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Though we come across a lot of applications and technicalities related to AI, the terms Artificial Intelligence, Machine learning, and Deep Learning are easily confused.
Before going into differentiating AI, Machine Learning, and Deep Learning, let's define a concept that's the essence of these three – an algorithm.
What Is an Algorithm?
It can be defined as a set of steps taken to accomplish a task. Algorithms need data to calculate and find an answer. The way algorithm handles simple as well as complex calculation makes it an effective tool than any human. Moreover, it works at a rapid pace and provides accurate information.
However, algorithms require training in order to process data, which in turn, also determine their efficiency and accuracy. As we know all squares are rectangles, but not every rectangle is a square. Similarly, just because you're using the algorithm to analyze data, it doesn't mean you're employing AI or machine learning technology.
Artificial Intelligence vs. Machine Learning vs. Deep Learning
As we mentioned earlier, while people use AI and machine learning interchangeably, these are not at all same. Here, you will understand the differences between AI, Machine Learning, deep learning, and how they are related to each other.
Well, Artificial intelligence is a broader umbrella under which machine learning and deep learning comes. AI is nothing but a technique which allows machines to mimic human behavior. Being a subset of AI, machine learning is all about how a machine can use data to answer questions, serve predictions, learn and change the algorithm.
The biggest example of machine learning is Google search. It has many machine learning systems that understand your query and adjust the results based on your personal interests.
Do you know that computers can be trained to think more like a human? Of all the technologies, neural networks are partly successful in doing so. This is because the neural network is an array of algorithms designed after the human brain. So, just like the human brain (which consists of multiple connected neurons) works to identify patterns, and categorize data, artificial neural networks do the same as well. Deep Learning is a subset of machine learning and primarily a study of multilayered neural networks. Unlike traditional neural networks, deep neural networks rely on more hidden layers and thus more depth.
Similar to AI, deep learning also needs training via a large amount of data. It needs exposure to hundreds, to thousands or millions of data points to deliver the right answer every time – rain or sun. Example of such technology is Google Brain. On learning, it tends to recognize images after running massive amounts of data.
Data Is at the Heart of the Matter
Data is important no matter what technology you are using. After all, flawed data can ruin your valuable insights and information. Therefore, you must know what data cleansing is. Basically, it involves data scientists who first detect the corrupt data, and then replace it with the right one. The process of data cleansing is of utmost importance because it ensures the reliability of your outcome. Since AI is governing more and more of our lives today; we need data that can be trusted.