Another Teacher COVID Story: Trudging Through

Personal Plights

This week has been a struggle. I had mentioned in a prior post how crazy it felt. Well, it got crazier. Now, my whole family has had to quarantine and I’m walking around looking like a plague doctor trying to care for them before I head back to Austin. Sometimes this year has felt like a waking nightmare. The thought of losing my grandmother and my family to this virus has brought those feelings to their peak. I don’t think I’ve ever cried so much in so few days. The only consistent thing throughout all of this has been PATHS UP with RSTEM but, even then, it has felt very inconsistent this week do to the family emergencies. I hope next week is better and that my family makes a fully recovery.

DeepMind, Lord of the Rings, and Vaccines

Straight from the recesses of my decomposing mind, I bring you my latest unhinged theory based on a conversation with special Guest Speaker, Arun Ahuja, that we had courtesy of one of our amazing cohort members–thanks Daniel! Arun was there to speak to us about Google DeepMind and all of the possibilities that were related to it, the video games, the labyrinths, the rewards systems that were designed specifically to allow the system to learn on its own. It was nothing short of amazing. Then, I had a thought about one of my favorite video games that I’d played: “Middle Earth:Shadow of War”. Regardless of how familiar you might be with this installation in the Lord of the Rings series of games, the premise is simple, as the main character you seek to conquer the world by strategically assassinating, taking control of, or collaborating with characters in the game. But, they use something called the Nemesis System to make this possible–and this is how it relates back to DeepMind–in which every single possible outcome is different and the non-playable characters can make their own choices to betray, join, or resist. The neural network system that Arun described sounded like how I imagined the Nemesis system would work, accounting for every eventuality, every move, like the AI that defeated the top Go player. So, I took this comparison further, I looked for confirmation that the Nemesis system in the game was somehow like a CNN but could not find concrete evidence to support my theory. (I spent five hours looking for it while caring for my family) I continued with the course though, and in the conversation with Arun asked if there was any credence in applying this DeepMind approach, with this Nemesis System outcome that I’ve seen and applying it to virology. My logic here was: if we can let an AI teach itself how to combat a virus and generate a subsequent vaccine, then allow the AI to account for every iteration in RNA mutation at any given sequence, we should—in what I’m sure is my flawed theory—be able to generate a vaccine for such things liked COVID-19. Then, we can expand upon that concept further and generate possible vaccines for viruses that do not yet exist.

Boom and Bust: Kaggle & Tensorflow

I felt really good about the Kaggle course working with Pandas for Python. There was definitely a familiarity to it in that it felt like commands for SQL and for using Pivot Tables for data. I think it was one of the few times I didn’t feel completely lost in the journey. Granted, I feel like at this point we have been learning so much in a short amount of time that I should still be grateful for what I can understand. Tensorflow has not been nice at all though. It has probably given me more grief than PyCharm did in the beginning. First it wouldn’t download, then it was corrupted, and finally it shut off my laptop for no reason. So, we are going to re-attempt Tensorflow tomorrow and Sunday because this week really left me with no time to do much else.