Recently, I attended a mandatory workshop hosted by the USF Office of Diversity and Inclusion. This workshop was mandatory for all graduate students that were funded by the department. The workshop began with the speaker asking everyone in attendance to declare their pronouns (she/he, him/her, hers/his, they, them, their, non-binary pronouns, and more). I guess I have been living under a rock (or in the basement laboratory of the Social Science Building with dead things that do not speak unless you give them a voice…) because I was not sure what the speaker was referring to until I heard other students announce their pronouns. I started to catch on before it was my turn to announce my pronouns (her/hers/she). At that moment, I realized that I was desperately in need of this workshop. During the workshop, I began looking around the room at my peers. I wondered how much of this type of knowledge was easily accessible and understandable to people outside of academia; no one outside of the Anthropology Department has ever asked me what my pronouns are…if no one has every asked me what my pronouns are – does this mean they do not know that gender should not be discussed in only binary language and/or are they not aware of terms such as transgender, cisgender, asexual, etc. ? If the answers to the aforementioned are “yes” than I am not surprised. I know many individuals that are “blissfully” ignorant to the changing of the times in which people that fall outside of the categories of male and female are now being rightfully permitted a voice and support in place of silence and discrimination. As anthropologists, humanists, and human beings in general, it is important for us to promote a diverse and inclusive community within our own network as well as outside of it by making people aware that there is in fact a problem. Text-mining is one way to accomplish this task but…again…how many people outside of academia even know what text-mining is…

There needs to be a way in which people within the academic community can translate the knowledge they receive in the classroom to individuals that are outside of the scholarly network. Any ideas?

Reverse engineering is the process of taking things apart and putting them back together to gain an understanding of the process, form, and function of something. For example, in our reading for this week, Steven Jones (2018), and others, are attempting to recreate and understand the inner workings of a historic center in Italy (to put it simplistically), called the Centro per L’Automazione dell’Analisi Letteraria — the Center for the Automation of Literary Analysis, by reverse engineering. The center was the brain child of Roberto Busa, a Jesuit scholar thought to be the father of humanities computing. The center was operational in the 1960s and is now nonexistent; the center used punched-card machines created by IBM to process data.

The process of reverse engineering this center mainly involves the analysis of photographs obtained from Busa’s archive and historical documents. This project is not just about elucidating how the punch-card machines worked or what the inside of this center looked it. In my mind, it is also about gaining an understanding of the political and social climate of the times, the social interactions of workers in close quarters, the technological availability to humans, and the collaborative nature of industries with “regular humans”.

The use of reverse engineering to recreate a period in time which is now lost is highly applicable to understanding and remembering the past. This is made possible with the advent of new 3D technologies. Jones is collaborating with other individuals to produce a virtual lab of the center which will allow others to experience a piece of history that might otherwise have been forgotten.

Jones, “Reverse Engineering the First Humanities Computing Center,” DHQ 12.2 (2018): http://www.digitalhumanities.org/dhq/vol/12/2/000380/000380.html

Unfortunately, if I want to put on my reductionist pants, most things in life are just systems of patterns that algorithms can infiltrate and mimic. As Greenfield (2018) essentially states, machines can learn the same things that humans can learn, such as how to play a game better than any human has in over 2,500 years (Go), how to replicate paintings that were produced by one-of-a-kind artists, how to swing a sword like a samurai, etc. The difference between a machine and a human is pure uncensored emotions. However, a machine does not need to possess emotions to create or do any of the things that humans do. Greenfield touches on this idea that a machine’s ability to exploit a human’s weaknesses is brute force. I would have to agree, but isn’t brute force a humanistic term? Does a machine understand the concepts of exploitation and brute force? No, not currently, but that is not to say that that possibility is off the table until the end of time (AI is evolving…). It is very interesting to think that the very machines that we are afraid of are being created by our own species. The desire for money and profit has completely overshadowed the fact that “we” might be creating instruments that will one day have the ability to take away our livelihoods, provide us with no security, and ultimately place us in metaphorical cages (and possibly actual cages if things get really wild).

On a note away from cyborgs that will one day rule the planet – it might be somewhat hard for me to imagine a world that is completely run by objects that were created using algorithms, AI, and machine learning when the automated voice from my credit card company can not distinguish between my saying a “one” or a “two”…but, I can not predict the future so who knows.

Greenfield, Adam. Radical Technologies: The Design of Everyday Life (Verso, 2018)