenfelder, Sabine. Existential Physics: A Scientist's Guide to Life's Biggest Questions (p. 74). Penguin Publishing Group. Kindle Edition.
- Keywords: Foundation_quotes Physics_quotes Closer_quotes Reality_quotes Slippery_quotes Heavy_quotes Mathematics_quotes Major_quotes Reason_quotes Fundamental_quotes Description_quotes Nature_quotes Divorce_quotes Everyday_quotes Experience_quotes Rely_quotes Rigor_quotes Consequence_quotes Observation_quotes Same_quotes Equivalent_quotes Explanation_quotes Different_quotes Way_quotes Set_quotes Axiom_quotes Exact_quotes Prediction_quotes Available_quotes Data_quotes Assign_quotes Know_quotes
Edwin_Hubble
Scientific Judgments
“Science deals with judgments on which it is possible to obtain universal agreement. These judgments do not concern individual facts and events, but the invariable association of facts and events known as the laws of science. Agreement is secured by observation and experiment — impartial courts of appeal to which all men must submit if they wish to survive. The laws are grouped and explained by theories of ever increasing generality. The theories at first are ex post facto — merely plausible interpretations of existing bodies of data. However, they frequently lead to predictions that can be tested by experiments and observations in new fields, and, if the interpretations are verified, the theories are accepted as working hypotheses until they prove untenable. The essential requirements are agreement on the subject matter and the verification of predictions. These features insure a body of positive knowledge that can be transmitted from person to person, and that accumulates from generation to generation.”
- Source: Edwin Powell Hubble. From manuscript on English Science in the Renaissance (1937), Edwin Hubble collection, Box 2, Huntington Library, San Marino, California. As cited by Norriss S. Hetherington in 'Philosophical Values and Observation in Edwin Hubble's Choice of a Model of the Universe', Historical Studies in the Physical Sciences (1982), 13, No. 1, 41. (Hetherington comments parenthetically that the references to court, judgment and appeal may be attributable to his prior experiences as a Rhodes Scholar reading Roman law at Oxford, and to a year's practice as an attorney in Louisville, Kentucky.)
- Keywords: Science_quotes Judgment_quotes Universal_quotes Agreement_quotes Survival_quotes Fact_quotes Association_quotes Event_quotes Law_quotes Observation_quotes Experiment_quotes Court_quotes Explanation_quotes Theory_quotes Generality_quotes Ex_Post_quotes Plausibility_quotes Interpretation_quotes Data_quotes Prediction_quotes Verification_quotes Hypothesis_quotes Falsification_quotes Proof_quotes Requirements_quotes Feature_quotes Positive_quotes Knowledge_quotes Transmission_quotes Accumulation_quotes Generation_quotes
Scientific Guesses
“[A scientist] naturally and inevitably ... mulls over the data and guesses at a solution. [He proceeds to] testing of the guess by new data — predicting the consequences of the guess and then dispassionately inquiring whether or not the predictions are verified.”
- Source: Edwin Powell Hubble, From manuscript on Francis Bacon as a scientist (1942), Edwin Hubble collection, Box 2, Huntington Library, San Marino, California. As cited by Norriss S. Hetherington in 'Philosophical Values and Observation in Edwin Hubble's Choice of a Model of the Universe', Historical Studies in the Physical Sciences (1982), 13, No. 1, 42.
Samuel_Karlin
Purpose of Models
“The purpose of models is not to fit the data but to sharpen the questions.”
- Source: Samuel Karlin, Delivered at the Eleventh R. A. Fisher Memorial Lecture, Royal Society (20 April 1983). In Carl C. Gaither and Alma E. Cavazos-Gaither, Statistically Speaking: a Dictionary of Quotations (1996), 140.
- Keywords: Purpose_quotes Model_quotes Data_quotes Fit_quotes Sharp_quotes Question_quotes
Ipek_Ozkaya
Data as Code
“We need to teach them [software engineers] how to evolve systems to incorporate such [AI] components, and we need to teach them to treat data as code.”
- Source: Ipek Ozkaya, “Application of Large Language Models to Software Engineering Tasks: Opportunities, Risks, and Implications,” in IEEE Software, volume 40, number 3, pages 4-8, May-June 2023, DOI: 10.1109/MS.2023.3248401.
Joel_Pel
Murphy's Law Equation
“A new mathematical foundation to one of today’s most recognized physical laws, Murphy’s Law. Experimental data matches very nicely with the theory presented in this equation.”
- Notes:
- PM is the Murphy’s probability that something will go wrong.
- KM is Murphy’s constant (equal to one).
- FM is Murphy’s factor, a very small number that can only be calculated on a 386-computer running Windows 3.1. Here we will approximate it by FM ≈ 0.01.
- I is the importance of the result or outcome of the event on a scale of 1 to 10.
- C is the complexity of the system on a scale of 1 to 10.
- U is the urgency on a scale of 1 to 10.
- F is the frequency on a scale of 1 to 10.
- e is Euler's number, a mathematical constant approximately equal to 2.71828.
- Source: Written by Joel Pel in the article “The Murphy's Law Equation” published 2007-05-21 in The Science Creative Quarterly.
- See Also:
Steven_Pinker
Information and Computation Medium
“Information and computation reside in patterns of data and in relations of logic that are independent of the physical medium that carries them.”
- Source: Steven Pinker (1997). How the Mind Works. New York, NY: W. W. Norton. ISBN 978-0-393-06973-0; eISBN 978-0-393-06973-0. Chapter 1 Section Reverse-Engineering the Psyche.
Robert_M_Pirsig
An Experiment is Never a Failure
“An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don’t prove anything one way or another.”
- Source: Zen and the Art of Motorcycle Maintenance: An Inquiry Into Values (1974), 102.
Todd_Rose
Data Point
“We just need the tools to understand each person as an individual, not as a data point on a bell curve.”
- Source: From the book “The End of Average: How We Succeed in a World That Values Sameness”, written by Todd Rose, published 2016 by HarperCollins, Kindle Edition, page 14. ISBN: 978-0062358387 (e-book).
- See Also:
- Keywords: Need_quotes Tool_quotes Understand_quotes Each_quotes Individual_quotes Data_quotes Point_quotes Bell_quotes Curve_quotes
Herbert_Simon
Computer Programming as Data Structures
“When we write a computer program in any language except machine language, we are really not writing down processes but data structures. These data structures are then interpreted or compiled into processes, that is, into machine-language instructions that the computer can understand and execute. Nevertheless, for most purposes, it is convenient for us simply to ignore the translation step and to treat the computer programs in higher-level languages as representing processes.”
- Source: Simon, H. A. (1996). The Sciences of the Artificial (Third Edition). Computers & Mathematics with Applications (3rd ed., Vol. 33). Cambridge, MA: MIT Press. ISBN: 9780585360102. Retrieved 2018-08-23 from https://monoskop.org/images/9/9c/Simon_Herbert_A_The_Sciences_of_the_Artificial_3rd_ed.pdf. Chapter 4, page 93.
- Keywords: Writing_quotes Programming_quotes Computer_quotes Program_quotes Language_quotes Exception_quotes Machine_quotes Process_quotes Data_quotes Structure_quotes Interpretation_quotes Compilation_quotes Instruction_quotes Understanding_quotes Execution_quotes Purpose_quotes Convenience_quotes Simplicity_quotes Ignore_quotes Translation_quotes Step_quotes Treat_quotes High_quotes Level_quotes Representation_quotes
Data for Planning
“The quality of design is likely to depend heavily on the quality of the data available. The task is not to design without data but to incorporate assessments of the quality of the data, or its lack of quality, in the design process itself.”
- Source: Simon, H. A. (1996). The Sciences of the Artificial (Third Edition). Computers & Mathematics with Applications (3rd ed., Vol. 33). Cambridge, MA: MIT Press. ISBN: 9780585360102. Retrieved 2018-08-23 from https://monoskop.org/images/9/9c/Simon_Herbert_A_The_Sciences_of_the_Artificial_3rd_ed.pdf. Chapter 6, page 146.
John_Tukey
Desire for an Answer
“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.”
- Source: John Tukey. A paper entitled Sunset Salvo. The American Statistician, Vol. 40, No. 1 (Feb., 1986), pp. 72-76. DOI: 10.2307/2683137.
- Keywords: Combination_quotes Data_quotes Ache_quotes Desire_quotes Answer_quotes Ensure_quotes Reasonable_quotes Extract_quotes Body_quotes
Shannon_Vallor
AI Data
“Only a modest subset of what is representable in digital form can be generated or stored in sufficient quantity and quality to be useful... An event that only happens once in a generation, or to one person in a billion, even if it is world-altering, is virtually invisible to a machine learning model.”
- Source: From the book “The AI Mirror” by Shannon Vallor. Published 2024 by Oxford University Press. ISBN 978-0197759066. Chapter 2, page 48.
- Keywords: Modest_quotes Subset_quotes Represent_quotes Digital_quotes Form_quotes Generate_quotes Store_quotes Sufficient_quotes Quantity_quotes Quality_quotes Useful_quotes Event_quotes Happen_quotes Once_quotes Generation_quotes Person_quotes One_quotes Billion_quotes World_quotes Alter_quotes Virtual_quotes Invisible_quotes Machine_quotes Learn_quotes Model_quotes Data_quotes AI_quotes Artificial_quotes Intelligence_quotes
Waldo_and_Boussard
LLM Performance
“GPTs [Generative Pre-trained Transformers] based on LLMs [Large Language Models] perform well on prompts that are more popular and have reached a consensus, yet they struggle with controversial topics or topics with limited data.”
- Source: From the journal article entitled “GPTs and Hallucination”, in Communications of the ACM, volume 68, number 1, 2025-01. DOI:10.1145/3703757.
Eugene_Wigner
No Definite Evidence
“[F. Werner, while a student in Princeton,] came to me and expressed his bewilderment with the fact that we make a rather narrow selection when choosing the data on which we test our theories. ‘How do we know that, if we made a theory which focuses its attention on phenomena we disregard and disregards some of the phenomena now commanding our attention, that we could not build another theory which has little in common with the present one but which, nevertheless, explains just as many phenomena as the present theory?' It has to be admitted that we have no definite evidence that there is no such theory.”
- Source: Eugene Paul Wigner, In 'The Unreasonable Effectiveness of Mathematics in the Natural Sciences,' Communications in Pure and Applied Mathematics (Feb 1960), 13, No. 1 (February 1960). Collected in Eugene Paul Wigner, A.S. Wightman (ed.), Jagdish Mehra (ed.), The Collected Works of Eugene Paul Wigner (1955), Vol. 6, 535.
- Keywords: Come_quotes Express_quotes Bewilderment_quotes Fact_quotes Narrow_quotes Select_quotes Choose_quotes Data_quotes Testing_quotes Theory_quotes Knowledge_quotes Focus_quotes Attention_quotes Phenomena_quotes Disregard_quotes Command_quotes Build_quotes Present_quotes Explanation_quotes Admit_quotes Definite_quotes Evidence_quotes
Shoshana_Zuboff
Life at the Data Interface
“Life at the data interface invites the worker into the abstract precincts of managerial work. It provides access to a broader view of the business as well as a deepened understanding of one's tasks and their role in the wider sphere of organizational functions. When work becomes synonymous with responsiveness to data, it engenders inquiry and dialogue, thus opening the way for workers to envision new possibilities and fresh alternatives to the reigning definitions of process, product, and organization.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 301. ISBN:0465032125.
- Keywords: Life_quotes Data_quotes Interface_quotes Work_quotes Organization_quotes Function_quotes Responsive_quotes Inquiry_quotes Dialogue_quotes Possibility_quotes Alternative_quotes Definition_quotes Process_quotes Product_quotes Invite_quotes Worker_quotes Abstract_quotes Precinct_quotes Management_quotes Provide_quotes Access_quotes Broad_quotes View_quotes Business_quotes Deep_quotes Understand_quotes Task_quotes Role_quotes Sphere_quotes Wide_quotes Synonymous_quotes Engender_quotes Open_quotes Way_quotes Envision_quotes Reign_quotes
Panopticon Coaching
“To be sure, some managers saw the enhanced certainty of their information about subordinates as an opportunity to accelerate learning and improve performance. They found that the data from the Overview System made it possible to survey a wide range of behavior for evidence of irregularities. To the extent that the sources of such irregularities could be discovered, the data could be used to 'coach' subordinates and to help them realign their behavior within acceptable standards... On the other hand, many of Cedar Bluff's managers were more interested in the possibility of obtaining foolproof data than in the opportunity of using such data for coaching. In the context of a conventional technology, managers had to rely on their subordinates for valid information. The quality of the data they received was likely to be a function of the quality of the reciprocal relationship they had established. In contrast, an informating technology could provide valid information under any conditions.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 324. ISBN:0465032125.
- Keywords: Panopticon_quotes Management_quotes Certainty_quotes Information_quotes Subordinate_quotes Opportunity_quotes Learning_quotes Improvement_quotes Performance_quotes Data_quotes Surveillance_quotes Behavior_quotes Evidence_quotes Irregularity_quotes Discovery_quotes Coaching_quotes Alignment_quotes Acceptability_quotes Standards_quotes Possibility_quotes Foolproof_quotes Convention_quotes Technology_quotes Reliance_quotes Dependency_quotes Validity_quotes Quality_quotes Reciprocal_quotes Relationship_quotes Condition_quotes Reliability_quotes Enhance_quotes Accelerate_quotes Overview_quotes System_quotes Survey_quotes Wide_quotes Range_quotes Source_quotes Help_quotes Realign_quotes Acceptable_quotes Manager_quotes Interest_quotes Context_quotes Conventional_quotes Rely_quotes Valid_quotes Information_quotes Quality_quotes Receive_quotes Function_quotes Relationship_quotes Establish_quotes Contrast_quotes Informate_quotes
Panoptic Power
“This rendering of panoptic power reflects an important evolution of the original concept. It rests on a new collectivism in which ‘the many' view themselves and each views ‘the other.' Horizontal visibility is created even as vertical visibility is intensified. The model is less one of Big Brother than of a workplace in which each member is explicitly empowered as his or her fellow worker's keeper. Instead of a single omniscient overseer, this panopticon relies upon shared custodianship of data that reflect mutually enacted behavior. This new collectivism is an important antidote to the unilateral use of panoptic power, but it is not a trouble-free ideal. Horizontal transparency breeds new human dilemmas as well.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 351. ISBN:0465032125.
- Keywords: Panopticon_quotes Power_quotes Importance_quotes Evolution_quotes Originality_quotes Conception_quotes Collectivism_quotes Other_quotes Tribalism_quotes Visibility_quotes Horizontal_quotes Vertical_quotes Model_quotes Surveillance_quotes Workplace_quotes Empowerment_quotes Omniscience_quotes Informant_quotes Supervision_quotes Custodian_quotes Data_quotes Behavior_quotes Antidote_quotes Unilateral_quotes Whistle_Blower_quotes Trouble_quotes Transparency_quotes Humanity_quotes Dilemma_quotes Render_quotes Reflect_quotes Many_quotes View_quotes Create_quotes Intensify_quotes Big_quotes Brother_quotes Member_quotes Explicit_quotes Keeper_quotes Overseer_quotes Rely_quotes Share_quotes Mutual_quotes Enact_quotes Collective_quotes Free_quotes
Highly Informated Database
“In the highly informated organization, the data base takes on a life of its own.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 393. ISBN:0465032125.
- Keywords: Informating_quotes Organization_quotes Database_quotes Data_quotes Life_quotes Control_quotes
Management Circles Around Data
“As the intellective skill base becomes the organization's most precious resource, managerial roles must function to enhance its quality. Members can be thought of as being arrayed in concentric circles around a central core, which is the electronic data base.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 396. ISBN:0465032125.
- Keywords: Intellectualism_quotes Skill_quotes Circle_quotes Organization_quotes Resource_quotes Management_quotes Quality_quotes Database_quotes Data_quotes Base_quotes Precious_quotes Role_quotes Function_quotes Enhance_quotes Member_quotes Concentric_quotes Central_quotes Core_quotes Electronic_quotes
Panopticon Recording
“Information systems that translate, record, and display human behavior can provide the computer age version of universal transparency with a degree of illumination that would have exceeded even Bentham's most outlandish fantasies. Such systems can become information panopticons that, freed from the constraints of space and time, do not depend upon the physical arrangement of buildings or the laborious record keeping of industrial administration. They do not require the mutual presence of objects of observation. They do not even require the presence of an observer. Information systems can automatically and continuously record almost anything their designers want to capture, regardless of the specific intentions brought to the design process or the motives that guide data interpretation and utilization.”
- Source: Zuboff, S. (1988). In The Age Of The Smart Machine: The Future Of Work And Power. New York, NY: Basic Books, p. 322. ISBN:0465032125.
- Keywords: Panopticon_quotes Information_quotes System_quotes Translation_quotes Display_quotes Computer_quotes Surveillance_quotes Universal_quotes Transparency_quotes Illumination_quotes Fantasy_quotes Constraint_quotes Time_quotes Space_quotes Dependency_quotes Physical_quotes Arrangement_quotes Administration_quotes Building_quotes Industrial_quotes Object_quotes Observation_quotes Automation_quotes Record_quotes Design_quotes Capture_quotes Intentional_quotes Process_quotes Motivation_quotes Guidance_quotes Interpretation_quotes Utilization_quotes Human_quotes Behavior_quotes Provide_quotes Age_quotes Version_quotes Free_quotes Laborious_quotes Keep_quotes Require_quotes Presense_quotes Observer_quotes Continuous_quotes Designer_quotes Regardless_quotes Specific_quotes Data_quotes
