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Difference Between Machine Learning and Artificial Intelligence

These two are terms commonly used in computer science, and there are various ways in which they differ from each other. In this article, we will discuss the major difference between machine learning and artificial intelligence. But let us first understand each of them individually in brief.

What is Machine Learning?

This term refers to a type of learning in which a machine becomes capable of learning on its own (despite no one programming it to behave that certain way). Machine learning is basically an application of artificial intelligence, and it provides any system with the ability to improve on its own by learning automatically.

We generate programs using machine learning by integrating both the output and input of those programs. In simpler words, “Machine Learning is the process by which a machine learns from its experience E with respect to a class belonging to task T. If we measure the performance of a learner by P, then a learner’s performance over the task T improves with its experience E with a performance P.”

What is Artificial Intelligence?

This term comprises two separate terms- Artificial and Intelligence. The term intelligence refers to the ability to think and understand, while artificial refers to something human-made or non-natural in nature. The concept of artificial intelligence being a system is a myth. It is not a system- but actually implemented in a given system.

There can be various definitions of the term artificial intelligence. A simplified one can be, “Artificial intelligence is a study that indicates how to train computers and devices as a result of which, they can perform things and activities that humans can do- but in a better way. Thus, AI is a type of intelligence that allows one to add all the human capabilities into the machines.

Difference Between Machine Learning and Artificial Intelligence

Parameters Machine Learning Artificial Intelligence
Definition and Meaning Machine learning refers to a subset of artificial intelligence that allows machines to learn and improve automatically based on past data without the need for explicit programming. Artificial learning is a technology that provides machines with the ability to simulate human behaviour.
Goal Machine learning aims at allowing various machines to adapt and learn from data so that they can provide an accurate output (on autopilot). Artificial intelligence aims at producing smart computer systems that can solve complex human problems faster than humans can do.
Mode of Operation In the case of ML, we basically teach different machines involving data to come up with accurate results by performing a task on its own. In the case of AI, we come up with intelligent systems that can perform any set of tasks just like humans.
Subsets The primary subset of ML is deep learning. The two crucial subsets of AI are deep learning and machine learning.
Range of Scope ML has a very limited scope. AI possesses a comparatively much wider scope.
The Approach of Machine Development ML works continually to develop machines capable of performing only a specified set of tasks (for which we train them). AI works continually to develop machines capable of performing several tasks that are way too complex.
Concerns The primary concern of ML is to ensure patterns and accuracy among them. The primary concern of AI is maximizing the overall chance of success for any given set of tasks.
Applications ML has its main applications among Search algorithms for Google, Auto-tagging of friends on Facebook, Online recommendations, etc. AI has its main applications among Humanoid intelligent robots, online playing games, Siri, Chatbot customer support, expert systems, etc.
Types ML has three major types. These are Reinforcement Learning, Unsupervised Learning, and Supervised Learning. AI also has three major types, depending on their basis of capabilities. These are Strong AI, General AI, and Weak AI.
Self-Correction ML comes with the concept of self-correction. It can do so as soon as it deals with a set of a new data type. AI can also undergo reasoning and learning- thus, it also has the concept of self-correction.
Type of Data Structure Machine learning can feasibly deal with both semi-structured and structured forms of data. AI is capable of dealing completely with unstructured, semi-structured, and structured forms of data with no hassle.

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