This is an introduction to how to use an AI agent that outputs predictive models and outputs output when a design is input to a design document, just like outputting a TV screen with a remote control. KAMG (KAMG) AI is a “No-Code knowledge assembly model generation AI agent” *. KAMG is an abbreviation for Knowledge Assembly Model Generation. You can practice using KAMG AI with the web app.

Traditional interactive agents interact with users through text or voice prompts. However, KAMG AI communicates through documents. This is to convey complex instructions in a structured and clear manner, and at the same time to facilitate knowledge assembly reflecting domain-specific knowledge.

Specifically, design instructions are delivered as a DDI (Design Document Interface) document, and the KAMG AI agent's machine learning module is responsible for generating models and implementing outputs based on this. The subsequent assessment phase is led by domain experts, and the LLM module supports expert judgment during this process.

Manufacturing companies that have internalized predictive model development capabilities through KAMG AI can secure the following advantages.

(1) Reducing defects and improving productivity through process optimization

(2) Rapid development response for customer-specific products

(3) Domain knowledge unique to enterprises is stored in design documents to enhance know-how security

(4) Reducing dependency on IT companies during maintenance, management, and improvement of predictive models

KAMG AI's uniqueness lies in its “document-based interactive predictive model generation agent.”

Domain veterans who lack AI, coding, and data analysis knowledge think they need an AI tool that can develop AI predictions that assemble their domain knowledge, so they developed Knowledge Assembly Model Generation (KAMG) AI. Model design and implementation were separated, and the design was divided back into initial design and finished design, and tasks were divided so that AI experts did the initial design and implementation, and domain experts did the finished design, evaluation, and utilization. Through division of tasks, the amount of AI, coding, and data analysis knowledge required to get started with AI prediction model development was minimized. If you start developing AI predictive models through KAMG AI, AI literacy will naturally improve as you repeat the process of developing and using predictive models. Use a web app to learn about designing, evaluating, and utilizing AI predictive models using Excel documents. Also, Q&A about learning content is possible using a chatbot using KAMG AI.

Introduction to using AI for causal basis, knowledge assembly, and predictive model generation

Introduction to using AI for causal basis, knowledge assembly, and predictive model generation