Thoughts on Data Science/Machine Learning and stuff like that

In this blogpost, I will not put a direct reference whatsoever, but will just share some of my considerations regarding data science related topics.

The cool terms of our age, especially artificial (or augmented) intelligence and machine learning are actually things that almost everyone are aware of. Take artificial intelligence. If I am to define that, I would even put manipulations as artifical intelligence enforcements to others, as the victims of that manipulation does not "really" utilize "natural neural networks" but just use sensory data, like sounds, images etc. Let's drop that for now, and use AI as the intelligence mechanism that is put to the non-living material by humans. If you can turn your owen's heat to 120 celcius and it only heats to that degree and maintain that for a specified period by again its user, that is really an AI acc. to this definition. In other words, the owen understands that I would like it to sustain 120 celcius for an hour, so it does that. If I set it to 150, it would do that. More than a mechanical temperature increase set, it also keeps the temperature at that point with relatively more complicated means. It has some sort of intelligence, that sometimes surpasses many humans'. This is quite more than just grabbing a stone and putting it on a higher place. 

Another flashy term is machine learning. An alphabetical sorting in Microsoft Excel is a machine learning application, let alone the linear regression almost every person with a little bit of quantitative analysis information keeps applying. Lock of a door and its key or child lock of cars are some loose examples of a machine learning applications, too.

Simply put, we don't need to learn quite elaborate examples of machine learning models to see their significance in our daily life, as well as for our future. Everyone needs to be familiar with at least a few types of machine learning methods, and developments in data science.