Category Archives: Week 7

Information design: text message

(fig. 1) The app/keyboard Slated translates your messages to other languages on real time. In this specific case it’s translating English-Tagalog

When we think about music, photos, videos and text and how we are able to produce them and transmit them using technology it can be a process described as incredible, even magic, but very little understood. Even though as users it is not clear to us what is happening ‘behind the scenes’, we continue to use this medium to communicate and transmit this information. It might look like magic but it was designed by us to perform that way and to be interpreted by us. Let’s further analyze the specific case of the text message.

We know how to interpret text messages, emails, pictures or sounds when we receive them because the meaning comes from the symbolic system that surrounds them and the social use of said structures (Irvine, Intro to Information Theory in Meaning Systems). For example, when we read a text message and we understand the words in it it’s because of language and symbols. When we read a text message we are not interpreting the digital bits that transmitted it to our device, we understand the meaning of said transmission.

(fig. 2) Martin Irvine, Introduction to the Technical Theory of Information (p. 7)

If we look at the image above we can state that a very clear set of encoding/decoding it’s happening in this process, or it is designed to, in order to transmit our cognitive system however we want to describe it: information, message, and/or meaning.

First we take our social-cultural meaning, in this example it could be the alphabet in one specific language. Second, in “pattern matching” each symbol is correlated to tokens that are specific to the medium is being used, in this case it would be specifically for the text message and whatever device we’re using to receive said message. Third, those signals are transmitted from one device to another, for that to happen they need to be converted into a language that can be registered and displayed in the receiver’s device. Then, it seems to me that there are two ‘decoding’ stages: first, our device decodes that digital signal into symbols in our screen, and then we decode those symbols into words or ‘meaning’.

However, we can not separate the digital transmission from the meaning entirely, because the existence of the ‘meaning’ and the need to transmit it is what motivates, or the reason for, having a digital signal transmission designed to encode this ‘message’ or ‘meaning’ and transmit it to another device, that in turn will decode it into a symbolic structure that we can interpret. Therefore, the characteristics of our cognitive symbolic systems are going to be a key part into the design of digital transmission systems, because we are the ones designing it and interpreting it. 

In the case of the text message, we can see a few characteristics that are necessary for our interpretation of the signal received:

  • Symbols: it can be the alphabet or images (such as emojis) to help us ‘decipher’ the message.
  • Visual representation of said symbols that is appropriate for the receptor. For example, visually it has to resemble the symbolic structure as much as possible: the size of the font has to be adequate for reading, the shape and order in which the symbols appear has to make sense in whatever language the message is communicated through.
  • It has to follow the rules of the symbolic structure that it is transmitting in order to be interpreted. In the case of the text message, it has to follow the rules of reading and writing.

What is interesting is that this process of encoding the cognitive symbolic structure into a digital signal, then to be sent and decoded again into the same cognitive symbolic structure is invisible to us, we can’t see this “semiotic envelope” (Irvine, Introduction to the Technical Theory of Information, p. 7). We cannot see it, but it’s happening right there in our hands. It is black boxed. Why? it seems to me that, as we’ve mentioned before in class, this is a case of “I don’t care how it works only that it works”.



  • (fig. 1) Darrell Etherington, Slated iOS 8 Keyboard Translates Your Text Messages to Other Languages in Real Time, Tech Crunch, extracted October 18, 2017,
  • Martin Irvine, Intro to Information Theory in Meaning Systems, (extracted on October 16, 2017)
  • (fig. 2) Martin Irvine, Introduction to the Technical Theory of Information, (extracted on October 16, 2017)

Achieving a common ground on Information and Meanings

Achieving a common ground on Information and Meanings

Grace Chimezie



I intend to use diagrams to explaining critical reasons on the need to achieve a common ground in information and meanings and other factors that contribute to the means of communication we have present in our artifacts.

As socially symbolic beings we always live in technically mediated symbolic systems and use information to exchange meanings. Where do meanings come from one may ask? All the information encoded and transmitted as digital data always presupposes the initial context of meaning  and the background of assumed knowledge that motivate, and frame the message or communication we encode.

Main features of the Signal transmission theory of information

Part of social science of information theory is grounded in a common culturally accepted series of tropes and narratives, if it quantitatively test the meaning of these tropes and narratives in a society constructured by them, and then, if it arrives at a certain reasonable conclusion based on those test. Information science then provides a somewhat ironically useful function of social self validation  ( for the information profession).

Information theory which provides meanings behind information technology acts as a technical devise for transmission, not screening the message for content, but simply, technically passing them from sender to recipient.

The role of information theory is not only to foster the development of transmission apparatuses, but more importantly, like a transmission apparatus or conduit itself, information theory is to transmit message of some sort without interference. The measure of success for information theory and cybernetics are their ability to preserve the system or organism from a hostile environment (e.g. Aircraft)

Take for example an Aircraft communication system

Diagram 2. 

Meaning systems 

The problem occurs however, when these tropes and narratives are given privileged authority over all other explanations and phenomenon in the field of information studies and in culture and society in general, and when the conduit model is used to evaluate and determine values that are contradictory or exposed to what it prescribes.

Understanding the limitations of technically important models when it comes to analysing the meanings and values of what we exchange in all our messages, communication and media systems.

The meanings of our messages come from the human symbolic systems that surround them and the social uses of technology mediated expression. We create meanings when we perceive signals, meanings are not properties of information signals.

Each single bit can be represented by a signal element. Each signal element takes some time to send. Bit rate: the number of bits that can be sent out per unit of time time.

According to Irvine (2014), We can’t separate the technological from social and collective cognitive meanings. Meaning is not a location it is an event. It happens in the process of using symbols collectively in communities of meaning making making.The meaning of information depends on the observer despite designers, scientist look to software to generate meaning.  Semantic networks and social functions of digital are not present as properties of the data.

To put in simpler terms

Meanings are the perceived signals we derive from an information. This is transmitted through binary codes called digitzation sent in bits through conduits. The argument as to which form takes greater precedence doesn’t weigh much, as social symbolic beings we cannot operate without the understanding of both.

On a lighter note it is great to see that most of these arguments originated from a teacher student relationship between Weiner and Shanon 


Historically, insofar as information science has been largely concerned with issues of information retrieval it not only has utilized the technology of electronic information transfer, but it has also tropically extended the notion of transmission and re-presentation from a technical to semantic and social level.

Both the professional and the social realms are professionally important because, from an ethical and political standpoint, professional knowledge must acknowledge the responsibility for social and political conditions and for historical developments not only within its traditional field but outside of it.


Irvine’s Youtube video 2014: Understanding Key concepts in Technology 

Ronald Day, 2000: The conduit metaphor information: the nature and politics of information studies: University of Oklahoma

Shannon, C.E. (1949). The mathematical theory of communication. In The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press.

Wiener, N. (1950). The human use of human beings: Cybernetics and society. Cambridge, MA: The Riverside Press.

“The book of faces” and the meaning of it

There is so much information around us. As Floridi puts it, Information is notorius for coming in many forms and having many meanings.  Over the past decades , it has been common to adopt a General Definiton of Information (GDI), in terms of data and meaning. That means that we can manipulate it, encode it, decode it as long as the data must comply with the meanings (semantics) of a chosen system, code or language. There has been a transition from analogue data to digital data. The most obvious difference is that analog data can only record information (think of vinyl records) and digital data can encode information, rather than just recording it.

But how is the information measured?

Claude Shannon, in his publication “A mathematical theory of communication”, used the word bit, to measure information, and as he said, a bit is the smallest measuring unit of information.  A bit has a single binary value, either 0 or 1.

When I think of information, I almost never associate it with data, but rather with meaning.  In a way, information to me serves the function of communicating a message. But, when we look at how is the message sent and delivered, is when we can see the data in it.

Now that we know the process, let’s take a look at Facebook, a social network that is changing our society.

History of Facebook

Facebook was created as part of a class project at Harvard. Mark Zuckeberg, created FaceMash, a software that compared two photos, taken from the student’s directory book, and the user decided which of the two photos was hotter. The cite was shutdown a few hours, since it violated copyrights and privacy. After this, Zuckerberg created “The Facebook”.

In 2003, there were no universal online facebooks at Harvard, but only papers that held photos and basic information of the students. So Zuckerberg, had the idea to create an online directory, and that’s how Facebook started.

From this example, we see the change going from analog to digital, and we see the function and  meaning behind Facebook, which was to create an online directory of Harvard students. But today, Facebook has evolved and changed it’s meaning and function.

Today, Facebook is participating in polls of political campaigns; we’re sharing information from different fields with our friends; we’re connecting with people from all over the world; we’re texting, sending messages, photos, videos etc…

The process of sending e text message through Facebook

When sending a text, we have audio that confirms it  and a symbol that shows up.  When you send a private message including audio, video, to your friend, the message is encoded through the interface of your phone or computer, then message is sent through the wireless network or  cellular company to the Facebook database software. The message then is decoded and sent to your friends. This process is done in seconds, and you never think of the information and data storage that happens behind the screen.

The information age and digital revolution, has helped people to create and design different technologies that use different means of communication.

Information theory helps to design physical architectures that can encode, decode and send a message but the combination of information theory and semiotics (signs, symbol) give a more meaningful experience to us as humans, and that to me means that one cannot go without the other, same as data and meaning go to the definition of information.


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

Floridi, Luciano Information: A Very Short Introduction. Oxford, UK: Oxford University Press, 2010.

Gleick, James Excerpts from The Information: A History, a Theory, a Flood. (New York, NY: Pantheon, 2011)

Irvine, Martin Introduction to the Technical Theory of Information

Phillips, Sarah. “A Brief History of Facebook.” The Guardian. Guardian News and Media, 25 July 2007. Web.

The Importance of Metaphors

Having taken CCTP-711: Semiotics and Cognitive Technology last year, many of the concepts and theories we read for this week were somewhat familiar to me. Thinkers like C.S. Peirce, Claude Shannon, and Warren Weaver have given us a useful foundation to understand information and its transmission in this age of digital media and knowledge. At first, I wondered how these concerns were relevant to this course, but as was said in the Professor Irvine reading, “In the context of electronics for telecommunications and computing, we can describe the question of “information” as a design problem.”

Information design, to me, is how we try and take these decontextualized bits and bytes of digital transmission, and turn them into a message than can be meaningfully absorbed by the intended recipient. But in order to do so, we must first have an understanding of communication theory. What is a message? Where does information reside? How do we communicate with each other? It seems to me that the dominant metaphor being used in both electronic and non-electronic conceptualizations of communication and information transmission is that of the packet or container being filled with content and then transported to the recipient via a conduit of some sort. This is chief metaphor employed when we learn about TCP/IP.

A “digital highway”

But just as with every metaphor, using this conceptual model comes with some limiting consequences. Meaning doesn’t actually reside inside a container that can be transported from one location/mind to another. It is a collectively derived process, more akin to the Cloud, from which we all maintain and pull from. Meaning making relies on centuries of cultural symbol building. You don’t send language or meaning from your mind to another in the unilateral manner assumed by the content-container-transport metaphor, it’s a much more communal and socially constructed process engaged in by not just the immediate actors, but the entirely of the society and culture they live in. The network, with all its various nodes and interconnectivity, is a much better metaphor than the transportation highway that we so often use. Understanding this truth is instrumental in becoming a good practitioner of information design.

Linear point-to-point metaphor for communication.

The cloud. Perhaps a better metaphor.


  1. Martin Irvine, Introduction to the Technical Theory of Information
  2. Luciano Floridi, Information: A Very Short Introduction. Oxford, UK: Oxford University Press, 2010.
  3. Ronald E. Day, “The ‘Conduit Metaphor’ and the Nature and Politics of Information Studies.” Journal of the American Society for Information Science 51, no. 9 (2000): 805-811.

Twitter is a Weird Place

Reply All, a podcast by Gimlet Media about the internet, has a recurring segment called “Yes Yes No.” In this segment, Gimlet CEO Alex Blumberg sits down with the two hosts, Alex Goldman and PJ Vogt, and asks them to explain something he’s found on social media, usually twitter. In each case, Alex reads the tweet, his voice sounding extremely perplexed, and then the co-hosts attempt, if they can, to decode all the levels of meaning behind what at first glance reads like nonsense. This segment highlights the difference between the signal-code-transmission model and meaning system models.

When Alex fails to understand a tweet, it isn’t because of a breakdown in the signal transmission model.

Figure 1: Knight 2012, Hartzell 2017, Tomwsulcer 2014

Above, I’ve created a very simplified signal transition model for “tweeting,” using Shannon’s design (Gleik 2011). Briefly, the user/information source, composes a message. This message is encoded (mediated through the smartphone’s touchscreen interface). The digitized message, now bytes, is transmitted in data packets over a wireless network, through the internet, where it is received by Twitter. Twitter then decodes these data packets and it’s software interprets the information, classifying, indexing, and storing it as well as running many other protocols for updating it’s platform. When twitter’s users launch the software application on their smartphones, the software sends an encoded request for information (again through wireless networks and the internet). After decoding this request, Twitter then encodes a new message in bytes made up of the combined information and transmits it back to the smartphones of users. Twitter’s application software receives the encoded information, decodes it, and displays it digitally on the screen. The platform is designed in such a way that the original message is reproduced as inscribed by the original user, but is combined with information provided by other users of the platform. The “noise” in the diagram above constitutes anything that interrupts the transmission of the data packets.

Unlike a text message designed for an individuated destination, Twitter is designed for a community of users. Twitter is designed to pattern match, which allows for the classification of information, creating a taxonomy of meaning. This classified information is aggregated and displayed on its platform. Additional algorithms create new information by tracking user interaction to determine the way information is displayed. This gives users, like Alex Blumberg, the option to see what information is getting the most attention. When Alex reads a confusing tweet, his inability to understand what is says isn’t due to a system breakdown, it is because the information Twitter is relaying comes from various users all with different reference points for interpreting what they are seeing on their digital displays. Alex has the ability to read the text and digital images below, but he doesn’t know what they mean.

Figure 2: Yes Yes No 2017

If the tweet above also has you confused, you can listen to the episode here. 

Denning and Bell describe information as always being comprised of two parts “sign and referent.” Humans determining meaning by  “the association between the two” (Denning and Bell, 2012, 477). The signs, or the symbols we perceive, are nothing unless we are able to associate them to a subject or idea. In the case above, the tweet and image work together to convey a whole series of meanings that can only be understood once the context is explained. In the “Yes Yes No” segment, Alex Goldman and PJ spend time providing the necessary history for decoding meaning, and once they do, the tweet can be read with ease.

Humans are constantly creating new links between symbols and things they perceive. These new linkages are expressed through language, which is socially constructed and constantly in flux. The orderly system of signal transition outlined by Shannon, which is essential for successfully mediating symbols through digital and electronic transmission, can never apply to the meaning of the messages without first freezing the relationship between meaning and language. And, as relationships are subjective, frozen language would then be forever encoded with other social constructs, such as power dynamics, and would reflect only this historical moment. Day describes this as “fulfilling the two paramount concerns for the U.S. during the Cold War period: controlling and idealizing linguistic and social normativity, and, relegating linguistic and social marginality and political contestation to minority or curiosity status, or simply, to being social or linguistic “noise.” (Day 2000, 811).

Day is extremely critical of using statistical methodologies to ascribe meaning to symbols; however, Denning and Bell’s article see ascribing meaning, the process of linking the sign and the reverent, as new information. That new information can then be processed by machines. This is a constantly generating system, as opposed to the lockdown described by Day. Twitter is designed to use algorithms to pattern match user preferences and promote content the software has statistically determined is relevant to its user’s interests. It is also designed to target advertisements toward specific demographics that the software has statistically determined are likely to buy certain products. In this sense, it is processing “meaning.” It should then be determined, to what extent these algorithms are fulfilling Day’s concerns and freezing social norms, or to what extent the systems is constantly adapting to knew information?

Works Cited

“#106 Is That You, KD? – By Gimlet Media.” Gimlet Media. Accessed October 18, 2017.

Day, Ronald E. “The ‘Conduit Metaphor’ and the Nature and Politics of Information Studies.” Journal of the American Society for Information Science 51, no. 9 (2000): 805-811.

Denning, Peter J., and Tim Bell. 2012. The information paradox. American Scientist 100 (6): 470-477.

Gleick, James. The Information: A History, a Theory, a Flood. (New York, NY: Pantheon, 2011).

Hartzell, Kathryn. Smartphone Screenshots. 2017

Knight, Gary. Now, Instead of Texting Each Other, You Can Text Other People. March 11, 2012. Friends with Mobile Phones Uploaded by JohnnyMrNinja.

Tomwsulcer. English: Young People Using Their Smartphones at a Party. The Ever-Present Use of Smartphones for Multiple Purposes Has Led Some Writers to Describe Young People as the “Thumb Tribe” or “Thumb Generation”. July 7, 2014. Own work.

“Yes Yes No.” Yes Yes No. Accessed October 18, 2017.



Tinder: Mapping of Digital Information and Meaning

I’ve never actually used Tinder, but the basic premise seemed perfect as a case study for how information as a means of sending technical information can be compared/contrasted with the meaning making that accompanies those signals being sent and received. For those not in the know, Tinder is an app designed for matching people who may or may not be strangers, based on the mutual understanding that they are already attracted to each other. (Or at least to their photos and profile). But technically speaking, what is happening behind the scenes before that understanding can actually take place?  What does a “swipe” mean in context? How do we know that? How can a digital process with only two signals, someone swiping left or right, result in contextual meaning?

In “The Information Paradox“, Denning and Bell resolve the ambiguity of this relationship, saying “The association between a sign and its referent is new information.” (Bell & Denning, 2012). So let’s take a look at how “swiping” acts as a sign for sending technical information, what its referent is for both users on a technical level, and how that association leads to “meaning making” for both of them.

On a procedural and technical level, the process toward this “matching” is pretty straightforward:

A user scrolls through profile photos of other users in their area using their finger on the screen to shuffle them either to the left or the right of the screen.

  1. Tactile gestures on the screen filter interest in the people being looked at. “Swiping” the photo to the left to discard them, and “swiping” their photo to the right saves that profiles information for future signaling and receiving of text communication. The same signals can be sent by touching the corresponding icons on the screen, “x” for pass or “heart icon” for liking.
  2. These signal are encoded digitally, and then signal electronic changes for whether the user should be labeled as available to initiate a match and conversation or whether matching and the initiation of digital contact should be ruled out.  
  3. The people whose profiles are being examined do not receive any signal based on these gestures UNLESS they happened to have “swiped” the other user’s profile photo to the right as well.
  4. At that point, both users are notified by the app of a “match” at the same time, and a text conversation is initiated in case they want to get together.

How then do these people draw meaning? “Information is the difference that makes a difference”. (Bateson, 2000). The difference between “left” and “right” is where meaning is drawn. Each user has become familiar with the procedural actions that lead to a “match” notification, and so they know that they both have “swiped right” as a show of potential attraction or interest. The absence of a “match” notification would have meant that the other user either hasn’t seen their picture, or “swiped left” as a a lack of interest.

Both users are simultaneous receivers of each other’s previous asynchronous signals of interest. In this context of ‘information as a design problem’, Tinder organizes digital information tied to the relationships between swipes and profile photos to “design and control patterns and quantities of electrical current (and radio waves) as signals that map onto human sign and symbol structures”(Irvine, n.d.). The difference between which database or bucket that each photo is swiped toward is ultimately the starting point for a slew of potential meanings, and the initiation of a more nuanced level of electronic signaling within the ensuing text conversation.  



Bateson, G. (2000). Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology (1 edition). Chicago: University of Chicago Press.
Bell, T., & Denning, P. (2012, December). The Information Paradox. American Scientist, (100).
Irvine-Information-Theory-Intro-820.pdf. (n.d.). Retrieved October 18, 2017, from
Irvine, M. (n.d.). Irvine-Information-Theory-Intro-820.pdf. Retrieved October 18, 2017, from
Rocchi, P. (Ed.). (2011). Logic of Analog and Digital Machines (UK ed. edition). New York: Nova Science Publishers.

Message, Shared Knowledge and Meaning Making

This week’s reading covers Shannon’s information theory. Information theory, combined with semiotics make the meaning-making process possible in this digital environment. As shown by Shannon’s original diagram of information transmission model, the information goes from the sender through a more complicating than thought procedure of encoding and decoding to reach the receiver.

Transmission of Message

Messages work after being transmitted because both the sender and receiver share the knowledge base of what the transmitted signal represents. For instance, in a text message, it is assumed that both the sender and receiver share the knowledge of the grammar, vocabulary, formal/informal usage of whatever language they are being transmitted in.

Language itself, no matter spoken or written, is a code for understanding and communicating the world as we perceive it to someone else. Thus beyond being able to receive the text, the receiver must understand the language or the communication rules for the signal to be able to correctly gather what the sender was trying to say. If the sender and receiver does not share the same language, they must try to find another channel to translate the information to a language that they understand. This gives the chance to introduce non-digital “noise” into the message. Mistranslation could happen when a word or phrase cannot be directly translated to another. Misunderstandings of words or phrases due to the language difference could lead to wrong decoding of the original message.


Meaning-making based on Shared Knowledge

In terms of images, the idea remains the same. A shared pool of previous knowledge is required to provide meaning. For example, people will recognize all these pictures as Mona Lisa, as they are familiar with the elements (such as the face, the gesture, or the tone of the color) in the original painting, and have already known them as symbols.

Another example goes in modern usage of images may be the Internet meme and jokes. What makes sense to one person may make no sense at all to another, because they’re not familiar with the background knowledge. They have no context with which to interpret the image given to them to understand what the sender is trying to say. For instance this picture would not make much sense to someone who is not familiar with the culture. Zhuge Liang, whose courtesy name is Zhuge Kongming, is a famous chancellor during the Three Kingdoms Period. This picture only make sense to people who speaks Chinese and knows the person. “Liang (亮)”, the person’s name, means light or brightness in Chinese, while “Kongming”, the courtesy name, means lights in holes or eyes. People normally see the name of a person as an entity and do not look into its actual meaning, thus this picture is funny for Chinese speakers as the image of the person changes according to the literal meaning of his name. However people from other cultures would not understand without the background knowledge or context.

(Literal Translation of his name:

Zhuge Brightness, Zhuge Darkness; Zhuge Light-in-the-Holes)



Martin Irvine, “Introduction to the Technical Theory of Information

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

Ronald E. Day, “The ‘Conduit Metaphor’ and the Nature and Politics of Information Studies.” Journal of the American Society for Information Science 51, no. 9 (2000): 805-811.

How information is being transmitted?

This week’s reading refreshed my understanding of the concept of information. In our daily life, information usually refers to facts and details that enhance our understanding. However, in the technical sense, information can be seen as a designed problem for transmitting encoded signals in a physical medium.

Information Model Theory

In Shannon’s model, we can see the process of how information is encoded and transmitted. Here we can use message app in our cellphone as an example, when we sending message to our friends, we are actually encoding information through the binary digit (bits) system. During this process, some system error (noises) can occur. And the information will be transmitted from the bits system to texts again through our friends’ decoding. Finally, in our friends’ cellphone they will receive our text message and interpret our message based on their understanding.

Though this model is essential for the daily digital and electronic system, it is insufficient for the meaning system.

Who Creates Meaning?

Meaning are not in anything or any system, the cognitive agents enact meanings based on their own understandings and interpretations. The social-cultural cognitive symbol users encode information, and the other cognitive symbol users receive and decode the information, during this process meanings are created.

In my own understanding, not only language, but the digital images need to be designed and transferred to visual information. As a visually inclined person, I love taking photos with my camera and enjoy others’ works.

(Photo from

During the process of taking pictures, the landscape we pick (the 3D space) will have different light rays, and those light rays will be captured and transferred by our digital camera. A good photographer will choose the best angel, place, and situation to shoot. In this way, he or she can encode some information through the photo: either expressing his or her emotion and thoughts, or promoting the beautiful landscape. And the audience will decode the photographer’s information through their own understanding and their own social-cultural background.

For example, my friend sees the beauty of nature from this photo. However; my first thought about this photo is the photographer must use tele-photo lens, and it’s really expensive.


Martin Irvine (n.p.), Introduction to the Technical Theory of Information.

James Gleick (2011), The Information: A History, a Theory, a Flood. NY: Pantheon

Peter Denning and Tim Bell (2012). The Information Paradox. American Scientist

Algorithmic Music in the view of information theory

From studying English text, Shannon declared a new concept, “H”, i.e. the entropy of a message, or the information. This makes me think of music which could be regarded as another kind of language. In this short article, I’m going to talk about “algorithmic music”, an emerging field of producing music by algorithm on the computer.


The basic operating principle is easy to follow.

1.1 Deconstruction

Imaging we going to compose a piece of “Beethovenish” piano Concerto, the first thing we need to do is to broke the 32 pieces of Beethoven Concerto into mini pieces, tag them and let the computer “study” these works.

1.2 Recombinancy

Similar with digitized speech, the procedure for a computer to produce a new melody with its data base is stochastic and is neither deterministic nor random. The note in the melody will be determined not only by the genre of the work but also the note right before it. Just like the digram “th” can frequently be find in English text, in music language, notes that can form harmonic intervals often show up together while it is always expected to hear a perfect cadence in the end of a paragraph. In this way, the task of recombine the notes to produce a new melody is replaced by doing a series of “yes” or “no” questions. With the given note A, the computer will first check note B. If the answer is “yes”, it will move on to the third note; otherwise, it will successively check note E, F and G until the answer is “yes”. When note D is suitable for the second place but the answers for H and I are both negative, the computer will jump note D to check note D.

2.Listening to music from a CD player

There are two ways for us to listen to music: alive and recordings. The communication system of listening to music from a CD Player is more complex than from a concert: the laser diode sends out laser to the disc while the photo diode receive the reflected laser and record the result as either “0” or “1”; then the player decode these “0”s and “1”s and transmit these electronic signals into sounds; hearing the sound, we may have different feelings from each other. According to Paolo Rocchi, “information always has two parts – sign and referent. Meaning is the association between the two”. A common sign-referent model in music is works written in majors carry a motion of “bright / grand”. But as for the understanding of a whole piece of work, different people have different sign-referent system thus resulting in different feelings.

Like English language, music also contains redundancy. In his paper, Weaver mentioned that redundancy is determined not by the free choice of the sender, but by the accepted statistical rules governing the use of the symbols in question. Leaving out some of the unimportant notes in the work will not change the meaning of the whole piece.


When doing background searching, I found that scholars from different fields have different answers of “what is information”. This reminds me of what Saint Augustine said: “What then is time? If no one asks me, I know what it is. If I wish to explain it to him who asks, I do not know.” Is information the same as time that cannot be explained?



James Gleick(2011), The Information: A History, a Theory, a Flood. New York, NY: Pantheon

Peter Denning and Tim Bell(2012). The Information Paradox. American Scientist

Martin Irvine, Introduction to the Technical Theory of Information

Warren Weaver(1953). Recent contributions to the mathematical theory of communication. A Review of General Semantics


meaning exist for information theory


In the signal transmitting process, signals, existing as bits, break through the physical constraints and minimize the contained information here. By putting choice half and half, like a flip coin, into the yes and no question, the signal created as binary digit as unit of information, thus make the breakthrough possible. Also, the Peter J. denning said that “transformation opens many new possibilities, most notably the creation of new information”(little confusing), the information is not change until being represented to observers and thus increase. To make transformation work, the computing process of this transformation must have an end instead of an infinite loop, while in the interaction systems, to apply it with intentions and meanings, it can be infinite. As the signals are waves or electric thing, they cost energy to some extend also.


For the classical information theory, Shannon is not taking the meaning part into consideration, he thought it is irrelevant. Shannon is more focusing on the technical part of transmission, the channels for this process instead of human involvement. But that is not the situation as long as the information theory putting into practice. As saying that “Meaningful’ means that the data must comply with the meanings (semantics) of the chosen system, code, or language in question. “ with my personal understanding, that when interaction happened, meaning will exist.

Meaning distribution

So it feels like meaning is distributed in this process, in our interaction with outside data and signs. Like the text being digitized into several bits, transmitted to our smart phone, then interpreted by decoding system within our phone, to become the symbolic sign we are familiar with, which is being described as “referent” within our brain. As we seeing the sign, we recognize its meaning from what we reference before. So it seems like a little similar to the distributed cognition from some aspect? Only standing from the artifacts foothold.



So as mentioned in the article: the information paradox, referent is hard to tell sometimes because with technology development, some of that also exist within digital devices. So for AI system, it now has the ability to deep learning, it is also gain social context and store in its brain, so can it calls the sign also generate meaning for a AI system when it knows how to cooperate signs like traffic lights?


Martin Irvine, Introduction to the Technical Theory of Information
Luciano Floridi, Information: A Very Short Introduction. Oxford, UK: Oxford University Press, 2010.
Peter Denning and Tim Bell, “The Information Paradox.” From American Scientist, 100, Nov-Dec. 2012.