Difference between Artificial Intelligence and Machine Learning
Artificial Intelligence vs Machine Learning
In computer science, artificial intelligence and machine learning are correlated to each other. These two are the most prominent of the technologies that have gained ground and have been gaining ground in the past and the present respectively and continue to be a raging boon to the academia around the globe.
As the two are related to each other, people sometimes also use them synonymously. However, they are functionally different from each other in their characteristics and composition. On a wider and broader level, both artificial intelligence and machine learning can be distinguished from each other.
Artificial intelligence, as the name suggests has its fundamental foundations on simulating and emulating human intelligence where a computer system is made to mimic human behaviour and intelligence. Artificial intelligence itself means, “a human-made thinking power”.
An artificial intelligence system need not be pre-programmed, but rather, uses its own intelligence to work with data and determine algorithms. It incorporates algorithms of machine learning such as deep learning neural networks and Reinforcement learning algorithm. Artificial intelligence is being used in multiple gadgets and devices today. Most popular of these are Siri, Google, Alexa, AI in Chess, etc.
Artificial intelligence on the basis of capabilities can be categorised into three types: Weak AI, General AI, Strong AI. We are currently working with Weak AI and General AI while the future of AI is Strong AI, which is said to be more intelligent than humans.
Machine learning is said to be a subfield or sub-class of AI. It is about exacting and withdrawing information and knowledge from the data. It complements machines to learn from past data without being explicitly programmed. Machine learning allows the computer to process information and makes predictions by relying on historical data without being programmed.
It uses large amounts of structured and semi-structured data to make predictions and produce accurate results on the basis of the same. It does not emulate human behaviour or replicate human thinking. It enables a machine to process information and generates results on its own accord. Online recommender system for Google search algorithms, Facebook auto-friend tagging suggestion, Email spam filter, etc.
Machine learning can be divided into three types: Supervised learning, Reinforcement learning, Unsupervised learning.