This blog describes my initial condition and mindset when starting something new. I believe that when people often wonder why someone is improving more than they are, even though they do the same things, the difference usually comes from the initial intention or mindset. Therefore, I am sharing my initial mindset.
So, FOMO(fear of missing out) is almost always the driving force that helps us move from a state of no knowledge to some knowledge.
This is a series where I will actively write and update blogs whenever I find something really cool to share. As I learn, I will share, so there will be many mistakes in definitions, implementations, and expectations. That's just part of the process you'll have to accept.
Before you start reading this, I want to share the concept of "Finite games vs Infinite games." This idea suggests that in finite games, we focus on winning, which isn't very helpful for mental growth and skill development because it often leads to stress and anxiety. On the other hand, an infinite games mindset is beneficial because you engage in activities simply for the sake of doing them (or just playing...). However, there is a downside to this; you need to switch to a finite game mindset at some point, or you risk remaining mediocre because playing with mediocre people can make you mediocre. I plan to make this switch by July 2025. So, I have about 6-7 months to play and improve my Math and ML skills to match any Data Science professional. This will likely involve implementing many ML algorithms. Just in case you missed this.
Why doing AI right now?
Usually, my answer to learning or doing anything difficult is "WHY NOT," but lately, I find that people often don't understand this mindset. So, I'm explaining it here. There are two parts to this theory:
Doing difficult things is important because it helps the human mind evolve and achieve the unthinkable. Will my learning make a Mars invasion possible? I don't think so.
Learning the ins and outs is much better than just knowing the basics. It's common knowledge that understanding a system deeply leads to better applications than using it as a black box (for example, AI forms are black box applications, whereas stable diffusion involves understanding the ins and outs).
Plan of action
hmm. i only know approach’s to learn anything new:
Top-Down Approach: Start by learning about the applications, then dive into understanding how they are implemented. The advantage of this method is that it's a very practical way to grasp concepts, allowing you to code from day one. However, the downside is that the quality of projects you can create is often low, like a Slack chatbot or a logo maker, which aren't very interesting.
Bottom-Up Approach: Begin by mastering the basics, then progress to understanding the implementation, and finally move on to creating applications. The advantage of this approach is that the applications tend to be of high quality, as they are carefully planned and supported by a deep understanding of the underlying technology. However, the time required to develop these applications is significantly longer, and the probability of completing a release is minimal.
I will think about this and update blog on my approach. because I want to do something before telling what i am going to do.
Overall this should have some 15-20 long, highly detailed blogs spread throughout the year 2025. So, keep an eye on it.