We are quickly approaching Thanksgiving and that is a day where I plan on relaxing and watching some football for a few hours. It is a day where I pretty much give up on trying to overcome distractions and give in to a day of rest. Unequivocally it is probably my most restful day of the year. Maybe that is something that I need to do more often. Thinking about that was peaceful enough in the moment to cause my thoughts to pinball over to overcoming distractions and tackling tough problems. Always running toward a purpose without rethinking that purpose could be counterproductive. On the other hand, being driven to action is inherently compelling. Some problems are so compelling that you are fueled by a near single minded purpose devoted to finding a resolution. Those types of problems fuel themselves. Everything outside of that bucket falls into the overcoming distractions and talking tough problems bucket.
The method that I use to overcome problems has always been based on how I deconstruct the hardest problems. I look at the form, function, structure, and assumptions (FFSA) within the problem space. Between those elements either a degree of contention exists or a path opens up to help understand how to move forward by overcoming either a structural barrier or limitations created by assumptions. That is something that I have been writing about for a long time now and it is something that always helped me find the footing necessary to begin to create change. Figuring out how to convert strategy into action takes a lot more than a great plan. You can take a great plan written out on a piece of paper and put it in a desk drawer. Within that desk drawer the darkness of the container is as bleak as the chance that strategy drives anything forward. Only on very rare occasions is complete inaction a method to drive success.
These questions have come to the forefront of my mind today based on some things that I have been wondering about recently. I finally setup my own Google Cloud Platform environment. That should allow me to really focus in on solving some very tough problems related to natural language process (NLP). Building out recommendation or decision engines within that space is very possible. Figuring out how to define or explain the relative degree of understanding that exists related to those decisions or recommendation is a much harder problem to overcome. Figuring out how to server up the right movie recommendation is much easier to derive algorithmically compared to understanding why that movie was the right offering. That is a tough problem to tackle, but one worthy of consideration including the time and effort that goes into that type of blocking and tackling.