Power Tools for Technical Communication:
Audience Characteristics

Study the following excerpts of technical text, and imagine the characteristics of the readers for whom these excerpts were most likely written. Describe those audiences and their characteristics in the boxes beneath each excerpt. When you've completed the descriptions, you can e-mail your work to your instructor, yourself, or both. You'll notice that the discussion is rather basic at first but rapidly becomes more complex.

  1. Neural network research — part 1:

    The exact workings of the human brain are still a mystery. Yet, some aspects of this amazing processor are known. In particular, the most basic element of the human brain is a specific type of cell which, unlike the rest of the body, doesn't appear to regenerate. Because this type of cell is the only part of the body that isn't slowly replaced, it is assumed that these cells are what provides us with our abilities to remember, think, and apply previous experiences to our every action. These cells, all 100 billion of them, are known as neurons. Each of these neurons can connect with up to 200,000 other neurons, although 1,000 to 10,000 is typical.

    The power of the human mind comes from the sheer numbers of these basic components and the multiple connections between them. It also comes from genetic programming and learning.

    The individual neurons are complicated. They have a myriad of parts, sub-systems, and control mechanisms. They convey information via a host of electrochemical pathways. There are over one hundred different classes of neurons, depending on the classification method used. Together these neurons and their connections form a process which is not binary, not stable, and not synchronous. In short, it is nothing like the currently available electronic computers, or even artificial neural networks.

    These artificial neural networks try to replicate only the most basic elements of this complicated, versatile, and powerful organism. They do it in a primitive way. But for the software engineer who is trying to solve problems, neural computing was never about replicating human brains. It is about machines and a new way to solve problems.

  2. Neural network research — part 2:

    The fundamental processing element of a neural network is a neuron. This building block of human awareness encompasses a few general capabilities. Basically, a biological neuron receives inputs from other sources, combines them in some way, performs a generally nonlinear operation on the result, and then outputs the final result. Figure 1 shows the relationship of these four parts.

    Figure 1. A Simple Neuron.

    Within humans there are many variations on this basic type of neuron, further complicating man's attempts at electrically replicating the process of thinking. Yet, all natural neurons have the same four basic components. These components are known by their biological names—dendrites, soma, axon, and synapses. Dendrites are hair-like extensions of the soma which act like input channels. These input channels receive their input through the synapses of other neurons. The soma then processes these incoming signals over time. The soma then turns that processed value into an output which is sent out to other neurons through the axon and the synapses.

  3. Neural network research — part 3:

    Recent experimental data has provided further evidence that biological neurons are structurally more complex than the simplistic explanation above. They are significantly more complex than the existing artificial neurons that are built into today's artificial neural networks. As biology provides a better understanding of neurons, and as technology advances, network designers can continue to improve their systems by building upon our understanding of the biological brain. However, the current goal of artificial neural networks is not the grandiose recreation of the brain. On the contrary, neural network researchers are seeking an understanding of nature's capabilities for which people can engineer solutions to problems that have not been solved by traditional computing.

    To do this, the basic unit of neural networks, the artificial neurons, simulate the four basic functions of natural neurons. Figure 2 shows a fundamental representation of an artificial neuron.

    Figure 2. A Basic Artificial Neuron.

    In Figure 2, various inputs to the network are represented by the mathematical symbol, x(n). Each of these inputs are multiplied by a connection weight. These weights are represented by w(n). In the simplest case, these products are simply summed, fed through a transfer function to generate a result, and then output. This process lends itself to physical implementation on a large scale in a small package. This electronic implementation is still possible with other network structures which utilize different summing functions as well as different transfer functions.

    Some applications require "black and white," or binary, answers. These applications include the recognition of text, the identification of speech, and the image deciphering of scenes. These applications are required to turn real-world inputs into discrete values. These potential values are limited to some known set, like the ASCII characters or the most common 50,000 English words. Because of this limitation of output options, these applications don't always utilize networks composed of neurons that simply sum up, and thereby smooth, inputs. These networks may utilize the binary properties of ORing and ANDing of inputs. These functions, and many others, can be built into the summation and transfer functions of a network.

    Other networks work on problems where the resolutions are not just one of several known values. These networks need to be capable of an infinite number of responses. Applications of this type include the "intelligence" behind robotic movements. This "intelligence" processes inputs and then creates outputs which actually cause some device to move. That movement can span an infinite number of precise motions. These networks do indeed want to smooth their inputs which, due to limitations of sensors, come in non-continuous bursts, say thirty times a second. To do that, they might accept these inputs, sum that data, and then produce an output by, for example, applying a hyperbolic tangent as a transfer function. In this manner, output values from the network are continuous and satisfy more real world interfaces.

  4. Neural network research — part 4:

    In currently available software packages these artificial neurons are called "processing elements" and have many more capabilities than the simple artificial neuron described above. Those capabilities will be discussed later in this report. Figure 3 is a more detailed schematic of this still simplistic artificial neuron.

    Figure 3. A Model of a "Processing Element".

    In Figure 3, inputs enter into the processing element from the upper left. The first step is for each of these inputs to be multiplied by their respective weighting factor (w(n)). Then these modified inputs are fed into the summing function, which usually just sums these products. Yet, many different types of operations can be selected. These operations could produce a number of different values which are then propagated forward; values such as the average, the largest, the smallest, the ORed values, the ANDed values, etc. Furthermore, most commercial development products allow software engineers to create their own summing functions via routines coded in a higher level language (C is commonly supported). Sometimes the summing function is further complicated by the addition of an activation function which enables the summing function to operate in a time sensitive way.

    Either way, the output of the summing function is then sent into a transfer function. This function then turns this number into a real output via some algorithm. It is this algorithm that takes the input and turns it into a zero or a one, a minus one or a one, or some other number. The transfer functions that are commonly supported are sigmoid, sine, hyperbolic tangent, etc. This transfer function also can scale the output or control its value via thresholds. The result of the transfer function is usually the direct output of the processing element. An example of how a transfer function works is shown in Figure 4.

    This sigmoid transfer function takes the value from the summation function, called sum in the Figure 4, and turns it into a value between zero and one.

    Figure 4. Sigmoid Transfer Function.

    Finally, the processing element is ready to output the result of its transfer function. This output is then input into other processing elements, or to an outside connection, as dictated by the structure of the network.

    All artificial neural networks are constructed from this basic building block - the processing element or the artificial neuron. It is variety and the fundamental differences in these building blocks which partially cause the implementing of neural networks to be an "art."

  5. Neural network research — part 5:

    Counter-propagation Network

    Robert Hecht-Nielsen developed the counter-propagation network as a means to combine an unsupervised Kohonen layer with a teachable output layer. This is yet another topology to synthesize complex classification problems, while trying to minimize the number of processing elements and training time. The operation for the counter-propagation network is similar to that of the Learning Vector Quantization network in that the middle Kohonen layer acts as an adaptive look-up table, finding the closest fit to an input stimulus and outputting its equivalent mapping.

    The first counter-propagation network consisted of a bi-directional mapping between the input and output layers. In essence, while data is presented to the input layer to generate a classification pattern on the output layer, the output layer in turn would accept an additional input vector and generate an output classification on the network's input layer. The network got its name from this counter-posing flow of information through its structure. Most developers use a uni-flow variant of this formal representation of counter-propagation. In other words. there is only one feedforward path from input layer to output layer.

    An example network is shown in Figure 5. The uni-directional counter-propagation network has three layers. If the inputs are not already normalized before they enter the network., a fourth layer is sometimes added. The main layers include an input buffer layer, a self-organizing Kohonen layer, and an output layer which uses the Delta Rule to modify its incoming connection weights. Sometimes this layer is called a Grossberg Outstar layer.

    Figure 5. An Example Counter-propagation Network.

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