Learning this course enables me to observe technologies from a brand new perspective. A lot of technologies are designed as cognitive-symbolic artefacts, especially the ubiquitous computing devices that root in human meaning systems. Here I will try to synthesize things I have learned to discuss computation primitively.
The design of computers is based on human meaning systems, which use signs to represent physical objects as well as abstract ones. From Morse Code, it has been common to use electricity to represent information. Shannon made it possible to break down analog signals into discrete ones for better and faster representation. Alan Turing defined computation and proposed Turing machine as a universal model for computation. All of these are correspondent with C.S. Peirce’s observation of human meaning systems. For Peirce, “meanings are chains of inferences from things physically given to cognitive responses[i]”. Another demonstration is computer language, in which syntaxes are assigned meanings to accomplish complex tasks.
It is said that the most fundamental difference between human and animals is the ability to make cognitive artefacts, which are designed to “maintain, display, or operate upon information in order to serve a representational function[ii]”. This assertion emphasized the importance of human meaning system again, which was epitomized by today’s computing devices. In order to offload human cognitive efforts and augment human intelligence, many beautiful visionary concepts were produced by pioneers, including the memex by Vannevar Bush and the Dynabook by Alan Kay, as we mentioned in past weeks. As the Internet comes to age, we distributed countless cognitive efforts onto computers, whose encyclopedic, spatial, procedural, and participatory affordances allow for uncountable achievements[iii].
However, computation is not necessarily all about computers, as Peter Denning argued in his What is Computation. He suggested a computational model of representation-transformation, which “refocuses the definition of computation from computers to information processes[iv]”. In this sense, computation even goes beyond the scope of human cognition, stepping into natural processes such as DNA transcriptions. This made me ponder about the nature of representation. If DNA transcriptions are seen as computation, is representation a natural or artificial process?
According to Denning, “a representation is a pattern of symbols that stands for something[iv]”. In this sense, representation definitely can be natural. Every pattern that stands for another thing can be seen as a representation, be it a piece of DNA sequence governing the production of a specific amino acid, an ultraviolet pattern indicating the center of a flower to a bee, an electron running around an atomic nucleus, or a neural firing caused by a potential difference and inducing a cascade of firings that lead to a specific thought in my brain. All these behaviors that translate external patterns into corresponding responses, I think, can be seen as information processes and instances of representational-transformation model, therefore a computation. They also meet the requirement of the interactive machine, because unlike Turing machines, they are dynamically interacting with the external environment[v]. Being a little infinitely regressive, I come to a conclusion that there’s nothing in the universe is not a computation. So, the whole universe is in a constant computational process.
Zooming in and getting back to the human scale, I suddenly realized that human meaning system might also be a natural representation, translating natural patterns into meaningful signs in the form of neural firing patterns and unceasingly creating new signs with other signs, in order to fully comprehend/represent the external world.
I know this thought is unconventional. Representation is mostly seen as a human cognitive activity. But if we broaden our definition for representation and computation into the non-human scope, we might get some insight of how to improve existing or design new computational paradigms.
[i] Irvine, Martin. n.d. “The Grammer of Meaning Making: Signs, Symbolic Cognition, and Semiotics.”
[ii] Norman, Donald A. 1991. “Cognitive Artifacts.” In Designing Interaction, 17–23. New York: Cambridge University Press.
[iii] Murray, Janet H. 2011. Inventing the Medium : Principles of Interaction Design as a Cultural Practice. Cambridge, US: The MIT Press. http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10520612.
[iv] Denning, Peter J. 2012. “Opening Statement: What Is Computation?” Computer Journal 55 (7): 805–10.
[v] Wegner, Peter. 1997. “Why Interaction Is More Powerful Than Algorithms.” Commun. ACM 40 (5): 80–91. doi:10.1145/253769.253801.