Category Archives: Week 7

Information Over Intelligence

Jalyn Marks

In text messages (and many other modes of communication), senders and receivers know whether or not the message has been viewed. That’s the direct feedback from the interface. However, there are different connotations and denotations of the actual content sent, depending on context, existing relationships between the communicators, etc.

James Gleick quotes Sir Thomas Elyot (16th century) to begin distinguishing information from intelligence, “Nowe used for an elegant word were there is mutuall treaties or appoyntementes, eyther by letters or message” (2011). I really like this quote because it communication is not solely reliant on cognitive or other biological functions; instead, communication is dependent upon access and education. We can ask: do the parties communicating with one another have the cultural, political, and economic backgrounds necessary to send and receive their messages?

Reference

Gleick, J. (2011). The information: A history, a theory, a flood.

Signal Transmission Theory of Information: A Subsystem for Meanings

Claude Shannon had a problem in electrical engineering to solve, namely, “the fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Though he uses the word “communication” what he means is “communications system design” (Irvine,4). Irvine draws the difference for us between E-information and information, where the prior refers to “the physical patterns of quantifiable units”, an engineering information perspective as opposed to the common understanding of information as knowledge/content/meaning (Irvine, 5).

Shannon’s seminal paper “A Mathematical Theory of Communication” tackled this problem by understanding how to transmit signals from one place to another in a reliable way, such that the decoder at the other end receives the same unperturbed message as the encoder encoded.

The main components of his model are –

INFORMATION SOURCE – where the semantic meaning is composed

MESSAGE – where the semantic meaning is translated

TRANSMITTER – the device that transmits the signal

TRANSMITTED SIGNAL- the encoded signal that has converted the message into an electromagnetic form

NOISE SOURCE- random noise or electromagnetic interference

RECEIVED SIGNAL –  the encoded signal that is received along with some noise.

RECEIVER – the device that decodes the signal that to convert into the message that was sent

MESSAGE – the original message that was sent by the information source.

DESTINATION

Shannon understood that the information structure had nothing to do with the content of the information and the simplest way to represent information potential is in bits – (yes and no answers that can generate 1 and 0’s corresponding to on-off states in a circuit.) i.e. if the transmitter from one end can successfully on-off certain switches on the other side via electromagnetic signals then that would represent a successful way of coding and decoding information, with min loss. (Denning & Bell, 472-3)

To this end he borrowed concepts like entropy – which would be the measure of the information and the entropy threshold would then defined the boundary between reliable and unreliable channels of signal transmission. (Denning & Bell, 473).

The signal transmission theory is useful when we consider it as a design intervention to the problem of signal coding. But the signal itself is a translation of our prior symbolic message. The engineering problem brackets out (to focus on its technical difficulties) the prior conception of the message. But when we look at the entire process as a symbolic cognitive technology, the chain roughly looks like this – cultural-social context – individual – intention- meaning – encoding – signal transmission -decoding – message reception – individual – interpretation and affect within a cultural-social context.

References –

Shannon, Claude Elwood . “A Mathematical Theory of Communication”. Bell System Technical Journal27 (4): 623–666.October 1948 DOI:10.1002/j.1538-7305.1948.tb00917.xhdl:11858/00-001M-0000-002C-4314-2.

Martin Irvine, Introduction to the Technical Theory of Information as Designer Electronics

Peter Denning and Tim Bell, “The Information Paradox.” From American Scientist, 100, Nov-Dec. 2012.

 

Miscommunitexting and how we find meaning in information

Victoria Gomes-Boronat

We have grown up in a time where communicating is both easier and harder than before. What do I mean? Well, with a few taps on a phone you are able to communicate with people halfway around the world. Through digital technology such as text messaging systems, we are afforded the ability to communicate with people anytime, anywhere. So why has it also made communication more difficult? When doing this week’s reading I couldn’t help to think about my (undergraduate) freshman year communications course. In the course, we discussed the concept of “miscommunitexting”. Miscommunitexting is when the intention or meaning of the informant’s text is misinterpreted by the informee due to overanalysis, different languages/symbolic systems, culture, generational differences, etc.  Miscommunitexting is possible/common because, as Irvine explains, “meanings, values, and interpretations are not physical properties of a symbolic medium (in this case, the text message); they are inferences and correlations made by agents (the informant and informee) in symbol-using communities,” (p. 13).

It is important to note that the “information” sent in the communication system does not refer to what we know as meaning or content, rather it consists of the bits that create representations of data i.e. numbers, letters & words, graphics patterns, etc and operations i.e actions. According to Shannon’s Information Transmission Model, a source sends a message -> an encoder generates a signal for the message; a channel stores and and carries the signal; a decoder converts the signal back into a message; the receiver receives the message, (Denning & Bell, 2012). The meaning is then interpreted by the agent reading the message (the symbols displayed on the screen).

The way we interpret the message depends on our language, culture, and symbol systems.  For example, my father is Spanish and my mother is Argentinian- although they share a language, there are some cultural differences in semantics. In Spain, there is a word that means swim cap, the same word in Argentina is understood as a female body part. The semantic differences due to culture could cause miscommunitexting seeing as the meaning would not be communicated, rather, the receiving agent would interpret the word with their own meaning. Ethnic culture is not the only factor in the way we interpret messages, the generation you are a part of, the interests you have, and your personality are all contributors to the way we interpret information.

I think that to a certain extent, people of a certain generation share the same understanding of texting language/etiquette. Most American Millenials/gen zers could probably tell you the difference between, “hey” & “hiii”, “okay” & “ok”, “I love you” & “Love ya”, while older generations might have more trouble discerning the differences in their semantics.

Then you must also take into account the personality and interests of the agents on each side. For example, one of my friends is the shortest texter. He will send one-worded responses all of the time. If he were a stranger, I’d be inclined to interpret his responses as disinterest or anger, however, knowing his personality, I know that’s not the case. He loves to draw and do hands-on activities in his free time so he prefers calls over texts- that way he can work on his drawings while also talking. I only knew the true meaning behind his short, straight-forward messages once I got to know him. On the other hand, if I have a friend who usually sends frequent, long, thoughtful texts and when she breaks her usual pattern and starts to send curt, infrequent messages, that signals to me that there may be a deeper meaning, for example, she may be extremely busy, upset at me, going through a hard time, etc. The message or information sent through the communication channel and decoded by the phone did not tell me this, but I used my knowledge of how she normally acts to interpret these meanings.

 

References

Denning, P. J., & Bell, T. (2012). The information paradox. American Scientist100(November – December), 470-477.

Irvine, M. (n.d.). Introducing information and communication theory: The context of electrical signals engineering and digital encoding. Unpublished manuscript.

Luciano, F. (2010). Information: A very short introduction. Oxford University Press.

Meaning in the Signal-Code-Transmission Model

Mary Margaret Herring

While the signal-code-transmission model of information satisfactorily accounts for the technical transmission of messages, it does not lend much help when decoding the meaning of a message. In Shannon’s original signal-transmission model of information, the source sends a message to the transmitter who encodes the message into a signal. At this point, noise may be introduced into the system. The message is then decoded and arrives at its destination (cited in Irvine, n.d.). This linear process perfect sense on a purely technical level. For example, a person sending a message by telegram would fill out the form with their message. Then, an operator would transcribe the message into Morse code to a receiver who would decode the message and deliver it to the recipient. For this reason, the information theory model is essential in understanding the mathematical and engineering processes needed to get signals from the sender to the receiver.

However, the signals linear model. As Irvine (n.d.) notes, the meanings of the messages are not “baked into” the medium. In the example of the telegram, the only thing that is actually sent is a signal. Information about the message’s cultural context or assumed background knowledge is not included. From this strictly technical point of view, whether or not the message is meaningful is irrelevant. Floridi (2010) writes that an advantage of digital systems is that they can be understood equally well represented semantically, logio-mathematically, and physically. With digital technologies, Floridi writes “[i]t is possible to construct machines that can recognize bits physically, behave logically on the basis of such recognition, and therefore manipulate data in ways which we find meaningful” (2010, p. 29). But, this still raises the question of how meaning systems can be included in a model where the meaning of the message is irrelevant.

Since the signal-transmission model explains how digital signals can be encoded and decoded into meaningful messages, perhaps it is time to apply the sign-referent interpretation proposed by Denning and Bell (2012). They argue that information contains both signs and referents that we use to make sense of digital information. Denning and Bell use the example of seeing a red light (sign) and our brains commanding us to stop (the referent). In the same way that we know to stop at a red light, we also know that a blue-underlined phrase usually indicates that the text contains a hyperlink to another file or webpage. By relying on these signs, we can make meaning from *perhaps* meaningless content.


References

Denning, P. J., & Bell, T. (2012). The information paradox. American Scientist, 100(November – December), 470-477.

Irvine, M. (n.d.). Introducing information and communication theory: The context of electrical signals engineering and digital encoding. Unpublished manuscript.

Luciano, F. (2010). Information: A very short introduction. Oxford University Press.

Shannon’s Transmission Model

Shannon’s Transmission Model

Yingxin Lyu

In Shannon1’s original diagram for the transmission model, there are five main elements: information source, transmitter, noise source, receiver, and destination. In addition, when inputting information source into the transmitter, it should be first encoded into binary language to protect the integrity of the source information. It should be decoded before it arrives at destination. Noise may appear in every part, which will lead to misunderstanding or lack of understanding of the information source.

Taking social network as an example, if a person wants to post some information on his or her facebook page, first, he or she need to what kind of information he want to post, photos, videos, or words, and that is information source. Then, the person will input the information with keyboard, or use camera to take photos or videos. In the process these information being stored in the smartphone, they have been encoded in binary language. Then, this coded information will be transmitted through Internet, and decoded into original format, words, photos, or videos, to be present on the screen of receivers. Moreover, the noise can exist in any part of the process. For example, the person who uploads the information may input some wrong information, or the internet doesn’t work well which leads to the incompletion of information uploading.

However, the signal-code-transmission model is not a description of meaning, because it only presents how a medium transmits information, and the function of understanding is only owned by human beings. According to The Information Paradox2, “Information always has two parts, sign and referent. Meaning is the association between the two.” Now the model only provide a way about how signs, as a part of meaning, being transmitted by media. As posts on Facebook, what people input words that they want to say, but the real meaning of these words still need other people to understand. Smartphones cannot replace humans’ brains to understand the meaning; they only present and transmit. The referent of a sign can be understood by human, and now it may also be received and processed by AI system, but only a medium worked as transmitting tool cannot describe and understand the meaning.

Even in the current society, the model can be applied in many digital products, like smartphones, interactive applications, computers, and so on. It is a very broad description of how information transmitting tools work. For these tools, they are actually machines, so they need to follow some rules to work, and the binary system provide them a rule to encode and decode information input. When they are working as the model, they are following the rule, translating each word or signal into the combination of 1 and 0, or performing the adverse process. The process is computational and mathematical. However, symbol system can never be simplified as such a mathematical process. Letters, Words, and sentences can be translated into binary language in a constant way, but translation from one language to another is a very demanding process. Even now, we got AI technology, virtual reality, and many other very advanced technology, but a human translator is still cannot be replaced by a kind of machine. The reason is that human’s symbol system is a great and complicated work that cannot be understood completely by machines which can only follow the rules.

References:

  1. Shannon, Claude E. “A mathematical theory of communication.” The Bell system technical journal 27, no. 3 (1948): 379-423.
  2. Denning, Peter J., and Tim Bell. “The information paradox.” American Scientist 100, no. 6 (2012): 470-477.