All You Need To Know About Artificial Intelligence - In A Nutshell Part 2

Check part 1 at:
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Another branch of machine learning is reinforced machine learning. It is explained in a short and efficient way by the video below where an MIT team build an autonomous RC car that adjusts its steering pattern to the environment. It is very important to mention that the behaviour of the car is learned by experience, not by pure "if this then that" algorithm.

Link below:

https://www.youtube.com/watch?v=opsmd5yuBF0

In general, reinforced machine learning is similar to learning how to play a guitar. If you put your fingers in an incorrect position, you will experience unpleasant sounds; if you do it better, the outcome will result in a pleasurable feeling of experiencing art. For humans, the reward is objective, whether it be money, power, comfort or happiness from helping others, but for computers we can set a clear and consistent goal. If the result of the actions is far from the expected result, the cost (difference between the outcome and the goal) will be high, which means that our actions are not satisfactory. By changing actions along the experiment we are able to learn. To simplify, you can think about reinforced machine learning as a branch of supervised learning when the experiment continues forever. It is often said that "we learn our whole life" and that is true.

The last branch of machine learning is unsupervised machine learning. Here we deal with unlabelled data and we have very little information about the structure of the data. Obviously, we have (or at least we should have) all the parameters of the data. Some of them can be missing due to technical problems or software bugs, but for simplification let's assume that we have all the data we want.

Let's work on another example:

Figure 4. Unlabeled data.

Obviously we, humans, can find patterns in this dataset. It looks like we have three big blobs. They represent something, but we do not know what. It might be a state of a system (stable, unstable, at risk of being unstable); it might be three species of a dog (based on the length and width of the body represented on the x and y axes). We don't know. Computers, however, need an algorithm to solve this clustering challenge. To make the problem even more complicated, we do not know how many labels we should use. We see three blobs but they might represent data about students who passed and failed a course, or people being guilty of crimes or not. In this case there are only two labels – pass or fail, guilty or innocent.

Figure 5. Clustering using KMeans algorithm for 3 clusters.

Figure 6. Clustering using KMeans algorithm for 2 clusters.

The biggest struggle with unsupervised learning is to decide how many labels we should use. If we use two labels for a problem that has three possible outcomes, we lose information. If we choose many labels, the information gain and clarity of the outcome is very, very weak.

I hope that this article gave you basic insights on how we can build our own Skynet. Why would we do that? To automate boring activities or just to improve quality of life. Hopefully our creation will be less aggressive and violent than the Terminator.

I am planning to make a series about the basics of machine learning, so any feedback is welcome.

Are you interested in the topic? Don't hesitate to contact me.

Wojciech Orzechowski

Contact me:

E-mail: [email protected]

LinkedIn: https://se.linkedin.com/in/wojciechorzechowski