There has been a lot of talk about machine learning in recent years, so we have decided to come up with a short and simple guide. We will talk about what it is, what it isn’t, as well as the practical applications of machine learning.
What Is It?
Machine learning is the study of algorithms that allow the machine in question to come up with answers to problems without explicit instructions, after some training has been done, of course. Machine learning is not AI, at least not in the traditional sense. It doesn’t try to copy human behavior, nor does it have a consciousness – it simply operates based on data given before. It bases its answers on previous experience.
There are several types of algorithms that we can use in building a system. Supervised learning deals with providing the system with the data, and the wanted output. During unsupervised learning, the machine finds similarities in data and puts them in groups together, based on the original instructions. These are just two of many more.
How Does It Work?
We collect data and put it into a comprehensive logical test. We provide both input and output for the test. After that, we create a model where the test can be performed. We use the same model to try out variables that the machine has not encountered before in order to test whether it grasped the principle at hand.
Think of the machine as a child. This child might encounter a pregnant person and ask them whether they are fat. This happens because the child has made the connection between being fat and having a large belly. Based on the child’s previous experience, there is only one reason why someone would have a large belly. Correcting the child (as well as making sure they understand what is okay to say in public) is how the child learns to differentiate between these two situations. The principle is very similar to that of machine learning.
As scary as some people may find the scope of the abilities machines possess, machine learning has a ton of practical applications, and it’s already in use. Detecting errors in production, predicting data related to sports scores and the weather, online ads and recommended videos and articles based on the ones you had at one time expressed interest in – all of these are examples of machine learning in action.
Without machine learning, you wouldn’t be able to rely on Google Assistant, Siri, Cortana, and Alexa. We use it every day to sort through data at previously impossible speed, sorting through the mail to see what messages are spam, which ones are not important, and those we want to read first. The practical uses will only continue to grow in number.