The three terminologies might be interchangeable, but they are not the same.
Artificial Intelligence is quite a popular term by now. Why am I talking about now? We were already exposed to such a kind of ideas right from the time movies like Terminator and Matrix were made. But what is happening now is that other than hearing only about AI, we get to hear about Machine Learning and Deep Learning as well. One thing is clear that is these three terminologies are not the same and their differences are still unclear among most of us.
Let’s get into some more details and help you to get a clear picture of them. An in-depth can help you to understand better.
First, begin with Artificial Intelligence, and then we will further discuss ML and DL one by one.
Machines that function like human Intelligence, can be loosely stated as Artificial Intelligence. What kind of human Intelligence am I talking about? To be more specific, I am talking about planning tasks, understanding user’s language, identifying objects and the ability for solving problems.
Thus, we can also say it like this, a machine that complete its tasks following a given set of algorithms, just like human intelligence can be called Artificial Intelligence.
Usually, there are two types of Artificial Intelligence. One is general and the other one is narrow. As discussed earlier, machines that perform task like human intelligence comes under general Artificial intelligence.
Now, there are machines that are able of performing specific tasks, which might have limited scope, but finishes the work perfectly that it is specialized on, like recognizing photographs, is a typical example of narrow AI.
Now what is Machine learning all about?
In a simple term, Machine learning is the possible way to achieve Artificial Intelligence. Rather than writing a series of complex programs, machine learning is the way of training the computer system, enabling them to learn how things actually work. Thus, they help the device to take decisions.
Training the computer system includes providing all kinds of data to algorithm and enabling them to learn information that needs to be processed, in an improved way.
For example, if you are to recognize human faces from millions of pictures available, you can do that correctly. The algorithm then builds up a model by itself for identifying the human faces accurately. Once they reach the accuracy level and start recognizing the pictures accurately, we get the confirmation that the machine has learned.
Now, let’s discuss Deep Learning.
Deep learning is very similar to Machine Learning. We can also term it as another technique for performing machine learning. It is said that the algorithms of Deep leaning are influenced by the human brain, the way they process the information.
As a human being, the way we recognize any model, gather various kinds of information for solving any task, the algorithm of deep learning performs similar kinds of tasks for machines.
As we receive any new information, our brain tries to decipher it. This is possible through a series of labelling and categorizing the information and comparing it with other known things before showing the final result. The same concept lies with deep learning as well. Thus, we can say Deep Learning is the advanced concept of Machine Learning. If we start comparing DL with ML we will notice, that DL required high-performance systems and a large amount of data for delivering correct results.
Hope, the differences between AI, ML and DL are clear and there is no room left for the confusion.