Cognitive computing is an umbrella term for systems intended to emulate human thought processes such as understanding language, reasoning and learning from experience in order to support people's decisions. The term became widely known in the 2010s, mainly through IBM Watson. It combines methods such as natural language processing, machine learning and knowledge representation; today people more often speak of AI assistants or language models instead.
An expert system is a computer program that stores the know-how of human specialists as rules and facts and draws conclusions from them. It typically consists of a knowledge base and an inference engine that applies the rules. Expert systems shaped AI in the 1970s and 1980s, for example in medical diagnosis or configuring technical equipment. Their ideas live on today in rule-based checks and knowledge graphs.
Natural language processing is the branch of AI that enables computers to analyse, understand and generate human language in text or speech. It covers tasks such as translation, classification, summarisation, sentiment analysis and question answering. Rules and statistical methods once dominated; today the field is shaped by large language models.
Artificial neural networks (ANNs) are computational models made up of many connected units (“neurons”) arranged in layers that process inputs via weighted connections. During training, the weights are adjusted step by step using examples, usually by backpropagation. ANNs are the foundation of deep learning and power image recognition, speech recognition and large language models. Despite their biological inspiration, they work far more simply than the human brain.
An insight engine is a form of enterprise search that brings together information from many sources, such as documents, emails, databases and applications, and analyses it with AI techniques. Rather than mere lists of hits, it delivers prepared answers, connections or recommendations, often in the context of the task at hand. The term was popularised mainly by industry analysts. Example: before a client meeting, a sales team sees all relevant contracts, tickets and notes in one place.
