Some of the main features of the signals transmission theory are, single unit, point to point, with containers and conduits. (Martin, 2021a, p. 14) However, while information passes through these transmissions, information and meaning are not the same. Information is described as, “structure-preserving structures,” which interpret patterns rather than “meaning.” (Martin, 2021a, p. 14) Furthermore, meanings cannot be transformed into substrates which are interpretable. (Martin, 2021a, p. 18) Meaning is not transferred through the system, but becomes the system. (Martin, 2021a, p. 19) Lastly, information is essential to digital communication because it is the first unobservable layer or substrate in a semiotic systems model, it is “measurable, quantifiable, predictable and designable.” (Martin, 2021a, p. 13)

E-information is unobservable, but media produced by that information can be viewed and interpreted. However, the process of the transmission is always the same, in the absence of errors, the bits which become bytes do not change, only our interpretation of the media they produce may vary. Humans apply meaning to symbols based on recognizable patterns over time, and E-information as a collection of patterns is an embodiment of our primary human symbolic systems. (Martin, 2021b, p. 2) If for example, an individual had never seen a dog, and is shown an image of a dog, they may have heard the word “dog” before, but because the individual had never seen a dog, how would they understand what the image was without being told? E-information is designed to be entirely structured, without structure, information transmission is not possible, which is contradictory to human symbolic understanding which has the capacity to change.  

Questions:

First, it is still unclear to me in the readings relating to the two learning objectives how E-information is designed as a substrate for symbolic meaning. Secondly, the terms “noise” and “distortion” were reintroduced in the Gleick reading, but it did not explain how “noise” translates over into computing. (Gleick, 2011) Lastly, in the Gleick reading, “ether” was mentioned, but not explained in detail. 

References

Martin, I. (2021a). Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding.

Martin, I. (2021b). Using the Principle of Levels for Understanding “Information,” “Data,” and “Meaning.”

Gleick, J. (2011). The information: A history, a theory, a flood (1st ed). Pantheon Books.

In the theory of Information, Shannon defines a message as a choice. In this term, Information separates from its semantic content; that is, Information transfers to the physical environment from the psychological one. To interpret the signal transmission in the theory of Information, according to Floridi, how Shannon deal with signals needs to be understood first- “he treated the signal as a string of discrete symbols.”  Combining with features of Information- Information is closely associated with uncertainty; there are probabilities in conveying Information; it’s difficult to transmit information from one point to another, and Information is entropy- a sender can overcome noise by using extra symbols for error correction instead of boosting the power. In addition, in the transmission, Shannon focused on how much of a message influences the probability of the next symbol on the base of statistical language structure so that it will allow for a saving of time or channel capacity. Relatively, he gave the formula and a new unit of measure- bit, and worked out how to calculate the redundancy in a piece of Information. Based on that, signal transmission (encoding) could be more effective.

According to Professor Irvine in Using the Principle of Levels for Understanding “Information,” “Data,” and “Meaning”,”  ‘information’ in electronic systems are designed as units of “structure-preserving structures,” which use the physics of electricity as a medium.” Information is encoded to the electronic signals that are communicable in the transmitting channel but unobservable. The traditional cultural or social meanings are not embodied in messages but shared by the group of people communicating. Therefore, those meanings (meta-information) cannot be represented in signal information.

In the theory, information is not content (it technically has no meaning). What we do with it is to utilize it to achieve more and faster communication (in our symbolic system). It cannot extend since it only occurs in the sender, channel, and receiver. Also, since it happens with the assistance of energy fields, electronic pulses, and signals used as binary representations, which we could not observe, so it can only be regarded as tools. “A fuller semiotic systems model provides a model of other levels of abstraction that explains how we use, express, perceive, understand, and interpret instances of tokens (physical-perceptible instances) of all our symbolic structures and media (language, writing, abstract symbols, graphs and diagrams, images, sounds, music, and video/film).” To be more specific, the information theory model is a subsystem of symbolic systems, which provides a level of abstraction for discrete symbols. It could not be interpreted in daily communication and become a new model for the symbolic system.

Questions:

Is token an observable unit in our symbolic system that is not discrete? And is this drawn to become the counterpart of signal in the information theory model?

A re-occurring theme/discussion point through out our conversations and readings, has been the concept behind de-black-boxing tech and the reason behind that is probably due to the fact that we struggle to truly grasp what is behind these machines and how do we really end up getting the “information” that we do in the formats that we do. How does an image get so easily captured and transferred from reality to the initial shutter of the specific camera that took the photo, to a digitalized form that can be re-used for multiple purposes, over an infinite time period in a gazillion different ways. You can send it, post it, delete it, put a filter on it, put a bunch of filters on it and photoshop it. No matter what you’re doing, you have managed to take something that exists in the physical real world and converted it to its digitized representation and depiction aka form. 

“E-information theory supports our
whole symbolic-cognitive system by providing the techniques for using electrical
energy quantities in time for physical substrates of all human sign and symbol
structures (data representations)” (Irvine, 2020, 4). We are basically trying to imprint our human and cultural understandings, meanings and values into an electronic format where automated calculations and interpretations can take place in order to take the information that has been fed into the machine or system and “spit out” new representations and meanings that yet have an overall meaning used for communication and interpretation. Physical electronics and machines such as our computers or smartphones have been created in order to present and hold, in a way “carry” these data representations that have been translated from human symbol meanings to bits and bytes, aka data. (“Data: Binary structure for units that can be assigned a first level of meaning” (Irvine, 2020, 3). Information theory is considered to be what we call “designer electronics” as it represents, processes and transmits designed binary electronics (what we have fed into the computer and converted it into the “language” it understands) as an “engineering solution to a semiotic problem” that of “imposing human logical and values on electronic and physical media for creating meaningful patterns” (Irvine, 2020, 2). 

Indicating a state of existence, the signal-code-transmission model does not replace the meaning of whatever the representation is, stands for. “Meanings are not properties of electrical structure” (Irvine, 2020, 9). We can understand and conceptualize the message that we have received through the transmission because of the pre-supposed meaning or notion we have attributed to it. The same way a color blind personal will view the color blue on his screen differently than a person who isn’t colorblind. However, the system itself does not make distinctions between the two blues, those are made solemnly by the person at the receiving end who conceptualize and see the color differently. The transmission just gets the job done of “carrying” the “message”/transmission from one designer electronic to the next. As Shannon also explains in his theories and findings, that “what a relay passes onward from
one circuit to the next is not really electricity but rather a fact: the fact of
whether the circuit is open or closed” (Gleick, 2011, 18). 

Whatever is transmitted is what will be shown. The system/computer does not have actual concepts or true understanding of what is happening, said, painted, shown, depicted, stated, etc. on the receiving end of a signal. The system/computer have zero pre-supposed cultural notions or any sort of conceptual belief about the E-information, that is up to the user, the real human. Leading to why the information theory model works for everything digital and electronic but “not extending to models for meanings, uses, and purposes of our sign and symbol systems” since that is ultimately left to the task of the human user. In reality, information isn’t something tangible, we can’t see it and most times we cannot touch it, especially if we are talking about E-information (Irvine, 2020). As long as the system runs and works properly what we see are “the effects of information shaped and “communicable” for internal processing in the systems” (Irvine, 2020, 13) that take place in seconds as the order come from binary switches and other processes that do not contain any further meaning other than on/off, 1/0, etc. The meaning-making comes in a later stage that takes place in our human brains that can unfortunately or fortunately, not work as fast as a computer’s processor and systems and therefore need time to process, conceptualize and interpret meaning found in the data using our own human symbol systems and cultural notions. 

Sources

Gleick, J. (2011). The Information: A History, a Theory, a Flood. Bantheon Books: NY

Irvine, M. (2021). Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding.

Martell, C & Denning, P (2015). Great Principles of Computing. The MIT Press: London

In doing the readings this week what was most salient to me was the similarities of signal transmission theory, the transmission of data, and the brain. Shannon’s information transmission theory as explained by Professor Irvine in his article requires the inception, transmission, and then interpretation of signal by a separate entity. This means that if I wanted to send a message or even an image what is required is the successful transmission of a message, even when marred by error, is left interpretable by the receiving entity. This is in many ways how the brain works.

Take for instance when we see, there is a transmission of information from our eyes, through a nerve, to the vision area in our brain to encode and decode that visual information. The brain doesn’t have access to the visual precept but many small precepts that are strung together to create whatever we are seeing. In the example of using an apple, our eyes break down that apple, and our brain reassembles the message. Now, not all messages will come through, and even some are conflicting but our brain using the information it has and all the other information around it will help fill in the likely gaps (not perfectly but works well enough). This representation is very much like how the internet works, the deconstruction and reconstruction of information to transfer it from one place to the other as mentioned by Professor Irvine and, Denning and Martell.

These signals by themselves have no semantic value though, though we may be able to see the apple at this stage we do not know what the apple is, how it tastes, what it’s color is, or all the things an apple can be used for. This is a completely different stage in the brain and in a lot of ways a different stage of computing. This meaning-making is the next step of the process for computing and requires more than just Shannon’s transference of information. This transference of information is important in making sure the data being received is consistent and interpretable but beyond that point, it requires another process to understand if the message itself is worthwhile.

I am excited to see where this goes and how computers are attempting to jump from information transmitters to information interpreters.

Reference

Irvine, Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding (2021).

Prof. Irvine, “Using the Principle of “Levels of Abstraction” to Understand “Information,” “Data,” and “Meaning” (Internet design) (2021).

Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015. 

My best friend just sent me a text message about her day. The perceptible text in this text message, i.e. the information, is encoded as bytes, transmitted in data packets, and then interpreted in software for rendering on my phone screen. When E-information is transmitted and received, it is being used as a symbolic medium to encode and decode information to be interpreted by my best friend and me.  We are simply observing and contextualizing representations in and representations out. Messages like the text my friend sent and assumed background knowledge cannot be represented in E-information. Rather, E-information is a sublayer in the technical design process that is all about encoding and representing interpretable representations. Behind the scenes of this text message are interconnected modules from wireless receivers to pixel-mapped screens. As I am reading this text message from my friend, I am able to gather meaning from it because I am a cognitive agent that understands material sign structures in living contexts of interpretation and understanding. electrical signal patterns are designed to be “communicable” “internally” (unobservably) throughout the components of a physical system (transducers, processors, memory units, digital network connections, interfaces), and “communicable” “externally” to the human users of meaningful signs and symbols through the system channels for “outputting” perceptible representations (usually patterns of pixels, and sound through audio interfaces). 

I quite like this example: 

Consider the parallels with the way we perceive and “decode” symbolic units using “natural” energy (light and sound) as medium. What we observe are effects and inferences from detected information: the light waves hitting our retinas from these text characters with features that we map to patterns, the acoustic waves of speech sounds and musical notes, the patterns of light registered from all the ways we use meaningful visual “information.” Using E-information-designed electronic devices like radios, TVs, and computer screens, we observe the effects of information shaped and “communicable” for internal processing in the systems. Meaning-making, the act of creating, expressing, and understanding meaning, is likewise unobservable (we can’t probe our minds or find the neural structures in our brains at the precise milliseconds we engage in conversations, interpret a page in a novel, recognize a song, or navigate directions along city streets with a GPS map), but we make reliable inferences from all the symbolic representations that we use every day.

As stated above, E-information is simply a layer in the process of sending and receiving text messages. But once those messages are received, it is up to the human brain to connect these symbols, whether they be pixels that make up images, letters, emojis, or however we beings define and practice communication. For humans to be able to interpret these representations,  we require a method of creating recognizable patterns with perceptible distinctions among the symbolic system being used (in this case text). If the states are randomly fluctuating in a substrate, humans cannot recognize patterns needed for interpreting and contextualizing. 

The signal transmission model is similar to a communication system. It has five essential elements:

-Information source is a source like person or machine who input different kinds of message.

-Transmitter is to encode or tokenize the information mentioned above, into specific pattern that fit the independent medium.

-Channel is the independent medium. Actually the information in the channel is unobservable, but we can control patterns of electrical current for signals so that the signals could be measurable.

-Receiver is to decoding information from the signal or we can say invert the operation of the transmitter.

-Destination is a person or machine who receive the decoded information.

In the theory, one important matter is the design of the transmitter and the receiver which are related to the method for encoding and decoding.  In addition, noise is also a factor that the theory focuses on. To overcome the noise source through the transmission and enable error correction, the model adds redundancy, namely uses extra symbols.

However, the model does not include the meaning. When Shannon built the model, he tried to eradicate the meaning of the message and focused on the engineering problem without meaning or semantics which are made by human’s collective practice or shared ideas.  When transmission, the information will be converted into bits when transmission and bits could not become information, let alone the meaning, until structured in a encodable pattern and output to human interpretable representations. What’s more, meanings, values, and interpretations are not physical properties or features of a symbolic medium (not an electrical structures); they are inferences and correlations made by symbol using communities. In other words, meaning cannot be in the channel or other physical things. The model only provides an essential abstraction layer in the designs of all electronic and digital systems which the meaning could not be described in.

Like Newton bridged the physics and the formula, Shannon made a bridge between information and uncertainty, entropy and chaos.  The bridge made the information into quantities, suitable for use in mathematical formula and finally link the human logic and symbolic values with electronic media. In other words, the theory forms a semiotic system for digital electronic data representation. In the system, representation, or instance, will be tokenized (encoded) and re-tokenize (decoded). However, as I mentioned above, the process only provides abstraction layer in the digital system, but not includes the interpretations of other symbolic structures, like language and graphs. Information theory cannot provide different kinds of abstract layers and cannot interpret instances unrelated to digital things. Therefore, it can only be a subsystem of the whole sign and symbol systems.

Reference

Irvine, Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding (2021).

James Gleick, & OverDrive, I. (2011). The Information. Knopf Doubleday Publishing Group. http://api.overdrive.com/v1/collections/v1L2BowAAAC4HAAA1k/products/d46545f2-0229-430c-b61f-314458ac6ed1

Computer and communication engineers specialize in systems that transmit information encoded as electromagnetic signals. For example, a microphone generates an electric signal as someone speaks, a magnetic disk records a copy of the signal, and a speaker generates a sound wave from that signal. A radio transmitter superimposes an audio signal on a radio frequency (RF) signal so that the RF amplitude tracks the audio signal, and a receiver subtracts out the RF signal to extract the audio. Engineers must be very precise and unambiguous about how they encode representations and their intended meanings. Otherwise, the physical systems they build will not work. Computer and communication engineers settled on the bit (short for binary digit) as their primary information unit. Claude Shannon introduced the term “ bit ”(Martell & Denning, 2015).

All we care about is what all that engineering adds up to when it succeeds in transmitting signals so that they become the physical basis for constituting the perceptible, meaningful patterns of our sign and symbol systems.

So we have an essential, foundational principle: the engineering principles for E-information (signals transmission and reception) form an intentionally designed subsystem for our primary meaning systems, that is, our sign and symbol systems (language and writing, mathematics, graphics, images, sound, film/video), which can be represented as data in the computing and E-information context (more on data later). In our contemporary electronics environment, we need the knowledge provided by both semiotics (the study of human symbolic cognition and sign systems) and the engineering theory of E-information (mathematics + physics) for all information systems engineered to use sections of the electromagnetic energy spectrum (electricity, radio waves, light waves (Irvine, 2021).

In the world of information science, the meaning is often tied to the notion of representation. A principle that underlies the whole concept of computation is that one state can be represented by another state. The states need not be in the same system. To give two examples: the words you type on a keyboard can be represented by voltages and current flows inside an electronic computer; music performed by a human artist can be characterized by a pattern of silver and black dote on a DVD. When a particular representation affects you, it has meaning to you. (Mayfield, 2013).

We need to understand the core concepts and design principles for E-information as a subsystem, and then go on to explain how the E-information subsystem is designed to serve our larger symbolic systems. We complete the whole picture with the knowledge provided by other fields (linguistics, semantics, pragmatics, semiotics, and other communication approaches), but not modelling those fields on the E-information transmission model.

As we’ve just reviewed, the signal-code-transmission model of information theory was initially developed as a set of models for transmitting error-free electronic signals in telecommunication systems where networks and radio frequencies’ physical limits and capacity could be precisely defined and engineered. This model provides an essential abstraction layer in the designs of all electronic and digital systems. It does not provide an extensible model for the larger sense of communication and meaning systems that these symbolic cognitive technologies allow us to implement. The meanings and social uses of communication are left out of the signal transmission model because they are assumed or presupposed as what motivates using signals and E-information at all. This is why we need to understand that the designs and engineering techniques for E-information are used for creating a data or semiotic subsystem using binary electronics.

“Information” in this context is thus primarily unobservable (we cannot observe energy fields, electronic pulses, or signals used as binary representations). (Irvine, 2021).

Why is the information theory model essential for everything electronic and digital, but insufficient for extending to models for meanings, uses, and purposes of our sign and symbol systems?

The transmission model of E-information is essential to understand. Still, it cannot be used for extrapolating to a model for communication and meaning more generally (though some schools of thought have tried unsuccessfully to use the model this way). The signal transmission theory is constrained by a signal-unit, point-to-point model, with the “conduit” and “container.”

The larger context surrounding E-information also includes what cognitive science research calls “meta-symbolic” knowledge, the understanding of meaning frameworks for meanings, and the essential meta-information (information about the information in the generic information sense) known to all communicators using a symbolic medium. This includes cultural knowledge of various kinds/genres of messages, social conventions, categories of meanings or cultural codes, and assumed background knowledge, which, of course, as meta-information is not – and cannot be — represented in the signal information, the E-information, itself. (Barwise, 1986 in Gleick, 2011).

References :

1. Gleick, J. (2011). The Information: A History, a Theory, a Flood. Bantheon Books: NY
2. Irvine, M. (2021). Introducing Information Theory: The Context of Electrical Signals Engineering and Digital Encoding.
3. Martell, C & Denning, P (2015). Great Principles of Computing. The MIT Press: London
4. Mayfield, J. E., (2013). The Engine of Complexity: Evolution as Computation. NY: Columbia University Press: NY

Although E-information transmission model  is very important to understand how an important layer in the semiotic systems functions, however, it can’t be used as a general model for communication and meaning. According to Professor Irvine’s piece, Introducing Information Theory, “the signal transmission theory is constrained by a “signal-unit, point-to-point model,” with the “conduit” and “container” metaphors.” This transmitted signal code model is not a description of meaning, because the technical aspect of the information model was not originally designed to interpret meanings as the human brain does. As a result, the content that passes through the conduit when the E-information is transmitted does not hold a meaning. Another important feature of the E-information is that it needs a symbolic medium for the purpose of “understanding of meaning frameworks for meanings” which is called, by cognitive science research, the “meta-symbolic” knowledge. 

Now we know that we need a medium for meaningful communication and representation, here comes the need for an essential layer in the semiotic systems, which is the “E-information theory.” In fact, in electronic systems, information uses the “physics of electricity” as the required medium to “impose regular interpretable patterns.” The information in this context is designed as units of structure-preserving structures.” As emphasized by Professor Irvine, these electrical signal patterns are designed to be “communicable” internally and externally; internally through the components of the physical system and externally in meaningful symbols to humans.  The physics of electricity as a medium is what makes the information theory model essential for everything electronic and digital.

Finally, It is important to mention here that information theory model is insufficient for extending to models for meanings, uses, and purposes of our sign and symbol systems because as described by Professor Irvine, “meanings” do not have the properties of a physical medium in the communication process. Meanings as generators of symbols are presupposed and taken for granted. This fact brackets the meanings off Shannon’s system problem.  

Case Study: 

In the mid 90s, the only means of shopping in Syria were few physical shops and a famous German shopping catalogue. The minimum time to receive the ordered items from Germany is two months. Paradoxically, last Christmas and through Amazon, I ordered a Sony headset at 10 am, and received it at 3 pm the same day. I can’t think of a better example than online shopping as a case study for this class. Using my Iphone device with its Apple iOS system, I logged into the Amazon app and chose the desired Sony headset. When I clicked “submit order,” my Iphone as a transmitter, used physics of electricity as a medium to impose regular interpretable patterns via electrical signals. These signals were communicated through the components of the physical system (the internet). Amazon (as a receiver) received the transmitted signal and interpreted it into meaningful symbols. My order was processed within Amazon system in a few hours. Amazon (as a transmitter now), sent other types of signals that were interpreted into meaningful symbols by the shipping company, as a receiver, to deliver the ordered item. This case made me pause and think about the importance of computer system as a solution and a mean of protection for human being during COVID.

Questions:

1. What is the difference between a symbol and a token? Is the token an encoded symbol?
2. Information theory is an engineering solution to a semiotic problem — it is a model for “designer electronics.” Why designer electronics not designed electronics?
3. Can you explain the concept of “electronic systems are designed as units of structure-preserving structure” in more details?

References

Martin Irvine’s, Introducing Information Theory (2021).

I think the first step to answering these questions is to figure out what information theory is. From my understanding it is just the explanation for how we can impose human logic and symbolic values on electronic and physical media. From this we get this offshoot that I think falls into the information theory concept something called E-information which is the digital electronics concept i.e. electrical engineering information where E-information = mathematics + physics of signals + time. This whole process is to preserve the pattern and quality of ta signal unit so that it can be successfully received at the end. Now the signal-code transmission model I think was the result of the information theory built by Claude Shannon to transmit electronic signals most efficiently over networks or broadcast radio waves merged with the question of how to represent data in discrete electronic units. This model is a way of transmitting error-free electronic signals in telecommunication systems, but it left out meanings and social uses of communication because they are assumed or presupposed. As a result, I believe the information theory describes how our primary symbol systems “encode” but do not provide a meaning to the symbol. This is what I got from the first text on the introduction to technical theory.  

For the next one, following a timeline of Shannon we see how the information theory came to exist, starting with the Differential Analyzer that was coordinated by a hundred relays, intricately interconnected, switching on and off in particular sequence. From this and his enjoyment in logic puzzles, Shannon realized in a deeply abstract way the possible arrangements of switching circuits lined up to symbolic logic particularly Boole’s algebra. Here we get his masters thesis in a machine that could solve logic puzzles the essence of the computer. Then he got interested in why the telephony, radio, television, and telegraphy all following the same general form for communication always suffer distortion and noise (static). But he took a job in Princeton and then WWII kicked off and he was assigned “Project 7” that applied mathematics to fire-control mechanism for anti-aircraft guns to “apply corrections to the gun control so that the shell and the target will arrive at the same position at the same time.” The problem was something similar to what plagued communications by telephone, the interfering noise and problem of smoothing the data to eliminate or reduce tracking errors. We go on a quick history of the telephone and are reintroduced to Shannon reading a text published in Bell System Technical Journal about the Baduot Code. Here information is the stuff of communication in which communication takes the place by means of symbols that convey some type of meaning. Fast forward to 1943 and Shannon is working as a cryptanalyst and enjoying tea with Alan Turing where they talked not about their work but of the possibility of machine learning. Here Shannon develops a model for communication and then I get lost in the math but from my understanding Shannon created this idea that natural-language text could be encoded more efficiently for transmission or storage. In which he develops a way to ensure end to end transmission.

From all of this we get the internet, which is designed with no central control. It’s a distributed packet switch network that ensures end-to end connectivity, so that any device could connect to any other device. I’m still having difficulty understanding what E-information is to compare it to properly compare it to the internet. The Internet is controlled by no one and everyone in the fact that the Internet packets (I think the information) are structure-preserving-structures between senders and receivers. At the same time, from my understanding, E-information is like the internet in that it uses electricity to create imposing regular, interpretable patterns that are designed to be communicable through a physical system to a human user. If this is right than wouldn’t the internet be a form of E-information?

Lingering Questions:

Where does E-information fit in the information theory?

What did the math that Shannon did to realize error-free transmission accomplish? I did not understand the math aspect behind his theories and would like a better explanation.

Where is the ALU and how does the ALU on my computer turn logic gates into actual images and symbols?

Why are there so many logic gates?