Computer Generated Courses at ITP

Assignment: Create your own poetry generator using these techniques. Use one of the generators implemented in this notebook as a starting point for your creation.

One of the most intriguing things for me at ITP is the course offering. Each class is eclectic in its own way. When reading the class descriptions, I always feel like I can only understand so much as they are so new to me. At the same time, having participated in different classes this semester, I find that the classes are intertwined and the knowledge can be applied with each other.

These thoughts lead me to create this ITP Course Mashup based on all the classes I’m taking here at ITP this semester.

I took the following elements from the original notebook and changed them to mimic the course description given to us at the beginning of the semester.

  • Split – split passage into separate words
  • Random – select random words from list
  • Random range – random number (for amount of credits)
  • Text wrap – putting texts together as a passage
  • Capitalize – turn texts into sentence case

It was difficult to get the course description to go beyond ‘Dadaism’ with the random function.  Therefore, for future development I’d like to work on the following.

  • Categorize words into nouns / verbs / adjectives
  • Eliminate repeated words

I had a lot more fun than I thought. Shuffling texts with preconceived notion is very entertaining.


Example results:

black1Artboard 6@3x-100

black1Artboard 1@3x-100

black1Artboard 5@3x-100

black1Artboard 7@3x-100

Transcription Assignment

Assignment: Transcribe and/or digitize a text that has never existed in digital form before. The resulting transcript should be in plain text format (i.e., a .txt file). The goal of this project is fidelity: try to make your transcription as true to the source material as possible.

I decided to record Dano’s Rest of You lecture for this exercise because I thought he had an interesting way of speaking. Dano doesn’t like to finish his sentences. Instead, he jumps around from topic to topic. I wondered how I could capture this

The transcription:

The voice file:

I tried to be as precise and accurate as possible with the transcription. I included the pauses, stuttering, repeated words etc. However, I still found myself filling in words I couldn’t hear to make it a complete sentence.

The biggest learning was transcription is full of BIAS!