Interrelatedness of Machine Learning, Artificial Intelligence, Deep Learning
Artificial intelligence (AI) emerged as a sub field of computer science as the quest for programming computers to solve tasks that humans are good at (natural language, speech, image recognition, etc.). Introductory sessions to AI learning are held at Best B. Tech College in Jaipur, Rajasthan.
The first machine learning algorithms were developed in the 1950s, inspired by early mathematical models of neurons in the human brain. In the following decades, neural networks with multiple neurons and layers were developed as well as algorithms to train them effectively.
Due to the increased focus on deep learning methods in the last decade, as well as the evolving technology stack and algorithmic techniques, the branch of machine learning that does not concern deep neural networks are now often referred to as traditional machine learning.
There is a close connection among the fields of machine learning, AI, and deep learning. AI can be understood as the quest for developing non-biological systems that exhibit human-like forms of intelligence. Approaches of learning AI includes symbolic models and reasoning, early versions of neural networks, and expert systems. In post 20th century, machine learning emerged as one of the most promising directions towards developing such artificially intelligent systems. In a nutshell, machine learning is about teaching machines or computers how to learn from data without the need for humans to hand-code specific rules.
While machine learning can be used to develop AI, not all machine learning applications can be considered AI. For instance, if someone implements a decision tree for deciding whether someone deserves a loan based on age, income, and credit score, we do not think of it as an artificially intelligent agent making that decision.
Approaches to AI that are not based on machine learning include the symbolic representations or logic rules, which can be written or programmed by observing a human expert performing a particular task. Systems that are developed based on observing human experts are called expert systems. The symbolic approach to AI is now sometimes called “Good Old-Fashioned Artificial Intelligence”. From a programming perspective, one can think of such expert systems as complex computer programs with many (deeply nested) conditional statements (if/else statements).
In sum, we can describe classic AI as a non-biological system that exhibits human-like forms of intelligence on one or multiple tasks. Machine learning is a field concerned with the development of algorithms that learn models, representations, and rules automatically from data. The differences become clearer to the students of Best B. Tech college in Jaipur, Rajasthan who study engineering in detail.
