Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions. On the other hand Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience. One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modelled after the human brain. The neural network helps the computer system achieve AI through deep learning. This close connection is why the idea of AI versus machine learning is really about the ways that AI and machine learning work together.

A Neuro Genetic hybrid system is a system that combines Neural networks which are capable to learn various tasks from examples, classify objects and establish relations between them and an extension of Artificial Intelligence whereas Genetic algorithm serves important search and optimization techniques. Genetic algorithms can be used to improve the performance of Neural Networks and they can be used to decide the connection weights of the inputs. These algorithms can also be used for topology selection and training networks. These are all subsection of AI.