- 1- Intelligence
- 2- Artificial Device
The ability to deal with cognitive complexity. Or in other words, it is the ability to apply knowledge and skills to do some kind of tasks.
The word artificial was used as a prefix to show the machine (computer) intelligence. In a field of ‘Computer Science’, AI, sometimes known as Machine Intelligence, is demonstrated by Machines to function like a human brain. The artificial intelligence is much complicated as the human brain itself.
It’s an idea of computer science to replicate the human brain. The term “artificial intelligence” is often used to describe machines that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving”. Any machine that takes any action and changes its working process as perceiving an environment change is associated with AI and the subset of AI is Machine Learning and in the same way, deep learning is the subset of Machine Learning.
It is an application of Artificial Intelligence that provides the machine to change its working condition and improve itself by previous experiences by the use of Programming and Algorithmic approaches.
Machine Learning is a mathematical, algorithmic and statistical approach in which machine trained on sample data also known as “Training Data” to perform a task without being technically pre-programmed or explicitly programmed to a certain task. ML (Machine Learning) algorithms are used in a wide variety of applications such as:
- Computer Vision
- Email Filtering
- Reinforcement Learning etc.
As we mentioned earlier it is a subset of Machine Learning and an approach of Computer Science to mimic the human brain by making the artificial neural network to perform the given task in different conditions and environment and learn itself by experiences and by giving some amount of data. It uses multiple layers to process the data (Information) to extract the desired result or higher-level features from raw data.
Artificial Neural Networks (ANNs) is a computing system and were inspired by biological (Human) brains neural network. It also trained on sample data same as Machine Learning approach but uses multiple layers of Nodes (a.k.a. Neurons) to filter or extract the higher-level featured data.