this weeks tasks:

• [x] Read Mixing movement and machine by Maya Man.
• [x] Read Humans of AI by Philipp Schmitt.
• [x] Pick one of the models above (BodyPose or BodySegmentation), following the examples above and ml5.js documentation, experiment with controlling elements of a p5.js sketch (color, geometry, sound, text, etc) with the output of the model.
• [ ] Read Open Sourcing the Origin Stories: The ml5.js Model and Data Provenance Project by Ellen Nickles and reflect on the the following questions:
  • What questions do you still have about the model and the associated data? Are there elements you would propose including in the biography?
  • How does understanding the provenance of the model and its data inform your creative process?
• [ ] Document your response to the readings as well as your p5.js sketch in a blog post and add a link to the post and your p5.js sketch on the Assignment 3 Wiki page. In your blog post, include visual documentation such as a recorded screen capture / video / GIFs of your sketch.

**Mixing movement and machine by Maya Man.**

Instead of asking, "What are all the things it can do?" a better question might be, "What do I want from it?" I find it fascinating how our relationship with machine models shifts over time—from curiosity to control. Initially, we're excited to explore, test, and play with the possibilities of these tools. But eventually, it becomes about commanding and harnessing their potential to fit our needs. Is this just a reflection of our human nature, wanting to dominate as the top of the food chain? This reminds me of Donna Haraway's Cyborg Manifesto, where she dissects the boundaries between human-animal and human-machine, questioning our urge to classify and control.

I also found it compelling how ml5 incorporates body language into its framework. For example, PoseNet "pushes the boundaries of digital interaction design beyond the current standard of clicks, presses, and taps." This language stood out to me because it perfectly captures what PoseNet does—it doesn’t just improve interaction design; it redefines the limits of how we can engage with technology, merging physical movement with digital interfaces.

**Humans of AI by Philipp Schmitt.**

“Once the images were collected, crowdworkers on Amazon's Mechanical Turk platform spent a combined 70,000 hours cleaning, segmenting, and annotating the dataset with 2.5 million instances of objects—a task that would have taken a single person, working 40-hour weeks with no time off, over 33 years.” This really puts into perspective the monumental effort involved in categorizing data for image recognition. It’s easy to overlook the sheer scale of human labor required to train these AI systems. The precision and patience behind this process often go unnoticed, but it’s a critical backbone of AI development.

This article reminds me of how much we tend to forget the hidden human labor that fuels AI. While we celebrate the capabilities of machine learning models, we seldom acknowledge the workforce behind the data preparation—those who meticulously label, organize, and refine the datasets that make AI's "intelligence" possible.

Experiment with controlling elements of a p5.js sketch:

• It assumed my hand was connected to my shoulder, although it can not see the rest of my arm it makes an assumption.

Open Sourcing the Origin Stories: The ml5.js Model and Data Provenance Project by Ellen Nickles and reflect

What questions do you still have about the model and the associated data? Are there elements you would propose including in the biography?

I’m curious about potential biases in the data, during collection or annotation…and how diverse the perspectives of annotators were. Were steps taken to address bias or ensure inclusivity? For the biography, I’d add transparency about who chose the datasets and why, details on the model's evolution, and any ethical considerations or social impacts the developers had in mind.

How does understanding the provenance of the model and its data inform your creative process?