What's the difference between Machine Learning and AI?

Artificial intelligence: A system is said to be artificially intelligent if it does “smart” things, that are typically associated with humans. Conventional computer science consisted of creating algorithms that could solve problems unambiguously, in a step-by-step manner, like sorting a list. In AI, the tasks do not typically admit a step-by-step solution, such as face recognition, self-driving cars, etc. Machine learning: This involves AI algorithms that learn pattern from data, to perform inference on new data. For example, given a lot of photographs labelled as of person A or not of person A, machine learning involves developing algorithms which when given a new photo can identify whether it is of person A or not. AI algorithms may or may not use machine learning; they can be rule-based. For instance, to identify names in text, a rule-based algorithm could use capitalization. Or, to create a chess playing agent, you can come up with a scoring function that assigns a score to the board state, by considering factors such as the no. of pieces of each player, the no. of pieces under direct attack by the opponent for each player, etc. Then, given a new state, you can try all possible moves and choose the one that results in the final state with maximum score. This is a rule-based system that does not need data of chess games. An ML system to play chess, on the other hand, would use data of chess games. Because the system knows which states in previous games resulted in a win/loss, it can use that information to approximate (“learn”) the true scoring function.