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English(EN) An evolutionary origin of collective decision making in humans and machines

人工智能集体智能 映射 人类层级制度

研究人员开发了一个框架,用于理解多层投票群体中集体智能的演化方式,超越了简单的平均化,以解决复杂、非线性的决策任务。他们确定了一种“边际反馈”支付结构,该结构仅在个人意见在其所在层级及以上层级起关键作用时给予奖励,以此激励持续的集体准确性。这种涌现的集体行为等同于机器学习中的多层感知器,表明人工智能中的层级制度和信用分配规则不仅是工程解决方案,也可能是自然的进化结果。 AI

影响 这项研究表明,在人类制度中观察到的集体智能和层级决策原则可以被机器学习模型所映射,有可能带来更强大、更先进的人工智能系统。

排序理由 该条目是一篇研究论文,讨论了集体智能的新框架及其与机器学习模型的关系。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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人工智能集体智能 映射 人类层级制度

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该条目是一篇研究论文,讨论了集体智能的新框架及其与机器学习模型的关系。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    人类与机器集体决策的演化起源

    Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has identified individual-level reward schemes that allow uninformed individuals to evolve collective intelligence from the bottom up…