Difference between Machine learning and Artificial Intelligence

Artificial Intelligence and Machine Learning play an important role in anything that is remotely related to Automation. AI and ML are the most advanced and popular technologies used for creating intelligent systems in different fields of engineering and science.

Although AI and ML are correlated, they are quite different from each other. AI is a wider concept that is used to build intelligent machines for the simulation of human thinking capability and behavior, whereas ML is an application of AI that allows machines to learn from data without being programmed explicitly.

Read this tutorial to find out more about AI and ML and how these two technologies are different from each other.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of science that broadly deals with the idea of making artificial manmade objects to think, learn, decide and work like a human brain while trying to solve a problem.

• Artificial intelligence mainly deals with programming of computers to detect patterns in new data, make decisions based on user's inputs, and produce outputs based on the user's inputs.

• In AI, all the rules are not explicitly defined and it is expected that the machine learns these rules by experience, using a reward-punishment mechanism.

• Today, AI is being used in designing self-driving cars, boats, object and facial recognition systems, and much more.

AI is like a basket containing several branches, the important ones being Machine Learning (ML), Robotics, Expert Systems, Fuzzy Logic, Neural Networks, Computer Vision, and Natural Language Processing (NLP).

While we highlight the features of ML in the next section, here is a brief overview of the other important branches of AI:

• Robotics − Robots are primarily designed to perform repetitive and tedious tasks. Robotics is an important branch of AI that deals with designing, developing and controlling the application of robots.

• Computer Vision − It is an exciting field of AI that helps computers, robots, and other digital devices to process and understand digital images and videos, and extract vital information. With the power of AI, Computer Vision develops algorithms that can extract, analyze and comprehend useful information from digital images.

• Expert Systems − Expert systems are applications specifically designed to solve complex problems in a specific domain, with humanlike intelligence, precision, and expertise. Just like human experts, Expert Systems excel in a specific domain in which they are trained.

• Fuzzy Logic − We know computers take precise digital inputs like True (Yes) or False (No), but Fuzzy Logic is a method of reasoning that helps machines to reason like human beings before taking a decision. With Fuzzy Logic, machines can analyze all intermediate possibilities between a YES or NO, for example, "Possibly Yes", "Maybe No", etc.

• Neural Networks − Inspired by the natural neural networks of the human brain, Artificial Neural Networks (ANN) can be considered as a group of highly interconnected group of processing elements (nodes) that can process information by their dynamic state response to external inputs. ANNs use training data to improve their efficiency and accuracy.

• Natural Language Processing (NLP) − NLP is a field of AI that empowers intelligent systems to communicate with humans using a natural language like English. With the power of NLP, one can easily interact with a robot and instruct it in plain English to perform a task. NLP can also process text data and comprehend its full meaning. It is heavily used these days in virtual chatbots and sentiment analysis.

Now, let's move on to discuss Machine Learning (ML) which is a prominent field of AI.

What is Machine Learning?

Machine Learning (ML) refers to ways by which a machine can learn without being programmed. ML empowers machines to learn automatically from existing data and algorithms and improve itself based on past experiences. In simple terms, machine learning is a data-driven application which can make its own decision based on varying inputs and can improve its decisions over time.

• ML is a subset of AI that uses complex programs that can understand through experiences and create predictions. It is a concept that creates complex algorithms for huge data processing and supports results to its users.

• In ML, the algorithms improve on their own by the frequent input of training data. ML algorithms use data to learn patterns and make predictions or decisions.

Machine Learning algorithms can be broadly divided into three categories −

• Supervised learning − In supervised leaning, the input data is labelled which is used as the training dataset. The algorithms use the provided training dataset to predict the output.

• Unsupervised learning − In such algorithms, the input data is unlabeled. The algorithm analyzes the input data, identifies patterns and extracts useful information to generate a possible outcome.

• Reinforcement learning − Here, the machines are not given any labelled input/output pairs. Instead, the machines are allowed to take a decision based on the input data and when the decision is right, they are rewarded. When the decision taken by the machine is incorrect, it is penalized. So, the machine learns to adapt through a repetitive trialanderror method.

The primary objective of machine learning is to learn from data and build models that can be understood and used by humans.

Difference between Artificial Intelligence and Machine Learning

The following table highlights the important differences between Machine Learning and Artificial Intelligence −

Key	Artificial Intelligence	Machine Learning
Definition	AI refers to the ability of a machine or a computer system to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, and making decisions.	ML is a type of AI that allows a system to learn and improve from experience without being explicitly programmed. It articulates how a machine can learn and apply its knowledge to improve its decisions.
Concept	AI revolves around making smart and intelligent devices.	ML revolves around making a machine learn/decide and improve its results.
Goal	The goal of AI is to simulate human intelligence to solve complex problems.	The goal of ML is to learn from data provided and make improvements in machine's performance.
Includes	AI has several important branches including Artificial Neural Networks, Natural Language Processing, Fuzzy Logic, Robotics, Expert Systems, Computer Vision, and Machine Learning.	ML training methods include supervised learning, unsupervised learning, and reinforcement learning.
Development	AI is leading to the development of such machines which can mimic human behavior.	ML is helping in the development of self-learning algorithms.

Conclusion

To conclude, the core concept of AI revolves around making smart devices and machines, while ML is all about making a machine to learn, decide and improve its results.

AI is a broad term that refers to the ability of a machine or computer system to exhibit intelligent behavior, while ML is a specific type of AI that involves training a system on data so that it can learn and improve over time.