1. Weaver talks a fair amount about redundancy in language, and how it helps make the crossword interesting as a form of play (p34). From a communication technology standpoint, it makes sense to have this sort of redundancy built into a system, in large part to work around any type of noise introduce int a transmission. you see this redundancy reflected throughout computing, particularly as one moves from discrete commands to higher level abstractions. In programming languages, for example, Assembly is as “close to the metal” as you can get with ‘natural’ language, and has a very limited command set. As one moves higher up the chain in abstraction, you’ll find redundancy in commands and instructions. C, C++, Java, javascript, etc. have a lot of redundancy, and allow a programmer multiple avenues of formulating a command. The common adage is “There’s more than one way to do it”, or TMTOWTDI. While this allows a programmer the ability to write programs/commands in whatever fashion they desire, it leads to computational inefficiency. How can we determine when this inefficiency outweighs the speed of communication? Is there another axis of information that can speak to this? (I’m thinking specifically of the difference between TCP and UDP, in that TCP is highly redundant and used for low bandwidth/high information (per byte) communication like text,  whereas UDP is used for High bandwith/low information (per byte) data like video). Relatedly, this redundancy in language offers one the opportunity to write in a larger stylistic format than a narrow, less redundant form. Though this may be the illusion of freedom, as it is still bound to the conventions of the language and base instruction set, it’s something to note.
2. Turing wraps his entire Imitation Game hypothesis in gendered terms. It’s almost like a tease. A a gay man in Britan, he had to express himself one way publicly, while living a private life that was quite different. Was this a coded message in the article, or an unconscious form of expression? Was he trying to parse for himself how to adapt and signal who he truly was in a repressive society? Is the mathematical challenge he set up for himself here ultimately a social one?
3. The models Weaver present and the Eames’ use in their communication diagrams clearly indicate the source, encoding, transmission, decoding and reception. It reminds me of Jim Campbell’s “formula for computer art” except that the communication theory misses the algorithm and memory aspects of the systems. It’s possible to extend the system to include these processes on either end (origin of transmission or reception of transmission) in an effort to indicate what happens when intention and understanding are incorporated into these ideas of communication.

1
Before developing his communication theory, Weaver makes clear that his use of the concept of information is absolved of meaningfulness and intent to influence, thus focusing on the level of technical problems of communication and excluding its semantic and influential levels (pp.30). In a process of abstraction, he is breaking information down to units and shows that messages are constituted as a series of discrete symbols – some of which get chosen freely from a definite field of possibilities (the extent of which is described as entropy, pp.31) while the rest are controlled by the statistical associations (pp.34).  It is interesting how such a process of quantification reveals the underlying structure of language and thus, the very nature of communication. However, to whom does it reveal it to? Weaver was a pioneer of “machine translation”, so is the receiver of his communication theory a machine rather than a human? If this is the case, does this suggest that (since his theory derives from the technical level of communication) the levels of semantics and influence (that is, meaning and intent) belong only to the human realm?
Today, the field of Artificial Intelligence investigates the ways in which meaning and intent are indeed produced from dynamic and complex statistical associations. How has communication theory evolved to accommodate this new reality?

2 (not really a question actually, just an observation)
It its telling to see that, even a figure as open-minded as Alan Turing – who himself did not exactly blend in the moral construct of postwar Britain – is nonetheless, a product of his environment: In order to prove the absurdity of the Theological Objection (which paired animals with machines due to their supposed inability to think), he wonders what a Christian would think of the fact that the Muslim religion considers women soul-less. However, Turing’s source is unknown (there is no such statement in the Qu’ran). This suggests that he is himself using a preconceived notion of theological origin – which is, paradoxically, exactly what he is arguing against.

However, if one reads between the lines of this unfortunate example, it looks like Turing identifies an emergent thread between animals, machines and women, decades before the intellectual movements of technofeminism and ecofeminism manifested themselves.

3
Turing explores the nature of possible machine mistakes as twofold (pp.58): these deriving from technical failures of mechanical or electrical kind, and those of logically wrong conclusions. The former is about what we often refer to as bugs or glitches, namely malfunctions of hardware – a natural consequence of the fact that a machine is a material artifact. Maybe, in a similar manner, machines are as much subject to failure as human bodies that suffer organ failures. On the other hand, the error of conclusion seems to differ in nature. It derives from the program or the software, as opposed to the hardware. Being universal, digital computers may be programmed to perform any set of instructions (pp.53), however there is no guarantee that this set of instructions has not inherited the logical gaps or bias of its author. Tellingly, man may reach a false conclusion following the method of scientific induction (pp.57) as much as the machine, since it is programmed by man (pp.58). In which ways are machine errors inevitably human?

REQUIRED

Warren Weaver, “The Mathematics of Communication”, Scientific American, 1949

Alan Turing, “Computing, Machinery and Intelligence”, Mind : A Quarterly review of Psychology and Philiosophy, 59(239), October 1950

Charles and Ray Eames, A Communication Primer, (Film) 1953. (online viewing)

RECOMMENDED

John McHale, “Information, Technology and Communications”, The Changing Information Environment, Westview Press, 1976.

Jagjit Singh, “Automatic Computers-Analogue Machines” and “Automatic Computers-Digital Machines”, Great Ideas in Information Theory, Language and Cybernetics, Dover Publications, 1966