摘要翻译：
在我一生中的大部分时间里，我都是一名计算机视觉专业人员，忙于图像处理任务和问题。在计算机视觉界，人们普遍认为人工视觉系统忠实地复制了人类的视觉能力，或者至少非常接近地模仿了人类的视觉能力。当有一天我意识到计算机和人类视觉几乎没有任何共同之处时，我感到非常惊讶。前者处理大量的数据，进行基于像素的计算；后者处理有意义的信息，涉及基于智能对象的操作。而两者之间的鸿沟是不可逾越的。为了解决这个困惑，我不得不首先回归和重新评估视觉现象本身，更仔细地定义什么是视觉信息，以及如何正确对待它。在这项工作中，我并没有像通常所接受的那样，受到生物学的启发。相反，我的灵感来自一个纯粹的数学理论，科尔莫戈罗夫的复杂性理论。我的工作成果已经在别处发表了。因此，本文的目的是尝试并应用在我的事业过程中所获得的洞察力在人脑的信息处理更普遍的案例和人类智能的挑战性问题。
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英文标题：
《Some considerations on how the human brain must be arranged in order to
  make its replication in a thinking machine possible》
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作者：
Emanuel Diamant
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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英文摘要：
  For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.
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PDF链接：
https://arxiv.org/pdf/1002.0184