Towards a Collective Brain
Amphithéâtre 301, Pôle API, Illkirch
Tracing knowledge acquisition and linking learning events to interaction between peers is a major challenge of our times. We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with Edelman's Theory of Neuronal Group Selection (TNGS), we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual as well as collective learning at different degrees of granu-larity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.
Since 2008, Philippe Lemoisson has been a Research scientist in CIRAD. Philippe Lemoisson’s activities are centred on collective knowledge building in the web paradigm. In 2012, he launched the ViewpointS research project involving both CIRAD and LIRMM. He has designed and co-develops VWA (ViewpointS WebApplication). 2000 to 2007: Manager of R&D projects in Computer Science. 1994 to 2000: strategic consulting in the Telecom area. 1987 to 1994: Information Systems design. 1982 to 1987: Vivendi/water adduction. Formation : PhD in Informatics, Université Montpellier in 2006. Graduate of the Ecole Nationale des Ponts et Chaussées in 1982. Graduate of the Ecole Polytechnique in 1977.
 Lemoisson P., Cerri S.A. (2018) ViewpointS: Towards a Collective Brain. In: Nguyen N., Pimenidis E., Khan Z., Trawiński B. (eds) Computational Collective Intelligence. ICCCI 2018.
Lecture Notes in Computer Science, vol 11055. Springer, Cham https://hal-lirmm.ccsd.cnrs.fr/lirmm-01865761