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Prompt:
Describe or narrate a scenario about a piece of clothing found a decade into a future in which “progress” has continued. Your description should address issues related to disease and elicit feelings of resentment.

Narrative:
The year is 2033. The world has advanced rapidly over the past decade due largely to the exponential growth of machine learning and artificial intelligence systems. Many of the large technology conglomerates from the 2020s have merged into a single corporation, OneWorld, that oversees and controls almost all global industries. The world is more interconnected than ever before. Borders have started to disappear, one central global currency is beginning to dominate markets and the world is largely at peace. Or so it seems.

This process of amalgamation was expedited by the Virus War, a series of bio-warfare attacks that brought the world to a standstill between 2025-2027. Airborne disease is now a part of daily life from the fallout from the war. Millions of people die each year because of illnesses contracted from breathing in contaminated air. Those in historically poor areas of the world continue to suffer the most. Everyone who can afford one now wears a BreatheClean, OneWorld’s most popular product. Those responsible for launching the Virus War have never been identified. Theories are rampant and varied. Many believe that OneWorld was in some way responsible, though no evidence has ever been uncovered. 

“What is this thing? A dirty bandage?”, Mason asks as he flings the unidentified object towards his Dad.

“Would you look at that. This dirty piece of fabric is a disposable facemask from years ago. I’m surprised that these things can still be found, let alone worn. You wouldn’t remember but when you were very young we even had some of these things for you”, his Dad replied.

“For me?’, Mason gawked, “why would I need a facemask that looks like this? This wouldn’t be able to protect me from anything…only my BreatheClean can. This looks like nothing more than a sock with some elastic bands on it”.  

“We’ve talked about this. Before the Virus War there was a pandemic that essentially shut the world down for a year or two.” Mason’s Dad reminds him, “many people think that that is what the Virus War grew from. They say that some government or maybe even some huge company saw some kind of potential in the fear and panic and decided to recreate it.”

‘That’s just nasty”, Mason scoffed, “how would killing millions of people be good for anyone?”.

His Dad shakes his head. “I’m not sure. What I do know is that there is one company that has beyond profited from the war. Look around, OneWorld touches everything. Our masks, our food, our vehicles, our global currency. Hell- even our colony on Mars and the mining operation on the Moon.” 

“Oh come on, Dad”, Mason mocks, “not that conspiracy theory. We have talked at school about how only wackos believe that OneWorld could be responsible for the war. It makes no sense. They are the ones protecting us”, he says as he taps his BreatheClean. “Plus we are learning about how they are trying to clean up our air, make it so that we don’t need to wear our masks anymore”.

Mason’s Dad looks down at the dirty piece of fabric at his feet. He kicks it and motions for Mason to follow him. Like most people, he knows that deep down OneWorld is in some way to blame. He doesn’t push the issue with Mason. He knows that Mason’s school is funded by OneWorld, so it would be a losing battle. 

OneWorld’s “BreatheClean”

Generated by Craiyon
Prompt: “hyper realistic person wearing hi-tech face mask”

For this week’s task, I explored the generative AI platform Craiyon. As outlined in some of this week’s module content, Craiyon, like other generative AI models uses algorithms to create images based on text prompts by the user. As with all generative AI models, Craiyon’s algorithm needs to be trained using sets of data. In the case of Craiyon, I would imagine that the model was trained using word and image association. 

As mentioned in this module’s content, a large issue with any generative AI platform is the data set or large language model that it was trained on. Bias is, therefore, an inherent part of any AI model since the programmers essentially decide what information will be used and what information will be excluded. This practice forces the programmers to place value on content and serve as judges of information. This is a flawed practice and can lead to the perpetuation of certain limited perspectives, ideologies and ways of thinking. In some cases, and as explored in this week’s module, this can result in models that have the power to exhibit hateful and discriminative responses.  

My goal with this week’s task was to see if I could uncover some possible bias built into the Craiyon algorithm. In my prompts, I opted to use the term “hyper-realistic” as this is a tip that I had seen before to have AI return realistic-looking images. My first prompt sought to determine if the platform seemed to be biased towards a certain race. To test this I wrote two different prompts that were race-neutral. The first prompt was: “hyper-realistic child playing baseball” (see image 1). The results were nine, very clearly white children. This led me to think that perhaps the algorithm had learned to associate baseball with white-looking children. While baseball reaches audiences around the world, to test this further, I selected a truly global sport- football/soccer. My prompt was: “hyper-realistic person playing football” (see image 2). The results this time were slightly more reflective of race around the world but still predominantly white. What was interesting to note from this prompt was largely the absence of female representation. To test this possible bias my third prompt was: “hyper-realistic company boss sitting at desk” (see image 3). Again, the results were overwhelmingly white in race, visibly old in age and limited in gender diversity. 

Based on these three prompts, I think it is clear that there are issues in terms of representation in this particular generative AI platform. While adjectives to specify race could be included in the written prompts, this highlights a deeper issue embedded into the algorithm that white and male are the default returns on generic human prompts. To push this hypothesis even further I decided to see if the platform also generated images that perpetuated stereotypes. I prompted the platform with: “hyper-realistic gang member” (see image 4). The generated images point to extreme stereotyping of race. Gang members, while largely negative forces in society, are present across every country and every race. Yet, at the results showcase, this platform sees gang members as exclusively black and asian. 

These results cast serious doubt on the ability of this algorithm to be truly representative of the world. Instead, it seems as though the algorithm draws upon long-standing inequalities and the perpetuation of stereotypes. While this is simply one generative AI text-to-image model, it would be worth examining the extent to which other platforms are similar or divergent in this sense. My intuition tells me that most, if not all, text-to-image platforms have these issues.

Image 1:

Image 2:

Image 3:

Image 4:

This week’s task was quite interesting, albeit challenging, as I was unfamiliar with network theory beyond a foundational understanding of the web and the connectivity of web nodes through algorithms. I have also never used Palladio as a tool through which to interpret data and realized that I am much more familiar with charts, graphs and tables and more attuned to Google Sheets than a visualization platform like Palladio. Palladio provides us with the visualization of the Golden Record track selection data within which I represent a node when looking at the data as a whole, or seemingly an edge when looking at each group individually. While using this platform to interpret data was an interesting experience, I struggled to understand how to truly leverage the site options to help me make strong conclusions, even after independent research. Perhaps this course could aim to scaffold this particular activity further moving forward to set students up for greater success and discussion. 

Looking through the visualized data I first sought to see which groups I was omitted from. I did not appear to be connected to Community 4 as I do not represent an edge connecting any of the song nodes. This makes sense as I did not select any of the tracks that are nodes represented in that community. A conclusion that could be drawn from this is that the individuals most connected within Community 4 may have had much different selection criteria for the tracks than I did. By contrast, individuals in that community may have had certain criteria that were similar to mine but may have assessed certain tracks using that criterion differently. This could be an interesting extension to visualize the criteria each participant used to guide their selections and cross-reference that with the tracks they selected.

By contrast, within Community 1 I seem to appear near the middle of the visualization and connect to four of the nodes. This points to the idea that other individuals in that community near the centre might have also had similar selection criteria for the activity. Namely, Bingying, Hassan and Louisa might share a similar interpretation of criteria. In Community 3 Hassan and I are again in a similar situation concerning the two tracks we both selected. In looking at all six communities at the same time, this connection between Hassan and I is again clear. 

This data visualization makes connections explicit and suggests possible conclusions, but cannot identify, with certainty, why specific songs were more popular and others were not. The visualization outlines, for example, that Bridget, Hassan Nisrine and myself selected many of the same tracks. What is missing from this interpretation is why that is. It could very well be that we shared a similar set of criteria in selecting our songs, or perhaps our interpretations of our unique criteria led to similarities. As mentioned above, I believe that the only way to truly understand why certain song selections were more popular than others would be to include categories that influenced selection. In that case, individuals would not only be linked to the categories of selection but also to their selected songs. 

Task 8: Golden Record Curation

I found the task this week to be especially interesting and challenging. Prior to this module, I was not familiar with the Voyager “Golden Record” and found myself diving down the rabbit hole, looking deeper into this whole ongoing event. I found it pretty fascinating that you can still chart the two Voyager crafts through NASA: https://voyager.jpl.nasa.gov/mission/status/

In curating this condensed version of the Golden Record I struggled to use my criteria to narrow down my selections, as each of the pieces had already been painstakingly curated in order to be included on the original record. My criteria appear similar to the goals outlined by the Golden Record team with my primary criteria being that: the tracks selected strive to represent the greatest possible diversity and reflect a true cross-section of humanity. I focused my curation around this idea because if extraterrestrials ever did find and listen to the record, I thought that it was only right to attempt to reflect the musical experience of humans as a whole across time and place. I had to fight my own musical bias throughout this process as I was not curating the tracks based on how “good” they sounded to me. In the end I am still not too confident with my list as I think it would be impossible to truly represent all of humanity’s musical texts in ten tracks. Here is my attempt:

Selection Criteria: Golden Record 2.0 (10 tracks)

The tracks selected strive to represent the greatest possible diversity and reflect a true cross-section of humanity.
The tracks attempt to strike a balance between genres and styles in order to offer the greatest variety possible.
The tracks serve as a time capsule highlighting music from various historical periods.
The tracks have value beyond the sound through meaning, such as by conveying aspects of the human experience (emotions, ways of thinking, methods of expression, ways of designing). 

Interview with Leather Bag:

Recently I sat down with Nick’s Leather Bag to discuss life.

For this redesign task, I personified my leather bag and let it speak for itself. Rather than solely changing the medium through which I explored the bag I wanted to also shift the perspective of how the bag is being viewed.

 In creating this task I needed to consider several things. Firstly, I needed to think about the conventions of an audio interview. This got me thinking about the literacy skills required to accomplish this: how to write interview questions, how an interviewer should deliver questions, what the progression of an interview tends to look like, how to use audio recording tools, what type of music is suitable for an audio podcast-style interview, etc. This shift in mode highlights, for me, many of the ideas presented in this module’s exploration of multiliteracies. Had I decided to create a video interview, this switch of medium would again require a shift in the literacy skills required. 

What really stuck with me from this module was the notion that literacy is design. This task helped consolidate this for me as I saw how I designed this audio interview beyond just the words being spoken. As the New London Group (1996) discusses, my task caused me to consider several design elements, namely: linguistic design, audio design and multimodal design. 

References:

The New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review 66(1), 60-92.

Task 6: An Emoji Story

I had a lot of fun with this task. I had never considered how emojis could be used instead of written text. As mentioned in our module, I use emojis as a way to both complement my written text and also to help clarify meaning and tone. In recent years I have also increasingly used emojis as a way to respond to messages. Oftentimes times a simple thumbs up emoji can capture my reaction to a message without needing to add any additional written text. I also noticed, however, as Bolter (2001) writes, “the picture elements extend over a broad range of verbal meanings: each element means too much rather than too little” (p. 59). I found this to be my primary struggle in using emojis as many of them can be interpreted in numerous ways.

When I first started the task I found myself attempting to translate my thoughts into emojis word for word. I quickly realized that there are a finite amount of emojis and not enough to represent every single word in a thought or sentence. For example, I found that verbs like “is” are impossible to represent through an emoji. As a result, I pivoted into focusing on nouns, action verbs and ideas in combination with one another to create meaning through the visuals. I started looking at the overall meaning of the title and the summary rather than the structure of the response. I think that this would be a great way to reinforce teaching and learning about summarizing and identifying the main ideas in texts as it forces one to really think in a big-picture sense.

For my task I started with the title. I don’t necessarily think that there was a particular rhyme or reason to this, other than the linear nature of my thinking concerning producing a text. I have always found that starting with a title gives me a launching pad for my text. Even when I write these tasks I have a title on my page. This could also be a product of my own obsessive behaviours.

References:
Chapter 4. Bolter, J. D. (2001). Writing space: Computers, hypertext, and the remediation of print (2nd ed.). Lawrence Erlbaum Associates.

Task 4: Manual Scripts

Manual Script:

Reflection:

I found the manual production of this text to be quite challenging. While I did not encounter too much trouble crafting the words of the text, it was the physical process of writing by hand that gave me difficulties. Clearly, my finger, hand and wrist muscles for this particular fine-motor activity are now vastly underdeveloped. It reminded me of being back in school and spending the first couple of weeks at the start of the year getting my handwriting stamina back to par. This activity made me realize how much of my daily text production is done through mechanized methods of texting and typing. Beyond jotting quick notes on Post-its, writing shopping lists and occasionally brainstorming on whiteboards, I almost exclusively use phone and laptop keyboards to write.

I wrote this text as a free-flowing journal of sorts, detailing a recent development of a particular hobby. And while I did not have many moments where I wanted to rephrase passages, I did have a couple of instances of misspellings. For the most part, I largely left them as is, but in at least one case I crossed out and word and wrote the intended word above. I realized that editing was not really a part of my process for this activity, as is common when I am typing and texting. I found myself trusting my inner narration more with my handwriting, likely due largely to the fact that if I wanted to change something it would require a much greater effort than simply doing so digitally. 

I would say that, without a doubt, I prefer using mechanized forms of writing, specifically typing, over handwriting. I believe that digitally typed text has many advantages over manually written text. Namely, speed and efficiency. When I type a text I can produce the actual letters at a greater pace. Over time, this means that I could produce much longer pieces in comparison, without suffering from hand fatigue or the need to get more writing supplies, like paper. In terms of efficiency, digitally produced text allows me to delete or even move around words or whole chunks of text. This makes the editing process much less tedious than handwritten texts. Beyond that, in digital text writing, there are now built-in functions that assist even further such as spelling/grammar check, word count check, Grammarly and more. 

The single greatest advantage, for me, of mechanical text production over manual text production is the tidiness of the text. I have always been somewhat insecure about the look of my printing and cursive writing. I went through school with my teacher always pointing out my “chicken scratch” or asking me to write more legibly. Mechanized writing has given me much more confidence in my writing as the focus can be directed toward the content and not the aesthetic.

Oral Anecdote:
I used the Google Chrome extension “Voice In” for speech-to-text. 

last night went to the Arctic Monkey show with a buddy a show that we had bought tickets for around a year ago I was a pretty big arctic monkeys fan back in the day but I kind of have a stopped listening to their music in recent years but I thought it would be an awesome opportunity to ReDiscover their music and really get back into them over the year as things go sometime it never really happened and I never really started re-listening their music so all of a sudden last night was the concert but I was still excited because it was a night out and it was going to be some good music and I get to hang out with a buddy so what also happened is that over the past year I’ve moved quite far away from where the venue was so initially when we bought the tickets I was living in Richmond and I could take the sky Train downtown and become you know pretty easy to get to The Venue but now I live in South Surrey and so to take public transit to and from the venue it would take about an hour and a half so I decided that I was going to drive and because it would be more convenient and I would save myself a bit of time at least I thought and yeah so I set off yesterday headed downtown to meet my buddy for some dinner before the show so we went to a pizza place I’d never been to in a in East Van can’t remember the name but it was pretty good had a good selection on alcoholic beer and I had a pretty good one that was Philip’s pilsner and I was pretty tasty after dinner we headed to the show and it was it was that moment where I said okay are we going to find parking or are we going to try and pay for parking but you know I’m trying to save some money so I decided that we were going to park in a residential area nearby the venue but not too far off but not too close because all the streets around the venue said you know permit parking only for residents of this that and the other street but you know we were looking at the cars didn’t really see any permits or anything so I don’t know if they register them online or how that all works so we took our chances I parked the car there hoping that it obviously wouldn’t get told we wouldn’t get a get a bill on it and so thankfully not but anyways we headed off to the concert of people it was pretty wild just all the way down the sidewalk usually when I’ve gone to concerts you know there’s tons of entrances so I think you know they can let people in pretty relatively quickly and there’s not a huge lines outside but in this particular venue it seems like there was only one main entrance and so the lineup was going down the sidewalk block blocks and blocks between this moment where he said okay you know should we go to the end of the line or should we you know head over towards the front of the line and at the same time we’re walking towards the back of the line so you know we had made her decision you know we’re not as young as we used to be so we said yeah we better follow the rules and enjoying the join the queue like everybody else and so there we get in there it’s pretty good the opening acts already happening so we find our seats and and yeah we’re just hanging out listening to the opening act and waiting for the Arctic Monkeys to come on and then the show finally starts starts with the bang it was really good show but I definitely didn’t know I would say you know a quarter or maybe you know a third of the songs I would imagine that many of them are from albums that I had never heard of or never listened to maybe they were too recent but in any case a lot of their hits from several albums ago they played a lot of those so it was great to hear them and you know to have a good time at the concert after the concert I decided that I would drive my buddy back downtown he lives on your Granville Street and so I said yeah you’re not going to Uber I already have my car so I don’t mind dropping off before I head back to South Surrey and then we get into downtown and it is madness I thought the trip was going to take you know even on Google Maps said it was going to take like 9 minutes to drop them off but we hit downtown and the Coldplay concert I guess they were also playing last night was just letting out tonight I’m assuming they played BC Place just based on the amounts of people that were coming where they were coming from so we were just deadlocked in traffic so what was supposed to be like a nine minutes drop off turned into you know a 30 minute Adventure downtown before I even started my Trek back to South Surrey but anyways all in all it was a really good time it was a good night and it gave me excuse to start listening to the Arctic Monkeys again

Reflection:

First and foremost, I found this week’s task to be extremely challenging. I never realized how much of oral communication is conversational based and that orating a longer story without a conversational partner is both awkward and somewhat uncomfortable. I was surprised by just how much casual daily spoken language is built on small bits of oral language at a time, usually interspersed with questions, prompts or at least non-verbal prompts from people you are speaking with. With the absence of these things, I struggled to tell a non-rehearsed story for five minutes. I needed to restart my story many times before I was able to just flow with it and keep it going. Initially, I kept getting stuck a sentence or two into the oration because I was stumbling over my words or something sounded “stupid”, for lack of a better word. Eventually, I just let go of those feelings and let the process happen.

This text deviates quite a bit from the conventions of written English. Most strikingly, the text is entirely composed of one huge, run-on sentence. Punctuation is absent from this text entirely. This causes the text to read like the ramblings of someone’s (my) inner thoughts more than an actual story. Beyond that, capitalization and the division of the text into logical paragraphs also make this text stand in contrast to standard English written conventions. These missing elements result in the text lacking emphasis, pacing and the indication of emotions. 

Overall, this text does capture the essence of my anecdote. The information is all presented in text form, though there are minor errors with how the “speech to text” program captured some of my words which does change the context. For example, in mentioning the pizza place we had dinner at, I made a point of saying that they had a good selection of “non-alcoholic beer”. In the text version, it reads as saying “good selection on alcoholic beer”. This error is both grammatically incorrect but also changes my intended meaning. At another part in the story, I was expressing how I hoped that my car would not get towed. The text reads “wouldn’t get told” which, again, changes the intended meaning. There are a few more examples of these misinterpretations of my story. This points to the fact that speech recognition software is certainly not error-proof. In truth, I think it would be quite challenging to design a program that is free of errors unless the system is trained extensively on the speech patterns of the individual speaker using it. Otherwise, so many factors can potentially impact how we pronounce and enunciate words. 

I would say that the most common mistakes in my text, beyond the issues with the software interpreting some of my words and transcribing them incorrectly, were simply mistakes in the way that I told the story. What I mean by that is that upon reading the story I was amazed with the amount of “filler” phrases that I used. Specifically, I seem to speak with a lot of “you knows” when I am orally telling a story, at least when orally telling an unrehearsed story. When that is translated into a written story it reads as though it should be a piece of dialogue and not a narration, per say. If I had sat down and scripted the story, I believe that the finished product would be more interesting overall. I would have paid close attention to the construction of my phrase and selected verbs and adjectives that really captured my feelings and actions more authentically. 

Through this activity it has become abundantly clear to me that oral storytelling and written storytelling are two very different types of texts that require unique skill sets. Beyond conversations, most oral storytelling I have done in the past has largely been in the form of formal presentations. These presentations are actually more like readings of written texts as I have often written scripts or at least developed talking points that I have practiced beforehand. When writing stories, one can spend any amount of time meticulously crafting the text. In oral storytelling, you need to deliver the text in that moment without having the time or luxury to undo, redo, take a break or consult other resources. At the same time, oral storytelling is more than the words spoken by the orator. Oral storytelling is also told through the use of one’s voice, one’s actions and body language, one’s context to a community and the place in which you are telling a story. 

A quote from one of our reading’s this week really stood out to me and helped me consolidate my thinking on this topic. Gnanadesikan wrote, “writing is generally done more deliberately than speaking, so finished written pieces are much more carefully crafted than a typical spoken  sentence.  Written  texts  can  thus  convey  their  message  more  precisely” (p. 5). While I agree with Gnanadesikan I also believe that oral texts require crafting of a different nature. It seems to me that some of the skills required to deliver a powerful oral text may have been lost over time. Being so reliant on written texts, and even using written texts for the purpose of delivering them orally has, I believe, caused us to underdevelop oration skills. We spend so much time in school preparing students to develop oral communication skills but often use formal presentations as the main means through which to achieve this. Perhaps more attention should be paid to helping students develop their otal communication skills without pre-planning the text and writing it out beforehand. 

References:

Gnanadesikan, A.E. (2011). The first IT revolution. In The writing revolution: Cuneiform to the 

Internet (pp. 1-12). John Wiley & Sons.

Does language shape the way we think? This is an interesting question that it touched upon throughout this module. While Dr. Boroditsky takes a scientific approach and attempts to explicitly answer this question, both Alexander MacDonald and Christine de Luca address this idea in their own ways. I think that language does play a part in shaping the way we think, how we see the world and how we interact with others. That being said, I think that language can also extend beyond what may be formally considered language. Alexander MacDonald makes me consider whether language has a broader term that includes any way of encoding and decoding texts. For MacDonald, I wonder if he thinks differently based on the way in which he receives and interacts with tap and dance texts. For example, I am curious if the rhythm and cadence of speech hold any stronger meaning for him when decoding spoken language. 

In both Dr. Boroditsky’s article and video she presents empirical research to back her claims. For me, many of her points resonated with my years living and working abroad. Oftentimes I chalked up the miscommunication I experienced abroad as a product of cultural difference. While I think that cultural differences are very much a real thing and have notable effects on understanding, I had never really considered the power of language. Before engaging with this module I had considered language to largely be a predetermined set of rules that allows for the formalic encoding and decoding of written and spoken texts. What I had not considered was the possibility that language impacts the ways in which we see and interact with the world around us. Looking back, I wonder how many of those instances of miscommunication were rooted in the languages we spoke and the fact that language can never truly be translated as it is much more than simply words.

References:

Boroditsky, L. (2011). How language shapes thought. Scientific American, 304(2), 62-65.

TEDx Talks. (2016, February 26). Oral tradition in the age of smart phones | Alexander MacDonald | TEDxFulbrightDublin [Video]. YouTube.

Wikitongues. (2014, September 21). WIKITONGUES: Christine speaking Shetlandic [Video]. YouTube.