"The Multiple Sides of Computer Science" by Matthew Minerd (2014)

It often takes some time for a new discipline to become recognized as an independent science. An excellent example of this is computer science. In many ways, this science still is a hodgepodge of several different sciences, each one having its own distinct character. For example, some computer scientists are almost indistinguishable from mathematicians. Many of the most difficult topics in pattern recognition and data communications require intensive mathematics in order to provide software solutions. Years of training in the appropriate disciplines are necessary before the computer scientist can even begin to work as a programmer in such areas. In contrast to those computer scientists who work with complex mathematics, many computer scientists work on areas of hardware development that are similar to disciplines like electrical engineering and physics.

However, computer science has its own particular problems regarding the unity of its subject matter. There are many practical applications for computing work; therefore, many computer scientists focus on learning a large set of skills in programming languages, development environments, and even information technology. All of these disciplines have a certain practical coloration that is quite distinct from the theoretical concepts used in other parts of the field. Nevertheless, these practical topics add to the broad range of topics covered by most academic programs that claim to focus on “computer science.” It can only be hoped that these disciplines will increase in orderliness in the coming decades.

What will be the effect of increased orderliness in computer science studies?

"Comparing Technologies: A Difficult Endeavor" by Matthew Minerd (2014)

Comparisons of technology are often difficult to make, not only because of the rapid pace of improvements but also because of the many new applications that are available as time progresses. If we were to consider the contemporary graphing calculator and the calculation capacities of computing machines from fifty years ago, there would be astounding improvements between these two devices. However, the improvements are not reduced merely to speed improvements. A graphing calculator also has numerous output capacities that far exceed those available much older computers, none of which had the ability to represent their output in any manner even closely resembling that of contemporary devices. Merely consider the display capacities of such a device. These enable users to input many new kinds of information, enabling design engineers to design new hardware functions to match the new means of collecting user input.

The situation is even more obvious when one considers the numerous functions performed by a modern “smartphone.” These devices are equipped with a panoply of features.  With all of these new functions come many new types of computational capabilities as well. In order to process images quickly, specialized hardware must be designed and software written for it in order to ensure that there are few issues with the phone’s operation. Indeed, the whole “real time” nature of telecommunications has exerted numerous pressures on the designers of computing devices. Layers of complexity, at all levels of production and development, are required to ensure that the phone can function in a synchronous manner. Gone are the days of asynchronous processing, when the computer user entered data into a mainframe, only to wait for a period of time before the processing results were provided. Today, even the smallest of digital devices must provide seamless service for users. The effects of this requirement are almost beyond number.

What is the effect of the features found on modern “smartphones”?