There is a natural evolution from the ideas that deep learning has empirical revealed to a theory of general intelligence. A common criticism of deep learning is its lack of good theory. Deep learning is like the supercolliders in high energy physics. It reveals the inner behavior of an artificial intuitive process. It reveals to us patterns of what does work.

从深度学习的经验揭示的思想到一般的智力理论都有自然的演变。 对深度学习的普遍批评是缺乏良好的理论。 深度学习就像高能物理中的超级碰撞机。 它揭示了人工直观过程的内部行为。 它向我们揭示了有效的模式。

To build up that theory we must walk back into the ideas of past thinkers. Thinkers who have never seen the empirical evidence. What will they conclude about their ideas if they had been exposed to evidence in deep learning?

要建立该理论，我们必须回到过去的思想家的思想中。 从未见过经验证据的思想家。 如果他们在深度学习中接触过证据，他们将如何总结他们的想法？

Let’s go back into the past to see how it informs the future. How far in the past do I need to go to find an informative theory? Perhaps I could go back enough to the precursors of AI. Back to the 1940s before computers where invented. Back to exploring Nobert Wiener’s cybernetics. After all, Deep Learning is a modern rendition of cybernetics.

让我们回到过去，看看它如何为未来提供信息。 过去我需要走多长时间才能找到有用的理论？ 也许我可以追溯到AI的前身。 发明计算机的历史可以追溯到1940年代。 回到探索诺伯特·维纳的控制论。 毕竟，深度学习是控制论的现代诠释。

But there is a problem here, it is related to the reason why Wiener was disinvited from the infamous Dartmouth conference that coined the term ‘Artificial Intelligence’. In the 1950s with the emergence of computers, there was a belief that artificial LOGICal system was the key to human intelligence. After all, isn’t logic what separates us from the brutes? This unfortunately led to decades of exploration in Artificial Logic that never achieved any semblance of general intelligence. But it was the discovery of Artificial Intuition (Deep Learning) that finally revealed a path to general intelligence.

但是，这里存在一个问题，这与为什么维也纳纳被著名的达特茅斯会议(Artartificial Intelligence，人工智能)不受欢迎的原因有关。 在1950年代，随着计算机的出现，人们相信人工LOGICal系统是人类智能的关键。 毕竟，是不是逻辑使我们与野蛮人区分开？ 不幸的是，这导致了对人工智能的数十年探索，但从未实现过通用的智能。 但是，人工直觉(深度学习)的发现终于揭示了通向一般智能的道路。

The problem however of Wiener and is a problem also inherited by Deep Learning is that it treats cognition as a dynamical system. It employs all the tools that we have inherited from centuries of doing physics.

然而，维纳的问题(也是深度学习所继承的一个问题)是将认知视为动态系统。 它使用了我们几个世纪以来从做物理中学到的所有工具。

We have today two competing ideas about how the brain works, one is based on formulas describing dynamical systems, both cybernetics and deep learning share this commonality. We also have a discrete computational kind that originates from the 1950s conference on AI (GOFAI).

今天，关于大脑如何工作，我们有两种相互竞争的想法，一种是基于描述动力学系统的公式，控制论和深度学习都具有这种共性。 我们还有一种离散的计算类型，它起源于1950年代的AI(GOFAI)会议。

Furthermore, we have what is known as the ‘symbolic grounding’ problem or the semantic gap. How does language which is discrete in nature achieve semantic grounding? How can we merge the connectionist and symbolist worlds?

此外，我们还有所谓的“符号基础”问题或语义鸿沟。 本质上是离散的语言如何实现语义基础？ 我们如何融合连接主义和象征主义世界？

We know intuitively that the solution for semantic grounding is related to the solution of general intelligence. But again, what thinkers of the past have thought of this problem? Apparently, there was one thinker, an American thinker who died impoverished in the early 1900s. A thinker who barely published any of his works but left behind an outstanding amount of theory. This American thinker in fact actually conceived of the universal gates used in computers today. His ideas on this were never noticed, until the ideas were reinvented to create computers in 1950s. The same American thinker was credited by Heisenberg himself for the idea of the uncertainty principle in Quantum Mechanics. This thinker was Charles Sanders Peirce.

我们直观地知道，语义基础的解决方案与通用情报的解决方案有关。 但是，再次，过去的思想家想到了这个问题吗？ 显然，有一个思想家，一个美国思想家，在1900年代初死于贫困。 一个思想家几乎没有发表任何著作，但留下了大量的理论知识。 实际上，这位美国思想家实际上构思了当今计算机中使用的通用门。 直到1950年代，他的想法被重新发明以制造计算机之前，他的想法从未被人注意到。 海森堡本人也认为同一位美国思想家是量子力学中不确定性原理的思想。 这个思想家是查尔斯·桑德斯·皮尔斯。

In my study of Peirce’s work on semiotics, it occurred to me that the common understanding of signs, that is one confined to the triad of icon, index and symbols, is an incomplete understanding of Pierce’s formulation. Here I will use Peirce framework of 10 genuine signs and map them into other common notions of information.

在我对皮尔斯的符号学研究中，我发现，对符号的共同理解(仅限于图标，索引和符号的三元组)，是对皮尔斯公式的不完全理解。 在这里，我将使用10个真正符号的Peirce框架，并将它们映射到其他常见的信息概念中。

The diagram above is difficult to understand because Pierce used uncommon words to describe characteristics of signs. Qualisign, Sinsign and Legisign can be mapped to the words Tone, Token and Type. Rhematic, Dicent and Argument can be mapped to the more common idea of Term, Proposition and Argument. Each sign is defined in a combination of three kinds of characteristics, there are 10 valid kinds as described by the diagram above. The Tone, Token and Type is associated to the sign itself. Icon, Index and Symbol is related to how as sign is related to the object it refers to. Term, Proposition and Argument is how a sign and its object is related to the interpretant.

上图很难理解，因为皮尔斯用不常见的词来描述标志的特征。 可以将Qualisign，Sinsign和Legisign映射到单词Tone，Token和Type。 可以将“ Rhematic”，“ Dicent”和“ Argument”映射到“ Term”，“ Proposition”和“ Argument”的更常见概念。 每个符号由三种特征的组合定义，如上图所示，有10种有效的特征。 音调，令牌和类型与符号本身相关联。 图标，索引和符号与符号如何与其所引用的对象相关联。 术语，命题和论证是符号及其对象与解释者的关系。

Aboutness

亲切感

Preparation and measurement characterize all that is communication in correspondence to Shannon’s framework. However, when we begin to introduce agents with an intentional stance, we find a new kind of information. This is information aboutness. It goes beyond how information is codified and into the semantics of information. We know this as icons in semiotics and references or pointers in computation. Information aboutness is of value only for intentional agents with memory. These are signs that capture similarity of what has been observed. Micmicry is an example of a procedural form of information aboutness. Information aboutness makes possible the recall of information that previously was encoded in memory.

准备和测量是与Shannon框架相对应的所有交流的特征。 但是，当我们开始有意地介绍代理商时，我们会发现一种新的信息。 这是有关信息。 它超越了信息的编码方式，而超出了信息的语义。 我们知道这是符号学中的图标和计算中的引用或指针。 关于信息的信息仅对有记忆的故意行为体有价值。 这些迹象表明已经观察到的相似之处。 模仿是有关信息的程序形式的一个示例。 信息的相关性使调用以前在内存中编码的信息成为可能。

Aboutness is captured by the icon-index-symbol triad in characterizing signs.

图标符号索引三合会在表征标志时捕获了亲密感。

Self

自

CT does not cover in its exploration of life is that characteristic of living things (or agents) to have what is known as an intentional stance. The intentional stance is a term coined by Daniel Dennett to refer to agents with behavior that are due to having cognitive capabilities. I use the term in the broadest of senses, which also includes the most primitive of cognitive capabilities (i.e., stimulus-response).

CT在其对生命的探索中并未涵盖生物(或主体)所具有的特征，即所谓的故意姿态。 故意立场是丹尼尔·丹内特(Daniel Dennett)创造的一个术语，指代具有行为的行为，这些行为由于具有认知能力而引起。 我在最广泛的意义上使用该术语，其中也包括最原始的认知能力(即刺激响应)。

Physics has the law of conservation of energy, which is basically an invariant property with respect to time. Analogously, biological agents have the intentional stance of preserving self or the conservation of self (aka survival and replication). Thus, information about self (see: AGI using self-models) is essential for all biological life.

物理学具有能量守恒定律，这基本上是时间的不变性质。 类似地，生物制剂具有保存自我或保存自我(即生存和复制)的故意立场。 因此，关于自我的信息(请参阅： 使用自我模型的AGI )对于所有生物生命都是必不可少的。

All genuine signs in Peirce’s framework involve an aspect that concerns the intepretant. The interprentant is the self in the context of a sign. In other words, Peirce framework is unusual in the sense that it introduces subjectivity in the the interpretation of signs. There’s more to discuss about this later.

Peirce框架中的所有真实符号都涉及一个与知识分子有关的方面。 解释者是符号背景中的自我。 换句话说，Peirce框架在将主观性引入符号解释的意义上是不寻常的。 以后还有更多要讨论的内容。

Entropy

熵

Entropy is a measure of information that finds its way into the vocabulary of macroscopic phenomena and in information communication capacity. Both Boltzmann and Shannon (years later) defined the use of entropy (a measure of a kind of information) in different fields but with similar equations. Boltzmann in his pioneering work in Statistical Mechanics defined entropy as a measure of statistical disorder. Shannon was unaware of the similarity of his measure of information with that of Boltzmann’s thermodynamic entropy. However, when searching for a name for his measure, Von Neumann recommended to Shannon that “nobody knows what entropy is, so in a debate you will always have the advantage.” Thus these two separate ideas of measures of information were eternally linked.

熵是一种信息量度，它可以通过宏观现象的词汇表和信息交流能力来获取信息。 玻尔兹曼(Boltzmann)和香农(Shannon)(数年后)都在不同的领域定义了熵(一种信息的度量)，但方程式相似。 玻尔兹曼(Boltzmann)在他的统计力学开创性工作中将熵定义为统计无序的一种度量。 香农没有意识到他的信息量度与玻尔兹曼热力学熵的相似性。 但是，在寻找自己的量度名称时，冯·诺依曼(Von Neumann)向香农建议：“没人知道熵是什么，因此在辩论中，您将始终拥有优势。” 因此，这两种独立的信息测度观念是永远联系在一起的。

Entropy is Term-Icon-Type. That is a characteristic of a sign, its form has a similarity with uncertain, disordered or useless information and it is defined by convention.

熵是术语图标类型。 这是一个标志的特征，它的形式与不确定，无序或无用的信息具有相似性，并且按照惯例进行定义。

Replication

复写

Replication is a key characteristic of life and is made possible via digitalization. Symbols are the embodiment of the digital in Semiotics. Digital information can be identified as Term-Symbol-Type. This differs from Entropy in that it a symbol and not an icon.

复制是生活的关键特征，并且可以通过数字化实现复制。 符号是符号学中数字化的体现。 可以将数字信息标识为术语符号类型。 这与熵不同，它是符号而不是图标。

Efference Copy

参考副本

Intentional agents learn the aboutness of information through environmental interaction. That is, learning and model building is achieved through intervention with the world. The expectations that an organism acquires is achieved by testing conjectures and predictions. Biological organisms are known to involve an efference copy or efferent copy. This is information about an organism’s own movement. This information explains why we can’t tickle ourselves or why we rub ourselves when we get hurt. Information about our own movements reduces the sensitivity of our sensors that are caused by our own actions. It allows us to maintain the stability of what we see despite the movement of our heads and eyes.

故意代理通过环境交互来了解信息的有关性。 也就是说，学习和建立模型是通过与世界的干预来实现的。 有机体获得的期望是通过测试猜想和预测来实现的。 已知生物体涉及有效拷贝或传出拷贝 。 这是有关有机体自身运动的信息。 这些信息说明了为什么我们不能为自己挠痒痒或为什么我们在受到伤害时会自己摩擦。 有关我们自身运动的信息会降低由我们自己的行为引起的传感器的灵敏度。 尽管我们的头部和眼睛移动了，但它使我们能够保持所见事物的稳定性。

Efference copies are classified as Term-Index-Token. This is a signal that an organism is aware of that refers to something that is caused by its own actions.

有效副本被分类为术语索引标记。 这是生物体意识到的信号，它是指由其自身行为引起的某种事物。

Affordances

客流

The next tier of information is what Gibson would describe as ecological affordances and what semiotics would describe as indexical signs. The value of this information is that it conveys to an intentional agent the possibilities and impossibilities that are available in a context. Constructor Theory revolves around the identification of possibilities and impossibilities of transformational tasks. In the realm of intentional agents, the recognition of possible and impossible potential actions is ideal for the preservation of self (i.e., survival). To learn about affordances requires the development and query of internal mental models of the world. This allows an organism to “see” what is possible or impossible without actual interaction with the world.

下一类信息是吉布森(Gibson)将其描述为生态能力的东西，而符号学将其描述为索引的迹象。 此信息的价值在于，它会将故意存在的可能性和不可能传达给故意的代理。 建构主义理论围绕着对转型任务的可能性和可能性的识别。 在故意行为者的领域中，对于可能的和不可能的潜在行为的识别对于保持自我(即生存)是理想的。 要了解能力，需要开发和查询世界内部的心理模型。 这使有机体无需实际与世界互动即可“看到”可能或不可能的事情。

Affordances are Proposition-Index-Type. The reason it is type is that affordances are innate index information that is shared by members of its species. An organism is born with innate cognitive capabilities that allows it to recognize affordances that its organism has evolved to be useful.

满足度是命题索引类型。 它是类型的原因是，提供能力是其物种成员共享的先天索引信息。 生物体天生具有先天的认知能力，使其能够识别其生物体已发展成为有用的能力。

Self Reference

自我参考

Let’s now delve deeper into the concept of information aboutness and usefulness with respect to self-models. That is, what can we say about information about the self that is referential and ultimately useful? Self-referential information involves higher information abstractions such as autonomy, introspection, and reflection. Brian Cantwell-Smith describes these kinds of information are based on notions of the self as unity, self as a complicated system and self as an independent agent.

现在让我们更深入地研究关于自我模型的信息的有效性和有用性的概念。 就是说，关于自我的信息具有参考性并最终有用，我们该怎么说呢？ 自我指称信息涉及更高的信息抽象性，例如自治性，自省性和反思性。 布赖恩·坎特威尔·史密斯 ( Brian Cantwell-Smith)将此类信息描述为基于以下概念：自我为一体，自我为复杂系统，自我为独立主体。

Autonomy is information on the self that recognizes its self-direction and agency. Information about autonomy is under Proposition-Index-Token sign. It is not of a certain type because these are signs that are of the individual and not the collective. The proposition is about itself and the signs of the environment.

自治是关于自我的信息，它可以识别自身的方向和能力。 关于自治的信息在Proposition-Index-Token符号下。 它不是某种类型，因为这些是属于个人而非集体的标志。 该主张是关于自身和环境的迹象。

Introspection is observations of a self’s cognitive processes. It allows reasoning about our own thoughts. Information about introspection is where the object of the sign is the thoughts of the interpreter. It is classified the same as autonomy but the proposition is about itself and the signs generated by itself.

内省是对自我认知过程的观察。 它允许我们思考自己的想法。 关于自省的信息是符号的对象是解释者的思想的地方。 它的分类与自治相同，但是命题是关于自身和自身产生的迹象的。

Reflection is a detached perspective of a self’s cognitive process and reasoning from the perspective beyond the self. It is classified the same as autonomy but the proposition is about itself and the signs generated by itself and the environment.

反思是自我认知过程和推理的独立视角，是超越自我的视角。 它的分类与自治相同，但命题是关于其自身以及由自身和环境产生的迹象。

There is ever-increasing complexity with different kinds of information required in the self-referential exploration of self. The object in that the sign is about is the same as that of the interpreter.

在自我的自我参照探索中，需要不同种类的信息的复杂性日益增加。 符号所涉及的对象与解释器的对象相同。

Coordination

协调

But we are not yet done with our emerging ontology of information. No man is an island, and no organism is independent of its ecology. In the Pi-calculus of distributed computation, information aboutness is known as a channel or vocative name. The Pi-calculus employs aboutness information as information coordination. Human civilization employs information coordination as a means of resource allocation. Money is an example of this kind of information. Money is essentially information about obligations and ultimately related to trust. But what is trust from the perspective of information? Trust is what I would fall into the same category known as information usefulness. However, trust is information about the self that is conveyed in interaction and communication with other-selves.

但是我们还没有完成我们正在出现的信息本体。 没有人是一个岛屿，没有任何生物是独立于其生态的。 在分布式计算的Pi-演算中，关于信息的信息称为渠道或称呼名称。 Pi演算将关于信息的信息用作信息协调。 人类文明利用信息协调作为资源分配的手段。 金钱就是这类信息的一个例子。 金钱本质上是关于义务的信息，并最终与信任有关。 但是，从信息的角度来看，信任是什么？ 信任就是我所说的信息有用性。 但是，信任是与他人进行互动和交流时传达的关于自我的信息。

A coordination sign or a vocative name is a Term-Index-Type sign. Resource signs like Money (a decentralized coordination mechanism) is like an affordance, however with a sign based on convention. That is, a Proposition-Symbol-Type.

协调符号或称呼名称是术语索引类型符号。 诸如Money(权力下放的协调机制)之类的资源标志就像是一种负担，但是具有基于惯例的标志。 即，命题符号类型。

Conversational Shared Experience

对话式共享经验

Sharing information context is an essential component for human cognitive development. Human eyes, specifically the white of our eyes, allow others to recognize what we are attending to. Human eyes have also evolved to understand the subtle changes in the color of our faces. These evolved capabilities reveal the importance of shared contextual information. A human’s ability to share their own experiences even without verbal language is an essential tool that accelerates cognitive development. One can even make the general assertion that the essence of being human is in the activity of sharing the human experience. One can therefore not comprehend human-compete intelligence without having a level of understanding of human experiential sharing. In art, there is a concept of the “beholder’s share.” That is, beauty is in the eye of the beholder, what is meant is that an interpretation of art is performed by its perceiver. However, good art is the kind of art where the artist is able to share an experience with its beholder. Da Vinci’s Mona Lisa’s smile, as an example, is sufficiently ambiguous such that it can morph to the preference of the beholder.

共享信息上下文是人类认知发展的重要组成部分。 人眼，尤其是我们的眼睛白，可以让其他人识别我们正在关注的内容。 人眼也已经进化为可以理解我们脸部颜色的细微变化。 这些不断发展的功能揭示了共享上下文信息的重要性。 即使没有口头语言，人们也可以分享自己的经验，这是加速认知发展的重要工具。 人们甚至可以断言，人类的本质在于分享人类经验的活动。 因此，如果不具备一定的对人类经验共享的了解，就无法理解人类竞争情报。 在艺术中​​，有一个“仁者共享”的概念。 也就是说，情人眼中的美丽，意味着对艺术的理解是由其感知者进行的。 但是，好的艺术是艺术家能够与旁观者分享经验的一种艺术。 举例来说，达芬奇(Da Vinci)的蒙娜丽莎(Mona Lisa)的微笑含糊不清，以至于可以改变情人的喜好。

I’ve written several times that to achieve AGI, that achieving conversational cognition is required. In my capability model, conversational cognition is at the highest steps. Conversational cognition makes possible cultural evolution. The notion in psychology of dual-hereditary theories proposes that human cognitive development is both biological as well as cultural. A recent position paper from DeepMind addresses specifically this perspective in“Emergence of Innovation from Social Interaction”. What’s interesting though is that humans have been able to converse for thousands of years with barely any technological progress. That is, the same tendency for bureaucratic organization exits also in the development of human society and cognition.

我已经写过好几次了，以达到AGI的要求，即达到对话认知 。 在我的能力模型中， 对话认知是最高的步骤。 会话认知使文化发展成为可能。 双重世袭理论的心理学概念提出，人类的认知发展既是生物学的又是文化的。 DeepMind最近发表的立场文件特别针对“ 社会互动中的创新的出现”这一观点。 但是有趣的是，人类几乎没有任何技术进步就能交流数千年。 也就是说，在人类社会和认知的发展中，也存在着官僚组织的相同趋势。

Human conversations involve Argument-Symbol-Type signs. Conversations in general are defined as the exchange of signs.

人类对话涉及“论据-符号类型”符号。 一般而言，会话被定义为交换符号。

Memes

模因

The scaling of shared human experience is what Richard Dawkins described as memes. Memes are the basis of an evolutionary model for cultural information transfer. Analogous to a gene, the meme was conceived as a “unit of culture” (an idea, belief, pattern of behavior, etc.). Memes are “hosted” in the minds of many individuals, and reproduce themselves by transferring from the mind of a person to the mind of another. It can be regarded as an idea-replicator that replicates itself by influencing the adoption of new beliefs by many individuals. Analogous to genetics, the success of a meme is dependent on the ubiquitous use and replication of its host. Daniel Dennett proposes that the development of human languages is a consequence of the spreading of memes.

理查德·道金斯(Richard Dawkins)称赞模范是人类经验共享的尺度。 模因是文化信息传递的进化模型的基础。 与基因类似，模因被认为是“文化单位”(一种思想，信念，行为方式等)。 模因被“寄托”在许多人的思想中，并通过从一个人的思想转移到另一个人的思想而自我复制。 它可以被认为是一种思想复制者，它通过影响许多人对新信念的采用来自我复制。 与遗传学类似，一个模因的成功取决于其宿主的普遍使用和复制。 Daniel Dennett提出人类语言的发展是模因传播的结果。

Memes, analogous to biological cells and viruses, have a two-level structure(i.e. A replicator-vehicle logic). Douglas Hofstadter in Metamagical Themas describes the structure of an effective meme:

模因类似于生物细胞和病毒，具有两级结构(即复制器-车辆逻辑)。 道格拉斯·霍夫斯塔特(Douglas Hofstadter)在《超魔幻世界》中描述了有效模因的结构：

System X:

系统X：

-Begin

-开始

X1: Anyone who does not believe System X will burn in hell, X2: It is your duty to save others from suffering.

X1：任何不相信系统X都会在地狱中燃烧的人，X2：拯救别人免受痛苦是您的责任。

-End.

-结束。

If you believed in System X, you would attempt to save others from hell by convincing them that System X is true. Thus System X has an implicit `hook’ that follows from its two explicit sentences, and so System X is a self- replicating idea system.

如果您相信System X，那么您将通过说服其他人相信System X是真实的来尝试使他们摆脱困境。 因此，系统X具有一个隐式的“钩子”，该“钩子”来自其两个显式语句，因此，系统X是一个自我复制的思想系统。

There is a bait that conceals the hook that allows a meme to propagate. Let’s frame this structure using the replicator-vehicle logic in CT. The vehicle, unlike in biology, is not physical but somewhat abstract. If we are to understand a vehicle as the mechanism that ensures sustainability, then it is the hook that is relevant here. The hook is the deception of information usefulness in the meme that encourages its own propagation. The concept of “unknown knowns” or willful ignorance has utility in the propagation of memes (and thus knowledge). It is indeed interesting that deception is a characteristic of information that originates not just in memes but in more primitive biological organisms. Camouflage and viruses are examples of deceptive strategies in biology. The physical replicator here would be the host mind that accepts the truthfulness and thus the utility of the original meme.

有一个隐藏钩子的诱饵，允许模因传播。 让我们使用CT中的复制器-车辆逻辑来构架此结构。 与生物学不同，媒介物不是物理的而是抽象的。 如果我们想将车辆理解为确保可持续性的机制，那么这里就至关重要。 钩子是模因中对信息有用性的欺骗，这种模因鼓励其自身传播。 “未知的已知”或故意的无知的概念在模因(以及知识)的传播中具有效用。 确实有趣的是，欺骗是信息的特征，它不仅起源于模因，而且起源于更原始的生物。 伪装和病毒是生物学中欺骗策略的例子。 此处的物理复制者将是接受真实性并因此接受原始模因实用性的主意。

Memes therefore involve two kinds of signs. The bait is a Proposition-Index-Type sign (the same as affordance, one where the index refers to the interpreting self) and the hook is a Term-Symbol-Type sign (the same as replication).

因此，模因涉及两种信号。 诱饵是Proposition-Index-Type符号(与提供相同，其中索引指的是解释自身)，而钩子是Term-Symbol-Type符号(与复制相同)。

Analogies

类比

Douglas Hofstadter has proposed that analogies are the fuel and fire of thinking. Information affordances are what makes analogies possibles. These are indexical information that binds concepts together and permits the combination of new concepts. This is what I would call “ingenuity” and it is entirely lacking in current Deep Learning models.

道格拉斯•霍夫施塔特(Douglas Hofstadter)提出类比是思考的动力和火力 。 信息提供能力使类比成为可能。 这些是将概念绑定在一起并允许新概念组合的索引信息。 这就是我所说的“ 独创性 ”，并且在当前的深度学习模型中完全缺乏。

The consequence of an analogy is the creation of signs with an Argument aspect, that is, one kind of genuine sign in Peirce framework. It is however involves the combination of Proposition based signs to form the Argument. Conventional analogies are formed in the identification of similarity (i.e. icons) or the identification of two similar indexes and thus leading to a more subtle connection between two concepts. Analogies do not work at the symbolic layer unless symbols refer to concepts which only happens when symbols are grounded. Said differently, the signs have meaning and that meaning either reveals a similarity or an index.

类推的结果是创建带有Argument方面的符号，即Peirce框架中的一种真实符号。 但是，它涉及基于命题的符号的组合以形成参数。 在识别相似性(即图标)或识别两个相似索引时形成了常规类比，因此导致两个概念之间的联系更加细微。 除非符号引用仅在符号接地时才会发生的概念，否则类比在符号层不起作用。 换句话说，符号具有含义，并且该含义或者揭示相似性或索引。

Hofstadter’s analogy making is a process that involves semiotics (aka semiosis). That is, a process that involves the interpretation of signs to create new signs. Can Peirce’s refined distinction of signs lend us a better understanding of general intelligence?

霍夫施塔特的类比制作是一个涉及符号学(又称符号学)的过程。 即，涉及对符号的解释以创建新符号的过程。 皮尔斯的精致标志区分可以使我们对一般智力有更好的理解吗？