量子计算机

• Quantum computer - Wikipedia, the free encyclopedia

  tags: Quantum, Computer

  • A quantum computer is a device for computation that makes direct use of distinctively quantum mechanical phenomena, such as superposition and entanglement, to perform operations on data. In a classical (or conventional) computer, information is stored as bits; in a quantum computer, it is stored as qubits (quantum binary digits). The basic principle of quantum computation is that the quantum properties can be used to represent and structure data, and that quantum mechanisms can be devised and built to perform operations with these data.^[1
  • There is a common misconception that quantum computers can solve NP-complete problems in polynomial time. That is not known to be true, and is generally suspected to be false
  • Quantum gates may be viewed as linear transformations. Daniel S. Abrams and Seth Lloyd have shown that if nonlinear transformations are permitted, then NP-complete problems could be solved in polynomial time. It could even do so for #P-complete problems. They do not believe that such a machine is possible.
  • A Turing machine can simulate these quantum computers, so such a quantum computer could never solve an undecidable problem like the halting problem. The existence of "standard" quantum computers does not disprove the Church–Turing thesis.

• Church–Turing thesis - Wikipedia, the free encyclopedia

  tags: no_tag

  • In computability theory the Church–Turing thesis (also known as Church's thesis, Church's conjecture and Turing's thesis) is a combined hypothesis about the nature of effectively calculable (computable) functions by recursion (Church's Thesis), by mechanical device equivalent to a Turing machine (Turing's Thesis) or by use of Church's λ-calculus:
  • Every effectively calculable function (effectively decidable predicate) is general^[1] recursive
  • The three computational processes (recursion, λ-calculus, and Turing machine) were shown to be equivalent by Alonzo Church, Stephen Kleene and J.B. Rosser (1934-6)^[2] and by Alan Turing (1936-7)^[3]
  • effectively calculable
  • effectively computable
  • Thus, as they stand, neither thesis can be proven
  • Informally the Church–Turing thesis states that if an algorithm (a procedure that terminates) exists then there is an equivalent Turing machine or applicable λ-function^[15] for that algorithm.

• Quantum entanglement - Wikipedia, the free encyclopedia

  tags: Quantum

  • Quantum entanglement （量子纠缠）is a quantum mechanical phenomenon in which the quantum states of two or more objects are linked together so that one object can no longer be adequately described without full mention of its counterpart — even though the individual objects may be spatially separated.

• Quantum superposition - Wikipedia, the free encyclopedia

  tags: no_tag

  • Quantum superposition （量子叠加）
  • Quantum mechanics has the exact same rules for multiplying and adding numbers associated with events, except that the quantities are complex numbers called amplitudes instead of positive real numbers called probabilities
  • The superposition principle says that the way to describe the world is to assign such a complex number to every possible situation, and that the way to describe how things change is to treat these numbers mathematically as if they were probabilities. Because these numbers can be positive or negative, quantum mechanics allows the counterintuitive phenomenon that sometimes when there are more ways for a thing to happen, the chance that it happens goes down. An event with a negative amplitude can cancel with an event with a positive amplitude.

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认知革命、行为主义和神经网络

人类是如何感知和认识这个世界一直是个巨大的谜，这里是我学习认知科学时的一些笔记。

• Cerebral cortex - Wikipedia, the free encyclopedia

  tags: no_tag

  • The cerebral cortex （大脑皮层，见右图） is a structure within the brain that plays a key role in memory, attention, perceptual awareness, thought, language, and consciousness.

• Cognitive revolution - Wikipedia, the free encyclopedia

  二十世纪五十年代认知科学的诞生也被称为是认知革命。
  tags: no_tag

  • The "cognitive revolution" is the name for an intellectual movement in the 1950s that began what are known collectively as the cognitive sciences.
  • 认知革命的定义：an all-out effort to establish meaning as the central concept of psychology […]. It was not a revolution against behaviorism with the aim of transforming behaviorism into a better way of pursuing psychology by adding a little mentalism to it. […] Its aim was to discover and to describe formally the meanings that human beings created out of their encounters with the world, and then to propose hypotheses about what meaning-making processes were implicated."
  • 认知科学的五条假设（原则）
    • The mental world can be grounded in the physical world by the concepts of information, computation, and feedback.
    • The mind cannot be a blank slate because blank slates don't do anything.
    • An infinite range of behavior can be generated by finite combinatorial programs in the mind
    • Universal mental mechanisms can underlie superficial variation across culture
    • The mind is a complex system composed of many interacting parts.

• Behaviorism (Stanford Encyclopedia of Philosophy)

  来自Stanford Encyclopedia of Philosophy。
  行为主义是已经过气的心理学分支，被认知革命革了命。

  tags: Cognitive Science

  • behave is what organisms do.
  • Loosely speaking, behaviorism is an attitude. Strictly speaking, behaviorism is a doctrine.
  • For such a person, there is no knowable difference between two states of mind unless there is a demonstrable difference in the behavior associated with each state.

• Connectionism (Stanford Encyclopedia of Philosophy)

  Stanford Encyclopedia of Philosophy

  tags: Cognitive Science

  • 定义Connectionism is a movement in cognitive science which hopes to explain human intellectual abilities using artificial neural networks (also known as ‘neural networks’ or ‘neural nets’). Neural networks are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. These weights model the effects of the synapses that link one neuron to another. Experiments on models of this kind have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure.
  • Connectionists presume that cognitive functioning can be explained by collections of units that operate in this way. Since it is assumed that all the units calculate pretty much the same simple activation function, human intellectual accomplishments must depend primarily on the settings of the weights between the units.
  • feed forward net.
  • backpropagation
  • Training nets to model aspects of human intelligence is a fine art. Success with backpropagation and other connectionist learning methods may depend on quite subtle adjustment of the algorithm and the training set.
  • Connectionist learning techniques such as backpropagation are far from explaining this kind of ‘one shot’ learning
  • One of the most attractive of these efforts is Sejnowski and Rosenberg's 1987 work on a net that can read English text called NETtalk.
  • Nets may be good at making associations and matching patterns, but they have fundamental limitations in mastering general rules such as the formation of the regular past tense.
  • For example, Marcus (1998, 2001) argues that Elman's nets are not able to generalize this performance to sentences formed from a novel vocabulary. This, he claims, is a sign that connectionist models merely associate instances, and are unable to truly master abstract rules.
  • Over the centuries, philosophers have struggled to understand how our concepts are defined.
  • Connectionism promises to explain flexibility and insight found in human intelligence using methods that cannot be easily expressed in the form of exception free principles (Horgan and Tienson 1989, 1990), thus avoiding the brittleness that arises from standard forms of symbolic representation.
  • The last forty years have been dominated by the classical view that (at least higher) human cognition is analogous to symbolic computation in digital computers.
  • The connectionist claims, on the other hand, that information is stored non-symbolically in the weights, or connection strengths, between the units of a neural net.
  • implementational connectionists seek an accommodation between the two paradigms
  • They hold that the brain's net implements a symbolic processor. True, the mind is a neural net; but it is also a symbolic processor at a higher and more abstract level of description
    • Cool, that's what I belive too! - post by ericwangqing
  • They complain that classical theory does a poor job of explaining graceful degradation of function, holistic representation of data, spontaneous generalization, appreciation of context, and many other features of human intelligence which are captured in their models
  • Such radical connectionists claim that symbolic processing was a bad guess about how the mind works
  • The failure of classical programming to match the flexibility and efficiency of human cognition is by their lights a symptom of the need for a new paradigm in cognitive science. So radical connectionists would eliminate symbolic processing from cognitive science forever.
  • distributed representation seems both novel and difficult to understand
  • The sub-symbolic nature of distributed representation provides a novel way to conceive of information processing in the brain. If we model the activity of each neuron with a number, then the activity of the whole brain can be given by a giant vector (or list) of numbers, one for each neuron.
  • So the brain amounts to a vector processor, and the problem of psychology is transformed into questions about which operations on vectors account for the different aspects of human cognition.
  • This suggests that neural network models serve as counterexamples to the idea that the language of thought is a prerequisite for human cognition. However, the matter is still a topic of lively debate
  • In a series of papers Horgan and Tienson (1989, 1990) have championed a view called representations without rules. According to this view classicists are right to think that human brains (and good connectionist models of them) contain explanatorily robust representations; but they are wrong to think that those representations enter in to hard and fast rules like the steps of a computer program.
  • Fodor and Pylyshyn's often cited paper (1988) launches a debate of this kind. They identify a feature of human intelligence called systematicity which they feel connectionists cannot explain. The systematicity of language refers to the fact that the ability to produce/understand/think some sentences is intrinsically connected to the ability to produce/understand/think others of related structure.
  • Since connectionism does not guarantee systematicity, it does not explain why systematicity is found so pervasively in human cognition.

• Mental Representation (Stanford Encyclopedia of Philosophy)

  Stanford Encyclopedia of Philosophy

  tags: Cognitive Science

  • The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science.
  • Contemporary philosophers of mind have typically supposed (or at least hoped) that the mind can be naturalized

• Church–Turing thesis - Wikipedia, the free encyclopedia

  tags: no_tag
  
  伟大的Church-Turing thesis：所有可计算集都是等价的，所有可计算都是图灵可计算。神经网络也是图灵机，神经网络计算模型等同于向量并行计算模型。
  

• • In computability theory the Church–Turing thesis (also known as Church's thesis, Church's conjecture and Turing's thesis) is a combined hypothesis about the nature of effectively calculable (computable) functions by recursion (Church's Thesis), by mechanical device equivalent to a Turing machine (Turing's Thesis) or by use of Church's λ-calculus:
  • Every effectively calculable function (effectively decidable predicate) is general^[1] recursive
  • The three computational processes (recursion, λ-calculus, and Turing machine) were shown to be equivalent by Alonzo Church, Stephen Kleene and J.B. Rosser (1934-6)^[2] and by Alan Turing (1936-7)^[3]
  • effectively calculable
  • effectively computable
  • Thus, as they stand, neither thesis can be proven
  • Informally the Church–Turing thesis states that if an algorithm (a procedure that terminates) exists then there is an equivalent Turing machine or applicable λ-function^[15] for that algorithm.

• Nature versus nurture - Wikipedia, the free encyclopedia

  tags: no_tag

  • The nature versus nurture debates concern the relative importance of an individual's innate qualities ("nature", i.e. nativism, or philosophical empiricism, innatism) versus personal experiences ("nurture") in determining or causing individual differences in physical and behavioral traits.
  • The famous psychologist Donald Hebb is said to have once answered a journalist's question of "which, nature or nurture, contributes more to personality?" by asking in response, "which contributes more to the area of a rectangle, its length or its width?"

• Tabula rasa - Wikipedia, the free encyclopedia

  tags: no_tag

  • Tabula rasa (Latin: blank slate) refers to the epistemological thesis that individual human beings are born with no built-in mental content, in a word, "blank", and that their entire resource of knowledge is built up gradually from their experiences and sensory perceptions of the outside world.
  • In computer science, tabula rasa refers to the development of autonomous agents which are provided with a mechanism to reason and plan toward their goal, but no "built-in" knowledge-base of their environment. They are thus truly a "blank slate".
  • Scientists recognize that the entire cerebral cortex is indeed preprogrammed and organized in order to process sensory input, motor control, emotions, and natural responses.^[5] This preprogrammed part of the brain then learns and refines its ability to perform its many tasks.

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认知科学（转译自http://plato.stanford.edu/entries/cognitive-science/）

认知科学（来自斯坦福哲学百科全书）

本文初稿于1996年9月23日，重修于2007年4月30号

认知科学是一门跨学科的研究，研究思维智力，相关学科包括哲学、心理学、人工智能、神经科学、语言学和人类学。它的起源，从学科思想来说起源于二十世纪五十年代中期多个领域的研究者开始从复杂的表示和计算过程来建立有关思维的理论；而从组织形式来说，则起源于二十世纪七十年代认知学科学术界的形成，起标志就是期刊《认知科学》（Cognitive Science）的创办。此后，来自北美洲、欧洲、亚洲和澳洲的超过60所大学开始了认知科学的研究，还有很多的大学设立了认知科学的课程。

1. 历史
   
2. 方法
   
3. 表示和计算
   
4. 理论方法
   
1. 形式逻辑
   
2. 规则
   
3. 概念
   
4. 类比
   
5. 影像
   
6. 连接主义
   
7. 理论神经科学
   
5. 与哲学相关
   
1. 哲学应用
   
2. 对认知科学的批评
   
3. 认知科学中的哲学话题

1. 历史

    至少从古希腊时代起，人类就尝试去理解思维和它的相关操作，例如哲学家柏拉图和亚里斯多德试图解释人类知识的本质。关于思维的研究长期以来一直是从属于哲学的领域，直至十九世纪产生了实验心理学 -- Wilhelm Wundt和他的学生开创了实验室方法，使得对精神操作过程的研究更加系统化。然而，在接下来的几十年，实验心理学开始被行为主义所主导，而行为主义事实上否认了思维的存在。行为主义者认为，例如J. B. Watson所言，心理学应当仅仅研究可观测的刺激和可观测的应激行为之间的联系，而对于意识和精神表示的言论被视为不是严肃的科学讨论，特别是在北美洲，行为主义对心理科学的统治延续到了二十世纪五十年代。直到1956年前后，学术界的大环境有了戏剧性的变化。George Miller从大量的研究中总结得出人类的思考能力是有限的，例如，人类的短期记忆能力大概在7项左右。他还提出通过精神程序的编码和解码处理将信息记录为块或者精神表示能够克服这个限制。当时，原始的计算机才刚刚出现几年，但是诸如John McCarthy, Marvin Minsky, Allen Newell和Herbert Simon等先行者就开始建立人工智能（Artificial Intelligence）的基础。此外，Noam Chomsky否定了行为主义者认为语言是一种学来的习惯的猜想，代之以通过一组规则构成的精神语法来解释对（人类对）语言的理解。上述的六位思想家被认为是认知科学的奠基者。

。。。待续 。。。