Slides from Lecture 1 of "Data: Past, Present, and Future",
Jan 17 2018.
New class on how data is impacting our professional, political, and personal realities. Taught by Profs Matt Jones and Chris Wiggins
Talk delivered 2019-06-25 as part of the Summer Institute in Computational Social Science, held at Princeton University https://compsocialscience.github.io/summer-institute/2019/
Video: https://www.youtube.com/watch?v=0suLWheVji0
title:
what should future statisticians, CEO, and senators know about the
history and ethics of data?
abstract:
What should our future statisticians, senators, and CEOs know about the history and ethics of data?
How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms?
I'll present content from a class co-developed over the past several years with Professor Matt Jones of Columbia's Department of History, based on material absent from both the curriculum for future technologists as well as for future humanists.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literac...chris wiggins
Data-empowered algorithms are reshaping our professional, personal, and political realities.
However, existing curricula are predominantly designed either for future technologists, focusing on functional capabilities; or for future humanists, focusing on critical and rhetorical context surrounding data.
"Data: Past, Present, and Future" is a new course at Columbia which seeks to define a curriculum at present taught to neither group, yet of interest and utility to future statisticians, CEOs, and senators alike.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
The weekly cadence of the course pairs primary and secondary readings with Jupyter notebooks in Python, engaging directly with the data and intellectual advances under study.
Throughout, these intellectual technical advances are paired with critical inquiry into the forces which encouraged and benefited from these new capabilities, i.e., the political dimension of data and technology.
Syllabus, Jupyter notebooks, and additional info can be found via https://data-ppf.github.io/
"Data: Past, Present, and Future" is supported by the Columbia University Collaboratory Fellows Fund. Jointly founded by Columbia University’s Data Science Institute and Columbia Entrepreneurship, The Collaboratory@Columbia is a university-wide program dedicated to supporting collaborative curricula innovations designed to ensure that all Columbia University students receive the education and training that they need to succeed in today’s data rich world.
"data: past, present, and future" day 1 lecture 2020-01-20chris wiggins
What should our future statisticians, senators, and CEOs know about the history and ethics of data? How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms? We've developed a course that introduces students, without prerequisites, to a historical view of our present condition, in which data-empowered algorithms shape our personal, professional, and political realities. The course attempts to integrate critical data studies with functional engagement with data (in Python via Jupyter notebooks), and interleaves an applied view of ethics throughout. The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Computing, cognition and the future of knowing,. by IBMVirginia Fernandez
How humans and machines are forging a new age of understanding.
-The history of computing and the rise of cognitive
-The world’s first cognitive system.
-The technical path forward and the science of what’s possible
-Implications and obligations for the advance of cognitive science.
-Paving the way for the next generation of human cognition.
Learning to trust artificial intelligence systems accountability, compliance ...Diego Alberto Tamayo
It’s not surprising that the
public’s imagination has
been ignited by Artificial
Intelligence since the term
was first coined in 1955.
In the ensuing 60 years,
we have been alternately
captivated by its promise,
wary of its potential for
abuse and frustrated by
its slow development.
Talk delivered 2019-06-25 as part of the Summer Institute in Computational Social Science, held at Princeton University https://compsocialscience.github.io/summer-institute/2019/
Video: https://www.youtube.com/watch?v=0suLWheVji0
title:
what should future statisticians, CEO, and senators know about the
history and ethics of data?
abstract:
What should our future statisticians, senators, and CEOs know about the history and ethics of data?
How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms?
I'll present content from a class co-developed over the past several years with Professor Matt Jones of Columbia's Department of History, based on material absent from both the curriculum for future technologists as well as for future humanists.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Data: Past, Present, and Future (Cornell Digital Life Seminar on Data Literac...chris wiggins
Data-empowered algorithms are reshaping our professional, personal, and political realities.
However, existing curricula are predominantly designed either for future technologists, focusing on functional capabilities; or for future humanists, focusing on critical and rhetorical context surrounding data.
"Data: Past, Present, and Future" is a new course at Columbia which seeks to define a curriculum at present taught to neither group, yet of interest and utility to future statisticians, CEOs, and senators alike.
The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
The weekly cadence of the course pairs primary and secondary readings with Jupyter notebooks in Python, engaging directly with the data and intellectual advances under study.
Throughout, these intellectual technical advances are paired with critical inquiry into the forces which encouraged and benefited from these new capabilities, i.e., the political dimension of data and technology.
Syllabus, Jupyter notebooks, and additional info can be found via https://data-ppf.github.io/
"Data: Past, Present, and Future" is supported by the Columbia University Collaboratory Fellows Fund. Jointly founded by Columbia University’s Data Science Institute and Columbia Entrepreneurship, The Collaboratory@Columbia is a university-wide program dedicated to supporting collaborative curricula innovations designed to ensure that all Columbia University students receive the education and training that they need to succeed in today’s data rich world.
"data: past, present, and future" day 1 lecture 2020-01-20chris wiggins
What should our future statisticians, senators, and CEOs know about the history and ethics of data? How might understanding that history provide tools and resources to future citizens navigating a future shaped by data empowered algorithms? We've developed a course that introduces students, without prerequisites, to a historical view of our present condition, in which data-empowered algorithms shape our personal, professional, and political realities. The course attempts to integrate critical data studies with functional engagement with data (in Python via Jupyter notebooks), and interleaves an applied view of ethics throughout. The intellectual arc traces from the 18th century to present day, beginning with examples of contemporary technological advances, disquieting ethical debates, and financial success powered by panoptic persuasion architectures.
Computing, cognition and the future of knowing,. by IBMVirginia Fernandez
How humans and machines are forging a new age of understanding.
-The history of computing and the rise of cognitive
-The world’s first cognitive system.
-The technical path forward and the science of what’s possible
-Implications and obligations for the advance of cognitive science.
-Paving the way for the next generation of human cognition.
Learning to trust artificial intelligence systems accountability, compliance ...Diego Alberto Tamayo
It’s not surprising that the
public’s imagination has
been ignited by Artificial
Intelligence since the term
was first coined in 1955.
In the ensuing 60 years,
we have been alternately
captivated by its promise,
wary of its potential for
abuse and frustrated by
its slow development.
Creating a Data-Driven Government: Big Data With PurposeTyrone Grandison
The U.S. Department of Commerce collects, processes and disseminates data on a range of issues that impact our nation. Whether it's data on the economy, the environment, or technology, data is critical in fulfilling the Department's mission of creating the conditions for economic growth and opportunity. It is this data that provides insight, drives innovation, and transforms our lives. The U.S. Department of Commerce has become known as "America's Data Agency" due to the tens of thousands of datasets including satellite imagery, material standards and demographic surveys.
But having a host of data and ensuring that this data is open and accessible to all are two separate issues. The latter, expanding open data access, is now a key pillar of the Commerce Department's mission. It was this focus on enhancing open data that led to the creation of the Commerce Data Service (CDS).
The mission at the Commerce Data Service is to enable more people to use big data from across the department in innovative ways and across multiple fields. In this talk, I will explore how we are using big data to create a data-driven government.
This talk is a keynote given at the Texas tech University's Big Data Symposium.
Presentación utilizada por por Anxo Sanchez (@anxosan) en la segunda sesión del Curso de Introducción a los Sistemas organizado por la Fundacion Sicomoro y Complejimad
A Glimpse Into the Future of Data Science - What's Next for AI, Big Data & Ma...Pangea.ai
We are living in the era of "the fourth industrial revolution". How did we get here? Read this presentation to explore current application trends in Artificial Intelligence (AI,) The Internet of Things (IoT), Big Data, and Machine Learning (ML) technology. Also, to discover the future implications of big data in our lives.
Read the original article here: https://www.pangea.ai/data-science-resources/future-of-data-science/
Work with a data science expert at Pangea: https://www.pangea.ai/
This is follow-up from the IBM Almaden Sept 27th meeting on "Regional Upward Spirals: The Co-Evolution of Future Technologies, Skills, Jobs, and Quality-of-Life"
Data Science from the Perspective of an Applied EconomistScott Nicholson
This is a talk I gave at Strata NYC 2011 about the contributions of applied economists to data science teams and how their analytical approach can differ from that of computer scientists (machine learning) and statisticians.
Big data, bad data -- Closing keynote at the Open World Forum 2013Rayna Stamboliyska
In this talk, I partnered with Dr. Rand Hindi (Snips) and Romain Lacombe (French Prime Minister's Commission Etalab) addressing various facets surrounding Big Data. My point was to critically discuss common misconceptions in both Big Data meaning and analysis.
Presentation developed for NH IEEE groups, and adapted for life long learning audiences. Big Data has reached a tipping point, where applications are multiplying, many using our personal data in ways we do not expect, or necessarily agree with. This is augmented by Artificial Intelligence which can detect far more subtle criteria than a human might. A peek into the future -- for better or worse. Related course syllabus at: https://is.gd/BigDataIssues
Science, Social Science, & Life Science
Science is based on empirical verification and faith in the law of uniformity (what happened in the past will happen in the future.). Science deals with Invariant Data and 5 variables.
Due to the consideration of infinity, which, by definition, never arrives, social science, which deals with around 50 variables, must be based on mathematical rigor.
When the final variable is Variant Data, it is calculated from a deterministic system and is not empirically verifiable.
Life or computer science, such as DNA and computer software, dealing with around 500 variables, must be based on logic due to unlimited complexity and the involvement of infinity. DNA itself is Big Data, and its effect is not subjected to empirical verification.
a mission-driven approach to personalizing the customer journeychris wiggins
Keynote talk at PyData NYC 2019 by Anne Bauer, Lead Data Scientist, The New York Times, and Chris Wiggins, Chief Data Scientist, The New York Times.
"Data science at The New York Times: a mission-driven approach to personalizing the customer journey"
How does The New York Times use data science to further its mission?
We'll talk about the use of machine learning throughout the company,
from social media promotion to targeted advertising to content
recommendations, and the cross-team collaborations that make it
possible.
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Creating a Data-Driven Government: Big Data With PurposeTyrone Grandison
The U.S. Department of Commerce collects, processes and disseminates data on a range of issues that impact our nation. Whether it's data on the economy, the environment, or technology, data is critical in fulfilling the Department's mission of creating the conditions for economic growth and opportunity. It is this data that provides insight, drives innovation, and transforms our lives. The U.S. Department of Commerce has become known as "America's Data Agency" due to the tens of thousands of datasets including satellite imagery, material standards and demographic surveys.
But having a host of data and ensuring that this data is open and accessible to all are two separate issues. The latter, expanding open data access, is now a key pillar of the Commerce Department's mission. It was this focus on enhancing open data that led to the creation of the Commerce Data Service (CDS).
The mission at the Commerce Data Service is to enable more people to use big data from across the department in innovative ways and across multiple fields. In this talk, I will explore how we are using big data to create a data-driven government.
This talk is a keynote given at the Texas tech University's Big Data Symposium.
Presentación utilizada por por Anxo Sanchez (@anxosan) en la segunda sesión del Curso de Introducción a los Sistemas organizado por la Fundacion Sicomoro y Complejimad
A Glimpse Into the Future of Data Science - What's Next for AI, Big Data & Ma...Pangea.ai
We are living in the era of "the fourth industrial revolution". How did we get here? Read this presentation to explore current application trends in Artificial Intelligence (AI,) The Internet of Things (IoT), Big Data, and Machine Learning (ML) technology. Also, to discover the future implications of big data in our lives.
Read the original article here: https://www.pangea.ai/data-science-resources/future-of-data-science/
Work with a data science expert at Pangea: https://www.pangea.ai/
This is follow-up from the IBM Almaden Sept 27th meeting on "Regional Upward Spirals: The Co-Evolution of Future Technologies, Skills, Jobs, and Quality-of-Life"
Data Science from the Perspective of an Applied EconomistScott Nicholson
This is a talk I gave at Strata NYC 2011 about the contributions of applied economists to data science teams and how their analytical approach can differ from that of computer scientists (machine learning) and statisticians.
Big data, bad data -- Closing keynote at the Open World Forum 2013Rayna Stamboliyska
In this talk, I partnered with Dr. Rand Hindi (Snips) and Romain Lacombe (French Prime Minister's Commission Etalab) addressing various facets surrounding Big Data. My point was to critically discuss common misconceptions in both Big Data meaning and analysis.
Presentation developed for NH IEEE groups, and adapted for life long learning audiences. Big Data has reached a tipping point, where applications are multiplying, many using our personal data in ways we do not expect, or necessarily agree with. This is augmented by Artificial Intelligence which can detect far more subtle criteria than a human might. A peek into the future -- for better or worse. Related course syllabus at: https://is.gd/BigDataIssues
Science, Social Science, & Life Science
Science is based on empirical verification and faith in the law of uniformity (what happened in the past will happen in the future.). Science deals with Invariant Data and 5 variables.
Due to the consideration of infinity, which, by definition, never arrives, social science, which deals with around 50 variables, must be based on mathematical rigor.
When the final variable is Variant Data, it is calculated from a deterministic system and is not empirically verifiable.
Life or computer science, such as DNA and computer software, dealing with around 500 variables, must be based on logic due to unlimited complexity and the involvement of infinity. DNA itself is Big Data, and its effect is not subjected to empirical verification.
a mission-driven approach to personalizing the customer journeychris wiggins
Keynote talk at PyData NYC 2019 by Anne Bauer, Lead Data Scientist, The New York Times, and Chris Wiggins, Chief Data Scientist, The New York Times.
"Data science at The New York Times: a mission-driven approach to personalizing the customer journey"
How does The New York Times use data science to further its mission?
We'll talk about the use of machine learning throughout the company,
from social media promotion to targeted advertising to content
recommendations, and the cross-team collaborations that make it
possible.
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abstract
The Data Science group at The New York Times develops and deploys
machine learning solutions to newsroom and business problems.
Re-framing real-world questions as machine learning tasks requires not
only adapting and extending models and algorithms to new or special
cases but also sufficient breadth to know the right method for the
right challenge. I'll first outline how unsupervised, supervised, and
reinforcement learning methods are increasingly used in human
applications for description, prediction, and prescription,
respectively. I'll then focus on the 'prescriptive' cases, showing how
methods from the reinforcement learning and causal inference
literatures can be of direct impact in engineering, business, and
decision-making more generally.
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https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931
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references: http://bit.ly/icerm
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with support from NSF award 1305023
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11. how did this end up in my news feed?
- math
- hardware
- system
- funding
- market
- regulation
- data
this was not possible 20 years ago.
- why?
- what did people do instead?
17. “In all cultures and all periods of
recorded human history, people
share the belief that the face alone
suffices to reveal innate traits of a
person.”
22. Statistical sciences always political
Dream of sciences of social difference
Central to development of
Statistics
And the
Data sciences
23. Florence Nightingale & Data Visualization
“Experience has shown that without
special information and skilful
application of the resources of
science in preserving health, the
drain on our home population must
exhaust our means. The
introduction, therefore, of a proper
sanitary system into the British army
is of essential importance to the
public interests.”
24. Florence Nightingale & Data Visualization
“Upon the British race alone the
integrity of that empire at this
moment appears to depend. The
conquering race must retain
possession.”
25. Every week
Scientific and mathematical development
Technologies and engineering
Driving forces: money, prestige, resources, Imperial competition
Power, ethics, and data intensive knowledge
26. Tech story: three chronological stages
Data and Math
Data and Engineering
Data and Technology
27. Data technologies
Census and government survey
Information processing machines and
digital computers
Always on network infrastructure
28. Power
How should social and political order be organized on basis
of science and engineering?
How do technologies transform the social and political order?
How do technologies augment and diminish democratic orders? Autocratic ones?
29. Power and politics*
New technologies mean new capabilities
These capabilities are first available to those in power
(cf., “The future is already here — it's just not very evenly distributed.” --Gibson)
How does this distribution of capability reorder power?
How are data-empowered algorithms an example of this dynamic
- of capability, and
- of reinforcing or distributing power?
* politics here meaning the dynamics of power, not to be confused with “voting”
40. Experimental design, hypothesis test, decision theory
To play this game with the greatest chance of
success, the experimenter cannot afford to
exclude the possibility of any possible
arrangement of soil fertilities, and his best
strategy is to equalize the chance that any
treatment shall fall on any plot by determining
it by chance himself.
-R.A. Fisher
41. World War 2: Turing and statistical cryptography
49. Weekly structure
Monday
Lecture and discussion
Expectation
arrive having done the week’s readings
Wednesday
Laboratory
Expectation
arrive with laptop ready to collaborate
51. Two tracks
more technical background track (60%)
● pursue a semester long project
culminating in a 15pp paper and any
associated code
●
● complete 3 problem sets
●
● short final presentation on paper
more humanistic background track (60%)
● write a 10 pp paper on a topic of their
choice
●
● complete 5 problem sets, these problem
sets will involve both computational work
and writing work
●
● short final presentation on paper
53. Required work
Postings on readings in slack
Problem sets (extensions of lab work, done in Jupyter)
Final paper
Your presence
54. Hardest part of class: Registration
CC + Barnard + GS
History-APAM 2901 Call number: 72327
SEAS
Applied Math 82901 Call Number: 72327
A+S and Consortium Grad:
History GR6998 section #10 Call number: 86347