Artificial Intelligence & Machine Learning (AI & ML)
Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.
Artificial intelligence is a field of computer science that makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence“, which means “a human-made thinking power.” Hence we can define it as,
Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.
The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithms and deep learning neural networks. AI is being used in multiple places such as Siri, Google’s AlphaGo, AI in Chess playing, etc.
Based on capabilities, AI can be classified into three types:
- Weak AI
- General AI
- Strong AI
Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.
Machine learning is about extracting knowledge from the data. It can be defined as,
Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.
Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate results or give predictions based on that data.
Machine learning works on an algorithm which learns on it’s own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of dogs, it will only give results for dog images, but if we provide new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender systems, Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.
It can be divided into three types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning