Terminology and Related Concepts | 2nd | week 2 |.
Before we deep dive into how AI works, and its various use cases and applications, let's differentiate some of the closely related terms and concepts of AI: artificial intelligence, machine learning, deep learning, and neural networks. These terms are sometimes used interchangeably, but they do not refer to the same thing. Artificial intelligence is a branch of computer science dealing with a simulation of intelligent behavior. AI systems will typically demonstrate behaviors associated with human intelligence such as planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation, and to a lesser extent social intelligence and creativity. Machine learning is a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned, without being explicitly programmed. Machine learning algorithms are trained with large sets of data and they learn from examples. They do not follow rules-based algorithms. Machine learning is what enables machines to solve problems on their own and make accurate predictions using the provided data. Deep learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making. Deep learning algorithms can label and categorize information and identify patterns. It is what enables AI systems to continuously learn on the job, and improve the quality and accuracy of results by determining whether decisions were correct. Artificial neural networks often referred to simply as neural networks take inspiration from biological neural networks, although they work quite a bit differently.