The socratic method as a tool for choosing machine learning models for corporate information systems
Abstract
The socratic method as a tool for choosing machine learning models for corporate information systems
Incoming article date: 09.12.2024The article presents an analysis of the application of the Socratic method for selecting machine learning models in corporate information systems. The study aims to explore the potential of utilizing the modular architecture of Socratic Models for integrating pretrained models without the need for additional training. The methodology relies on linguistic interactions between modules, enabling the combination of data from various domains, including text, images, and audio, to address multimodal tasks. The results demonstrate that the proposed approach holds significant potential for optimizing model selection, accelerating decision-making processes, and reducing the costs associated with implementing artificial intelligence in corporate environments.
Keywords: Socratic method, machine learning, corporate information systems, multimodal data, linguistic interaction, business process optimization, artificial intelligence