Watching sports and researching

Everything for this domain is now fully set up technology-wise. The new daily writing plan is in full effect. You cannot stop the signal, but you certainly can ignore it. However, technology won’t be a blocker to distribution this time around. I even used the vaunted Substack Notes feature for the first time since April of 2023. That particular channel of discourse is going to be used to share links to papers, articles, or other manuscripts that I think are worth reading. Notes I publish don’t ever seem to generate very much engagement, but they are at least interesting. They are akin to screaming or maybe whispering into the loudest wind. With that additional channel of content distribution now up and running, things are now broken into four distribution patterns. Those 4 channels include: notes, a daily writing effort, research notes, and AI-specific missives. Figuring out the best way to divide up the content is going to be a part of the journey.

Tomorrow, my television viewing journey is going to include watching the NBA finals on ABC using my trusty over-the-air antenna plugged right into the television. Last night, the NHL game I watched was absolutely chaotic and full of fights. Like a lot of fights happened. We will see what happens in the next game or if the Panthers have gained the initiative in the series. It’s more likely that the NBA finals this year end up going to 7 games, but the NHL finals might go the distance as well. I have watched every NHL finals game so far this year. This next game will prove to be an interesting series for the future of the game. The teams left standing play very different styles of hockey. We will see which style the league ends up tilting toward going forward. We have seen both hockey and basketball styles changing over the last 10 years, but I think we are at an inflection point for hockey.

Overall, my research interests got focused on the future and development of machine learning and AI-related topics. Several hundred research notes were written, and a lot of them were interesting deep dives. They were timely and ultimately helped build toward a solid understanding of the foundation, trends, and future patterns. Part of the problem with building up a foundation of AI knowledge involves questions of both breadth and depth. One solid approach would be to sit down and read "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig. I have two different versions of the textbook on my shelf. Reading those hundreds of pages is a substantial commitment in both time and energy. Part of the problem at that point would be that you would have a solid foundation, but would end up lacking the next few layers of a multi-layer puzzle where the top layer is what people are so excited about. Over the last 18 months or so, the world of foundational models and LLMs has become omnipresent and perhaps oversaturated.