I’m sure you have heard of the terms artificial intelligence, machine learning, and deep learning. Do you end up using these interchangeably? Probably the answer is yes, not just you but many working in the field of information technology do that. However, I must tell you that these words are not the same thing, they differ. In this blog post, we will focus on elaborating on machine learning, artificial intelligence, and deep learning; how they differ and their significant usage will also be covered.
Artificial Intelligence (AI) – What it is?
Artificial Intelligence, commonly known as AI, refers to the creation of machines that reduce human efforts by performing human like tasks. This indicates that machines will be trained to carry out all the works that human can do with ease. These machines will also make mistakes, learn from them, and gain adequate experience to take decisions on their own.
Artificial Intelligence is a kind of fundamental science and a unique approach to enhance the technological aspect in different fields of business. It has the ability to process images, signals, natural language, numerous databases, and do the unthinkable. If you think machine learning and artificial intelligence are same, you are wrong. All machine learning is AI, but all AI is not machine learning. So now, we know that Machine Learning is a subset of Artificial Intelligence. This might have cleared half of the confusion.
AI vs ML vs DL – Let’s Know the Real Difference
Artificial Intelligence and machine learning are frequently used interchangeably in the realm of big data. However, you must know why not to use them vice versa.
Artificial Intelligence or AI is a wider concept than machine learning. AI can address all the issues of computers and mimic the significant functions carried out by human beings with ease. When machines perform tasks based on the algorithms in a useful or intelligent way, it is known as AI. As machine learning is termed as a subset of Artificial Intelligence, its role is to focus on the capability of the machines to accept a certain amount of data and understand it. It alters the algorithms simultaneously when they learn about the data it is going to process.
Algorithm is Important
Before proceeding further, let’s understand the meaning of algorithm. Algorithms are a set of rules that are followed to resolve complexities in a computer system. Machine learning consumes these complex data and carries out calculations to give the desired outcome. The calculations can be simple or complex. Algorithms are trained to learn the methods of classification and processing the information. The accuracy and efficiency of the algorithms are hugely dependent on the training provided. Utilizing an algorithm to find the correct answers are not a part of machine learning or the AI that was used.
Nowadays, people tend to use machine learning or AI to state that an algorithm is utilized to analyze the data and take a decision. Using the algorithms to predict any sort of outcomes is never considered as machine learning. Utilizing these outcomes of the predictions to enhance your future predictions is termed as machine learning.
Training computers or operating systems to think and take decisions like human beings is achieved partially by using neural networks. These networks are a set of algorithms that works like a human brain. As human beings use their brains to recognize a pattern and classify it, neural networks also perform the same tasks for computers. The brain can consistently try to add some basic sense to the information that is being processed. In order to do this function, it initially labels the information and assigns the items to the specific categories. Whenever we as human beings come across something new or unique, we definitely try to compare the same to an already recognized item to understand what it is, similarly, neural networks are meant to recognize all the new items that are processed in the computers.
What is Machine Learning (ML)?
ML is an application of AI that empowers the systems to automatically learn and improve from the experience without being explicitly programmed to do so. However, ML focuses on computer programs that can access data, make use of it, learn from it to become intelligence. I would call machine learning as machine intelligence, which is being the focus here. Intelligence machines are an outcome of a combination of machine learning and AI. Now, one more important role player is deep learning, which is a subset of ML is. Let’s know what Deep Learning is.
Deep Learning (DL) Defined
Deep learning, as the name states, means learning deeply to the next level and is a part of machine learning. The basic concept of this type of learning relates to neural network of brain, which means learning deeply in many layers that are involved in the artificial neural networks. Sometimes an artificial neural network can have single layer of information, while a deep network may have many. These layers are known as nested hierarchy of decision trees. Learning these layers is termed as deep learning.
Now you know the key technology behind the driverless cars. Deep learning is a technology that enables computers to do what comes to humans naturally. Here machine works and acts like human brain.
The Bottom Line
Now you know that Machine Learning, Deep Learning and Artificial Intelligence are related, but they are not the same thing. They work on a common principle but their functions and usage differ. I have already said that ML is a subset of AI, so by now you should also know that DL is a subset of ML. This should have given you a clear picture of DL, ML and AI. Next time when you make use of these terms don’t make the mistake again, it isn’t an elusive thing to do.
The words look small, but they are a big thing in the field of technology. Technology is like a feeling; it is deeper than it looks. Technology is changing every day for better, and its impact on society is visible with naked eye. Those who think these technologies are a trend they need to rethink! They are not just a trend, but are the three powers that will define the future.
In a future post we will discuss the bigger picture of deep learning, which probably is not so trending word for many of us. Till then keep smiling, keep learning!