This document provides a generic overview of the differences between Machine Learning and Artificial Intelligence.
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Artificial Intelligence (AI) and Machine Learning (ML) are correlated elements of computer science. Each of the two technologies are some of the highest trending in the industry today and used for creating intelligent software.
Artificial Intelligence is the larger concept that is used create intelligent machines that can simulate human thinking capability and behavior. Machine Learning is an application or subset of AI that allows machines to learn from available data without being explicitly programmed to do so.
Artificial intelligence is a field of computer science that enables a computer system to mimic or simulate human intelligence. The two words "Artificial" and "Intelligence" translate to "a human-made thinking power."
An AI system does not require programming in advance. It uses algorithms that work with the AI intelligence already present in the system. This involves machine learning algorithms, such as "reinforcement learning" and "deep learning" neural networks.
Some examples of AI implementations in used today include: Siri, Google AlphaGo, and various chess playing implementations.
Machine learning is a subfield of AI that enables machines to learn from historical data or experiences within a specific domain without being explicitly programmed. ML is reliant on extracting knowledge from the data. ML models are composed of complex algorithms. Through trial and error, the algorithms are refined to best explain patterns and best predict future outcomes and user behaviors.
ML focuses on enabling machines or software to learn without human guidance, similar to how humans learn by recognizing patterns in the world and remembering the patterns through learned rules. Machines analyze information provided and create a model that explains the patterns. The information gathered can then guide future behavior.
The primary benefit of ML is automation of effort. For example, in cases where you already know what you want to know, ML accelerates how quickly your goal is can be achieved. In cases where you do not know what you want to know or cannot identify a useful pattern in a dataset, ML can find a useful pattern and bring it the forefront for investigation.
Some examples of ML implementations in use today include online recommendation systems, such as Google search algorithms, email spam filters, and Facebook automatic friend tagging suggestions.