摘要翻译：
图灵测试(TT)检查的是人类的智能，而不是任何假定的一般智能。它涉及到反复的互动，需要以适应人类对话伙伴的形式学习。与图灵机(TM)的定义相比，这是一个宏观级别的后Hoc测试，图灵机(TM)是一个先验的微观级别的定义。这就提出了一个问题，即学习是否只是另一个计算过程，即是否可以作为一个TM来实现。在这里，我们认为学习或适应与计算有根本的不同，尽管它确实涉及可以被视为计算的过程。为了说明这种差异，我们比较了(a)设计TM和(b)学习TM，并为论证的目的对它们进行定义。我们证明了存在一个定义良好的问题序列，这些问题不能有效地设计，但可以学习，以有界停顿问题的形式出现。综述了人类智能的一些特点，包括：交互性、学习能力、模仿倾向、语言能力和语境依赖性。一个解释其中一些的故事是社会智力假说。如果这是大体上正确的，这就表明，如果人工智能要通过TT，就需要相当长的一段时间的文化适应（在上下文中进行社会学习）。虽然总是有可能将学习的结果“汇编”成一个TM，但这不是一个设计的TM，也不能不断地适应（通过未来的TTs）。我们得出三件事，即：一个纯粹“设计”的TM永远不会通过TT；没有所谓的一般智力，因为它必然涉及学习；学习/适应和计算应该明确区分。
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英文标题：
《Learning, Social Intelligence and the Turing Test - why an
  "out-of-the-box" Turing Machine will not pass the Turing Test》
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作者：
Bruce Edmonds and Carlos Gershenson
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最新提交年份：
2012
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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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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英文摘要：
  The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
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PDF链接：
https://arxiv.org/pdf/1203.3376