- Expert systems
An expert system is an AI system that learns and mimics a human being in decision-making abilities. They do not use conventional programming approaches to solve complex problems. Rather, they use logical connotations to solve problems. These systems are mainly used in the medical field to operate healthcare facilities by doing things such as detecting infections. Furthermore, they are also used in the banking and finance sectors for loan investment and analysis.
This is a branch of AI that focuses on the design and development of robots. It deals with the design, construction and operating robots by taking advantage of science and engineering techniques which make robots highly versatile. Robots are deployed to help humans handle tedious and bulky tasks that are often dangerous or repetitive. Some of these tasks include controlling computer systems, transforming information and manufacturing. For instance, NASA uses robots to lift heavy objects in space. Robots act as AI agents that work in the real-world environment to accomplish specific results.
- Fuzzy logic
This is a computing approach based on the principles of degrees of truth as opposed to using modern computer logic that is Boolean. We sometimes encounter problems and difficulties recognizing whether some conditions are true in the real world. Their fuzzy logic gives flexibility for reasoning, leading to inaccuracies and uncertainties for any condition. Fuzzy logic modifies uncertain information by measuring the degree to which the hypothesis is correct. This branch of AI is used for reasoning about uncertain concepts.
- Machine learning
Machine learning (ML) is a branch of AI that is highly demanding compared to the others. This is the branch of science that enables machines and computer systems to process, assess, analyze and interpret data with the aim of getting solutions that we face in real-life. Computer systems can learn and take some actions by themselves because the level of data provided through machine learning is sufficient. Algorithms are used to help machines make predictions based on the based outcomes. ML algorithms and techniques help train models with the available data, which will predict and adjust to future outcomes. Some major categories of ML include supervised learning, unsupervised learning and reinforcement learning.
- Neural network
Neural Network is a branch of AI associated with using neurology to help computer systems and machines perform certain tasks. It is also known as “Deep Learning” because it uses artificial brain neurons to learn and solve complex problems, like the way the human nervous system does it. It is used in various areas, including risk analysis, market research, forecasting, predictions in stock markets and detecting fraud. Social media face verification algorithms also use this technology.
- Natural language processing
Natural Language Processing (NLP) is the science of getting insights from natural human language to communicate with machines. It makes computers understand and interpret human language and interactions. The process entails machines getting human sound from interactions and converting it to a machine-understandable form such as text format. The text is then converted back to components by the computer systems, making it capable of understanding human intentions. An example of an organization that uses NLP is Twitter which uses it to filter tweets. Amazon uses it to better customer experiences by processing their reviews.