Qu’apprend-on des machines apprenantes?
L’ouvrage d’Adrian Mackenzie, professeur au Département de sociologie à l’Université de Lancaster, est d’un genre inédit au sein de la littérature émergente, mais encore peu étendue en sciences humaines et sociales, qui explore le fonctionnement du machine learning (ML). Les avancées spectaculaires de cette branche de l’intelligence artificielle (IA) depuis quelques années ont éclipsé les autres approches en la matière et ont soudainement transformé l’IA en un problème social et politique. Plusieurs auteurs ont déjà insisté sur la nécessité de focaliser le regard sur les outils de l’IA, en pointant les limites des travaux qui ne traitent que des effets sociaux des « algorithmes ». Comme le fait remarquer l’anthropologue des sciences et des techniques Nick Seaver, la plupart des travaux sur le sujet s’agitent au sujet des « algorithmes » ou le « big data », en insistant sur leurs effets néfastes, voire catastrophiques, pour la société sans jamais préciser exactement ce qu’ils sont. Le transfert des connaissances et des perspectives entre les spécialistes en IA et en SHS (d’ailleurs dans les deux sens) est pourtant indispensable pour en proposer une critique informée et efficace.
The project of cultural sociology developed by Boris Dubin and his colleagues is often remained out of frames in the discussion of theirs works. Thorough consideration of the principles of cultural analysis which is based on the works of Boris Dubin and Lev Gudkov in the field of sociology of literature can give us an opportunity to reevaluate the place of this project in the history of the humanities in last decades. Ноwever the study of the evolution of the project reveals some conceptual tensions. Further discussion of these tensions seem to be useful as for the productive reception of this project as for the comprehension of the significance of Dubin’s works of 1900s – 2000s in its proliferation.
This chapter addresses major dimensions of Internet-related inequalities in contemporary Russia including relevant regional, urban/rural, income, gender, occupation and age-related predictive variables commonly used in order to operationalize differences in socioeconomic positions of individuals and families and, correspondingly, in their access to the Internet. The analysis is based on multiple data sources – from 2007-2010 Russian Federal State Statistics Service Household Budget Survey data to Public Opinion Research Foundation (FOM) Internet Use Survey (2002-2011) and other opinion and market research agencies’ data on Internet coverage among different population groups. In addition to examining causes of a gap in access to Internet using computers and mobile phones, current policies aimed at closing the digital divide as well as prospects and possibilities of convergence between different groups of population in patterns of information technologies usage will be briefly analyzed.
The article in memoriam summarizes theoretical approaches to sociological analysis of innovation developed by Vladislav Kelle. The focus is made on the key papers describing Russian scientific and innovation system. Key theoretical considerations and observations complemented with the recent statistical data on innovation development of the country.
In November 2014, Team DESCARTES led by Newton Lee and sponsored by the Institute for Education, Research, and Scholarships (IFERS) was among one of the 104 teams registered with the Defense Advanced Research Projects Agency (DARPA) for the first-ever Cyber Grand Challenge (CGC). Only 28 teams, including Team DESCARTES, made it through two DARPA-sponsored dry runs and into the CGC Qualifying Event in June 2015. We proposed a system—Distributed Expert Systems for Cyber Analysis, Reasoning, Testing, Evaluation, and Security (DESCARTES)—that would be a fully autonomous cyber defense system that is capable of autonomous analysis, autonomous patching, autonomous vulnerability scanning, autonomous service resiliency, and autonomous network defense.
Online social networks have become an essential communi- cation channel for the broad and rapid sharing of information. Currently, the mechanics of such information-sharing is captured by the notion of cascades, which are tree-like networks comprised of (re)sharing actions. However, it is still unclear what factors drive cascade growth. Moreover, there is a lack of studies outside Western countries and platforms such as Facebook and Twitter. In this work, we aim to investigate what fac- tors contribute to the scope of information cascading and how to predict this variation accurately. We examine six machine learning algorithms for their predictive and interpretative capabilities concerning cascades’ structural metrics (width, mass, and depth). To do so, we use data from a leading Russian-language online social network VKontakte capturing cascades of 4,424 messages posted by 14 news outlets during a year. The results show that the best models in terms of predictive power are Gradient Boosting algorithm for width and depth, and Lasso Regression algorithm for the mass of a cascade, while depth is the least predictable. We find that the most potent factor associated with cascade size is the number of reposts on its origin level. We examine its role along with other factors such as content features and characteristics of sources and their audiences.
This book provides an in-depth comparative analysis of inequality and the stratification of the digital sphere.
Grounded in classical sociological theories of inequality, as well as empirical evidence, this book defines ‘the digital divide’ as the unequal access and utility of internet communications technologies and explores how it has the potential to replicate existing social inequalities, as well as create new forms of stratification. The Digital Divide examines how various demographic and socio-economic factors including income, education, age and gender, as well as infrastructure, products and services affect how the internet is used and accessed. Comprised of six parts, the first section examines theories of the digital divide, and then looks in turn at:Highly developed nations and regions (including the USA, the EU and Japan); Emerging large powers (Brazil, China, India, Russia); Eastern European countries (Estonia, Romania, Serbia); Arab and Middle Eastern nations (Egypt, Iran, Israel); Under-studied areas (East and Central Asia, Latin America, and sub-Saharan Africa).
Providing an interwoven analysis of the international inequalities in internet usage and access, this important work offers a comprehensive approach to studying the digital divide around the globe. It is an important resource for academic and students in sociology, social policy, communication studies, media studies and all those interested in the questions and issues around social inequality.
The article presents an analytical overview of the theoretical and empirical studies devoted to the interpretation of place names and place-(re)naming practices in social sciences. The author suggests a classification based on the distinction of two key approaches to the place-(re)naming practices — cultural and critical. The article focuses on the critical approach formed under the influence of such trends in the contemporary sociology as: 1) the surge of interest to the collective memory and practices of commemoration; 2) the expansion of sociological theory into geographical disciplines and the emergence of ‘social production of space’ theories; 3) the domination of critical research orientation. Finally, the author discusses some limitations of the critical approach and the ways to overcome them with the help of theoretical and methodological resources of neopragmatic sociology.