Tasks:

• [x] Read Language models can only write ransom notes by Allison Parrish and review the The Foundation Model Transparency Index.
• [x] Experiment with prompting a language model in some way other than a provided interface (e.g., ChatGPT) and document the results in a blog post. You can use any of the code examples above and/or try a variety of LLMs locally with ollama. Reflect on one or more of the following questions:
• How does the concept of LLMs as “ransom notes” influence your perception of using these models creatively?
• How does the hidden origin of the text that LLMs generate affect your sense of authorship or originality in your creative work?
• How does the metaphor of "collage" used to describe LLMs align with or differ from your creative process?
• How would you compare working with an LLM to other forms of text generation, such as using a Markov chain?

Markov chain: A Markov chain is a statistical model that predicts future states based on current states, often used in text generation to create sequences of words or characters by analyzing the probability of one element following another in a given corpus.

Reflect on one or more of the following questions:

Read Language models can only write ransom notes by Allison Parrish and review the The Foundation Model Transparency Index.

1. How does the concept of LLMs as “ransom notes” influence your perception of using these models creatively?

   In her lecture at NYU’s Digital Theory Lab, Allison Parrish compares large language models (LLMs) to ransom notes, where words and phrases are stitched together with no trace of their original context. Kinda of like taking away the originality (like copy right). Unlike true collages, which keep a connection to their sources and carry lots of depth, LLMs erase the individuality of each piece they use. And if this is the case? Who do we now credit? This brings me back to artist AI, where AI has taken art work and art styles and use for there own productions. Letting thousands of artist lose credibility. I feel like a possible solution would be providing a short bibliography of where the work was taken from, or give credit to the artist used to create such a artwork.

2. How does the metaphor of "collage" used to describe LLMs align with or differ from your creative process?

   The "collage" metaphor for LLMs hits differently than how I approach creative work. When I make something, I’m pulling from different inspirations, such a Pintrest, Tiktok and other artist, but I’m intentional about showing the uniqueness of each piece and keeping its story alive. It’s like creating a layered collage where each part still has its own voice and contributes to the whole in a meaningful way. But with LLMs, it’s more like a ransom note—pieces are cut and pasted from everywhere, losing their original essence and context. They get smoothed together into something that feels unified but lacks the depth or individuality of a real collage.

Large language models (LLMs)

“I would argue that LLM output works the same way: each token, taken from its source, is “de-authorized,” its material form and original context rendered irrelevant, and subsequently used to create the illusion of a uniform voice. The people responsible for tokens coming to be arranged in this way—the engineers, the product designers, the marketing departments, the CEOs of “artificial intelligence” companies—are rendered effectively anonymous.”

• Parrish shows that, rather than drawing from thoughtful, curated collections, LLMs rely on impersonal masses of text that strip language of its richness and meaning. She advocates for computational collages that highlight the unique sources and histories behind each “cut,” creating a sense of interconnection that respects the human stories embedded in language.

  It’s almost as if the original voices are erased, and there’s this weird anonymity to the whole thing. If LLMs could somehow acknowledge where each piece came from or give credit, they might feel more like authentic collages rather than just a mashup with no real connection to the sources.

• Parrish describes large language models as digital collage makers that piecing together ‘fragments of text’ from countless sources in a way that resembles ransom notes. Each “cut” /token is pulled from a vast sea of online content, decontextualizing the original words and creating a “collage” of ideas and phrases.

Attempting to run Ollama:

Downloaded to VS Code

Did not work so now I try running locally

Running the p5.js locallt