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Fundamental Concepts of Artificial Intelligence
- Machine learning, which was coined in 1959, is a subfield of AI that involves the creation of programs that perform actions without explicit instructions.
- ML is divided into three categories: Supervised Learning, which learns from labelled data; Unsupervised Learning, which identifies patterns in unlabelled data; and Reinforcement Learning, which learns from interacting with the environment.
- Deep Learning (DL), inspired by the workings of the human brain, involves the use of artificial neural networks to analyse different factors of data.
- Natural Language Processing (NLP) is another AI field that involves machine interaction with human language.
- Computer Vision (CV) is a subset of AI that allows computers to perceive the world around them by learning to grasp high-level awareness from digital images and videos.
- Robotics is a branch of AI that uses engineering to design machines that can interact with the physical world, often integrating with other AI fields such as CV and NLP.
- Expert Systems are AI programs that imitate an expert’s decision-making skills; they are the first successful types of AI software.
- Weak AI solves specific issues as compared to Strong AI that matches or outperforms human intelligence across a range of cognitive tasks.
- The Turing Test is a practical method to determine machine intelligence that involves a human interrogator and two participants: a human and a machine.
- Artificial consciousness, or machine consciousness, is when non-biological systems exhibit consciousness, which has made people wonder if said systems should have rights and moral standing.
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