“《超越学科的认知基础》2015秋颜峻学习报告”版本间的差异
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2015年10月25日 (日) 06:36的版本
第一周
关键词
认知(Cognition)[1]
Thomas Kuhn[2]
the Structure of Scientific Revolutions[3]
Leslie Valiant[4]
Probably Approximately Correct. (PAC)[5]
Probably Approximately Correct Learning[6]
group learning
Collaboration
Multidimensional study
Comparison
Illustrated by Gu Xueyong
本文
I had the class on Wednesday, to be honest, it made me confused, for I had no idea what I shall do and how. The name of the course is appealing and the central concept is novel from my perspective, thus I determined to be fully engaged in this course and figure out what I might get from it.
At the very beginning of the class, some terms were intricate, as it puzzled me how these distinct subjects or territories got correlated, some having nothing to do with others intuitively. It is the process of coping with the task that really capacitates me to get gradually understand the essence of the course which is designed to facilitate the formation of learning patterns.
What is the pattern? Of course, it is the methodology of study and can be roughly attributed to three aspects:
Multidimensional study
Multidimensional study means to integrate diverse learning sources. The sources are generalized, which comprises of learning materials, technology applied and platforms etc. Take learning quantum mechanism as an example, learning itself may be dull without interests. However when accompanied by videos and experiments, things starting get quite different. Not only the learning process becoming more absorbing, but the comprehension of the entire territory rises to an unprecedented level which is absolutely beyond my expectation.
Collaboration
collaboration plays critic part in learning process because students from different department varies from their knowledge network to their approaches to cognize, thus we can get complemented by working together.
Finally, it is contrast that really contributes to the improvement of your learning skills. Take the homework as an example. It requires us to read two literatures and list names of persons, institutions and key technologies. I read two chapters from two books, one chapter each, the first being Ecorithm (Probably approximately correct) and the second being The Route to Normal Science (the Structure of Scientific Revolution). The two chapters are designated as 1 and 2 respectively and here is the comparison.
Github
- Introduction[7]
- Mainpage[8]
- Help:git.pdf
Teambition
Comparison
Comparison does not mean comparing two irrelevant issues or references, instead it urges us to visualize things through different perceptions and form an interrelated network to reinforce our interpretation of certain concepts and theories etc.
Both books describe the revolution of science. The 1st book refers to the terms of CS while the 2nd book describes it using variable examples and facts[1]. However, both chapters are telling the same main point. The science evolves from experience and facts accumulated and gradually based on systematical learning. Learning is then divided by two categories; one is explanatory learning[1] in the 1st book (normal science or paradigm in the 2nd book), the other being machine learning (paradigm theory predictions and fact gathering). The most significant difference between these two books is their terminology that is restricted by the writers’ research territory. While Leslie Valiant utilizes words like PAC and ecorithm as evolving algorith[1], Thoma S. Kuhn stress more on the history and the development of electric and optic physics[2].
Illustrated by Gu Xueyong
Key Persons
1. John Von Neumann, Einstein, Newton, Alan Turing, Johannes Kepler. 2. Aristotle, Ptolemy, Newton, Franklin, Lavoisier, Lyell, Planck, Einstein, Hauksbee, Gray, Desaguliers, Du Fray, Nollett, Watson, Boyle, Boerhaave, Hutton, Cavendish, Coulombs and Volta.
Key Technologies
1. Computers, Einstein’s theory of general relativity, Newton’s laws, Kepler’s Laws of Planetary Obits, Alan Turing’s calculation, Turing test, Ecorithm and PAC. 2. Aristotle’s Physica, Ptolmey’s Almagest, Newton’s Principia and Opticks, Franklin’s Electricity, Lovoisier’s Chemistry, Lyell’s Geology and quantum mechanism.
Key Organizations and Constitutes
参考文献
[1]Probably Approximately Correct
[2]The Structure of Scientific Revolution
第二周
关键词
Commensurability[11]
Comparability[12]
Communicability
非定域性(non-localization)
Uncertainty principle[14]
波粒二相性（wave-particle duality)[15]
量子隧穿效应（quantum tunneling）[16]
Lawrence Lessig[17]
CodeV2[18]
四力学说
Code Is Law
Norms Law Architecture Market
本文
Last class, I learned a lot, the most important of which is not about the knowledge itself but the gradual formation of the quantum world view as well as the essence of general education.
The conservation laws are paradigm or normal science which people deem true and consequently quantum has correlation based on non-localized interaction which is beyond my previous perception of the world. I used to believe that interaction between mass is conducted by fields such as electric fields and magnetic fields, however it seems that things in microcosmos behave quite different.
Besides, what impressed me most was Shuai Tianlong's speech delivered in the class. This is a world of pluralism, without knowledge of various fields, we cannot perceive things from multidimensional aspects. What is more, Shuai said that if we wanted to communicate with people from different territories, we need to stand in the same platform with others. It does not mean that we need to reach the same level as those specialists do, we just need to know some basic concepts, history and technologies of the field, which is accord with the Road Since Structure that I am going to talk about below.
First, I have to say that the task is quite hard for us this week. The two videos uploaded on the Internet contain quantities of terms, which hinder our understanding of the videos, especially the Code Is Law. Honestly speaking, I can get the principle while watching “the structure of revolution” for I had referred to the book before, however, the code is law is completely a new concept for me, from the terms to the structure. Besides, I cannot quite get the point of the image consists of four words, law, norms, market and architecture, thus I need to refer to the book.
This experience concords with the content that presented in Road since structure regarding three aspects stressed by Thomas Kuhn.
Translation only requires understanding of words and expressions in both languages while interpretation requires understanding of the culture that the language is accommodated, thus two different language system are internally incommensurable and incomparable[1], just like what I felt watching the Video code is law, knowing every sentence the speaker issued without getting the principles of the speech.
This also reminds me of the reading experience last week. The two books, the Structure of Scientific Revolution[2] and Probably Approximately Correct[3], are actually talking very similar topics but in different lexical systems, or we can say it is the systems that historians and programmers reside that really should account for the variance. To interpret both of them well, we need to translate the words in one system another and this way they are comparable.
The second thing is that while you learn something you need to learn a lot of words within this territory simultaneously instead of learning isolated words. Take code is law as an example, if we take it for granted that law, norms, market and architecture are four different aspects[4], we may not be able to get a holistic view of the system interconnected by them.
The last but not the least, to understand the concepts well, it is required that we have already know some basic things within the territories thus it is possible for us to communicate, which is also in accordance with what teacher Shuai Tianlong told us last class.
Previously, when explaining some metabolic pathways of biochemistry, I always complain that some people just cannot understand so easy ones and now I fully realized my mistake. To make others understood, it is the primary thing to keep them familiar with basic words and expressions. This is by all means the biggest harvest I have ever got in this class.
Key Characters
Aristotle[19]
Newton[20]
Galileo[21]
Volta[22]
Joseph Banks[23]
Ludwig Boltzmann[24]
Planck[25]
Quine's Word and Ohject
Adam and Eve
Edan
Mary Hesse[26]
Kitcher[27]
Key Technology
Boyle’s law
Ptolemaic astronomy
Copernican astronomy
Aristotelian physics
Newtonian physics
Aristotelian motion: the change of position or mass.
Newton mechanics: the force equals mass multiplies acceleration.
Volta’s electric battery
Leyden jar
Electrolysis
Ohm's law
Quantum theory
Black-body problem
Probability theory
Kinetic energy E
Resonator
Quantum
£ = hv , h is the Planck Constant and the v is the frequency of the resonator
Oscillator
Principle
Element
Compound
Incommensurability
Comparability
Communicability
Reference determination
Translation
Interpretation
Phlogiston
Dephlogisticated air
Texonomic category
Lexical structure
Procedure of interpretation
Interpretive strategy
Quality-bearing principle
Key Organizations and Constitutes
参考文献
[1]A Mathematical Theory of Communication
[2]The Structure of Scientific Revolutions
[3]Probably Approximately Correct
[4]CodeV2
Appendix
Videos about quantun
From BBC
第三、四周
关键词
Conceptual system
Metaphorically structure
Metonymy[30]
Metaphor(比喻)
Spatial experience
Social experience
Emotional experience
Structural metaphor
RATIONAL ARGUMENT IS WAR
LABOR IS A RESOURCE
TIME IS A RESOURCE
Rational animals
Material resources
本文
上周，帅律师讲了法律的基本结构以及特点，同时还简单介绍了一下Lawrence Lessig的CodeV2[32], 四力学说让我印象较为深刻，但是我还是有一些疑问。尽管从逻辑推演的严密性来看，四力学说形成了一套自洽的体系，但是这样的一个模型的价值何在，它能否指导实际的生产工作，能否给人类创造价值？此外，我还希望进一步了解四力学说提出的过程，比如说为什么是市场，架构，网络，法律这几个里起到了推手作用，而将其他的因素忽略，而不仅仅是四力如何相互作用。
同样的问题还发生在学习《the Structure of Scientific Revolution》和《Road Since Structure》的过程中，我不能够理解作者为什么使用长篇大论，反复论证说明一个问题。从亚里士多德到牛顿，再从牛顿到爱因斯坦，科学理论的革新无不是经过了上百年才出现的[1]，即使他的科学进化模型是正确的，科学革新的速度能够加快吗？在询问了顾老师后，我得到了一部分解答，通过使用互联网以更高的效率集中各方力量是有可能加速科学的发展的。
本周阅读了《Metaphors We Live by》这本书，在此之前,我先阅读了teambition中的《隐喻的认知研究 解读 <我们赖以生存的隐喻>》[2]，在这篇文献中，并没有给出研究隐喻的认知基础的意义，而只是简单地概括了书的主要概念和内容，这是一个不足之处。随后，我阅读了原著的十二、三章。在阅读的过程中，我才发现这项研究的意义是非常巨大的。
在生活中，我们把对事物的理解和接受当作理所当然，但是仔细一想，我们对事物建立一个感知模型是基于已知的事物上的，按照这个逻辑，我们认知的源头在哪，哪些是我们不需要通过所谓的隐喻就可以认知的。哪些是构成认知的基本元素？从逻辑上来看，这个问题似乎无解，但是书中却给了我们一种可能的机制。人类认知的基础是对空间的感知，这种感知是通过亲身经历建立起来的，通过实物和方位我们能够表述许多概念，进一步通过隐喻将抽象物质具象化，使得抽象的物质具有了某些实物的可操作性等等[3]。
除此之外，我还意识到一个人的认知水平是和他/她的隐喻系统紧密相关的，不同人对同一个事物有着不同方面以及不同层次的理解可能就是因为不同人的隐喻体系不同。这让我想起了上个星期接触的Commensurability, Comparability, Communicability[4]。不同领域的人有各自一套的隐喻体系，而要想让不同领域之间的人有所交流，首先要站在一个相同的基础上，这也是词条构建的重要性，而词条的构建恰恰也是在搭建一套隐喻体系。
可以说，书中的观点重新塑造了我的认知观，感觉收获颇丰。
关键人物
Lawrence Lessig [33]
亚里士多德[34]
牛顿 [35]
爱因斯坦 [36]
关键技术
关键组织和制度
参考文献
[1] Road Since Strucutre
[2]《隐喻的认知研究 解读 <我们赖以生存的隐喻>》
[3] The Structure of Scientific Revolution
[4] Metaphors We Live By
第五周
关键词
Namespace[39]
Homonym[40]
Metonymy[41]
Block chain[42]
数字社会
合法交易
计算架构
计算法律学
Watson(computer)[43]
Watson律师
Smart contract(智能合约）
Selectica
Smart Oracle
智能合约五项基本原则
数字ID
自主管理
Windhover[44]
ID3[45]
架构[46]
OpenPDS[47]
OMS[48]
本文
本周蔡老师初步地介绍了一下数字社会的基本概念其中又重点地介绍了数字社会的发展，人工智能的历史，以及计算法律学研究的一些内容。在讲解的过程中，蔡老师对计算机给人类带来的影响和冲击进行了反复强调。其中令我印象最为深刻的是“计算机是算得最快的笨蛋，然而它现在却有了常识”。
可以说从一定程度上，我的传统观念受到了一定的冲击，无论是智能合约还是Watson律师都让我对计算机发展的迅猛感到吃惊。吃惊之余，对于计算机拥有常识以及计算机能够被用于法律学的前景还是持有怀疑的态度。首先，计算机程序，就拿Watson来说，顶多只是一个存储着死知识的代码，人脑神经元之间的联系和整合能够迅速地将两件事物或者概念相互关联，形成一个常识的网络，这是计算机所不能企及的，在人工智能等领域要想有突破性的进展，需要设计更加智能的算法或者架构实现跳跃计算。在跳跃计算的基础上，我认为所谓“常识”之间的交联才能成为现实。
此外，Watson研究癌症无可非议，而且一直以来我都有这种想法，希望借助机器的优势进行科学研究。特别是在学习了本课程以及阅读了Thomas Kuhn的《the Revolution of Scientific Resolution》对科学发展的基本规律,特别是Kuhn Cycle[1]有了一定的理解后，我认为计算机技术的进步能够大大地加速科学革命。一方面是计算机有计算速度上的优势，能够帮助人们解决许多繁琐的事物，比如文献调研并且还能通过文献的统计分析对研究热门方向提供指导。计算机也为群体学习提供了一个良好的环境，推动知识之间的交流，加速科学的发展[1]。
尽管如此，计算机还是不能够代替人在研究过程中的关键作用，这种差异是由人类神经系统特殊的第二信息系统，人类对语言和抽象信息的理解，使得逻辑能够在不同的事物中跳跃，这是我一直认为计算机不能做到的。
最后，在上课的过程中，我始终有一些疑问，诸如智能合约五项基本原则以及Windhover的数字身份，信任和数据管理原则等等这种类似规定有何意义。可能是由于不同的专业语汇[2]以及比喻体系[3]的问题，蔡老师在上面讲的非常投入，然而我却不能够体会到重要性，这一点和《Road Since Structure》以及《Metaphors We Live by》中的论述是一致的，commensurability的重要性进一步体现。
关键人物
John McCarthy[49]
关键技术
AI[50]
ID3
架构
OpenPDS
OMS
Watson computer
Smart Oracle
Selectica
关键机构和制度
智能合约五项基本原则
Windhover的数字身份，信任和数据管理原则
参考文献
[1]Revolution of Scientific Revolution
[2]Road Since Strucutre
[3]Metaphors We Live by
