Yesterday I spent some time thinking about my research trajectory and where my writing efforts should be placed. Intellectually I know that I should use the conference cycle to help motivate my writing efforts and keep them on a tight publishing schedule. Over the last few years that has not really happened in a consistent way. I have written and put together content for a series of talks, but I never finished taking that content and putting it into a journal article format or working it into a conference paper. That is probably one of the first things that should be on my writing plan that is in the process of being reworked. My writing schedule works and I turn out content every week. That is a proven effort at this point. It has worked for well over a year within the machine learning content space. All of that content exists in a well contained Google Doc with weeks 1 to 104 planned out for Saturday delivery. At the start of the year, I did take the first year of that content and put it into a manuscript form and it was edited by a professional to make sure it was ready to be shared in print. That cycle will continue until the end of the 104 planned posts. 

Please keep in mind that my writing plan is not a theme of the year or anything like that. It should be an organized and thoughtful research trajectory from the start to the finish. The other constraint that I put on it is to very clearly view it as a measure of what I could do with the time that I have in front of me. If I only had 5 years to muster up writing efforts, then what should that time be spent on and the most important things should be closer to the beginning of the journey than the end of it. Time is incredibly unforgiving and before you know it from our perspective things will move along with or without the time being put in at the keyboard to create resplendent prose. Within the moment we know time is about to pass, but we have the ability to only elect action or inaction. 

My 5 year writing plan as of March 3, 2022:

• Year 1 – Heavy ML focus for the rest of 2022
  • Finish writing a collected series of ML/AI essays on Substack and combine them into a manuscript, “The Lindahl Letter: On Machine Learning Year Two.” This manuscript should include both years one and two. 
    • Weekly Substack posts
    • Manuscript generation at the end of the year
    • Will need to be edited by a professional before the print edition goes live
  • Rework last years speaking engagement talks into academic papers. This could be one combined paper or potentially 5 different papers depending on how the initial effort shapes up.
    • “What is ML Scale? The Where and the When of ML Usage.”
    • “The ML scale problem: Thinking about where and when to use ML, ROI models, synthetic data, repeatable frameworks, and teams.”
    • “Applied ML ROI – Understanding ML ROI from different approaches at scale.”
    • “Demystifying Applied ML – Building Frameworks & Teams to Operationalize ML at Scale.”
    • “Figuring out applied ML: Building frameworks and teams to operationalize ML at scale. V3”
  • Rerun the MLOps Github research and turn that content into a paper
• Year 2 – For 2023 I want to pivot into studying sentiment analysis and modern polling methodologies. At this point, I will have written 104 essays on ML/AI and should probably refocus on a specific topic that is material to ML/AI, but adjacent to it as an area of research. It’s possible by 2023 that quantum computing will be a huge topic for research and will end up getting some attention as well.
  • Automated sentiment analysis paper
  • Sentiment analysis and machine learning essays for Substack
  • Modern polling methods essays for Substack
  • The breakdown of modern polling paper
• Year 3 – 2024 will include a return to writing about local government administration and technology. It will be 20 years since earning my master of public administration degree. By this time my writing should be as crisp and focused as it will ever be and my perspective on technology will be well considered from my previous work on ML/AI. 
  • Technology and local government administration
  • The intersection of public administration and technology
  • How technology influences the practice of governing 
  • How government uses ML/AI technology
• Year 4 – 2025 will probably be the year where quantum computing has broken down modern encryption frameworks. 
  • Changes and uses in encryption technology
  • Encryption and society
  • Quantum encryption
• Year 5 – 2026 is going to be a year where my backlog should be highly full. The previous 4 years of this writing plan should have created a ton of leftover writing works.
  • A reflective work on ML/AL
  • Did open source MLOps technology survive?
  • Did the serverless trend pan out in the cloud?