The number of potential outputs from GenAI is virtually limitless. In contrast, GenAI must construct a text fragment from individual words and letters, ensuring that it is grammatically correct, comprehensible, and accurately represents the process. In classical ML, the model merely needs sufficient training to confidently categorize a text fragment. Although the progression from classical ML to GenAI might seem incremental, it poses a fundamental technical challenge.
Beyond merely classifying existing text, it can generate new text based on specified criteria-such as operator instructions that outline a process to resolve a particular root cause of a machine breakdown. The basis for such models may be deep neural networks, support vector machines, or other methods. For instance, an ML model might be trained using specific text fragments-such as operator incident reports in which machine breakdown descriptions are classified into specific root causes such as “end of tooling life” or “operator error.” Based on this training, the model can process previously unseen text fragments of incident reports and judge what caused the incident.
Classical ML algorithms discern patterns within observed data, enabling them to generalize these insights to new, previously unseen data. To discuss the applications of GenAI, it is essential to first define how it differs from “classical” machine learning (ML).
