As the image implies, we have a connectedness that stretches beyond ourselves. The use of imagery such as this provides a decent visualization of how our brain uses algorithmic principles to function. I am wondering how, in the coming weeks, I will learn this as it appleies to the various topics we have discussed in this course. Another question would be how, as the next image shows, can computers use our spoken and written words, to create algorithms for use in mental health treatment and beyond? (Pestian, et al, 2017).
To branch away from the aforementioned, I have had several comments on my Ethnography, but more pointedly, on the poem I submitted as part of it. A couple of comments were from classmates in EDC17, and a few others from MOOC participants. I think this may be the sum and substance of the MOOC I studied, and which I found myself immersing into rather than simply being an outside “participant.”
The purpose of the MOOC, the REAL purpose I now am starting to realize, goes beyond the stated objectives of the course, which were to share experiences and thoughts about Cascadia. As some have mentioned, my Ethnography drew them in and caused them to spend an unexpected amount of time looking through my collage of pictures and texts. It seems my Ethnography served a purpose beyond its stated objective as well. Rather than turning into a dry, sterile presentation, I found creating it drew from memories and experiences that have long been filed away in my brain. How we we remember is a fascinating realm in which to dive into. Good and not so good memories: we can either dredge them up, churn them up, recreate them, or remake them. It is interesting how present circumstances or perspective, can cause us to see the same memory as good or bad.
Perhaps Weeks 8 through 10 will help me understand the algorithms at play in bringing past memories back to the forefront of consciousness, as I learn how different apps use those algorithms to help us create, express, and even sustain, creativity outside of ourselves.
Pestian, J. P., Sorter, M., Connolly, B., Bretonnel Cohen, K., McCullumsmith, C., Gee, J. T., Morency, L.-P., Scherer, S., Rohlfs, L. and the STM Research Group (2017), A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial. Suicide Life Threat Behav, 47: 112–121. doi:10.1111/sltb.12312