Actualités

5 articles

(English) Neurons spike back

This article by Dominique Cardon, Jean-Philippe Cointet and Antoine Mazières retraces the history of artificial intelligence through the lens of the tension between symbolic and connectionist approaches. From a social history of science and technology perspective, it seeks to highlight how researchers, relying on the availability of massive data and the multiplication of computing power have undertaken to reformulate the symbolic AI project by reviving the spirit of adaptive and inductive machines dating back from the era of cybernetics.
The full english version may be accessed here.

(English) Open doctoral and post-doctoral positions ! D/L: Sept 30, 2019

The CNRS CMB Computational Social Science Team based in Berlin is recruiting several doctoral students and post-doctoral researchers, to work under the ERC Consolidator grant “Socsemics”, focusing on internet echo chambers and polarization. These offers take place in an interdisciplinary context and touch a variety of domains: computational social science, political science, NLP, information visualization, sociology of the internet, social network analysis, complex network modeling, essentially.

Detailed job offers may be found here with a deadline for application set at Sept 30th, 2019.

The team presentation is available either as a video or as a website, while the “Socsemics” ERC project has its own website

Extensive information on the scientific content and context are available in the above-mentioned job offers – interested applicants may nonetheless feel free to contact Camille Roth (roth[@]cmb.hu-berlin.de) to discuss this further.

(English) Appraising algorithmic biases

Algorithmic Distortion of Informational Landscapes“, by Camille Roth, has just been published in Intellectica 70(1):97-118 –
This review paper focuses on biases induced by recommendation algorithms. It explores the state of the art along a double dichotomy: first regarding the discrepancy between users’ intentions and actions (1) under some algorithmic influence and (2) without it; second, by distinguishes algorithmic biases on (1) prior information rearrangement and (2) posterior information arrangement.
An open-access pre-print may be downloaded here.