AI and machine learning have an interesting relationship, with both fields seeking to create machines capable of solving complex problems, but differing in their goal, scope, and application.
First of all, AI systems have loftier goals, a much wider scope, and the ability to be applied to multiple different tasks, whereas machine learning systems have a specific goal, a narrow scope, and are commonly used for a single application.
The goal of AI is to create an artificial intelligence system capable of simulating human intelligence, meaning a system that can solve a wide variety of different types of problems.
In contrast, the goal of machine learning is to help an AI system learn faster, so it can complete its work quicker, and at a higher degree of accuracy. Machine learning can therefore be thought of as an optimization process or training technology used to make AI systems even more powerful.
AI’s scope is far broader than machine learning’s since AI systems are designed to handle a multitude of tasks, whereas machine learning programs are typically dedicated to perfecting a single task.
And in terms of application, AI systems may be used to control a whole series of different processes requiring many different algorithms and forms of intelligence all operating in unison, whereas the typical machine learning program is likely tasked with only a single process.
In short, AI has loftier goals, a broader scope, and far wider running applications than machine learning, but both fields are incredibly important to the modern economy.