You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth.
Overview
Artificial Intelligence: The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by the human 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 human can do better.”Therefore It is an intelligence where we want to add all the capabilities to the machine that human contain.
Machine Learning: Machine Learning is the learning in which machine can learn on its own without being explicitly programmed. It is an application of AI that provides the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definitions of the 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:
| ARTIFICIAL INTELLIGENCE | MACHINE LEARNING |
|---|---|
| AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as an ability to acquire and apply knowledge. | ML stands for Machine Learning which is defined as the acquisition of knowledge or skill |
| The aim is to increase the chance of success and not accuracy. | The aim is to increase accuracy, but it does not care about the success |
| It works as a computer program that does smart work | It is a simple concept machine takes data and learn from data. |
| The goal is to simulate natural intelligence to solve complex problem | The goal is to learn from data on a certain task to maximize the performance of machine on this task. |
| AI is decision making. | ML allows the system to learn new things from data. |
| It leads to developing a system to mimic human to respond behave in a circumstances. | It involves in creating self-learning algorithms. |
| AI will go for finding the optimal solution. | ML will go for the only solution for that whether it is optimal or not. |
| AI leads to intelligence or wisdom. | ML leads to knowledge. |
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