Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.
In short, according Bernard Marr (Forbes) the best answer is that:
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
And,
Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
And,
Deep Learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as Deep Neural Learning or Deep Neural Network.
More differences between Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence (AI):
- AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as a ability to acquire and apply knowledge.
- The aim is to increase chance of success and not accuracy.
- It work as a computer program that does smart work.
- The goal is to simulate natural intelligence to solve complex problem.
- AI is decision making.
- It leads to develop a system to mimic human to respond behave in a circumstances.
- AI will go for finding the optimal solution.
- AI leads to intelligence or wisdom.
Machine Learning (ML):
- ML stands for Machine Learning which is defined as the acquisition of knowledge or skill.
- The aim is to increase accuracy, but it does not care about success.
- It is a simple concept machine takes data and learn from data.
- The goal is to learn from data on certain task to maximize the performance of machine on this task.
- ML allows system to learn new things from data.
- It involves in creating self learning algorithms.
- ML will go for only solution for that whether it is optimal or not.
- ML leads to knowledge.
Deep Learning (DL):
- DL requires a lot of unlabeled training data to make concise conclusions while ML can use small data amounts provided by users.
- Unlike ML, DL needs high-performance hardware.
- ML requires features to be accurately identified by users while DL creates new features by itself.
- ML divides tasks into small pieces and then combine received results into one conclusion while DL solves the problem on the end-to-end basis.
- In comparison with ML, DL needs much more time to train.
- Unlike DL, ML can provide enough transparency for its decisions.