Author Archives: Roxy

About Roxy

I am a graduate student from Georgetown University. 4 years ago, I felt language ability was my highest priority. That is why after being recommended to the Xi’an Jiaotong University, I chose Bilingual as my major. I served as Project Manager of the Re-paper Project on campus, which aimed to optimize my university’s paper recycling system. We first put recycling bins for waste paper in classrooms. Then, we focused on training cleaning staff how to classify waste paper and increase their income as a payback, and cooperated with a local recycling. We also noticed that improving the environmental awareness of students can help out the administration rather than simply publicizing our project. After six months, paper recycling improved considerably on campus. At this point, I began to realize the power of communications. I, then, began an internship at the Xinhua News Agency. This institutional focus on exactitude led me to raise the standard for my own work. I translated news from all over the world and also broaden my knowledge. I consulted reports from different countries to report the objective one. I also learned to keep attention to details after finishing the road book for our photography team in Tibet. Having experienced the communication fields as an amateur, I would now like to concentrate on it.

Talk to me in Spanish, French, English, and Chinese (Roxy)


Thanks to Google Translate, we can realize some communication we never thought that might happen. With GNMT (Google Neural Machine Translation) system, Google had improved his performance by a lot. What human beings’ cognitions does it distribute? How to improve Google Translate? In this paper, I will trace the history of the technology applied in machine translation first, including the PBMT (phrase-based machine translation) system and the GNMT (Google Neural Machine Translation), explain the poor behavior of Google translate by comparing the differences of understanding a sentence between human beings and machines (with examples), and analyze the human beings’ cognitions distributed by Google Translate.

1. Introduction

My friend Melissa, a graduate student in the translation of English and Chinese, had told me that: “As a translator, when I saw the latest progress of Google Translate, I totally could understand the anxieties and fears of the textile workers in the 18th Century when they saw the steam engine. Google Translate, the industry leader of machine translation, is a useful technology for people to extend and distribute their cognitions.

Communication is really important to human beings, the social beings who have to live in society and deal with each other. Languages are always the biggest barrier for people to understand the outside world. Semiotics is the study of signs. According to C.S. Peirce, there are three kinds of signs: icons, indices, and symbols. Icons represent things by simply imitating them; indices convey the idea by being physically connected with them, and symbols convey the meaning because of their usages. The most typical and common example of a symbol is language ( Irvine, 2016a). The semiotic feature of language makes language impenetrable to machines. Google had already improved its algorithm a lot, but still had some flaws.

2.The Main body of the essay

2.1Technical Overview

2.1.1 The introduction of Machine translates

Machine Translation is the use of software to translate a source language into the target language. Machine translation system can be divided into two categories: rule-based and corpus-based. The former’s resources are dictionaries and rule bases, the latter are corpora with the statistical mean.

Only a few years ago, PBMT (Phrased-Based Machine Translation) system was the mainstream approach of machine translation. Google Translate was based on this algorithm as well. Google machine translation basically uses technology-based statistical machine translation method. It takes a large number of bilingual web content as a corpus, and then selected the most corresponding words of the original language. The “Phrase”, here, in “phrased-based” means the smallest unit of translation.

2.1.2 How PBMT works?

First of all, PBMT breaks up the sentences into phrases according to the syntax of this language. Syntax, grammar, describes the rules and constraints for combining words in phrases and sentences that speakers of any natural language use to generate new sentences and to understand those expressed by others(Irvine, 2016b). Here is an example, we can see the syntax tree of the sentence “Once when I was six years old I saw a magnificent picture in a book, called True Stories from Nature, about the primeval forest(From The Little Prince written by Anntonie de Saint-Exupery, translated from the French by Katherine Woods ).”%e5%b1%8f%e5%b9%95%e5%bf%ab%e7%85%a7-2016-12-16-%e4%b8%8b%e5%8d%8810-26-14

Then, it will match each phrase to the target language from its big data.

Finally, it is necessary to rephrase the target language phrases so that it can conform to the syntax of the target language.

During the whole translation process, it is also necessary to use other lower-level NLP (Natural Language Processing) algorithms, such as Chinese word segmentation, part of speech, syntax structure, etc. Admittedly, Google’s technology is advanced, but it still sometimes generates all kinds of translation jokes. The reason is that statistical method, unlike human beings with knowledge, needs a large-scale bilingual corpus. The accuracy of translation directly depends on the size and accuracy of this corpus. This way of translation will eventually generate the incorrect translation because of the error propagation since any error in the middle link will continue to spread down, and lead to the wrong final result. Therefore, even if the accuracy of a single system can be as high as 95%, the accumulation of minor error will cause an unacceptable result.

2.1.3 How does GNMT work?

In September 2016, Google has been published “Google`s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation” on

With the same corpus, the GNMT (Google Neural Machine Translation) system can achieve the same result with less workload compared with the PBMT (phrase-based machine translation) system. The word “neural” in GNMT means it can pay attention on the words you’ve inputted. In the past, the translation engine would only look at each word, one by one, and provide you the language matching with it. But the GNMT will “read” the words at first. For example, if the texts you’ve input is an excerpt of a news that had already translated into French by the news website. The Google Translate will then provide you the same chunk from the French page.

The diagram below shows how GNMT translates a Chinese sentence into an English sentence.

This model follows the common sequence-to-sequence learning framework with attention. It has three components: an encoder network, a decoder network, and an attention network.

Formal languages are defined with respect to a given alphabet, which is a finite set of symbols, each of which is called a letter. This notation does not mean, however, that elements of the alphabet must be “ordinary” letters; they can be any symbol, such as numbers, or digits, or words (Clark, A., f 2013). First, “The encoder transforms a source sentence into a list of vectors, one vector per input symbol.” Here, it encodes each Chinese character into each vector. “Given this list of vectors, the decoder produces one symbol at a time, until the special end-of-sentence symbol (EOS) is produced. The encoder and decoder are connected through an attention module which allows the decoder to focus on different regions of the source sentence during the course of decoding

%e5%b1%8f%e5%b9%95%e5%bf%ab%e7%85%a7-2016-12-19-%e4%b8%8b%e5%8d%8812-45-40With GNMT system, Google Translate can achieve a better result under the standard of human assessment. With the help of bilingual human assessors, the sample sentences from Wikipedia and news website can reduce (Wu, Y. 2016).


Data from side-by-side evaluations, where human raters compare the quality of translations for a given source sentence. Scores range goes from 0 to 6, with 0 meaning “completely nonsense translation” and 6 meaning “perfect translation.”

Here are some examples of a translation produced by PBMT, GNMT, and Human.


Machine translation is far from being perfect. The encoding of sentences into vectors, regardless of its language feature, the content may be uncontrollable, and lead to errors. GNMT will still make significant mistakes that human translators will never make, such as misspellings and misinterpretations of rare terms. However, GNMT represents a major milestone.

2.2 Google Translate V.S. Human Beings

“As the engineering branch of computational linguistics, natural linguae processing is concerned with the creation of artifacts that accomplish tasks” (Clark, A.2013) NLP is the main source for machines to understand a sentence, most NLP tasks require the annotation of linguistic entities, with class labels: A part-of-speech tagger, for instance, assigns a part of speech to each word.

When I saw the news of Google ‘s new GNMT system, I was very interested. So I used a Chinese article to test Google Translate. The article I chose was very logical, it is an introduction of an earphone. The result I received was surprisingly good, the choices of words are very accurate and suitable.

But this matter is not that simple.

2.2.1 the Semiotics of language

The linguistic sign units have two sides, the concept and sound-image. Saussure will use sign [signe], signified [signifé] and signifier [signifiant] to mention word, concept, and sound-image respectively. (Martin, I. 2016 a)


An English-speaker can understand the meaning of words. He can follow the command to pick the red paper among 10 pieces of colorful paper. “But a computer cannot understand the meaning of red, just as a piece of paper cannot understand what is written on it (Hausser, R.1999).”

Let’s see Google Translate’s performance when dealing with a more flexible and oral article.



And this is the translation from a Chinese-English translation company: “If the performance of GTX 950 is taken as a benchmark of 100%, then the performances of GTX 1050 and GTX 1050Ti can reach 110% and 140% respectively. They also excel the previous generation models in terms of power consumption. Apart from saving energy, lower power consumption means less heat generated, which is a good news for the game players with small-chassis computers for which ventilation can be an issue.“

We can see that the google translation version, compared with the one translated by human, is barely acceptable.

2.2.2 The Beauty of Language

The machine cannot understand the beauty of language. Poems are fantastic because they have rhythm and verbal poetic images. Here is an example,  this is an ancient Chinese poem.

陆游 《卜算子·咏梅》











(The Diviner-Ode to the Plum

By Lu You

Tr. Zhao Yinchuan

Beside the broken post bridge there

It blows, solitarily sane

The dimming dusk it can hardly bear

And there’s the slash of wind and rain


It contends for spring with no one

That horde of flowers, let them flare

It falls into dust, trundled to none

Its aroma welling as e’er)

In this poem, the rhyme is following a certain pattern. The human translator can understand it and find the words rhythm with each other to accomplish the translation. But the machine can never tell this difference. It will follow the big data’s command and output the most common combination of these words.

In addition, “Broken Bridge(断桥)” “Dimming Dusk(黄昏)” “Wind(风)” “Rain(雨)” These verbal poetic images jointly create a lonely and clear atmosphere. We can see the picture imaginatively: a plum is opening in a desolate spot next to a broken bridge, the evening wind and rain scatter the plum into the mud, but it still maintains its aroma.

Language sign, according to Saussure, has (1) the arbitrary (i.e., unmotivated) structural relation of sound and meaning in any natural language (the foundation of language as a symbolic system), (2) speech sounds, word forms, and meanings are elements in a system of interrelations, within which, and only within which, they function as constituents of a language; and (3) the recognition of two dimensions of meaning — the “context-free” sense (like dictionary meaning) and social-cultural value (meaning in contexts of use) (Saussure, 1959). These features make language can only be understood by the people who understand the system of this language (signifier), and the meaning of this symbol (signified). This limitation is the biggest barrier for the machine to fully understand one sentence.

“Broken Bridge(断桥)” “Dimming Dusk(黄昏)” “Wind(风)” “Rain(雨)” these words are accepted by the Chinese society to signify a specific meaning. These verbal poetic images can accurately express the feeling of the author so that the readers receive an aesthetic experience.

2.2.2 The Formation of Language

Finally, it cannot match the same target language if the formation of the source language is changed.



Now, Google Translate can deal with the translation between English and Chinese. But if I add an auxiliary on the same sentence, the translation cannot reach the ideal result.

The process of GNMT is purely a process of fitting functions. Through this fitting function, if the source language changes its formation, it will map different target language, even if they have the same meaning. So, adding a few irrelevant words will change the result enormously.

2.3 Distributed, Extended, and Embodied Cognition

Google has already completed the experimental and commercial practice on its TensorFlow platform (TensorFlow™ is an open source software library for numerical computation using data flow graphs) with Tensor Processing Units. This technology

2.3.1Distributed cognition between individuals and technology Beyond Direct Manipulation: Graphical Interface

The interface is the access for people to manipulate the things existing on the screen. An important research issue for the field of human-computer interaction is how to move beyond current direct-manipulation interfaces (Hollan, J.2000). This web page allows us to interpret actions such as, input the source language, change the source language, get the output of the target language, share the texts, listen to the texts, etc. Some of the actions can be realized in the real world, but some have no easy counterpart.


As users become more familiar with an environment they situate themselves more profoundly(Hollan, J.2000). “Everything we take for granted about graphical “interfaces” – software controlled pixel mapping with an “interactive” software layer engineered to track pointing devices and defined regions for user-activated commands (icons, menus, links) – were developed in this context for “augmenting human intellect” and organizing all forms of symbolic representations and expressions. “(Martin I., 2016 c)This website, like the most website, uses graphical interfaces (icons) to connect the users to the website.  Provide knowledge

Listen: From the icon “listen”, Google Translate will read this text. Google Translate recorded a human’s voice of thousands and thousands of carefully-chosen sentences to make the voice of it. These sentences are chosen to contain all the sounds in one language and all the combinations in this language (For example, in English, the /s/ sound will change to accommodate the letter in front of it). They by following divide these sentences into sound tokens. The voice we can hear is the combination of these tokens. The technology could provide us with the knowledge and skills that are unavailable from internal representations (Zhang, J., 2006). We could imitate the sound provided by Google to speak another language.

Translation (Website translation, Camera instant translation):

@Aidan Mechem twittered his experience with Google Translate.


With notebooks, we don’t have to memorize everything happens in the daily life; with Google Translate, we don’t have to learn Spanish to communicate with a Spanish-speaker. Google Translate is an affordance of our cross-language communication.

2.3.2 Distributed cognition across individuals. Cross-cultural communication

Instead of spending times in learning other languages, Google Translate provide you the most convenient and efficient solution of understanding other languages. The function of “share”, and “read phonetically” democratize the cross-language communication. Google Translate Community

Languages are much more complex than the machine can understand. The language may be unclear without the context. The sentence like “J’ai votre nom” can be understood in two different meanings: “I have all of your names” and “I have your name; you are one person I respect”. That is why Google Translate Community is available for anyone to correct the translation. Even if the meaning is clear, idioms and specific terms and expressions make translation very hard for machine translations to translate precisely. The meaning of one sentence may change with different social context or the status of the reader and writer. On analyzing the distributed cognition across individuals, reductionists insist that the cognitive properties of a group can be entirely determined by the properties of individuals”; the interactionists insist that the interactions among the individuals can produce emergent group properties that cannot be reduced to the properties of the individuals(Zhang, J., 2006). The communities’ advises can be really important for Google to expand its corpora and by following to provide better service.



3. Conclusion:

Machine learning is a process of identifying the vectors. For example, there are two boxes of fruit, including apples and oranges. The machine will first identify their vectors: (red, with handle, sweet) = apple, (yellow, no handle, acid) = orange. Facing a green apple, the machine can find that this green apple is relatively close to the apples’ vectors, and will identify it as an apple.

In the linguistic field, NPL is the way for the machine to analyze one sentence. GNMT system is a new algorithm for Google Translate to pay attention to the connection between the source language and target language. With these algorithms, Google Translate has actually been incredibly successful, in my point of view. But there are some big problems when translating: for example, from Chinese to English, Google Translate can’t tell where Chinese words start and stop since there aren’t spaces between Chinese words.

An English speaker will say “it’s Greek to me” when he cannot understand a word; a Greek speaker will say “it sounds like Chinese” when he encounters the same difficulty; a Chinese speaker addresses this situation as “it’s Heavenly Script to me”. Google Translate can break down the barrier of language for us. I think Google Translate can be improved to bring a revolution in the near future to the cross-language communication.







Martin, I. (2016 a). The grammar of meaning systems: Sign systems, symbolic cognition, and semiotics. Unpublished manuscript.

Martin Irvine. (2016b). Introduction to Linguistics and Symbolic Systems: Key Concepts. Unpublished manuscript.

Martin Irvine. (2016c). Introduction to the Technical Theory of Information. Unpublished manuscript.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … & Klingner, J. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv preprint arXiv:1609.08144.

Carey, J. (2002). A cultural approach to communication. McQuail’s reader in mass communication theory, 36-45.

Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. OUP USA.

Clark, A., Fox, C., & Lappin, S. (Eds.). (2013). The handbook of computational linguistics and natural language processing. John Wiley & Sons.

Zhang, J., & Patel, V. L. (2006). Distributed cognition, representation, and affordance. Pragmatics & Cognition, 14(2), 333-341.

Hausser, R., & Hausser, R. (1999). Foundations of computational linguistics. Berlin: Springer.

Ferdinand de Saussure, Course in General Linguistics. 1911-1916. English translation by Wade Baskin, 1959. Excerpts.

Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: toward a new foundation for human-computer interaction research. ACM Transactions on ComputerHuman Interaction (TOCHI), 7(2), 174-196.

Chandler, D. (2007). Semiotics: the basics. Routledge.

Short, T. L. (2007). Peirce’s theory of signs. Cambridge University Press.

Bergman, M., & Paavola, S. (2010). The commens dictionary of Peirce’s terms-Peirce’s terminology in his own words.

Denning, P. J., & Bell, T. (2012). The information paradox. American Scientist, 100(6), 470.

Human Evolution

This is my first year in CCT. Before this CCT program, if you ask me what is a computer, I will answer that computer is a device that can carry out some arithmetic or logical operations. But at the end of 12 weeks of study of this course, I will answer that computer, is a computing artifact and an affordance of humanity’s cognition.

In these weeks, we go through the history of semiotics from C.S. Peirce’s theory of signs to the Saussure’s semiotic model, the history of meaning systems and symbolic representation from the earliest records of symbolic expression to computational language processing and artificial intelligence, the history of models of computation, the history of computing systems from memex by Vannevar Bush and the Dynabook by Alan Kay to the laptop and other computing devices today. After dipping into these theoretical backgrounds, I can see that my thinking of this world and human beings has been changed.

Humanity Memory V.S. Computer Storage

Nowadays, there have been major breakthroughs in the human brain understanding; some of them can directly change our daily life. However, media on human brain knowledge is far less than common sense. Common people, including me, have a lot of misconceptions about the human brain. The most popular one is the analogy between the human brain and the computing systems. This misunderstanding mainly comes from science fiction. Many science fiction works describe humanity’s memory as a data that can be erased and revised. For example, in the Matrix, people can learn martial arts or how to drive a helicopter by directly receiving codes.

Human’s memory is not recorded in the brain, but grows in the brain. The brain remembers things through neurons. There is an example: a patient had no pain nerves, which means that he can remain awake throughout the craniotomy. The doctor found that one of his nerves jumped when he saw the picture of Jennifer Aniston, and another one jumped when he saw the photo of Bill Clinton. The human brain arranges a specific nerve for each person he knows. In computing, the most semiconductor memory is organized into memory cells. It is easy to save a file on the hard disk, just change the arrangement of bits (0 or 1), but it is impossible to change people’s memory.

Computing Devices & Human Beings

After reading those science fiction works, I could not help but think of the day that the artificial intelligence will destroy the human world. Now, a lot of field people are working on the advantages of human beings relative to artificial intelligence. We have the unique human expectations of the future. We are the only species has the ability to understand the long-term future and make plans for it. In the book “The Future of the Mind”, the Japanese physicist, Kari Daishi, points out that the biggest difference between human beings and other species is that our brains understand the concept of time. So that the long-term interests will drive us to give up some short-term interests, and the collective development will drive us to give up some personal interests.

Of course, no one can predict the extent to which technology can be developed. Maybe one day, the machine can fool the human, and can even affect mankind.

However, after learning knowledge of semiotics, I see them from different angle, I regard these cognitive-symbolic artifacts as the extensions of human cognition. If you look at the history of computing systems, computing systems are getting smaller to smaller. At beginning, its size is about a room. In 1970s, we had the personal computers. Now, I have a laptop in my bag everyday, and we wear Google glass and all the other wearable devices. The next step is that they will be under our skins. So they become closer and more intimate to us, we are absorbing computers into us. In this society, we are bringing technologies to us, and they become parts of us. We are no longer independent from our technology. They change our identity of who we are. In the past, we thought ourselves ending by fingertips. After we are expanding ourselves with the help of those computing systems. Some people may concern about that the artificial intelligence and technologies may replace people from their jobs. It is true that some jobs will disappear, but it will create more new jobs for people. That is what we are doing, using technologies to create new things for people to do and further evolving to new creatures.

The development of human society (Roxy)

Let us go first with the development of human beings. There could be two clues to analyze the history of mankind. The first one is “extension”, or “ affordance”. According to Marshall McLuhan, the development of science and technology depends on the continuous substitution of men’s extension. When people afford moving on the wheels and rockets, we extend our legs; when people afford washing on washing machine, we extend our hands; when people afford watching on TV, we extend our eyes.

The second clue is “interaction”. Although interface, nowadays, “has a narrow range of meaning as a shared boundary across which two separate components of a computer system exchange information, ” as showed in the first Wikipedia’s entry of interface. From an etymological perspective, this word, in 1874, means “ a plane surface regarded as the common boundary of two bodies.” In its broadest sense and also the modern usage, this term, “interface is anything that connects two (or more) different systems across the boundaries of those systems. “

Basically, interface exists since the dawn of mankind. From the moment we use language to substitute the gesture. We can feel the interface between language system and gesture system. The sensory systems of human beings could interact with each other. From the moment we start to draw patterns on the pottery. We can perceive the interface between the aesthetic system and practical system. Outside systems which could fulfill different human needs could interact with each other. We then apply interface to the computational system. We also interact with other species: in the nomadism civilization, people interact with sheep, horses and dogs; in the agriculture civilization, people interact with rice and wheat; in the industrial civilization, people interact with oil and coal.And now, in the information era, with the help of WIMP (windows, icons, menus, and pointer devices), we can detect the interface between the human system and the computational system. Human beings and machine systems could interact. In the future, we will create more interfaces, for example, through recently the heated project “The Reality Editor”, we can read the interface between the computational system and the material system.

“The Reality Editor  is a new kind of tool for empowering you to connect and manipulate the functionality of physical objects. Just point the camera of your smartphone at an object built with the Open Hybrid platform and its invisible capabilities will become visible for you to edit.” Drag a virtual line from one object to another and create a new relationship between these objects. With this simplicity, you are able to master the entire scope of connected objects. This tool could help users to maximize their strength, such as spatial coordination, muscle memory and tool-making. For example, it could help you to turn off the light in your bedroom without letting you to stand up and walk to the switch.

The interactions between human and human, human and object, and human and information will decide how this society operates. In the next step, I think the computing may focus on the small things, the details. The critical details can make the difference between a friendly experience and traumatic anxiety. A problem cannot be solved with the unhandled details.  With the Internet, people are tagged. More specific targets mean more segments of the market, which requires better detail-optimization. 


What is the difference between the HCI and interaction design?



Computer Language and Computational Thinking (Roxy)

At least, I learned something connected with semiotics before, when I was an undergraduate student. But computer science, or computation, is a brand-new field to me. Before I touch this field, I felt it is really cool and challenging. It is like one of the greatest inventions and social changes has a great influence on the human beings. So I finished all the free course connected with Python on the codecademy website. After coding with my own hands, I felt computation is not harder than I thought. In Python, all the sentences quoted by “”” and “”” or led by // are symbols that mean things, since the computer will automatically ignore these words. Besides, other sentences without quotation mark or slashes can be treated as the symbols that mean things, but they can only be read in a particular form.

According to Prof. Dasgupta,  computer science is an artifact. Compared with natural science, which devotes itself to figure out the existing functions of the organs, fossils, or oxygen, computer science, as an artifact, whose purpose and functions are all defined by human beings, is decodable. As an outsider, I really hope coding and computation can be as easy as possible, so I cannot help but ask why computers cannot use natural language to code. English may cannot perform this task, but French or German which are more rigorous than English still could not meet the requirements. There is an impassable gulf between natural language and computer language. I think the root of this gulf is that there is a huge difference between how do human beings think and how do computers understand. People use language of thought to communicate, but computers are trying to understand all the descriptive language.

But we can see the shrinking gap between computer language and natural language from the history of coding. People use 0 and 1 as the basic component of coding at first. It is very hard to memorize, to check, and read by human beings. But now we have a java script, python and so many other computer languages. They are really similar to the natural language, except for the fixed form and arrangement.

With the help of computer language, not only we could code, we can also have . The core of computational thinking is the problem-solving skill, which is reformulate a difficult problem into one we know how to solve (Jeannette M. Wing.) For example, IOS system is a relatively closed system, compared with Android. Yes, it has some open-source bits, but the vast majority of the operating system are closed-source. There is no real possibility of changing the settings by an application. So, how do some music apps realize the function that can display the scrolling lyrics on the screen when the phone is locked. Even if the iTunes and Music cannot implement function. Those computers reformulate this hard question into one they know how to solve. They photoshopped every lyric on the same posters slide by slide, and change the posters one by one every several seconds. To a user, it looks like the poster never changes, only the lyrics are rolling. I think this is really a smart practice. If we could apply this computational thinking into daily life, it still could solve some hard problems.



Why they cannot define a same framework or arrangement of different computer language?


[1] Denning, Peter J., and Craig H. Martell. Great Principles of Computing (Cambridge, MA: MIT Press, 2015).

[2] Wing, Jeannette. “Computational Thinking.” Communications of the ACM 49, no. 3 (March 2006): 33–35

[3] Subrata Dasgupta, It Began with Babbage: The Genesis of Computer Science. Oxford, UK: Oxford University Press, 2014. Excerpts: Prologue and Chapter 1.



Coding and Decoding a text message- Roxy

“Sign” , according to C. S. Peirce, is a static unit or individual representational form that can be interpreted as part of a collective sign system. Signs can be signs are because they can magnify the particular feature of a particular object. For instance, in French, a “mother-in-law” is called as “ la belle-mère.” “Belle” means beautiful and pretty. “mère” means mother. It is also true in China, we call a mother-in-law as “qin jia mu”, here, qin means close and related by blood. We can see that we have to emphasize that we are soooo close because we are not that close in reality. Language, here as a symbol, functions in the daily life.

Thanks to the internet, people in this world closely connected to each other as never before. The online version of communication can get rid of the facial expressions, gestures and tones, it is easier to be interpreted. So, how a text message, an email message, or social media message works? What kinds of communication acts understood by communicators are involved?
In The Information Paradox, Shannon’s words were cited to explain the first theoretical model of a mathematical theory of communication. “ A source sends a message. An encoder generates a distinct signal for the message, as prescribed in a code book. The channel is the medium that carries signals from the source to the receiver. a decoder on the receiver end converts the signals back to their original form, using the same code book, and the message has arrived.”

In the first step, the encoder generates a distinct signal for the message. Senders play a decisive role in a communication action. The sign can only be a sign when it is interpreted by a sender in a particular way. So, if the senders’ intention is unknown, this sign cannot be interpreted. A German man has taught his dog Adolf to give a nazi salute when hearing “Adolf sit, give me the salute.” In this case, the situation is much more complicate. The sender of this sign is only a trained dog who cannot understand the meaning of this gesture, so the dog is not violating Germany’s anti-Nazi laws. The sender also decides the way of encoding. A signal can be encoded in various ways, but there exist better options of one idea. A lot of outside factors can matter. For example, If a signal is highly required by an environment, this signal can be sent in many ways. When I want to answer a yes-no question, I can say “yes”, “sure”, “correct”, “of course”, etc. But If I want to mention a term, I can only use “algebra”, or “physics”.

In the last step, a decoder converts the signals back to their original form. The process of decoding is answering a series of yes-no questions. Although the answer of each question can only be 0 or 1, but the probabilities of 1 and 0 are different. I have to distinguish “me” and “not me”, and then the “is a person” -“ is not a person” question. The “is a tiger”-“not a tiger” question. We can see that we have to cut a sign into several questions and then decode them respectively. A receiver has to choose the same code book of interpreting the codes. Although we do have some conventions, they cannot cover every aspect, detail, and trivia of life. That could be the first reason the receiver may not get the sender’s meaning.

Another reason that may distract the receiver is the noise exists in the medium that carries signals from the source to the receiver. Sometimes we can abstract the thing we talk about from the real world. When we sit in a theatre, we can tell the difference between on stage and in reality. That is because there is a intangible wall between you and the play. But, usually, we recognize this world by taking them as an entity.

I can never make sure that the text message I sent can be full understood by you.


Nazi salute. (2016, October 8). In Wikipedia, The Free Encyclopedia. Retrieved 11:29, October 8, 2016, from
Irvine, Martin. “Introduction to the Technical Theory of Information.”
Denning, Peter J., and Tim Bell. 2012. “The Information Paradox.” American Scientist 100 (6): 470–77.
Hall, Stuart. “Encoding, Decoding.” In The Cultural Studies Reader, edited by Simon During, 507-17. London; New York: Routledge, 1993.
I have two questions:

1.Do we have some expression in particular languages cannot be translated or deciphered by other languages? I mean, if you can explain a 15-letter English word by 1000 Chinese words, you can still translate it.

2. Why different languages can be unbalance? In Chinese, we use different words to indicate elder cousins, younger cousins, male cousins, female cousins, maternal cousins, and paternal cousins, but in English, there is only one word: cousin. And in French, they use “quatre-vingt-dix-neuf” to indicate 99, quatre means 4, vingt means 20, dix means 10, neuf means 9. It is really like a formula: 99=4*20+10+9. Why they don’t have the words like ninety.

Pop Art and Semiotics (Roxy)

Pop art is an art movement that emerged in the mid-1950s in Britain and the late 1950s in the United States. In pop art, Material is sometimes visually removed from its known context, isolated, and combined with unrelated material. (Wikipedia) Pop art is the abbreviated version of popular art, since one of its aims is using images of popular culture in the art to narrate, parody, or to satirize.

These are examples of famous pop art.



According to C.S. Peirce, human thought is based on signs in symbol systems, each of which have a structure of material and cognitive relations. Language can be the most obvious symbol. But, actually, human beings cannot only rely on the language to express their feelings, since we are facing such a complex world. We can count on other symbols, visual symbols must be one of the popular ones. Compared with language, the components of visual symbols can be more arbitrary, creative, and abundant.

Although some linguist and philosopher, such as Susanne Langer, denied applying the laws of syntax that govern language on the analysis of articulation. I still think there exist a lot of similarities underneath the differences. I, here, use a famous Asian artist: Kusama Yayoi and her arts as an example to interperate.


u=955618547,2833888708&fm=21&gp=0The parallel constraint-based architecture of interpreting the theory of processing is used here to analyze the art work.Pretty same as the figure of linguistics, the processing of art can also be divided into three parallel structures: the spots structures, the syntactic structures, and the conceptual structures. (Jackendoff)

WechatIMG13The Spots Structures
Rather than the lexicon of linguistics, we can see clearly that Kusama Yayoi’s art works are composed by thousands of spots. They are the basic components of her art works. She, herself also mentioned that even earth is one of the millions of spots, when you finished one sport, you have already finished the universe and this world. In her art works, those spots are just like cells, or molecules, which are the origin and basis of the life.

WechatIMG15The Syntactic Structures

The combination of the spots also has its own syntax, so that to express. This syntax can also branch outward to color and size of the spots.

We can also see another obvious feature of her work, the infinite. She uses millions of spots to change the original formation of a stuff, and creates a continuing relation between this stuff and the outer space. So when facing this art work, it is hard for the audience to tell the reality and the world she created.

The Conceptual Structures

Pumpkin is one of her representative works. To Kusama Yayoi, pumpkin is an index of food. She had experienced the World War 2, pumpkins are the only food for them to survive through the famine. From then, pumpkin becomes her favorite vegetable. She sees pumpkin as the most humorous vegetables, and the flexible one as well. So in her art works, we can see the pumpkin is soft instead of having hard skin.



[1] Daniel Chandler, Semiotics: The Basics. 2nd ed. New York, NY: Routledge, 2007. Excerpts.

[2 Ray Jackendoff, Foundations of Language, selections on the “Parallel Architecture” model of language as a combinatorial system. Chap. 5.5, pp. 123-128; Chap. 7, pp. 196-200.

Language can be everything—Roxy (Jiange)

What is Language

Before the reading, if someone ask me what is language, I will tell him that language is the ability to use a complex system of communication to express feelings, and languages are totally different from each other and arbitrary. There exist 5000-7000 kinds of languages in the world now. But, after the reading, I will say everything could be language, there must be millions of ways to express thoughts. Not only the languages, spoken or signed, can be language; all those auditory, visual, or tactile stimuli to the brain can also be languages. “It is a abundantly clear that the brain is organized in terms of numerous interacting areas that together determine our experience of the world and our intentions to act.” (Jackendoff, 130)

Brain is like a CPU, the input are all kinds of stimuli, and the output depends on the things you familiar with. All man are born equal, yes,it is also true to a linguist. A 8-month-old baby can have the ability to identify all the different existing pronunciations, there are more than 800 pronunciations! But later, if his brain stop being stimulated by the particular sound, he will lose this ability. A Chinese-Speaker cannot pronunciate /l/ sound and /rrrr/ sound, without training. A Japanese-speaker cannot tell the differences between /r/ and /l/, my english-speaking classmates cannot pronunciate my name……

So long as there is an input, stimuli to the brain, the brain can help us to feel this world.

Here is also another interesting device. BrainPort device, created by Wisconsin-based Wicab, Inc., consists of a pair of sunglasses with an embedded camera, a hand-held CPU, and an electrode-laden paddle which sits on the user’s tongue which is placed on the user’s tongue. The camera will capture the visual information, transfer them into electric impulses and stimulate the brain through the tongue.

Besides, output methods can be various. I think we all had inner voice sometimes, which means we can think in words. Some people may curious about how deaf people think in words. Actually, blind people’s dream may contain those feelings, so deaf people can think in those gestures and signs. So it doesn’t matter which language is your mother tongue, we all think in f-language, or f-mind.

Language can exist in everywhere.



[1] “Irvine-Linguistics-Key-Concepts.pdf.” Google Docs. Accessed September 22, 2016.
[2] “Radford-Linguistics-Cambridge-Excerpts.pdf.” Google Docs. Accessed September 22, 2016.
[3] “Jackendoff-Foundations-of-Language-Excerpts.pdf.” Google Docs. Accessed September 22, 2016.
[4] BrainPort V100. (2015, Nov. 30).BrainPort V100 Vision Aid [Video file].Retrieved by

Some examples of the evolutionary stages of the symbolic species

Darwin and Wallace had described natural selection as “ a struggle for existence”, but to human beings, it is better to be described as “ a struggle for control”. Human beings not only have the response to the potential mates, the preys and predators, but we also respond and have the motivation to control other species. So, although brains are the parts of the body, just like animals, they had evolved to store information about an external reality, and were available to be utilized to generate effective behaviors on, and syntactically coherent statement, on the world. (Barrett, 14)

I want to illustrate examples here to demonstrate the theories mentioned in the papers to articulate my understanding. I don’t know whether they are correct……..

To explain the evolutionary process of human cognition, Renfrew proposed four evolutionary stages. The first stage, episodic culture, the culture and capabilities founding our closest living primate relatives, is the transition from hominids to Homo erectus. This is maybe the beginnings of self-awareness. The mirror test is an attempt to determine whether a non-human animal possesses the ability of self-recognition. In this test, an animal is anesthetized and then marked on an area of the body of the animal cannot normally see. When the animal recovers from the anaesthetic, a mirror is provided. If this animal can touch or investigate this mark, it is taken as an indication that this animal perceives the reflected image as itself, has the primitive self-awareness (Wikipedia).

The second stage, mimetic culture, the culture to produce conscious (languages are not excluded), is an evolutionary step beyond episodic culture and a foundation for symbolic representation and language. Mimetic skill requires an individual has the skill to memorize, define and rehearse the body’s movements in a systematic way. There are some examples of Homo erectus’s behaviors, such as some rituals, dances and marks on clay tablets. Although the mimesis are not language, but they do allow us to better communicate.

The third transition, linguistic or mythic culture, the characteristic of early Homo sapiens. Myth, according to Donald, is the primary function of language in a culture dominated by linguistic cognition. In this stage, people can synthesize symbolic art and symbolic language. The symbolic pictures, such as the one founding southern European caves,were used to explore and develop the mythic ideas.

The fourth transition, external symbolic storage employing symbolic material culture, the characteristic of early agrarian societies with permanent settlements, monuments and valuables. With the development of writing, people’s memory was no longer restricted to the bounds of the body, but could be held in external storage systems.

On the one hand, we benefit from this evolution. From the moment when we curved elaborate marks on clay tablets, we know we can handle those symbols. We can communicate with each other efficiently.

On the other hand, we are also hunted by this brain evolution. The development of the brain provides human beings thousands of mood. Those sad and jealous feeling always hurt us.




Kate Wong, “The Morning of the Modern Mind: Symbolic Culture.” Scientific American 292, no. 6 (June 2005): 86-95.

Merlin Donald, “Evolutionary Origins of the Social Brain,” from Social Brain Matters: Stances on the Neurobiology of Social Cognition, ed. Oscar Vilarroya, et al. Amsterdam: Rodophi, 2007.

Terrence W. Deacon, The Symbolic Species: The Co-evolution of Language and the Brain. New                York, NY: W. W. Norton & Company, 1998.

Colin Renfrew, “Mind and Matter: Cognitive Archaeology and External Symbolic Storage.” In Cognition and Material Culture: The Archaeology of Symbolic Storage, edited by Colin Renfrew, 1-6. Cambridge, UK: McDonald Institute for Archaeological Research, 1999.

John C. Barrett, “The Archaeology of Mind: It’s Not What You Think.” Cambridge Archaeological Journal 23, no. 01 (2013): 1-17.

Wikipedia contributors. “Mirror test.” Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 6 Sep. 2016. Web. 15 Sep. 2016.

Symbols and ideas

After finishing the readings of this week, I can learn the rudiments of semiotics and linguistics, by the summary of the major findings and the introduction of those abstract conceptions. Peirce illustrated three kinds of signs: icons, indicators and symbols, and he designated symbols as linguistic signs. At university, I learned English and French as my major, so I was introduced to linguistics and applied linguistics at the same time. That is the reason I want to demonstrate my understanding of semiotics by illustrating the comparison between different languages (English, French, and Chinese).

Color words are the signs to us, with the meaning of identifying the different colors. For example, when indicating black, a Mandarin-speaker will call it as “hei”, while a English-speaker will call it as “black”, and a French-speaker will call it as “ noir”. When they are addressing this color, they are not particularly indicating the blackboard’s black or the hair’s black, they are indicating the color extracted from the hair or the blackboard, which is an entirely abstract idea. This way of outputting can simplify the communication connected with color, by getting rid of the tangibility of the items with the specific color. SO that people can talk about this colorful world, without holding different carriers of different colors.

“After acquiring language and experiencing symbol structures in images, objects, and sounds in a culture, we also somehow know that things can mean something beyond their materiality as things or the mere perception of what strikes our eyes, ears, and other sense organs” ( The Grammar of Meaning Making: Signs, Symbolic Cognition, and Semiotics, Irvine, P4-5). Since people with different background were experiencing the different symbol structures, their understanding of same thing can be different. So except for indicating the color conceptions, the color words can refer to other ideas.

On the one hand, this color-conception correspondence can show people’s generality. Black, as one of the basic color word, except for indicating the black color, can have the meaning of evil, unfortunate, and sad. For example: (1) There are still some black sheep in our society.  (2) Experts fear that at least a quarter of those rocks are now missing, presumably stolen or sold on the black market. (3) 他总是让我给他背黑锅。(He always let me to be his scapegoat.) Here I think people’s fear of black can be tracked back to the ancient times, where there was no light at night. There is always a horrible and unknown world hidden in the darkness of night. So, facing black, even people from different backgrounds can have the same feeling.

On the other hand, color, as a carrier of culture, can show its own particularity of different culture. Yellow, as the most respected color for Chinese people, suggests the royalty and other good things. Because it is the color of plowland, which peasants lived their life on. There are words like 黄道吉日( a lucky day), and 飞黄腾达 (be successful), has the color of yellow. While in English, blue is used to denote the good things. Such as blue blood and blue book. The reason of using blue is because this is the color of respected sea.

This example is just intended to be the beginning of my understanding of semiotics. I hope it can help me to get booted up. Overall, I think semiotics is really an interesting field, worth digging in.



Irvine, Martin. “The Grammar of Meaning Making: Sign Systems, Symbolic Cognition, and Semiotics.” Google Docs. Accessed September 6, 2015.

Irvine, Martin. “Signs, Symbolic Cognition, and Semiosis: Intro.” Google Docs. Accessed September 6, 2015.