Scrolling through your social media feed, you may have noticed ads on Tesla or BMW releasing their new and revolutionary technology and implementing in their latest car models. A sought-after and futuristic concept that is on the market right now are self driving cars. Even though it isn’t a new concept, it is yet to be perfected and in our everyday lives.

But the question arises, how do these vehicles work? I mean, someone has to program it right? How does it know where to go, when to go, when to stop and when to yield to traffic, and so on. For a human, it can take a couple of days to weeks to learn how to drive and they get better as they earn more experience throughout their driving experience.

But for a robot, the process by which they learn and adapt by observing and analyzing is called machine learning. The term sounds pretty intuitive, right? Machines… learn. But it may not be as simple as you think.

Just teach your Toyota Prius how to drive on its own, easy, right? Nope. Machine learning is a topic that is increasing in interest and demand: it is a glimpse into our future.

How? you may ask. Machine learning consists of different methods of gathering data, processing them, and “teaching” the machine without being explicitly programmed. It is going to be near impossible to program a car about every turn on a road, and even if you do, who is going to account for all the other cars?

In layman terms, it is a tool required for machines and robots to keep up with the ever increasing population and environmental change in today's world-- a tool needed to make the machine work on-the-fly-- and to apply human ways of learning into robots.

When applying machine learning to a program, you are giving the robot/ machine its own brain, its own auditory, and visionary senses. Let’s take a look at a real world problem that we are trying to solve nowadays using machine learning. How do you detect a person’s emotions by just showing the machine a face of the person?

Scientists have fed millions of pictures of what different people’s faces look like when expressing different emotions, and in this way, the machine finds a pattern among the faces showing the same emotion and creates an algorithm that gets more accurate as more data is added. It is near-impossible to get the accuracy to a 100% but more data gathering and research is going on. Similarly, a car will know at what rate to speed up and slow down, take turns, and abide traffic signs based on the past data that has been fed into it.

Demand in this industry is increasing rapidly as more and more questions arise and people start their quest to look for answers. Our goal in CommunityAI is to help you all learn a piece of machine learning so that you will be able to understand the fundamentals and be able to apply it to your science projects and programming concepts.

So the next time you see the advertisement of a self-driving car, impress your friends when you explain how it actually works!