History of data
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Description
Book Introduction
Human's hidden desires and hidden power relationships
All about data, shown without hiding anything!
The 2024 Nobel Prize was a one-man show for AI.
Following the Nobel Prize in Physics going to AI machine learning researchers, the Nobel Prize in Chemistry has also gone to researchers in the field of AI.
AI swept two of the three Nobel Prizes in science: the Nobel Prize in Physiology or Medicine, the Nobel Prize in Physics, and the Nobel Prize in Chemistry.
As AI gains attention in the field of science and technology, the importance of data, which forms the basis of AI technology, is increasingly highlighted.
Data has now become a tool for predicting, evaluating and controlling the behavior of all human societies.
We have entered an era where who will have future power will be determined by who collects, processes, and handles more data.
So when did humanity begin collecting information about people and using it as a tool to predict and manage the future? How did data become a more powerful tool than anything else?
This book traces the history of data, tracing how data has been created and utilized throughout human history, as well as how new mathematical and computational techniques have been competitively developed to leverage such data to operate more effective socioeconomic systems.
Furthermore, it traces the development of data over the centuries, from the census to the rise of statistics and eugenics to Google search, exploring how data has reshaped the power structure of society and the nature of power revealed in the digital age, shedding new light on the present and future of power between corporations, the state, and citizens.
In modern scientific and technological society, data means power.
Even the power to define what is true is based on data.
The authors of this book argue that while technology and mathematics are at the heart of the history of data, it is ultimately a story about the volatile games played between states, corporations, and citizens.
Furthermore, it analyzes how data reinforces social inequality and distorts reality, and what risks a system where everything is determined by data poses to us.
For all users and developers living in the age of technology, this book will serve as a guide for those who want to understand where technology is headed and how we can collectively shape that future.
In the process, you will also understand the truth about data and the history of power.
All about data, shown without hiding anything!
The 2024 Nobel Prize was a one-man show for AI.
Following the Nobel Prize in Physics going to AI machine learning researchers, the Nobel Prize in Chemistry has also gone to researchers in the field of AI.
AI swept two of the three Nobel Prizes in science: the Nobel Prize in Physiology or Medicine, the Nobel Prize in Physics, and the Nobel Prize in Chemistry.
As AI gains attention in the field of science and technology, the importance of data, which forms the basis of AI technology, is increasingly highlighted.
Data has now become a tool for predicting, evaluating and controlling the behavior of all human societies.
We have entered an era where who will have future power will be determined by who collects, processes, and handles more data.
So when did humanity begin collecting information about people and using it as a tool to predict and manage the future? How did data become a more powerful tool than anything else?
This book traces the history of data, tracing how data has been created and utilized throughout human history, as well as how new mathematical and computational techniques have been competitively developed to leverage such data to operate more effective socioeconomic systems.
Furthermore, it traces the development of data over the centuries, from the census to the rise of statistics and eugenics to Google search, exploring how data has reshaped the power structure of society and the nature of power revealed in the digital age, shedding new light on the present and future of power between corporations, the state, and citizens.
In modern scientific and technological society, data means power.
Even the power to define what is true is based on data.
The authors of this book argue that while technology and mathematics are at the heart of the history of data, it is ultimately a story about the volatile games played between states, corporations, and citizens.
Furthermore, it analyzes how data reinforces social inequality and distorts reality, and what risks a system where everything is determined by data poses to us.
For all users and developers living in the age of technology, this book will serve as a guide for those who want to understand where technology is headed and how we can collectively shape that future.
In the process, you will also understand the truth about data and the history of power.
- You can preview some of the book's contents.
Preview
index
Prologue Data: The History of Truth and Power
Data: The Key to Understanding Modern Society
About this book
PART 1: The Birth of Data
Chapter 1: Warnings About Data Becoming Power
The threat of data has become a reality
The Limits of Technological Determinism
Chapter 2: Defining Society in Numbers
Governing the world with statistics
Finding the regularities of the world
The Birth of the Average Person
A new science about humans
Interpreting Humans with Statistical Laws
The Science of Individual Differences
For a new science of human improvement
Chapter 3: Scientific Solutions to Social Problems
Eugenics born in crisis
Correlation of data
Mathematical statistics, the foundation of eugenics
The Truth Revealed by Eugenic Statistics
Interpret society through a scientific lens
Causation, not correlation
Data-driven racism
New biometric program
Causes of differences between races and classes
Chapter 4 The Science of Individual Differences
Analyzing the causes of poverty
From correlation to causation
The dangers of statistical work
The Birth of Intelligence Testing
Human life becomes data
The Arrogance of Scientific Racism
Chapter 5 What is the data for?
The Science of Beer Brewing
Science for Truth
Science for Decision Making
Algorithm of Truth
The development of statistics brought about by war
PART 2 Evolving Data
Chapter 6 War and Data
Bletchley Park's Secret Project
American cryptography
Bayes' theorem defines the probability of miracles.
Investing in bigger, faster computers
Data for non-encrypted communications
Data Becomes Business
Chapter 7: In Search of the Principles of Human Intelligence
Turing designs a thinking machine
Oppose data
The Birth of Artificial Intelligence
What is intelligence?
Funding for AI
Imitate the knowledge of experts
Find the rules for acquiring knowledge
Back to data
Chapter 8: The Age of Big Data
Technologies for data processing
The Value of Information and the Resurgence of Privacy
Weakened privacy
Generating value from data
Chapter 9 Self-Learning Machines
Mimicking the human neural network
Learning based on pattern recognition
The Amazing Success of Machine Learning
From artificial intelligence to machine learning
Soviet data industry
Finding the best answer with a neural network
Machine learning algorithms
Netflix Awards
Chapter 10: Evolving Data Science
Attributes of Data Science
Tools for data analysis
data mining
The structure of the Google search system
From data mining to big data
Artificial intelligence
Embracing Statistics and Data Science
The Rise of the Data Scientist
Ethics without expertise
PART 3: Data Becomes Power
Chapter 11: The Ethical War Surrounding Data
For ethical algorithms
From Tuskegee to Belmont
The principles of the Belmont Report
Applying it to Silicon Valley
Having ethics
Limitations of technical solutions
To avoid being surrounded by regulatory agencies
Chapter 12: The Birth of the Attention Economy
Attention has become more valuable
The Meaning of Attention in the Internet World
The price of free information
Advertising and Reality
The emergence of venture capital
The consequences of the attention economy
Chapter 13 Solutions Beyond Solutionism
First Power: Corporate Power
Second power: state power
Third Power: Civil Power
civil power
Unstable games
Acknowledgements
main
Search
Data: The Key to Understanding Modern Society
About this book
PART 1: The Birth of Data
Chapter 1: Warnings About Data Becoming Power
The threat of data has become a reality
The Limits of Technological Determinism
Chapter 2: Defining Society in Numbers
Governing the world with statistics
Finding the regularities of the world
The Birth of the Average Person
A new science about humans
Interpreting Humans with Statistical Laws
The Science of Individual Differences
For a new science of human improvement
Chapter 3: Scientific Solutions to Social Problems
Eugenics born in crisis
Correlation of data
Mathematical statistics, the foundation of eugenics
The Truth Revealed by Eugenic Statistics
Interpret society through a scientific lens
Causation, not correlation
Data-driven racism
New biometric program
Causes of differences between races and classes
Chapter 4 The Science of Individual Differences
Analyzing the causes of poverty
From correlation to causation
The dangers of statistical work
The Birth of Intelligence Testing
Human life becomes data
The Arrogance of Scientific Racism
Chapter 5 What is the data for?
The Science of Beer Brewing
Science for Truth
Science for Decision Making
Algorithm of Truth
The development of statistics brought about by war
PART 2 Evolving Data
Chapter 6 War and Data
Bletchley Park's Secret Project
American cryptography
Bayes' theorem defines the probability of miracles.
Investing in bigger, faster computers
Data for non-encrypted communications
Data Becomes Business
Chapter 7: In Search of the Principles of Human Intelligence
Turing designs a thinking machine
Oppose data
The Birth of Artificial Intelligence
What is intelligence?
Funding for AI
Imitate the knowledge of experts
Find the rules for acquiring knowledge
Back to data
Chapter 8: The Age of Big Data
Technologies for data processing
The Value of Information and the Resurgence of Privacy
Weakened privacy
Generating value from data
Chapter 9 Self-Learning Machines
Mimicking the human neural network
Learning based on pattern recognition
The Amazing Success of Machine Learning
From artificial intelligence to machine learning
Soviet data industry
Finding the best answer with a neural network
Machine learning algorithms
Netflix Awards
Chapter 10: Evolving Data Science
Attributes of Data Science
Tools for data analysis
data mining
The structure of the Google search system
From data mining to big data
Artificial intelligence
Embracing Statistics and Data Science
The Rise of the Data Scientist
Ethics without expertise
PART 3: Data Becomes Power
Chapter 11: The Ethical War Surrounding Data
For ethical algorithms
From Tuskegee to Belmont
The principles of the Belmont Report
Applying it to Silicon Valley
Having ethics
Limitations of technical solutions
To avoid being surrounded by regulatory agencies
Chapter 12: The Birth of the Attention Economy
Attention has become more valuable
The Meaning of Attention in the Internet World
The price of free information
Advertising and Reality
The emergence of venture capital
The consequences of the attention economy
Chapter 13 Solutions Beyond Solutionism
First Power: Corporate Power
Second power: state power
Third Power: Civil Power
civil power
Unstable games
Acknowledgements
main
Search
Detailed image
Into the book
Those in power are reluctant to examine the historical processes that created and dominated their current power.
This is because, when we look at complex history, the necessity and legitimacy of the power they hold becomes unstable.
By examining the often unpredictable processes by which technologies have gained prominence, history can jeopardize so-called "technological determinism," the idea that the growth of particular technologies themselves drives history.
--- p.33
As Lisa Gitelman noted years ago, “raw data is an oxymoron.” This is because every step of the data collection process involves human choices—what to select, how to sort, who to include and who to exclude.
All data collection involves very different infrastructures to sort, store, and process that information, along with cognitive biases on the part of the collectors.
In 1600, 1780, and 2022, data was not discovered, but created.
How did this data become so powerful? How were the structures for collecting, storing, and analyzing it created?
--- p.42~43
He said his achievement was to show that even moral phenomena, when observed through data, resemble celestial phenomena.
“The more we observe individuals, the more their individual peculiarities, whether physical or moral, are erased, and only the general facts remain distinct as societies exist and persist.” Understanding human society means understanding these general facts, and this is made possible by accumulating a lot of data about the society and its people.
--- p.52
Philip Rogaway writes, “Crypto is redistributing power.”
Data “defines who can do what and through what.”
World War II proved it.
Codebreaking fundamentally changed the global power dynamics by helping the Allies achieve decisive victories in the Pacific and Europe through better intelligence.
--- p.135
Researchers in the late 1980s and 1990s abandoned the goal of using computers to simulate human thought or to understand human cognition.
The pursuit of 'what works' rather than 'what is true or beautiful' encouraged experimentation with a wide variety of algorithms and attempts to understand data in all sorts of ways.
The field of machine learning has slowly but surely embraced these values—a tradition of practical engineering rather than pure science.
It seemed to follow the industrial approach more than the academic one.
To achieve this, machine learning researchers have increasingly increased computational time, albeit in an uneven manner, an approach that is feasible at least in well-funded research labs.11 This eclecticism has led machine learning to utilize a wide variety of algorithms across many research fields.
--- p.219~220
There are many roots in data science.
It involves a lot of engineering as well as sophisticated mathematics.
It's relevant not only to university lecture halls, but also to sales departments and politicians' war rooms.
The impure nature of data science tells a key story we're tracking.
In other words, increasingly automated decision-making models are being combined with large-scale infrastructure that enables such processing.
Data science emerged from the combination of statistics, machine learning, and the analytical processing of data within large and small companies.
To understand the rise of data science, we need to move between the world of computational statisticians, who warn of "over-mathematicalization," and the developments within industry.
This is because, when we look at complex history, the necessity and legitimacy of the power they hold becomes unstable.
By examining the often unpredictable processes by which technologies have gained prominence, history can jeopardize so-called "technological determinism," the idea that the growth of particular technologies themselves drives history.
--- p.33
As Lisa Gitelman noted years ago, “raw data is an oxymoron.” This is because every step of the data collection process involves human choices—what to select, how to sort, who to include and who to exclude.
All data collection involves very different infrastructures to sort, store, and process that information, along with cognitive biases on the part of the collectors.
In 1600, 1780, and 2022, data was not discovered, but created.
How did this data become so powerful? How were the structures for collecting, storing, and analyzing it created?
--- p.42~43
He said his achievement was to show that even moral phenomena, when observed through data, resemble celestial phenomena.
“The more we observe individuals, the more their individual peculiarities, whether physical or moral, are erased, and only the general facts remain distinct as societies exist and persist.” Understanding human society means understanding these general facts, and this is made possible by accumulating a lot of data about the society and its people.
--- p.52
Philip Rogaway writes, “Crypto is redistributing power.”
Data “defines who can do what and through what.”
World War II proved it.
Codebreaking fundamentally changed the global power dynamics by helping the Allies achieve decisive victories in the Pacific and Europe through better intelligence.
--- p.135
Researchers in the late 1980s and 1990s abandoned the goal of using computers to simulate human thought or to understand human cognition.
The pursuit of 'what works' rather than 'what is true or beautiful' encouraged experimentation with a wide variety of algorithms and attempts to understand data in all sorts of ways.
The field of machine learning has slowly but surely embraced these values—a tradition of practical engineering rather than pure science.
It seemed to follow the industrial approach more than the academic one.
To achieve this, machine learning researchers have increasingly increased computational time, albeit in an uneven manner, an approach that is feasible at least in well-funded research labs.11 This eclecticism has led machine learning to utilize a wide variety of algorithms across many research fields.
--- p.219~220
There are many roots in data science.
It involves a lot of engineering as well as sophisticated mathematics.
It's relevant not only to university lecture halls, but also to sales departments and politicians' war rooms.
The impure nature of data science tells a key story we're tracking.
In other words, increasingly automated decision-making models are being combined with large-scale infrastructure that enables such processing.
Data science emerged from the combination of statistics, machine learning, and the analytical processing of data within large and small companies.
To understand the rise of data science, we need to move between the world of computational statisticians, who warn of "over-mathematicalization," and the developments within industry.
--- p.242~243
Publisher's Review
From the birth of statistics to algorithms,
How has data science transformed the structure of society?
The history of data begins with the introduction of the term 'statistics' into English in the 18th century.
'Statistics' was originally knowledge about the state and its resources, and was not a discipline for gaining insights such as quantitative research directions or predictions.
Beginning in the 18th century, Europeans charted figures on deaths, crimes, and diseases to strengthen government power and inform policy, and recorded many more aspects of human life in abstract mathematical terms.
And we began to develop new mathematical tools to record and examine such vast amounts of data.
The research of the Belgian astronomer Quetelet was the basis for statistics to become established as a new discipline that shows the flow of human society.
He applied the analytical methods used by astronomers to government statistics in an attempt to discover patterns in human society.
And furthermore, it dramatically changed the way we understand the world by endowing such regularities with properties that reveal meaning and reality.
The method of analyzing and interpreting statistical figures later led to the study of Francis Galton in the social trend of Britain expanding its influence throughout the world through imperialism, giving birth to a new discipline called eugenics.
Eugenics had numerous negative consequences, but his research led to a completely new approach to understanding human differences.
Statistics don't just represent the world.
It changes not only the way we categorize and perceive the world, but also the way we categorize others and ourselves.
And so it changes the world.
Statistics, which began with the research of an 18th-century astronomer, has completely transformed the face of human society.
Today, thanks to the wealth of data available about diverse populations, scientists, salespeople, the military, and spies can better understand and target individuals.
We also live in a world where our individuality is quantified compared to other Internet users, and where advertising algorithms use those quantified differences to compete for our attention.
Therefore, examining the history of data will provide us with an accurate and objective perspective on the current state of our society.
The data revolution ushered in an era of machine learning and artificial intelligence!
It was World War II, one of the greatest tragedies in human history, that propelled statistics to the next level as a new discipline focused on data.
During World War II, a group of scholars gathered at London's Bletchley Park used data to decipher German codes, helping the Allies achieve decisive victories in the Pacific and Europe through better intelligence, decisively shifting global power relations and leading to the birth of machine learning, which later became the foundation of artificial intelligence.
This book traces the birth of digital computing, from the military application of data for decryption to its application in business and technology since World War II.
It also provides a fascinating behind-the-scenes look at how the public's demand for the protection of digitized personal information, and the efforts to realize it, have progressed and been frustrated as power shifted from corporate power to state power and then to "citizen power."
We explore how the field of "artificial intelligence" emerged, faded, and then reemerged in the form of "machine learning" as data on citizens, consumers, and adversaries grew.
Machine learning, once a largely overlooked field in artificial intelligence, has enjoyed tremendous success since the turn of the millennium, making it a term now used interchangeably with artificial intelligence.
Machine learning approaches began to spread beyond academic centers and industry labs in the 1990s and 2000s.
Those who advocate industrial-scale machine learning and apply it to business and government activities began to be called "data scientists" in the 2010s.
This book offers a fascinating account of how tools were developed that allow everyone from scientists to journalists to leverage machine learning, and explains the principles that have made machine learning central to the infrastructure that mediates communications, science, journalism, and politics today.
Who really owns the data?
Today, the term 'data' is used to refer to data-driven, algorithm-based decision-making systems that surround us in almost every field.
This book clearly depicts the intellectual transformation surrounding data, which became the foundation of our science-and-technology society, how new technological and scientific capabilities were developed, and who supported, developed, or funded these capabilities and transformations.
It also provides a fascinating account of the competition surrounding such a shift, and how this new capacity reconfigured power—changing who could do what, where power came from, and to whom it was directed.
The authors of this book clearly reveal the historical tensions between corporate power, state power, and civil power, as well as the current tensions.
To this end, we focused on the role of data in establishing truth and fostering competition among such powers.
It also seeks to show how society as a whole has arrived at its present state, exposing the small coincidences, subjective design choices, and deceptions that make it seem as if things were 'just that way'.
Understanding these transitions and contingencies can help us understand how similar problems have been solved in the past.
Building on this historical understanding, we can gain new insights into how to dismantle and reassemble the skeleton of systems that often empower the powerless, but often empower the powerful.
How has data science transformed the structure of society?
The history of data begins with the introduction of the term 'statistics' into English in the 18th century.
'Statistics' was originally knowledge about the state and its resources, and was not a discipline for gaining insights such as quantitative research directions or predictions.
Beginning in the 18th century, Europeans charted figures on deaths, crimes, and diseases to strengthen government power and inform policy, and recorded many more aspects of human life in abstract mathematical terms.
And we began to develop new mathematical tools to record and examine such vast amounts of data.
The research of the Belgian astronomer Quetelet was the basis for statistics to become established as a new discipline that shows the flow of human society.
He applied the analytical methods used by astronomers to government statistics in an attempt to discover patterns in human society.
And furthermore, it dramatically changed the way we understand the world by endowing such regularities with properties that reveal meaning and reality.
The method of analyzing and interpreting statistical figures later led to the study of Francis Galton in the social trend of Britain expanding its influence throughout the world through imperialism, giving birth to a new discipline called eugenics.
Eugenics had numerous negative consequences, but his research led to a completely new approach to understanding human differences.
Statistics don't just represent the world.
It changes not only the way we categorize and perceive the world, but also the way we categorize others and ourselves.
And so it changes the world.
Statistics, which began with the research of an 18th-century astronomer, has completely transformed the face of human society.
Today, thanks to the wealth of data available about diverse populations, scientists, salespeople, the military, and spies can better understand and target individuals.
We also live in a world where our individuality is quantified compared to other Internet users, and where advertising algorithms use those quantified differences to compete for our attention.
Therefore, examining the history of data will provide us with an accurate and objective perspective on the current state of our society.
The data revolution ushered in an era of machine learning and artificial intelligence!
It was World War II, one of the greatest tragedies in human history, that propelled statistics to the next level as a new discipline focused on data.
During World War II, a group of scholars gathered at London's Bletchley Park used data to decipher German codes, helping the Allies achieve decisive victories in the Pacific and Europe through better intelligence, decisively shifting global power relations and leading to the birth of machine learning, which later became the foundation of artificial intelligence.
This book traces the birth of digital computing, from the military application of data for decryption to its application in business and technology since World War II.
It also provides a fascinating behind-the-scenes look at how the public's demand for the protection of digitized personal information, and the efforts to realize it, have progressed and been frustrated as power shifted from corporate power to state power and then to "citizen power."
We explore how the field of "artificial intelligence" emerged, faded, and then reemerged in the form of "machine learning" as data on citizens, consumers, and adversaries grew.
Machine learning, once a largely overlooked field in artificial intelligence, has enjoyed tremendous success since the turn of the millennium, making it a term now used interchangeably with artificial intelligence.
Machine learning approaches began to spread beyond academic centers and industry labs in the 1990s and 2000s.
Those who advocate industrial-scale machine learning and apply it to business and government activities began to be called "data scientists" in the 2010s.
This book offers a fascinating account of how tools were developed that allow everyone from scientists to journalists to leverage machine learning, and explains the principles that have made machine learning central to the infrastructure that mediates communications, science, journalism, and politics today.
Who really owns the data?
Today, the term 'data' is used to refer to data-driven, algorithm-based decision-making systems that surround us in almost every field.
This book clearly depicts the intellectual transformation surrounding data, which became the foundation of our science-and-technology society, how new technological and scientific capabilities were developed, and who supported, developed, or funded these capabilities and transformations.
It also provides a fascinating account of the competition surrounding such a shift, and how this new capacity reconfigured power—changing who could do what, where power came from, and to whom it was directed.
The authors of this book clearly reveal the historical tensions between corporate power, state power, and civil power, as well as the current tensions.
To this end, we focused on the role of data in establishing truth and fostering competition among such powers.
It also seeks to show how society as a whole has arrived at its present state, exposing the small coincidences, subjective design choices, and deceptions that make it seem as if things were 'just that way'.
Understanding these transitions and contingencies can help us understand how similar problems have been solved in the past.
Building on this historical understanding, we can gain new insights into how to dismantle and reassemble the skeleton of systems that often empower the powerless, but often empower the powerful.
GOODS SPECIFICS
- Date of issue: October 25, 2024
- Page count, weight, size: 428 pages | 150*220*30mm
- ISBN13: 9791198347039
- ISBN10: 1198347031
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