Semantics in AI refers to the meaning behind words and sentences and how computers understand that meaning. The goal of semantics in AI is to enable machines to understand and interpret human language in a way that is similar to how people understand it.
For example, a natural language processing (NLP) system might use semantic analysis to determine the sentiment behind a sentence — whether it is positive, negative, or neutral. An AI system that provides customer service might use semantics to understand the intent behind a customer's questions, and provide a relevant response.
Another example is an AI-powered search engine, which uses semantic analysis to understand the meaning behind a query and provide relevant results. By using semantics, these systems can understand the relationships between words and concepts, and provide more accurate results.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of computer science that enables machines to interpret and comprehend human language for various tasks.
Text-to-Speech Translation (TTS)
Text-to-speech (TTS) is a technology that converts written text into lifelike speech.
Generative Question Answering (GQA)
GQA is an AI capability that involves generating new and contextually relevant answers to questions by synthesizing information from various sources.