Difference between Artificial Intelligence, Machine Learning, and Deep Learning: Clearing the Confusion
Once in our lifetime, Almost all of us have been confused about Machine Learning, Artificial Intelligence, and Deep Learning. You do not need to worry We are now going to clear that confusion. Although they are sometimes used interchangeably they are different from each other. The diagram below gives clarity on the topic and below that we attempt to differentiate between them.
In this article, we will try to give a clear understanding of these three topics with examples. Deep Learning is a subset of Machine learning and Machine Learning is a subset of Artificial Intelligence.
Now, We attempt to clarify what Artificial Intelligence is.
Artificial Intelligence(AI)
The term Artificial Intelligence was first coined by John McCarthy in 1956. According to him, Artificial Intelligence is "the science and engineering of making intelligent machines, especially intelligent computer programs". In general, It is that branch of computer science that deals with the simulation of Intelligent behaviors in computers ie. to make the machine able to imitate intelligent human behaviors. It is that discipline which deals with making machine smart. Whether it's a car, a refrigerator, a robot, a torch or a software application If you are making them smart it is AI. Artificial Intelligence is a very broader concept.
Whenever a machine solves a problem based on the set of rules (algorithm) to solve the problem, this intelligence of Machine is Artificial intelligence. For example, if a machine moves an object from one place to another place that act is performed on the basis of given rules.
Intelligent Machines are categorized into three groups as follow:
Strong Artificial Intelligence
It is also called Artificial General Intelligence(AGI). It refers to those AI which exhibits human intelligence ie they can act, understand, think, and make decisions like human do. In theory, Strong AI can do everything that a human can do. There is not yet any Strong AI. It is believed that to make a true Strong AI, Machines should be made conscious.
Weak Artificial Intelligence
It is also known as Artificial Narrow Intelligence(ANI). This is a limited and most common type of Intelligence. It refers to that intelligence which can do one work really well which means it has narrow scope of what it can perform. The idea is not to mimic human behavior but just to simulate human behavior. Thus it is nowhere near matching human Intelligence. Even the smartest AI of today are weak AI. These AIs are very intelligent at completing the specific task they are assigned.
Super Intelligence
It is also called Artificial Super Intelligence(ASI). It is that state in which Artificial Intelligence surpasses human Intelligence. These AI are theoretically best at everything either that be medical or engineering or accounting etc. Even the most bright human talent can not come near to this intelligence. Super AI is not likely to exist for many decades or more.
Some Examples where Artificial Intelligence are applied are:
- Self Driving Cars
- Sophia: The Humanoid Robot
- etc.
Machine Learning(ML)
Machine Learning is first coined in 1959 by Arthur Samuel. According to him, Machine learning is "Field of study that gives computers the ability to learn without being explicitly programmed". Machine Learning is a subset of Artificial Intelligence. It focuses more on developing programs that teach computers to change when exposed to new data. It brings Artificial intelligence via learning through data. Data is the most integral part of the Machine Learning process. It is the process of training algorithms so that they can make a decision.
There are three types of learning as follow:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
You can learn about Machine Learning in detail here.
Some applications of Machine Learning are:
- Snapchat Filters
- Google Search
- Netflix Search
Deep Learning(DL)
Deep Learning is one of the most popular methods of machine learning. It is rising exponentially. This field of machine learning is inspired by the working mechanism of our brain cells called neurons. It was first coined by Igor Aizenberg in 2000. It is that class of Machine Learning that deals with the use of multiple layers to progressively extract the higher-level feature from the raw input. It is based on feature engineering which is the discipline of extracting features from raw input. Deep Learning is also sometimes termed as Deep Neural Network or Deep structured learning. They are based on Artificial Neural Networks.
There are many popular architectures of deep learning. Some of them are:
- Deep Neural Network
- Deep Belief Network
- Recurrent Neural Network
- Convolutional Neural Network
If we talk about the Artificial Neural Network there are three different layers as follow:
Input layer: Layer which accepts the input data and passes it to the hidden layer.
Hidden Layer: All the calculation is performed in this layer. There can be many hidden layers.
Output Layer: This layer is responsible for computing and giving the output.
We have also discussed in detail about the convolutional neural network in our another article. Find it here.
In the end, I would like to show you the very famous image used to distinguish between AI, ML, and DL
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