Category Archives: Week 2

Symbols and Computational Thinking – Week 2 (Yanjun)

To be honest, before starting this class, I have never 1) imagined about learning “computing and meaning of code” in the future, and 2) think about the “why” behind all the appearances of computation, programming, coding, etc.

I have always considered “computer” and its related products and procedures as a “black box”, or say, a “myth” — something that I can never figure out unless I learn about programming language and how to code. However, through the introduction of Prof Irvine and after reading these articles, I now start to think about the reasons and roots behind the seemingly scared “computer” and what exactly is preventing me from trying to know more about the existence of computing.  

“Computing systems, software programs, and digital data are all based on, and are designed to serve : (1) the human capacity for symbolic thought; (2) our core, shared, human symbolic systems (like writing and images); and (3) the need for symbolic forms to be embodied in physical media for representation and communication.” 

I think this description perfectly explained why there will be computers, programming language as well as computing, which enabled me to understand the computing from human cognition angle. Also, when it is connected with “symbols” and “interpretation”, it would be easier for me to understand the logic behind computing.  Computer and program are platforms that carry different signs, programming and coding is the interpretation procedure of ideas. Take video games as example, the production of video games includes original text script and art designs that will later be coded into a complicated program by using programming language, and players receive information and get to know the story based on the interpretation done by game programmers. 

Video games as the latest form of human expression will be my focus for this course.



Introduction to the Topics and Methods of the Course, Martin Irvine 


Qi Wang week 2

After finishing this weekly reading, I am surprised and gained some new perspective of understanding computing. To be honest, I was scared of coding and computing at first. Because I thought these are computers’ languages, which is a new language for humans, and humans need to adapt their languages. Besides, there is no logic in computing, it is the way how they work. However, in Computing and the Meaning of “Code”, it comes up with a new way about computing, which is a computing system, a symbolic system, and programs that are designed to serve our way of thinking (Irvine). We humans already gained the way of symbolic thinking. Unlike other species, human has the capacity and tendency to think in the pattern, abstraction, and generalized way. This special character can be traced to human evolution. This is totally different from my original idea which is that computing is a brand new thing. Now I feel I have some confidence in learning to code because it is rooted in humans.

Another surprising fact I found is that the procedure of computing is closely linked. Once human gets the ability of symbolic think to create patterns and symbols, we already build the cornerstone. With these bricks, we generalize patterns and relations. Then we get the solution, that is a logical and clear path. I really like the math example here (the =, >, <), it is a good way to show people without computing knowledge about how the procedure works.

In the Venn diagram, it shows that computer science is multidisciplinary. It is affected by cognitive science and cognitive Linguistics, therefore, I am wondering if the computing can, in turn, affect our cognitive thinking? Every profession trains people in a specific way of thinking. From the book A First Look at Recent Definition of Computing, we know computing or programming focuses on the relations in the process and pays attention to the meaning of symbols, transmission, and output results (Dasgupta). It is a very straightforward way to solve problems. And the job of programmers is to connect human needs with machine implementation. Many algorithms and models are abstractions of the real world, so it is easy to bring thinking into real life.


Subrata Dasgupta, It Began with Babbage: The Genesis of Computer Science. Oxford, UK: Oxford University Press, 2014.

Irvine week 2 Introduction to the Topics and Methods of the Course



Understanding computing from a different prespective

Chutong Wang
The readings gave me a whole new view of computing. The line between natural and artificial is actually blurrier than I ever imagined. Cause if I look at what I learned in social science or biology classes, the principles of computing are incredibly similar with the principles of human communication or social resources distribution or the principles of the communication and transportation of chemicals in our bodies. Computing is not merely a study of a specific technology or tools (although master those techniques are quite important), but it offers a methodology of solving problems and a view of information. 
And what’s more, computing is a reasonable production belonged to human symbolic cognitive system. It evolved progressively and logically. This is really a good news for me since I used to regarded my computer as my best friend but also the mysterious guru that I can never see through. 
And by the way if I could not fully understand some terms like “string” even if I google it now, will it affect my future study of this course, cause I indeed struggle with all these terms but can just get a vague understanding.

Qasim: Week 2

As Professor Irvine says, “computer systems are a projection of what human symbolic minds can do”. This parallel draws to what the nativity of computing was meant to be. In what was meant to be machine to simplify our lives, evolved rapidly into a conscious that is equivalent or more powerful than the human mind; in terms of speed, knowledge, and accuracy. The sui generis development of data and power enlightened us and notably, “The emphasis of computing shifted from machines to information processes, both artificial and natural” (Peter J. Denning et als.). What seemed like an innocuous shift, has instead rapidly changed the discourse of how we knew society. Most recently, we have seen how computing has the power to manipulate elections, democracy and perception of good and bad.

De-Blackboxed: “The act of computation is, the, symbol processing: the manipulation and transformation of symbols. Numbers are just one kind of symbol; calculating is just one kind of symbol processing”. (Dasgupta). This is the paradigm of the foundations of how we have allowed for computing to leak beyond the scope of being a tool within science but be defined as a new method of thought and discovery (Denning et als.)

For future analysis: bias in computing. Is there such thing as true bias? Many algorithms are trained by pattern -> implemented by a human developer.

Computing, symbols and questions


Danae Theocharaki 

Point (4) “How can we get one category of symbols (binary
representations) to “mean” all our other kinds of symbolic
representations (languages, text characters, emojis and graphical
symbols, images, numbers and mathematical operations, sounds
and music, etc.)?” made me think of the overall ideas around symbology. 

How are symbols culturally unique? Does everyone see/recognise symbols in the same way? Cultures have and still use symbols that are often time unique to them. So who decides how, when and why to create these “electronic” symbols specifically those shared by a large number of the human population i.e. emojis also referred to as “universal validity” by Dr. Irvine? 

I was really intrigued by the idea of “ownership”, especially in terms of computing. As Dr. Irvine mentions in his introductory piece: “why we all “own” the principles of computing
as a consequence of being human.” 

Great Principles of Computing

Computing as a science field VS technology field 

Domains: AI, cyber security, cloud computing, big data, graphics, computational science 

“Each window sees the inside space in a distinctive way; but the same thing can be seen
in more than one window.” p. xvi

Interesting to read about computational metaphors. Not only does technology change a long with new human discoveries, needs, development, etc. but so do humans because of technology. 

Principles of Computing: communication, computation, coordination, recollection, evaluation, design 

I really liked the representation of figure 1.10 as it also (for me at least) created a familiarity with the quote noted above from the preface. I’m curious to explore these concepts more and have a better understanding of them in terms of their role, benefit and action in computing but also in how we explore it. 

“The second difference is that the structures of computing are not just
descriptive, they are generative. An algorithm is not just a description of a
method for solving a problem, it causes a machine to solve the problem.” p. 15

“A First Look at Recent Definitions and Conceptions of Computing.”

symbol – action – symbol processing 



We chose what symbols to use as they define what they mean to us, why and how they are important to us 

“We do not interact with computers by reading programs; we
interact with programs running on computers.” p. 3, this goes back to this concept expressed both in “Introduction to the Course” and in Great Principles of Computing, that the actual computer, the “machine”, is a means to an end and not the actual end.  

Fordyce, Week 2

Computing is built off of the development of language systems and is, generally speaking, a system of algorithms and representation. Computers, being programmable machines, complete operations based off of the input given and are made up of both hardware and software (NIOS). The concept of collectively understood representations are something I would like to further explore under the context of modern computing (Irvine). While exploring, I would like to disrupt the conventional means by which we collectively understand. Computing, while viewed as an objective means to an end, is deeply rooted in biases. These biases are inherently exclusive when creating commonly understood “representations”. Something I want to question and explore in class, and in computing more broadly, is how we can make “representations” more inclusive, while also making them collectively understood.