ViewpointS：迈向集体大脑
ViewpointS: towards a Collective Brain
摘要：
追踪知识获取并将学习事件与同伴之间的互动联系起来是我们这个时代的主要挑战。我们已经构思，设计和评估了一个新的范式，我们称之为ViewpointS的网络交互，构建和使用集体知识。通过利用与Edelman的神经元群体选择理论（TNGS）的相似性，我们推测它可以被隐喻地认为是一个集体大脑，特别是在跨学科表征的情况下。在本文中，我们提出了我们的建议的原因（和限制），旨在成为一个有用的整合工具，为未来的定量探索个人以及集体学习在不同程度的粒度。因此，我们正在挑战当前的每一种方法：语义网中的逻辑方法，挖掘和深度学习中的统计方法，基于权威和信任的推荐系统中的社会方法;不是在他们自己喜欢的操作领域，而是在他们的集成弱点远离人类大脑的整体和动态行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
