Optimally, once a self-driving car model can be developed and consistently achieve an F-score that outperforms the human equivalent we need all humans to stop driving in favor of the better alternative. Unfortunately, this would no doubt be an ethical and legal nightmare. People don’t want to give up their autonomy to a “machine” even if it is theoretically safer. Or not yet at least. In this post I want to explore a bit about why that might be, and what advantages a human driver could have over a purely digital one. The most dangerous aspect of when applying machine learning to problems vital to life or organizational viability in the context of the reading is ML's inability to adapt to unfamiliar input. This is a well-founded concern considering the behavior of AI as pointed by Zhi Quan Zhou’s Case Against Mission-Critical Applications of Machine Learning and the subsequent author response. In the short term, it is wise to continue to rely on machine learning as a simple tool ...
This post is a bit of a retrospective on small personal project I worked on a few years ago and what I hoped at the time to be something unique. While the name came a few months after its inception, it is very apropos for my intention and execution of each project. Each track was spliced into samples, and re-constructed into a final product is similar yet unique to that of its original self. You may think that this sounds similar to how most remixes are produced, and you would be right. Similar techniques are used by music producers to create VIP (variance in production) mixes of their own songs. What makes SnS remixes unique are the restrictions placed on the creation process from the onset of the project. Before I list out what those restrictions were (spoilers: there were only two), I want to speak a little on the inspiration for this project. During my time in studies in audio engineering and acoustics at Belmont University, we learned a lot about what is now a largely antiquated...