1 hr 26 min
Episode 20: Data Science: Past, Present, and Future Vanishing Gradients
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- Technology
Hugo speaks with Chris Wiggins (Columbia, NYTimes) and Matthew Jones (Princeton) about their recent book How Data Happened, and the Columbia course it expands upon, data: past, present, and future.
Chris is an associate professor of applied mathematics at Columbia University and the New York Times’ chief data scientist, and Matthew is a professor of history at Princeton University and former Guggenheim Fellow.
From facial recognition to automated decision systems that inform who gets loans and who receives bail, we all now move through a world determined by data-empowered algorithms. These technologies didn’t just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search.
DJ Patil, former U.S. Chief Data Scientist, said of the book "This is the first comprehensive look at the history of data and how power has played a critical role in shaping the history. It’s a must read for any data scientist about how we got here and what we need to do to ensure that data works for everyone."
If you’re a data scientist, machine learning engineer, or work with data in any way, it’s increasingly important to know more about the history and future of the work that you do and understand how your work impacts society and the world.
Among other things, they'll delve into
the history of human use of data;
how data are used to reveal insight and support decisions;
how data and data-powered algorithms shape, constrain, and manipulate our commercial, civic, and personal transactions and experiences; and
how exploration and analysis of data have become part of our logic and rhetoric of communication and persuasion.
You can also sign up for our next livestreamed podcast recording here!
LINKS
How Data Happened, the book!
data: past, present, and future, the course
Race After Technology, by Ruha Benjamin
The problem with metrics is a big problem for AI by Rachel Thomas
Vanishing Gradients on YouTube
Hugo speaks with Chris Wiggins (Columbia, NYTimes) and Matthew Jones (Princeton) about their recent book How Data Happened, and the Columbia course it expands upon, data: past, present, and future.
Chris is an associate professor of applied mathematics at Columbia University and the New York Times’ chief data scientist, and Matthew is a professor of history at Princeton University and former Guggenheim Fellow.
From facial recognition to automated decision systems that inform who gets loans and who receives bail, we all now move through a world determined by data-empowered algorithms. These technologies didn’t just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search.
DJ Patil, former U.S. Chief Data Scientist, said of the book "This is the first comprehensive look at the history of data and how power has played a critical role in shaping the history. It’s a must read for any data scientist about how we got here and what we need to do to ensure that data works for everyone."
If you’re a data scientist, machine learning engineer, or work with data in any way, it’s increasingly important to know more about the history and future of the work that you do and understand how your work impacts society and the world.
Among other things, they'll delve into
the history of human use of data;
how data are used to reveal insight and support decisions;
how data and data-powered algorithms shape, constrain, and manipulate our commercial, civic, and personal transactions and experiences; and
how exploration and analysis of data have become part of our logic and rhetoric of communication and persuasion.
You can also sign up for our next livestreamed podcast recording here!
LINKS
How Data Happened, the book!
data: past, present, and future, the course
Race After Technology, by Ruha Benjamin
The problem with metrics is a big problem for AI by Rachel Thomas
Vanishing Gradients on YouTube
1 hr 26 min