Always finishing things

Coming up with ideas and thinking about the future is a lot easier than always finishing things. Mountains of ideas are hard to keep up with and the time commitment to close out something that only took an instant to create can be extreme to finish. Right now I can sketch out the chapters of a book on applied machine learning on a sheet of paper. Actually taking the time to write that book would be a large multiple month commitment of my time. So many people are probably writing that book that it does not seem like a reasonable use of my time at the moment, but it is an example of where a few minutes of work would translate to months of effort to close that effort out. That is why always finishing things is harder than it sounds. You will quickly find your list of potential things to do quickly faces a situation with over-supplied ideas and undersupplied time. That is one of those things that is always the hallmark of having to make choices about our time. We stare out at the perfect possible future and know that only so much can be done in one lifetime. Inherently that is what makes contributions to the academy of academic thought so compelling. You get the chance to contribute to a body of work with intergenerational continuity. Permanence is an interesting thing to associate with an idea, but it is a rather powerful one. Great ideas are sold, stolen, and reused based on their merit. The best ones keep on moving along from thinker to thinker. 

My time is always something that needs to be better accounted for. It is a scarce resource and applying my time to problems should be done based on some reasonable heuristic. Always finishing things is very time consuming. It is done without any filtering heuristic. Always doing anything is potentially problematic. You have to account for the situation and think about what is actually being done. For the next week or so I’m going to take stock of the things I’m spending my time doing by using some note cards to keep track of the larger blocks of things that are going to be consuming my time. Yesterday I had decided to spend a block of my time reading 2-3 peer reviewed machine learning literature reviews a day until that exercise was exhausted. I’m expecting that the literature review reading will show up on the note cards, but the times when it does not will be insightful as well. It will give me a look at what needed to happen and what happened instead based on how I’m committing my time.  

Some time later… I went out to Google Scholar and started a search for, “machine learning literature review.” During the search autocomplete suggested that I might be interested in a search for, “machine learning literature survey.” Apparently, both searches are pretty common and I’ll keep that “survey” term in my back pocket for after I read a few literature reviews. After completing that first search the Google search algorithm had a few related searches to suggest that included: machine learning algorithms, pattern recognition machine learning, machine learning mitchell, supervised machine learning, machine learning classification, uci machine learning, machine learning intrusion, and machine learning repository. I can tell the web of ideas that will spill out from reading machine learning literature reviews is going to include a lot of spokes into very specific lines of inquiry. To try to stay as current as possible I applied a filter to only show me articles since 2020. I’ll have to use the more recent publications to work my way back to the more foundational scholarly work on the subject.

The first 3 articles I encountered based on relevance to the search for articles since 2020 were as follows:

1. https://arxiv.org/abs/2007.11354v4 This one was free and easy to download. 
2. https://www.sciencedirect.com/science/article/abs/pii/S0305054820300435 This one cost $39.95 to read and was abandoned. 
3. https://www.sciencedirect.com/science/article/pii/S0952197619302672 This one was free to download.