In (partially) learning Python, I was reminded of Latour’s network of human actors and nonhuman actants. Programming languages enable an ongoing communication between humans and objects, objects and objects, and humans and humans. Through the use of Python, one can execute commands to the computer and receive results, the various softwares can send commands to other softwares, and one can communicate to others who are also proficient in the programming language. In addition, one can specify the intended audience (human or machine) through the use of particular symbols. For instance, the three opening and closing quotation marks (“””) ensure that the computer will not read certain sections of the code; however, other programmers can read these sections in order to better understand the author’s choices or how the code functions.

While using Python and reading the articles for this week, it occurred to me that programming languages consist entirely of performatives, in the linguistic sense of the term. A performative occurs when what the speaker says correlates exactly with what she does. For instance, if I were to say “I promise to learn to code,” I am also doing exactly that: promising to learn to code. In this way, language not only says or explains or details or means, but also performs actions. Programming languages appear designed in such a way that every utterance is also a performance. As Prof. Irvine remarks, bits include “not only data (numbers that mean things) but executable instructions (numbers that do things)” (4).

While the Python exercises assisted in understanding some of the concepts of the reading, they did not convince me of the benefits of applying computational theory to all aspects of the world and existence. For instance, Wing discusses how “computational thinking will have become ingrained in everyone’s lives when words like algorithm and precondition are part of everyone’s vocabulary” (34), and she seems to suggest that the adoption of these new(er) metaphorical understandings will be an improvement. So far, I am not entirely convinced. Why should we think of the choice between lines at a supermarket as “performance modeling for multi-server systems”? What is gained through the application of this new(er) metaphor set? We’ve discussed before the danger of the “mobile army of metaphors,” the way in which “truth” results in a metaphor losing its metaphoricity. However, none of the theorists for this week appear to treat these metaphors critically (or even to acknowledge their existence as metaphors). How cautious and critical should we be in employing computational metaphors, and what are the boundaries (if there are boundaries) of their usefulness?

Martin Irvine, An Introduction to Computational Concepts (introductory illustrated essay)

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

Binaries are fundamental to our understanding of the world and our lives. Night and day. Alive and dead. Love and hate. Close and far. Of course we soon realize there’s nuance to these supposed oppositions – that they’re not as simple as they seem. They’re inevitably on some sort of common spectrum – time, state of a living organism, emotion, physical (or metaphorical) distance. Distinguishing between these seemingly complete, holistic states is useful for our understanding. It can be especially useful when we need to decide whether to take action or not (eg. fight/run, buy/sell).

The readings this week gave me new insight into why these oppositions are so useful – especially Hillis’s description of how important restoring logic is for designing a consistently functional system. He writes “…the implementation technology must produce perfect outputs from imperfect inputs, nipping small errors in the bud.” His description of the hydraulic computer also clarified for me the famous Bateman quote, “a difference that makes a difference.” These differences are what create meaning and are a fundamental part of how we comprehend our existence. In order for them to accomplish that, they must be definitive – on or off, right or left, or we can’t perceive them.

The main thing the Code Academy exercises demonstrated for me (and I admit I didn’t finish the tutorial) was what I believe Wing is referring to when she talks about layers of abstraction. The various tools for analyzing (eg. length) and manipulating (eg. uppercase) different types of input offer insight into how these different inputs relate to one another and to the broader system. This also relates (I think?) to the distinction between meaning and doing made by George Dyson in the description of the von Neumann project in Dr. Irvine’s introduction.

This is similar to the way our minds are capable of storing different dimensions of information – we can understand the function of an object, its size, and its color and keep those classes of information clear so that if we encountered a different object of the same function but a different size and color, we could tell that the difference was in those categories. Similarly, with these broader categories of binaries, we examine the difference to understand something new, but we are aware, at least on some level, of the fact that other aspects of what we’re comparing (eg. hair, skin, and diapers in the case of the babies above) are the same.

Dr. Irvine writes in his introduction, “Human cognition is both massively parallel and has many simultaneous states, many not even at the threshold of awareness.” Binaries help us make sense of our world by allowing us to hold all these different states at once, but they also fit into an organizing, dare I say unifying structure. The lightbulb can be on or off, but either way it is a lightbulb.

I found myself thinking a lot as I was reading this week about the binary of analog and digital. Humans are capable of perceiving both nuance and distinction. Evans (p. 11) suggests this complexity when he writes about how color translates to computing: “There are arguably infinitely many different colors, corresponding to different wavelengths of visible light. Since the colors are continuous and not discrete, there is no way to map each color to a unique, finite bit sequence. On the other hand, the human eye and brain have limits. We cannot actually perceive infinitely many different colors; at some point the wavelengths are close enough that we cannot distinguish them. Ability to distinguish colors varies, but most humans can perceive only a few million different colors.”

I wonder if a connection can be made between this digital/analog binary and Debray’s concept of transmission/communication. Debray writes about passing information through time, in effect representing a difference in a sustainable way. Communication may be anything we perceive, so that the sound of the wind in the grass may mean one thing to me and another to you. But if I want to capture something about what the wind in the grass meant to me, I’ll have to make some decisions about how to represent it so others can perceive something similar. Can we think of the transmission as the message we want to encode and must therefore render definitively rather than leaving it up to interpretation?

This week’s materials focus on computational thinking and why it’s beneficial to us. So I kept asking myself: Why do we need computational thinking? As for me, I had a hard time trying to figure out how to ask research questions and how to write high quality research paper. I think there’s something about computational thinking that are paralleled with research processes.

Both Daniel Hills and Jeannette Wing mentioned that computational thinking is about hierarchy, thinking at multiple levels of abstraction. I think it is also true for how to boiling down a research question. Research questions are not answered simply. Usually we need to boil down the major question into different little answerable questions and link every one of them logically to answer the major one. Today Professor Tinkcom talked about how to do academic writing to different audiences (as a guest lecture, new content this year, and I am a TA so I was there.) He said that academic writing is about problem solving and it always start with questions that you genuinely want to know. He also mentioned how to work on each part of your writing and how to finally put them together. I think the process of breaking down and then synthesize is just like the level of abstraction in computational thinking. For example, when programming with Python, you want to find out how much positive words, neutral words, and negative words are there in one article. Like what professor Irvine said, computation always starts with functions, not with physical technologies. So now the question is “find out the words”. It’s something conceptual we want to do. So next we want to ask ourselves: what does it mean by positive/neutral/negative? There are many resources we can refer to online sentiment words dictionaries computer scientists build that categorize every English word into three categories: positive words, neutral words, and negative words. Then we might think, recognizing a word to be positive/neutral/negative is subjective and varies according to different contexts. So how can we use the static sentiment words dictionaries? Well, we can do some adaptation to the dictionary. Then we run Python program to compare each word in the article with the dictionary to get the result about the percentage of positive/neutral/negative words. Also, when we write Python scripts, there are many symbols we need to define as well.

The thinking process I described above actually is not computation specific. The logical problem solving process exists in many ways of our life. I think what I learned most from reflecting computational thinking is how to logically connect each part of my research to better answer the major question that I raise. Sometimes I read research articles that jump into conclusions or the conclusion is not answering the major question (I think I make the same mistakes too.)

Another thing I think is interesting about computational thinking is that not all programmings and algorithms are dealing with a grand question, instead most of the time, it solves questions that sound like minor and not important. For example, we want to use a program to do math. We all can do math but computers can do it faster, over and over again. Computers are not doing anything that at a very high level of complexity but it’s humans who boil the question down to many sub-questions and use the automaticity feature of computer to tackle problems.

Boolean logic relates to the idea of binary, which depends on the idea of a single difference. A difference can be embodied in hardware, by making two ends to a pipe, and therefore introducing binary options in mechanical terms. Understanding the idea of Boolean logic helps us what information really is. Traditionally, “a fact” comes to mind when we think of information, whereas information is really about the difference – the way Gregory Bateson puts it, it is the “difference that makes a difference. ”

As Shannon demonstrated in his master’s thesis, “A Symbolic Analysis of Relay and Switching Circuits,”[1] electricity can also be used to embody the Boolean logic. Irvine describes binary units (bits) as the “interface between logic-mathematics and electronics.” Computation, therefore, builds on our cognition. While the concept of computational thinking is used to describe how humans think in computer science terms, we can go further and describe how computers think in human terms, and mention cognitive coding to describe how computer programs depend on human cognition to begin with. While software depends on human cognition, however, computers are more powerful than humans. We owe computers’ power to their processing capacity and memories.

The more one gets invested in computing, it becomes obvious that a good code is more than a good algorithm. The procissing capacities of computers not only form their power, but also their limitations. A good code should take into consideration the processing power of computer, and be as smart as possible.

Consider the following Python algorithm that multiplies any three input variables:

def functionMultiply(a,b,c):

mul=a*b

mul=Mul*c

return(mul)

This algorithm, however, can be improved in the following way:

def functionMultiply(a,b,c):

mul=a*b

return(mul)

The concept of computational thinking becomes more tangible once we take into consideration the limitations of computer. Wing eloquently describes this perspective by stating that computational thinking involves: “making trade-offs between time and space and between processing power and storage capacity.”[2]

This quote leads us to think about the elegance in coding design. Less code, less iterations, and rather applying more of algorithm and human intelligence, and relying less on the memory: “It is judging a program not just for correctness and efficiency but for aesthetics, and a system’s design for simplicity and elegance.”[3]

All of the software programs are designed to facilitate human thinking. In Java, the following statement prints a string:

print.out.ln(“Hello World”)

In Matlab, the following code clears the memory:

clc;

And finally, considering the following algorithm that I just wrote last week for my homework:

a <- a + theme(axis.ticks =element_blank(),

panel.background=element_blank(),

axis.text.x=element_blank(),

axis.text.y=element_blank(),

axis.title.x=element_blank(),

axis.title.y=element_blank(),

panel.grid=element_blank())

Even if you are not familiar with the R, Java, or Python, it is intuitively clear that the command “print.out.ln” somehow prints out something, “clc;” is clearing, and in the R code, I was creating something transparent or empty. Therefore, the coding process is eased not only through analogies such as “data sets”, etc., but also through the linguistic analogies with the English language.

What is really deblackboxed, from my perspective, is the way coding and predictive algorithms are altering our online exposure. The content we are formed online have became a means for us to make sense of the world: we consume news, media, entertainment, make buying decisions, and communicate through interfaces. Our interactions with computers, however, are used as data that predictcs the type of content we might be interested in consuming, the type of product we might look for, or to expose us the person that we might have known. Therefore, our online exposure is personalized for us – which brings us to the intersection of capitalism and computation.

[1] Shannon, C. E. (1938). A symbolic analysis of relay and switching circuits.American Institute of Electrical Engineers, Transactions of the, 57(12), 713-723.

[2] Wing, J. M. (2008). Computational thinking and thinking about computing.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.

[3] Wing, J. M. (2008). Computational thinking and thinking about computing.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.