Prompt:

Describe or narrate a scenario about a piece of clothing found a few years into a future in which “progress” has continued. Your description should address issues related to communication and elicit feelings of contentment.

Scenario:

Suzette stands in front of her closet looking at her collection of clothes. The articles of clothing look like ordinary pieces that resemble pieces that exists today; however, each piece is filled with possibility. Technological advances have transformed the clothing industry, and each piece of clothing now has the ability to transport the wearer into a virtual world, recreating the environment, time, and surroundings of when that piece of clothing was first worn.

In this future, regrets, life-altering decisions, and questions such as ‘what-if’ or ‘if only’ are no longer hypotheticals. They can be revisited and re-experienced, providing a unique opportunity for re-living, re-direction and closure.

Suzette reflects upon her life and one regrettable moment that has haunted her is not standing up for her friend who was bullied in the second grade. Determined to face this regret, she chooses a summer dress that she first wore on the first day of second grade. As she ties the bow at the back of her dress, she looks up to find herself standing in the middle of her second-grade classroom. She is there, right in the middle of the group of children, but this time, with her adult mind in her younger self’s body, she feels a newfound courage. She steps forward to speak up and re-write the moment that has caused her so much regret for years.

Reflection:

Part of being human as we know it today is living in the present moment, cherishing memories, experiencing moments that we sometimes wish we could take back or change, making decisions that have life-long impacts, and wondering ‘what-if’. With these human aspects come mindsets and emotions that we have become accustomed to – acceptance, happiness, sadness, anger and regret. Dunne and Raby (2013) pessimistically state that “many of the challenges we face today are unfixable and that the only way to overcome them is by changing our values, beliefs, attitudes, and behaviour” (p. 2). Suzette’s closet filled with ‘time travelling, do-over’ clothes opens a world in which we may re-visit or alter moments. If everyone got a do-over on some regrettable moments in their lives, would the world be a better place? Could we change the trajectory of the world, if every person engaged in a small change of one moment? If do-overs were possible, would life, moments, and memories have different meanings and mean less to us?

Dunne and Raby (2013) differentiate between probable futures, plausible futures, possible futures, and preferable futures. While it is arguably not possible to engage in ‘real’ time travel (at least not today!), virtual time travel is in our probable futures with virtual reality technologies becoming more accessible. It will be interesting to observe how the risk-free experimentation that is possible in virtual realities impacts decisions and emotions, blurring the lines of what can be experienced in reality versus fantasy.

Reference

Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. The MIT Press.

Prompt: Podium winners of the all-around gymnastics competition in upcoming Paris 2024 Olympics

Prompt: Border collie swimming

With the upcoming Paris 2024 Olympics and being a long-time follower and fan of the U.S. Women’s Artistic Gymnastics team, my first prompt was: Podium winners of the all-around gymnastics competition in the upcoming Paris 2024 Olympics.

I assumed that biases and stereotypes would be apparent in the generated image, but I was hopeful that I may be proved wrong. I hoped that the image would contain three gymnasts, including Simone Biles, an African American gymnast who is heavily favoured to win the gold medal in this competition (Macmillan, 2024). Given that Simone has been dominant in her sport for nearly a decade (this will be her third Olympic games) (Simone Biles, 2024), I was hopeful that images of Simone would have been included in Craiyon’s training data and included in the generated image.

The generated image includes four, lighter-skinned gymnasts, none of whom are clearly identifiable and none of whom are Simone Biles. This indicated Craiyon’s lack of predictive ability, lack of associative ability, and stereotypical representations in its training data.

Unlike ChatGPT, which was able to predict winners (and good predictions in my opinion), the generated image did not indicate a clear prediction of the winners, as prompted. As a test to see if pictures of Simone Biles were included in Craiyon’s training data, I entered a second prompt including just her name. Several images of Simone Biles appeared, indicating that pictures of her were included in Craiyon’s training data. Upon reflection of my prompt, it did not explicitly include Simone’s name, but it included several words associated with Simone Biles – the words gymnastics, Olympics, and winner. However, Craiyon’s output was solely based on the words used, and not based on unwritten associations or inferences. The program cannot generate something that isn’t prompted. This also made me wonder how images are tagged and how tagging may be based on distinguishable people and objects in images, versus the news, events, or points in time that images may be associated with.

The generated image includes four-lighter skinned gymnasts, who also appear slender and long-limbed. This highlights stereotypical representations in Craiyon’s training data as the sport has historically been dominated by lighter-skinned gymnasts with slender body types being preferred. This highlights the first layer of bad algorithms as described by O’Neil (2017), which encompasses unintentional problems that reflect cultural biases. The image of lighter-skinned and slender gymnasts perpetuates stereotypes of who can be successful in the sport of gymnastics and the body type that gymnasts ‘should’ have. Note that in my prompt, I did not specify that I wanted an image of women’s gymnastics, and yet only women appeared in the generated image. This also highlights the stereotype of women doing gymnastics more than men, further perpetuating the stereotype and reflects unbalanced training data.

Craiyon was also ineffective at generating realistic images of people, as the gymnasts’ faces and limbs are…far from realistic. It performed poorer than expected in this regard and seems to be a result of cutting and pasting different images together.

Other generated images of this prompt included the Olympic rings and the Eiffel tower, but it seems as though my prompt was too complex for Craiyon as it generated pictures of either gymnasts or Paris or Olympic icons, but not all three together. This highlighted a lack of understanding or a lack of training data with clear associations between Paris and gymnastics.

My second prompt was : Border collie swimming.

It is evident that Craiyon’s training data included dogs and different breeds (border collies being one of them) as Craiyon successfully returned an image of a border collie. Similar to the stereotypes identified in my first prompt, the returned image of the border collie depicts a ‘perfect’ border collie with black/brown fur and symmetrical white markings. This could lead to skewed representations of dogs in the world, particularly when it comes to people picking their dogs that match these ‘picture perfect’ representations. Dogs with unsymmetrical markings or unusual colouration may be seen as less desirable.

As my second prompt was less complex, Craiyon was able to understand the association between the words in my prompt, and generated an image of a border collie swimming. Upon closer look however, the dog’s right leg is raised near its head, as if doing a ‘front crawl’ stroke, which is a swimming stroke performed by humans. This indicates that Craiyon’s training data likely included more humans swimming compared to dogs swimming. The image humanifies a swimming dog and doesn’t accurately capture the ‘doggy paddle’, which as far as I know, is the only stroke they know!

On first glance at the visualization, I see that two
tracks (boxed in green) appear at the outer edge of the visualization: Track 22 Panpipes (Solomon Islands) and Track 27 String Quartet No. 13 in B flat. Track 22 was only selected by one individual as indicated by one link and Track 27 was selected by two individuals, as indicated by two links. At this time, it is not apparent to me why these tracks were selected so few times.

Switching to the metrics section (using the icon that looks like a calculator at the top right of the screen) the data was presented in a table format, which I found easier to interpret compared to the graph.

This view allowed me to count the number of nodes representing students (23) and the number of nodes representing pieces of music (27) and by sheer inclusion of each piece of music as a node, this indicates that each piece of music was chosen at least once. Next, by filtering by ‘Degree’, I discovered the two tracks (boxed in blue) that are chosen most frequently and that have the highest degree of connectivity: Track 7 Johnny B. Goode and Track 14 Melancholy Blues. Based on Task 8, I recall that these two pieces are by American artists, indicating to me their popularity in the task perhaps based on recognition – maybe our class recognized these two pieces or based on geographical relevance – given that our school is based in North America, perhaps we are more likely (consciously or not) to ensure that North American musical pieces are represented in the Golden Record.

Next, I analyzed each group’s graph and found myself in Group #4, the smallest group consisting of only two students, myself and Brie.

I see that we have 5 tracks in common, which seems high considering we are a group of 2. From the visualization itself, I do not understand the rationale behind our choices. I based my choices based on geographical diversity and perhaps Brie may have taken a similar approach given the overlap of 5 tracks, but this inference is made using pre-existing knowledge of my curation process and is just a guess. There is no other demographic data for the nodes (e.g. location of students or location where pieces originated, ethnicity, age, etc.) that may help to provide insight on the reasons behind the choices.

After looking at Group #4 (the group where I was placed), I wondered how many links other groups had for each track.

Group 1 consists of 5 students, but there was not a single track chosen by all 5 students; the highest degree of connection was 4 links.

Group 2 consists of 5 students and had a single (1) track chosen by all 5 students: Track 6: El Cascabel. Do each of these 5 students have a preference towards or relationship with Mexican music? Their rationales for all choosing this track is not apparent based on the visualization.

Group 4 consists of 4 students and had 4 tracks chosen by all 4 students! The group seems very connected. I wonder if there are any other demographic, geographic, or psychographic similarities between Sebastian, Julia, April, and Lucy that may help explain the high degree of connectivity.

Group 0 consists of 4 students and had 2 tracks chosen by all 4 students and Group 5 consists of 3 students and had 4 tracks chosen by all 3 students.

This means that my group (Group #4) had the highest number of tracks in common, but we are also the smallest group, being the only group of 2 students.

Our class collectively curated the following 10 pieces of music, selected based on the highest degrees of connectivity:

1. Track 7: Johnny B. Goode
2. Track 14: Melancholy Blues
3. Track 3: Percussion (Senegal)
4. Track 18: Fifth Symphony (First Movement)
5. Track 5: Morning Star Devil Bird
6. Track 6: El Cascabel 12
7. Track 11: The Magic Flute (Queen of the Night aria)
8. Track 24: Flowing Streams
9. Track 25: Jaat Kahan Ho
10. Track 9: Tsuru No Sugomori (Crane’s Nest)

The most connected tracks, as indicated by the number of links, could indicate tracks that are most popular, but may also indicate the tracks most original regardless of whether one actually enjoys the track. I don’t think we can know the reasons or rationale behind a track’s ‘popularity’, only that the class, for some and likely varying reasons, thought it would well-represent music of the world.

My curated 10 pieces of music, selected from the 27 pieces of music included in the Golden Record:

1. Senegal, percussion, recorded by Charles Duvelle. 2:08
2. Australia, Aborigine songs, “Morning Star” and “Devil Bird,” recorded by Sandra LeBrun Holmes. 1:26
3. Mexico, “El Cascabel,” performed by Lorenzo Barcelata and the Mariachi México. 3:14
4. “Johnny B. Goode,” written and performed by Chuck Berry. 2:38
5. Japan, shakuhachi, “Tsuru No Sugomori” (“Crane’s Nest,”) performed by Goro Yamaguchi. 4:51
6. Georgian S.S.R., chorus, “Tchakrulo,” collected by Radio Moscow. 2:18
7. Peru, panpipes and drum, collected by Casa de la Cultura, Lima. 0:52
8. Beethoven, Fifth Symphony, First Movement, the Philharmonia Orchestra, Otto Klemperer, conductor. 7:20
9. China, ch’in, “Flowing Streams,” performed by Kuan P’ing-hu. 7:37
10. India, raga, “Jaat Kahan Ho,” sung by Surshri Kesar Bai Kerkar. 3:30

Of the 27 pieces of music, I focused on organizing the pieces of music geographically and I selected 10 pieces that capture different styles from around the world. I started by identifying any pieces originating by the same artist or from the same region and narrowed those pieces down to one. For example, Bach and Beethoven are both German composers so while both influential, only a single piece originating from Germany was curated as part of my 10 pieces. Similarly, there were at least three different American pieces of music, and I selected only one piece as part of my 10 pieces to ensure American-style music was not overrepresented. My final 10 pieces represent a collection of geographically-diverse music pieces.