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Difference between Machine learning and Artificial Intelligence

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  • Difficulty Level : Easy
  • Last Updated : 12 Jul, 2022

Artificial Intelligence and Machine Learning are the terms of computer science. This article discusses some points on the basis of which we can differentiate between these two terms. 

Artificial Intelligence comprises two words “Artificial” and “Intelligence”. Artificial refers to something which is made by humans or non natural thing and Intelligence means the ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present humans can do better.” Therefore It is an intelligence that we want to add all the capabilities to a machine that human contains.

Machine Learning is the learning in which a machine can learn on its own without being explicitly programmed. It is an application of AI that provides the system the ability to automatically learn and improve from experience. Here we can generate a program by integrating the input and output of that program. One of the simple definitions of Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.” 

The key difference between AI and ML are:  

1.AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as the ability to acquire and apply knowledge.ML stands for Machine Learning which is defined as the acquisition of knowledge or skill
2.The aim is to increase the chance of success and not accuracy.The aim is to increase accuracy, but it does not care about success
3.AI is aiming to develop an intelligent system capable of performing a variety of complex jobs. Machine learning is attempting to construct machines that can only accomplish the jobs for which they have been trained.
4.It works as a computer program that does smart work.Here, the tasks systems machine takes data and learns from data.
5.The goal is to simulate natural intelligence to solve complex problems.The goal is to learn from data on certain task to maximize the performance on that task.
6.AI has a very broad variety of applications. The scope of machine learning is constrained.
7.AI is decision-making.ML allows systems to learn new things from data.
8.It is developing a system that mimics humans to solve problems.It involves creating self-learning algorithms.
9.AI will go for finding the optimal solution.ML will go for a solution whether it is optimal or not.
10.AI leads to intelligence or wisdom.ML leads to knowledge.
11.AI is a broader family consisting of ML and DL as its components.ML is a subset of AI.

Three broad categories of AI are :

  1. Artificial Narrow Intelligence (ANI)
  2. Artificial General Intelligence (AGI)
  3. Artificial Super Intelligence (ASI)

Three broad categories of ML are :

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning
13.AI can work with structured, semi-structured, and unstructured data.ML can work with only structured and semi-structured data.

AI’s key uses include-

  •  Siri, customer service via catboats
  • Expert Systems
  • Machine Translation like Google Translate
  • Intelligent humanoid robots such as Sophia, and so on.

The most common uses of machine learning-

  • Facebook’s automatic friend suggestions
  • Google’s search algorithms
  • Banking fraud analysis 
  • Stock price forecast
  • Online recommender systems, and so on.
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