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AI顶级国际会议近5年引用量排名前十的论文

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所属报告：

【研报】计算机行业深度：为什么要重视人工智能的规模优势-210530（21页）.pdf

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当前 AI技术以深度学习算法为核心，基于神经网络的主流算法难以实现通用的智能化，细分场景落地时需要结合行业 Know-how、客户需求痛点以及数据，可以产生实质性价值。根据企鹅号 FMI团团 2021 年 5 月 6 日的信息，知名外媒《Towards Data Science》按照‚谷歌引用次数‛这个指标，统计了近五年来发表在各大国际顶级会议（如 NeurIPS、AAAI、ACL，ICML、EMNLP 等）上引用量排名前十的论文。我们发现，深度学习、基于NN（Neural Network，神经网络）的框架依然是学界主流。根据通用近似定理（Universal Approximation Theorem），神经网络的计算能力可以近似一个给定的连续函数，但是没有给出如何找到这个网络以及是否是最优解，实际中往往通过经验风险最小化和正则化原则进行参数学习，由于神经网络强大的计算能力，容易产生在训练集上的过度拟合，使得算法难以产生较强的通用性。目前我们应用的安防监控、自动驾驶、语音识别、地图导航等场景都是深度学习 AI技术在图像视觉、语音识别、自然语言理解等领域的应用，在各个细分场景落地时都需要结合所在行业的 Know-how、客户的需求痛点以及客户的真实数据，才能产生落地应用的价值，通用的人工智能从当前来看依然存在较大的现实差距。海康威视高级副总裁徐习明曾于 2018 年对这一论调曾发表过评论，‚今天的人工智能还是一种弱人工智能，基于深度学习的算法精度会无限逼近 100%，但永远无法达到。随着‘准确率’提升，最后竞争的更多是场景落地能力‛。2021 年 5 月 29 日，腾讯副总裁、腾讯 AI Lab 和 Robotics X 实验室主任张正友在接受新京报记者采访时表示，‚强人工智能之路很漫长，需要找到新算法新技术‛。当前的 AI 是场景化的 AI。

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