Data Science

Sandy Pentland - Reinventing Society In The Wake Of Big Data

Speaker(s): Professor Kenneth Benoit, Kenneth Cukier | The ubiquity of "big data" about social, political and economic phenomena has the potential to transform the way we approach social science. In this talk, Professor Benoit outlines the challenges and opportunities to social sciences caused by the rise of big data, with applications and examples. He discusses the rise of the field of data science, and whether this is a threat or a blessing for the traditional social scientific model and its ability to help us better understand society. Kenneth Benoit (@kenbenoit) is currently Professor of Quantitative Social Research Methods at LSE. He is also Part-Time Professor in the Department of Political Science at Trinity College Dublin, and has previously held a position at the Central European University (Budapest). He is currently Principal Investigator on 5-year grant funded by the European Research Council entitled QUANTESS: Quantitative Text Analysis for the Social Sciences. Kenneth Cukier (@kncukier) is the Data Editor at The Economist, following a decade at the paper covering business and technology, and as a foreign correspondent (most recently in Japan from 2007-12). Previously he was the technology editor of the Wall Street Journal Asia in Hong Kong and worked at the International Herald Tribune in Paris. In 2002-04 he was a research fellow at Harvard's Kennedy School of Government. He is the co-author of "Big Data: A Revolution that Will Transform How We Work, Live and Think" (2013) and "Learning with Big Data: The Future of Education" (2014) with Viktor Mayer-Schönberger. Simon Hix (@simonjhix) is Professor of European and Comparative Politics and Head of Department of Government at LSE.

The Challenge of Big Data for the Social Sciences [Audio]

To have any chance of success, highly creative, collaborative uses of new forms of digital media must be a cornerstone of any strategy for progressive social and environmental movements. Four leading innovators in this domain share their insights. Hosted by Matthew Monahan, Namaste Foundation. With: Ben Knight, co-founder of Loomio; Edward West, co-founder of Impact Hub Oakland and Hylo social network; Ingrid Sanders, founder of PopExpert, a crowd-sourced, community-driven platform. 

Recorded Saturday, October 17, 2015 at the National Bioneers Conference in San Rafael, California.


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Digital Media: Collaboration and Movement Building in a Data-Driven Society

Todd Toler from Wiley explains how research communication is evolving beyond the typeset article into formats that are richer, more discoverable, and create a dynamic research pipeline.

Episode 2: Transformations in Access and Data

How will the role of data science in democracy be transformed as software expands the public’s ability to conduct our own experiments at scale? In the 1940s-70s, debates over authoritarian uses of statistics led to new paradigms in social psychology, management theory, and policy evaluation. Today, large-scale social experiments and predictive modeling are reviving these debates. Technology platforms now conduct hundreds of undisclosed experiments per day on pricing and advertising, and the algorithms that shape our social lives remain opaque to to the public. Democratic methods for data science may offer an alternative to this corporate libertarian paternalism.

In this talk, hear about the history and future of democratic social experimentation, from Kurt Lewin and Karl Popper to Donald Campbell. You’ll also hear about CivilServant, software that supports communities to conduct their own experiments on algorithms and social behavior online.

J. Nathan Matias is a Ph.D. candidate at the MIT Media Lab Center for Civic Media, an affiliate at the Berkman-Klein Center at Harvard, and founder of CivilServant. He conducts independent, public interest research on flourishing, fair, and safe participation online. These include research on harassment reporting, volunteer moderation online (PDF), behavior change toward equality (PDF), social movements (PDF), and networks of gratitude.

Nathan has extensive experience in tech startups, nonprofits, and corporate research, including SwiftKey, Microsoft Research, and the Ministry of Stories. Nathan’s creative work and research have been covered extensively by international press, and he has published data journalism and intellectual history in the Atlantic, Guardian, PBS, and Boston Magazine.

Slides: http://cmswm.it/cmsw-matias-slides

MIT Comparative Media Studies/Writing

Nathan Matias: "Authoritarian and Democratic Data Science in an Experimenting Society"

In this special episode of the Data Show, O'Reilly's Jenn Webb speaks with Max Ogden, director of Code for Science and Society. Recently, Max and Code for Science have been working on the ongoing rescue of data.gov and assisting with other data rescue projects, such as Data Refuge; they’re also the nonprofit developers supporting Dat, a data versioning and distribution manager, which came out of Max’s work making government and scientific data open and accessible.

Saving the World--Or at Least the World's Scientific and Government Data

In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware. Through his startup Nervana (since acquired by Intel), Rao has been at the forefront of building a next generation platform for deep learning and AI.

I wanted to get his thoughts on what the future infrastructure for machine learning would look like. At least for now, we’re seeing a variety of approaches, and many companies are using heterogeneous processors (even specialized ones) and proprietary interconnects for deep learning. Nvidia and Intel Nervana are set to release processors that excel at both training and inference, but as Rao pointed out, at large-scale there are many considerations—including utilization, power consumption, and convenience—that come into play.

Data Science

Solange Martinez (Sol)