Week 5 Readings

Rhetoric (in The Software Arts) – Warren Sack

Rhetoric, on the other hand, is a form of argumentation pursued infant go large crowds peoples by many who know nothing about the topic and who are apathetic if not antagonistic. 

In the reading Sacks want to tackle “How does the Ancient Greek demonstration become the Silicon Valley standard pitch of today?” He thenbrings up Aristotle’s approach to rhetoric, Aristotle recognise that at times an audience has no knowledge within a field or aren’t able to follow arguments based solely on scientific and logical principles. In these instances, rhetoric is a necessary element that is incorporated through persuasive language and techniques in order for truth to be taught. On the other hand Plato’s definition of a Sophist, (those who teach the art of rhetoric) was one of a liar that makes things up to win an argument with a disregard for truth and reality. A sophist is one who engages arguments in a theatrical way, closer to the art of imitation than one of ‘mathematics’. 

Interestingly, this connects to Winograd’s demo for the SHDRLU program where he feels unease with the concept of demos due to the following reasons:

• The method he employed to implement a demo is largely patterned. 
• The demo has a low probability of performing correctly.
• The program was built more as a prop for a solo performance rather than a tool to be shared with others. 

Similarly, the anxiety and unease that scientists feel as they move away from the ideal of deductive demonstration and further into the territory of theatrical performance and a spectacle.

“The ideal of deductive demonstration in the seventeenth century, with the arrival of empirical experimentation— served as the model for the creation of inductive demonstrations in the form of computer simulations and “demos” that seamlessly fuse automatic inference with the art of imitation.”

Taking note of the importance of deductive demonstration during the seventeenth century it is vital to understand its differences. Deductive demonstration happens when one makes an inference based on widely accepted facts or premises. For example, a beverage can be defined as a substance that is “drinkable through a straw” therefore we can deduce that soup is a beverage. With this ideal way of demonstration in combination with the rise of empirical experimentation led to the notion of inductive demonstration. Inductive demonstrations is the act of making an inference based on observations of a sample.

It seems as though through inductive demonstration, inferences made from empirical observations and experiments eventually merges with the art of imitation. Once a demonstration was performed for the larger public, it eventually transforms into a rhetorical performance through the act of demonstration itself. 

“Contemporary forms of induction are computational. They are only called that in the technical literature of computer science and applied mathematics. In public, such as in journalists accounts of “big data” computational forms of induction are called “machine learning.”

Today machine learning performs by utilising a number of techniques to inductively infer patterns from a large database. The way in which we obtain data is no longer through a comprehensible and straightforward inductive demonstration such as Boyle’s air pump demonstration mentioned earlier in the reading. Instead, data is almost imaginary its not created but rather “captured” or mined” through machine learning. Data essentially embodies aggregates of our online activities, this particular information is important to fuel corporations and generate capital.

In Boyle’s case the public was persuaded through a multistep argument while today we are persuaded to accept something that is presented by corporations like Google, Amazon or Netflix. Without any knowledge of how this inductive inference arrived at the solution it provides. “We are not collaborators, we are corroborators, appreciators”   

Managing Opacity: Information Visibility and the Paradox of Transparency in the Digital Age – Cynthia Stohl, Michael Stohl, & Paul M. Leonardi

“Transparency implies that third parties can clearly follow the chain of activity and decision making that led to a certain outcome, opacity implies that such activity and decision making are hidden from view and cannot be seen or assessed by third parties.”

This reading categories the nuance beneath what it means to possess transparency within three main categories: availability of information, approval to disseminate information, and accessibility of information to third parties. Depending on if these three attributes are found at equally high levels, it could determine how information is presented in ways that make it easy or difficult for others to access. Some examples used in the reading that I found interesting was the concept of “inadvertent Opacity” to conceal information (data) in plain sight. This happens when too much ‘visibility’ of data is offered to the public, rendering it meaningless due to the recipients cognitive limitations. 

“The transparency paradox goes beyond the idea that when there is an abundance of information available, it is often difficult to obtain useful, relevant information. The transparency paradox indicates that availability, accessibility, and approval of information need to be managed to produce not only visibility but effective use of that information.” 

Additionally, skill was another category that stood out, the reading points our that there are at least two skills the seeker needs to have. The first being mechanical, as data is stored within a location or format. Mechanical knowledge presents the foundational skills for the acquisition of data or information. Secondly, the interpretive skills to make sense of the data, these skills are often rooted in culture and experience. The jargon employed are standard and meaningful in context.