2018 International Conference on Computational Social Science

Redefine business in the 21st century at the intersection of computer science and social science

Save the date for this interdisciplinary event designed to connect a diverse community of researchers — academics, industry experts, open data activists, government agency workers and think tank analysts — who are dedicated to advancing social science through computational methods.

For four days, IC2S2 will be the epicenter of computational social science. Convening hundreds of attendees from more than 20 countries, the impact of IC2S2 is influential, innovative and global.

After successful events in Helsinki, Finland; Evanston, IL; and Cologne, Germany, the 4th Annual IC2S2 will return to Evanston and the Kellogg School of Management at Northwestern University. Join us in 2018 to explore the future of social research and the biggest questions facing the field of computational social science today.

Source: www.kellogg.northwestern.edu

What is consciousness, and could machines have it?

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.


What is consciousness, and could machines have it?
Stanislas Dehaene, Hakwan Lau, Sid Kouider

Science  27 Oct 2017:
Vol. 358, Issue 6362, pp. 486-492
DOI: 10.1126/science.aan8871

Source: science.sciencemag.org

Expanding and reprogramming the genetic code

Nature uses a limited, conservative set of amino acids to synthesize proteins. The ability to genetically encode an expanded set of building blocks with new chemical and physical properties is transforming the study, manipulation and evolution of proteins, and is enabling diverse applications, including approaches to probe, image and control protein function, and to precisely engineer therapeutics. Underpinning this transformation are strategies to engineer and rewire translation. Emerging strategies aim to reprogram the genetic code so that noncanonical biopolymers can be synthesized and evolved, and to test the limits of our ability to engineer the translational machinery and systematically recode genomes.


Expanding and reprogramming the genetic code
Jason W. Chin
Nature 550, 53–60 (05 October 2017)

Source: www.nature.com

A proposed methodology for studying the historical trajectory of words’ meaning through Tsallis entropy

The availability of historical textual corpora has led to the study of words’ frequency along the historical time line, as representing the public’s focus of attention over time. However, studying of the dynamics of words’ meaning is still in its infancy. In this paper, we propose a methodology for studying the historical trajectory of words’ meaning through Tsallis entropy. First, we present the idea that the meaning of a word may be studied through the entropy of its embedding. Using two historical case studies, we show that this entropy measure is correlated with the intensity in which a word is used. More importantly, we show that using Tsallis entropy with a superadditive entropy index may provide a better estimation of a word’s frequency of use than using Shannon entropy. We explain this finding as resulting from an increasing redundancy between the words that comprise the semantic field of the target word and develop a new measure of redundancy between words. Using this measure, which relies on the Tsallis version of the Kullback–Leibler divergence, we show that the evolving meaning of a word involves the dynamics of increasing redundancy between components of its semantic field. The proposed methodology may enrich the toolkit of researchers who study the dynamics of word senses.


Neuman, Y., Cohen, Y., Israeli, N., & Tamir, B. (2017). A proposed methodology for studying the historical trajectory of words’ meaning through Tsallis entropy. Physica A: Statistical Mechanics and its Applications.

Source: www.sciencedirect.com