Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

 

Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon

Science  19 Aug 2016:
Vol. 353, Issue 6301, pp. 790-794
DOI: 10.1126/science.aaf7894

Source: science.sciencemag.org

This report is the first in a series to be issued at regular intervals as a part of the One Hundred Year Study on Artificial Intelligence (AI100). Starting from a charge given by the AI100 Standing Committee to consider the likely influences of AI in a typical North American city by the year 2030, the 2015 Study Panel, comprising experts in AI and other relevant areas focused their attention on eight domains they considered most salient: transportation; service robots; healthcare; education; low-resource communities; public safety and security; employment and workplace; and entertainment. In each of these domains, the report both reflects on progress in the past fifteen years and anticipates developments in the coming fifteen years. 

Source: ai100.stanford.edu

We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one’s behavior has on others’ well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactions between peoples’ behaviors within a given network, and the implications in a variety of settings. Finally, we highlight some empirical challenges inherent in the statistical analysis of network-based data.

 

Networks: An Economic Perspective
Matthew O. Jackson, Brian W. Rogers, Yves Zenou

Source: arxiv.org

Since more than 50 years scientific and nonscientific communities are accustomed to computational facilities, which increase steadily in speed and efficiency of calculations, in particular in processor performance, memory size, and storage capacity. Mitchell Waldrop analyzes the current situation in the chip producing industry and predicts the end of Moore’s prophecy of an exponential growth in computational capacities [1]. Here, an attempt is made to view with the eyes of a user the spectacular development of computers, its benefits for mathematics, science, and engineering as well as the possible consequences of its end.

 

The end of Moore’s law: Living without an exponential increase in the efficiency of computational facilities
Peter Schuster

Complexity

Source: onlinelibrary.wiley.com