Symbolic Machine Learning is a term from the fields of Artificial Intelligence, Digital Transformation, and Big Data and Smart Data. It concerns how machines learn by processing knowledge using symbols and rules, in a way similar to humans. This means that the system doesn't just analyse data but also understands how different terms relate to each other and can derive rules from this.<\/p>\n
Unlike other machine learning approaches, where computers recognise patterns from vast amounts of data, Symbolic Machine Learning works with defined symbols and logical rules. This makes the machine's behaviour easier to understand and explain, which is particularly important when decisions need to be transparent.<\/p>\n
For example, imagine a programme designed to handle customer service requests. Symbolic machine learning helps to correctly assign the meaning to words such as \u201einvoice\u201c, \u201ecomplaint\u201c and \u201edelivery\u201c and to generate meaningful responses from them. The machine is thus given rules like \u201eIf the word \u201acomplaint\u2018 appears, then forward the request to the complaints department.\u201c This makes its use secure, understandable, and particularly attractive to companies.<\/p>","protected":false},"excerpt":{"rendered":"