Today we are bombarded with terms that are often a bit obtuse. And there are closely related terms that seem to be synonyms or overlap related terms. How are the terms artificial intelligence, deep learning, machine learning, and neural networks related to each other and how are they different?
This quick post should help you finally get a handle on the similarities and differences if you haven’t already!
First, we’ll start out with “dictionary” definitions:
- Artificial intelligence: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.(Google)
- Machine learning: is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.” (Wikipedia)
- Neural networks: a computer system modeled on the human brain and nervous system. (Google)
- Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. (Wikipedia)
- Artificial intelligence consists of computer systems that aspire to have human-level intelligence or better and use inputs that humans use such as vision, listening, decision-making, and action. The traditional method of getting machines to act like humans has been through human-created computer algorithms.
- Machine learning gets rid of the human programmer. The machine programs itself.
- Neural networks focus on trying to replicate the human brain and its functions by creating computer systems that model the human brain.
- Deep learning is a type of machine learning but uses “non-linear” processes.
To help you better grasp the differences and similarities between these terms/fields here are videos on each: