So, the first week has been concluded and therefore I should also summarize my newly gained point of view and knowledge. I won't be hiding the topics of interest I wrote in the class as it is not a big secret. It's the opposite, I would like to even emphasize why I wrote those topics.
I will start with the one I personally had an interest in for a long time: "AI in Health Industry using globally unified data from every hospital". I won't dive into the story of how I also wanted to be a doctor when I was a kid, but then fear of responsibility to deal with human life stopped me. But the idea of unification of experiences and results of all the hospitals maybe not in the whole world at first, but in one specific country sounds like a great opportunity to make healthy decisions easier and more helpful. With the AI in the sphere as well, it perhaps would've been even quicker to analyze one's issues and compare them to the rest cases. In the long run, unifying all hospitals would have helped less developed countries with their healthcare system and eventually lowering the number of death caused by incorrect or even inexperienced decisions of doctors. However, looking realistically at how the world works I understand that this "idea" probably will never see a light. Simply, it is impossible to make every country co-operate while it is nearly impossible to make one country to be responsible.
Now, let's get back to real problems that can be solved and my second topic of choice: "Application of machine learning in the petroleum industry". As I have mentioned previously, the company I worked at before coming to the US (and probably where I will be working later on after going back to Azerbaijan) is connected to the state oil industry. Implementation of ML in the said industry is not a new topic, but if we consider how many different areas the ML could be applied to, then there is always a need of bringing new implementations. While I'm still more familiar with computer science than the oil industry, it will be my responsibility to fully analyze the required fields and find that particular area where at the moment it is more required to use ML. There are few restrictions I have right now (or at least of those that I could identify yet). For example, a time limit is the main factor at the moment. I need to have some kind of outcomes at 3 stages within the next 10 months including the next spring semester. The first one is mid-august which is related to this class's requirement. NExt deliverable should be in December, and lastly fully working (prototype) by May 2022. Another limitation would be having enough data to apply the ML on. According to my company's policies, it's prohibited to use their data outside the company but there is also open data provided by the Norwegian Petroleum Directorate which could be used in the middle of research. Of course, this project by no means can be considered as a small one, therefore I must use all the knowledge in project planning I acquired within my bachelor's years and plan every step accordingly. Time, resources, risks, deliverables all should be well planned and documented.