Categories
Mandatory Tasks

Speculative Futures

Describe or narrate a scenario about a pill found a century into a future in which society as we know it has come apart. Your description should address issues related to the brain and elicit feelings of fervor.

The Flippancy Pill 

It’s 2125. Our fractured society finally acknowledged what began with a conversation I had a century ago.

During a discussion about a media bias diagram—reliability on the vertical axis, political leaning on the horizontal—I decided to really go for it. Not in saying I preferred left-leaning news, but in laying out my theory of the fundamental difference between left and right.

“At risk of being reductive,” I said, “the difference is your ability to stomach social constructionism—whether you can bear the idea that reality is determined by social processes through and through. Every time you uncover something apparently ‘natural,’ you can dig deeper and find it’s dependent on social processes. Right-leaning people lack this tolerance; they believe in certainties and a natural order.”

My colleague responded, “You want to see the evidence, right? That’s what left-leaning people want.”

I thought: No, show me the critique.

I was sidestepping the traditional talking point about how the right is rigid and the left is open-minded. Instead, I was saying, “I don’t claim to be less rigid—I just firmly LIKE social constructionism. I find it more interesting, more dynamic, and possibly more humane, though I’m not even sure about that.”

This inspired the “Flippancy Pill”—now neurologically rewiring millions with feverish intensity, making it impossible to retreat to the safety of ego-stroking frameworks. Under its influence, when my colleague said “you want to see the evidence, right,” she couldn’t use “evidence-seeking” as a way to create comforting ideological alignment.

The pill breaks down the arguments that lead to “safety” in your position—that sense of complacency my colleague was trying to goad me into where we both reassure ourselves we’re simply “evidence-based.” What if instead, on both left and right, you had to admit you’re just appealing to WHAT YOU LIKE? “That’s my perspective, and if you don’t like social constructionism, that’s unfortunate for you, because social constructionism is cool.”

Pierre Poilievre perfectly illustrated this divide, saying: “What binds us together is the Canadian promise: that anyone from anywhere can do anything, that hard work gets you a great life in a beautiful house on a safe street WRAPPED IN THE PROTECTIVE ARMS OF A SOLID BORDER.”

As a social constructionist, I can’t stomach this. It’s horribly generic—”anyone from anywhere can do anything”—it’s so generic as to be completely meaningless. Like saying, “Because the sky is blue, I can build rockets.”

I don’t believe hard work guarantees a great life—what you’re born into determines outcomes far more. What if social structures, not natural laws, determine outcomes? Hard work within an unjust system may yield little, while privilege within that same system can produce success with minimal effort.

Poilievre’s statement is an overwrought accumulation of American talking points to the point of absurdity. He’s laying out his view of what’s naturally good, while I’m saying “get with reality”—thereby ironically accusing him of pie-in-the-sky thinking from my socially constructed worldview.

Categories
Optional Tasks

Text-to-Image

Cultural Fusion in AI Image Generation

I tested three different culinary fusion prompts with an AI image generator to analyze how it combines different food traditions.

For my first prompt, I asked for “pad Thai plated at a three Michelin star Western restaurant with French techniques.” I was expecting something along the lines of micro food, slow food movement aesthetics, or maybe a deconstructed pad Thai with nitrogen infusion–you know, the works. Instead, I got a regular old-looking pad Thai with some vegetables arranged around the side of the plate. Nothing about it screamed “three Michelin stars” or “French techniques.” It was just… pad Thai.

This made me wonder if the AI isn’t really that good at handling hybrid concepts. It seems to grab onto the main food item–pad Thai–and then just adds a few decorative elements around it rather than truly transforming the dish as requested.

My second experiment was “South American grits and cornbread reimagined with Indian spices and presentation style.” The result was a total smorgasbord–lots of color, lots of spices, lots of different elements arranged on the plate. The cornbread was only a tiny piece of the ensemble, and one of the side bowls had what looked like a pile of orange spice (probably meant to be turmeric). The center bowl was actually divided in half with grits on one side and what looked like lentils on the other – this was the most creative element of the dish. Oddly enough, there was another separate bowl that also contained grits at the side. What caught my eye were strips of raw corn arranged on the plate and off the plate surrounding were small clay containers of spices and then a random half lime just sitting there.

The AI seemed to be following a “more is more” principle, throwing in limes and chilis and all sorts of elements, but it completely failed to integrate these things together. Instead of reimagining Southern staples with Indian influences, it just placed traditional versions next to Indian elements.

My final test was “Japanese sushi prepared with Mexican ingredients and presentation.” Again, the results were uninspiring–it looked like regular sushi with nothing that screamed “Mexican.” The wasabi was arranged in artful dollops, and there was a prawn head decoratively placed along with other unrecognizable garnish items behind the sushi, and also poking out from behind this wall of ancillary items, another piece of sushi. There was a separate clay kind of mini trough behind the main plate, very deemphasized, with very finely sliced chilis–the only remotely Mexican element, but it was barely noticeable. The maki in the center looked pretty unappetizing with little orange maggoty things spilling out from under the seaweed casing, and these certainly weren’t recognizable Mexican ingredients.

What became clear is that these AI systems struggle with true fusion. They can recognize “pad Thai” and “sushi” as concepts, but when asked to transform them through the lens of another culinary tradition, they fall short. Instead of reimagining dishes, they just place elements from different cuisines side by side–an additive rather than transformative approach to fusion.

It looks like when the AI sees “pad Thai” or “sushi” in a prompt, that’s what it focuses on, and everything else just becomes window dressing. The French techniques or Mexican influences barely show up. I’m guessing the AI was probably trained on tons of regular food photos – like thousands of pictures of normal pad Thai – but not many examples of actual fusion cuisine. So when I ask for something more creative and out-of-the-box, it just defaults to what it knows best and throws in a few random elements from the second cuisine as an afterthought.

I noticed the AI went overboard with the Southern-Indian fusion attempt. Instead of blending the two styles together, it just piled up a bunch of separate elements on the same plate. Grits here, spices there, cornbread off to the side. It’s like the AI only knows these cuisines as lists of ingredients rather than as cooking styles that could actually mix together in interesting ways.

What struck me most was how conventional all three images were, despite my explicitly asking for fusion. The pad Thai remained just pad Thai, the grits and cornbread stayed recognizably Southern with Indian items merely alongside them, and the sushi showed no Mexican influence whatsoever. It’s as if the AI has a hard time breaking out of established categories to create something truly innovative–exactly the kind of creative leap that defines real culinary fusion.

A pattern emerged across all three images: the AI relied heavily on decorative garnishes and artistic presentation to create the impression of sophistication rather than actually reimagining the dishes themselves. The prawn head in the sushi image was never meant to be eaten—it was purely decorative, like the scattered vegetables around the pad Thai or the raw corn strips with the grits. These exterior elements seemed to compensate for the lack of imagination in the edible components. The AI appeared to understand “fancy food” as “regular food with artistic garnishes” rather than genuinely innovative fusion cuisine.

This experiment got me thinking about what these image generators can and can’t do yet. The AI made some decent-looking food pictures, but it seemed to miss what I thought I was asking for. I’m no expert on fusion cuisine, but even I know it’s supposed to be more than just putting a bowl of grits next to some Indian spices. I expected the AI to blend things together more – not just place different foods side by side. Maybe these systems just haven’t seen enough examples of real fusion cooking, or maybe they’re just not designed to mix concepts in that way. Either way, I found the results pretty revealing about current limitations.

Categories
Optional Tasks

Attention Economy

User Inyterface Game: Dark Patterns in Action

This game was purposely frustrating – I literally felt my life wasting away before my eyes as I clicked endless pictures trying to “verify I wasn’t a human being.” The seconds bled away when help boxes were slow to disappear and misleading buttons did unexpected things. When I clicked “help,” instead of getting assistance, it just told me “Please wait, there are 409 people in line” – making me realize how many futile, unnecessary tasks the game forces on users.

The cookie acceptance was particularly revealing. When presented with “Not really, no” as an option, I felt the nonchalance in that phrasing was irksome. I actually care about cookies and website access to my browsing history, but the design made me feel I was selling a piece of myself just to progress quickly – a need for speed created by the interface itself.

Form fields were nightmarish. Having to delete placeholders before typing was annoying enough, but then the placeholder text still appeared as I typed over it! This second-guessing of my sanity showed how these dark patterns deliberately disorient users. Email entry forced an unnecessary horizontal movement to a drop-down menu instead of just letting me type the domain.

The terms and conditions used clever double negatives: having to untick “I do not accept” meant I had to not-not-accept, which actually meant accepting. This manipulation of syntax shows how interfaces exploit language confusion.

I did not even notice all these password stipulations the first time I played the game:

  • “Your password is not unsafe”
  • “Your password requires at least 10 characters”
  • “Your password should have at least 1 Capital letter”
  • “Your password must have at least 1 Numeral”
  • “Your password needs at least 1 letter of your email”
  • “Your password can have at least 1 cyrillic character”

I did a hack where I asked for a random password generator which allowed me to blow through this part (I didn’t have to look up what a cyrillic character was). I did this intuitively because I suspected there would be some absurd requirements – I didn’t even see all the requirements until my second playthrough because of how difficult the font was to notice.

The age slider increment by twos was frustrating – I wanted to put 124 (to match my August 1, 1900 birth date) but could only toggle between 123 and 125. The months appeared out of order in the dropdown menu for date of birth, which was “cool” (by which I mean incredibly frustrating).

Perhaps most devious was placing tick boxes ABOVE the pictures for the human verification. Since boxes are intuitively expected below images, I clicked what I thought were boxes for the pictures above, but they were for those below – forcing me to start over. This was especially confusing because I had to scroll up to reveal the top row, initially assuming standard layout.

What struck me most was realizing I simply wanted to complete the game – exactly the behavior these dark patterns promote. Websites often use similar techniques to make users “sign away their lives” just to accomplish simple tasks. This game effectively simulates that manipulative dynamic.

 

Categories
Mandatory Tasks

Network Assignment Using Golden Record Curation Quiz Data

When I first saw that I occupied a small nodule in the network, I thought it was a badge of pride—I’m an outlier who chose different picks than anyone else. But the explanation isn’t so simple.

When filling out the quiz, I only chose 7 tracks. This happened because I included two non-“musical pieces” in my Golden Record curation: “United Nations Greetings/Whale Songs” and “Sounds of Earth” (though how are whale songs not music, am I right?). The reason I ended up with 7 instead of 8 tracks is because the music titles sometimes had different names—an English description versus their original language on the podcast page. It was only later that I discovered there was a YouTube link with all the Golden Record tracks, and these names corresponded to the ones on the quiz. One track got lost in the mix because I didn’t want to spend time cross-referencing names.

So the only reason I’m an outlier is because me and two other colleagues chose LESS than the required 10 songs, and I chose the LEAST at 7. My opportunities for connections were greatly impacted. I think it’s good that the quiz didn’t force you to choose 10 songs, as that would have misrepresented what I thought should go on the record (I misinterpreted “music” to mean only musical tracks and not tracks in general—but I want whale songs with Louis Armstrong!). But this creates a limitation in the network.

This exemplified the fact that the visualization can fail to capture the reasons behind my different engagement with the task, which has significant political implications. When someone lacks access to information (like me missing the YouTube page) or doesn’t have the cultural proficiency to complete something in the expected way, they appear differently in the data visualization – but this misrepresentation doesn’t actually reflect their true preferences, values, or identity. It’s a powerful observation about how data collection processes can systematically misrepresent people based on access barriers rather than actual differences in perspective or preference.

I was in a 17/17 facet group with Sourabh—a lonely group of just 2 people—and because there were only 2 of us, none of the three songs we shared became nodules or circles. At first I thought you need at least three people sharing a song to get a circle, BUT when I combined our 17/17 group with the 29/29 facet group, Track 14 (Melancholy Blues) only had Isabella and me sharing it, yet it was still a circle. So circles must come about due to the interrelationships and connection density. This visualization choice of which selections become circles further reinforces whose preferences get emphasized and whose remain peripheral, which connects to broader questions of representation.

These visualization patterns reveal deeper power dynamics at work. My choices were really established by the task parameters, yet I wasn’t given much weight in this network. There’s no way the visualization can reveal that it’s built on the hidden assumption that whale songs aren’t considered ‘music,’ but that assumption effectively misrepresented my actual preferences and pushed me to the margins of the network. There’s almost a power dynamic embedded in the assignment that implies whale songs aren’t music and dictates what constitutes a music record (spoken word and whale songs cannot go on a music album; flute and drums can).

Also, while the visualization shows our preferences, it can’t show our motivations. One person might choose Bach for his mathematical precision, another because of his position in the Western canon. This small-scale example gives me insight into a much more serious problem: how marginalized groups might be misrepresented in data visualizations not because their perspectives aren’t valid, but because the data collection methods themselves contain barriers that disproportionately affect certain populations. What you’re measuring isn’t musical preferences but people who completed the task “correctly” from one particular perspective.

Categories
Mandatory Tasks

Golden Record Curation

Tracks for inclusion, with justification:

1. United Nations Greetings / Whale Songs – Voyager Golden Record

The necessity of wild juxtapositions. Outlandishness of project, postulating communication we can scarcely perceive. Information-poor language of gilded representatives versus the impossibly noble, majestic feast of information of whales.

2. Sounds of Earth – Voyager Golden Record

The clash between mathematical precision of planetary frequencies, which could be accused of ponderousness, with the ecstatic simplicity of striking a flint creates a perfect tension. Given broad historical perspective, incredibly over-privileging of humanity’s auditory footprint, but I kind of like that. Including an EEG of a representative of earth brilliant yet absurd.

3. Ketawang: Puspåwårnå (Kinds of Flowers), Performed by Pura Paku Alaman Palace Orchestra – Nonesuch Records

Chimes and chanting – that combination is indelible. Also need to represent symbolic thinking, and this Javanese mapping of flowers to philosophical states perfectly demonstrates our genius for correspondences (though utterly incomprehensible from an intergalactic perspective). Categorizing beauty through flowers – there’s a simplicity of intention revealed there, which I love.

4. El Cascabel by Lorenzo Barcelata, Performed by Antonio Maciel and Los Aguilillas with Mariachi México de Pepe Villa – Bicycle Music Company

Dynamic and great. Mariachi horns sound like the apocalypse; it’s always been that way for me. This music captures how our species races (sometimes joyfully, other times exhaustedly) toward oblivion.

5. Jonny B. Goode by Chuck Berry – Universal Music Enterprises

We need Chuck Berry. Chuck Berry captures frenzied hormonal youth – a universal language of sexuality that aliens might recognize (not that Bach is devoid of sexuality). His music represents both our cultural cycles and primal energy – revolutionary once, now simultaneously revered yet quaintly distant.

6. Mariuamangi by Pranis Pandang and Kumbi of the Nyarua clan – Recorded by Robert MacLennan

Maximum drone effect. Need to represent fundamental gestation periods of humanity, when literally NOTHING is happening. The recording captures something primordial, those extended periods of stasis punctuating our existence where change remains imperceptible.

7. Chakrulo by Georgian State Merited Ensemble of Folk Song and Dance – Melodiya Studio in Tbilisi, Georgia

Indispensable Georgian folk song. Shows we are not above acknowledging our darker, aggressive nature – destructive tendencies, capacity to make beauty therefrom. Drinking and violence need to be represented, puncturing the sterility of space.

8. Melancholy Blues by Louis Armstong and His Hot Seven – Columbia Records

The blues is more than a genre – it’s the definitive aestheticization of suffering. All about humanity’s resilience and defiance in the face of hardship, with a profound emotional depth that transcends intellectual achievements.

9. Mugam by Kamil Jalilov – Smithsonian Folkways

This represents our interpretation of cosmic mysteries – a sound journey into the unknown. Haunting, exploratory quality. Shows long before we actually went into space that we had imagined its vastness through our traditional instruments.

10. Dark Was the Night, Cold Was the Ground by Blind Willie Johnson – Legacy Recordings

Perfect moans and slide guitar. Is there any more universal experience than isolation? The stark, haunting quality, and the ground – there is no ground in space, yet we need ground to stand on and ground for communicating without words.

 

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