ViewpointS: a collective brain

Understanding and forecasting brain functions is the major challenge of our times. The focus of this endeavor is understanding and forecasting learning events, such as the dynamic adaptation of beams connecting neuronal cards in Edelman's Theory of Neuronal Group Selection (TNGS). We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge byWeb interactions that we called ViewpointS. By exploiting the similarity with the 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 brain functions as well as of collective wisdom at different degrees of granularity. 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.

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Main Authors: Lemoisson, Philippe, Cerri, Stefano A.
Format: conference_item biblioteca
Language:eng
Published: Springer
Online Access:http://agritrop.cirad.fr/598056/
http://agritrop.cirad.fr/598056/8/ID598056.pdf
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spelling dig-cirad-fr-5980562022-06-01T12:58:53Z http://agritrop.cirad.fr/598056/ http://agritrop.cirad.fr/598056/ ViewpointS: a collective brain. Lemoisson Philippe, Cerri Stefano A.. 2020. In : Brain function assessment in learning: Second International Conference, BFAL 2020, Heraklion, Crete, Greece, October 9–11, 2020, Proceedings. Frasson Claude (ed.), Bamidis Panagiotis (ed.), Vlamos Panagotis (ed.). IIS. Cham : Springer, 34-44. (Lecture Notes in Artificial Intelligence, 12462) ISBN 978-3-030-60734-0 International Conference on Brain Function Assessment in Learning (BFAL 2020). 2, Crète, Grèce, 9 Octobre 2020/11 Octobre 2020.https://doi.org/10.1007/978-3-030-60735-7_4 <https://doi.org/10.1007/978-3-030-60735-7_4> Researchers ViewpointS: a collective brain Lemoisson, Philippe Cerri, Stefano A. eng 2020 Springer Brain function assessment in learning: Second International Conference, BFAL 2020, Heraklion, Crete, Greece, October 9–11, 2020, Proceedings Understanding and forecasting brain functions is the major challenge of our times. The focus of this endeavor is understanding and forecasting learning events, such as the dynamic adaptation of beams connecting neuronal cards in Edelman's Theory of Neuronal Group Selection (TNGS). We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge byWeb interactions that we called ViewpointS. By exploiting the similarity with the 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 brain functions as well as of collective wisdom at different degrees of granularity. 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. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/598056/8/ID598056.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-3-030-60735-7_4 10.1007/978-3-030-60735-7_4 https://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=221121 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-60735-7_4 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-3-030-60735-7_4
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description Understanding and forecasting brain functions is the major challenge of our times. The focus of this endeavor is understanding and forecasting learning events, such as the dynamic adaptation of beams connecting neuronal cards in Edelman's Theory of Neuronal Group Selection (TNGS). We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge byWeb interactions that we called ViewpointS. By exploiting the similarity with the 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 brain functions as well as of collective wisdom at different degrees of granularity. 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.
format conference_item
author Lemoisson, Philippe
Cerri, Stefano A.
spellingShingle Lemoisson, Philippe
Cerri, Stefano A.
ViewpointS: a collective brain
author_facet Lemoisson, Philippe
Cerri, Stefano A.
author_sort Lemoisson, Philippe
title ViewpointS: a collective brain
title_short ViewpointS: a collective brain
title_full ViewpointS: a collective brain
title_fullStr ViewpointS: a collective brain
title_full_unstemmed ViewpointS: a collective brain
title_sort viewpoints: a collective brain
publisher Springer
url http://agritrop.cirad.fr/598056/
http://agritrop.cirad.fr/598056/8/ID598056.pdf
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