Whenever general people try to define Artificial Intelligence or Machine Learning, they usually end up giving them the same definition. Especially in practice, both are used interchangeably, as both usually mean supervised learning. But in theory, they both are two different terms in various cases.
“It is the study of how to train the computers so that computers can do things which at present human can do better.”
It has three types, which are Weak AI, General AI, and Strong AI.
“It is the study of computer algorithms that improve automatically through experience as it is a subfield of Artificial Intelligence.”
It also has three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
#Where are the basic differentiating points then?
- AI is a computer program that does smart work. On the other hand, ML works with a simple concept: it takes data and learns from data.
- AI aims to make smart computer systems like humans to solve complex problems, whereas the aim of ML is to allow machines to learn from past data without programming explicitly.
- AI prioritizes success to accuracy. But ML prefers accuracy to success.
- AI has two main subsets: Machine Learning and Deep Learning. Deep Learning is the main subset of Machine Learning.
- AI evolves into a system that can mimic a humane response whereas ML just creates algorithms for self-learning.
- The scope of ML is narrower than AI.
- ML just goes for a solution whereas AL goes for the optimal solution.
- AI looks for intelligence or wisdom. But ML searches for knowledge.
- ML deals with structured and semistructured data. But AI not only deals with structured and semistructured data but also unstructured data.
- The two of their applications are different. AI is applied in Siri, customer support using catboats, Expert System, online game, the humanoid robot, etc. Ml is applied in an online recommendation system, Google search algorithms, Facebook auto friend tagging suggestions, etc.