[TRINITY 데모 영상] 기자간담회 발표영상 / Ontology 기반 의사결정 지원 Agentic AI 개발 플랫폼

Summary

The video explains how AI agents based on ontologies can revolutionize business processes. Ontologies, used to categorize and structure knowledge, enable AI to analyze complex relationships and predict outcomes. The transcript highlights various applications, such as sales analysis, project management, and employee recommendations, showing how businesses can leverage AI agents to automate tasks, detect anomalies, and optimize decision-making. By incorporating both structured data and business-specific knowledge, AI agents provide significant efficiency improvements, making them essential for companies facing challenges like rising labor costs and data complexity.

Takeaways

• 😀 AI agents powered by ontologies can drastically improve business efficiency by automating tasks such as sales analysis, employee recruitment, and inventory management.
• 😀 Ontology is the philosophical study of the nature of being, which helps define and categorize things in the world, enabling computers to interpret and manage structured knowledge.
• 😀 By defining specific categories and relationships, ontology can be used to predict actions, like forecasting a spouse’s reactions or solving business problems such as identifying supply chain issues.
• 😀 With AI and ontologies, businesses can rapidly find causes of complex issues and suggest actionable solutions, making operations more efficient in various fields such as finance, human resources, and manufacturing.
• 😀 Triniti, an AI solution, can reduce time spent on business tasks like sales anomaly detection by quickly defining ontologies and deploying AI agents to handle analyses and report generation.
• 😀 Traditional data systems often lack clear standards and business knowledge, but Triniti's knowledge store allows for learning from existing business manuals, documents, and structured data (e.g., databases, CSVs).
• 😀 By combining structured data with ontology-based knowledge, AI agents can offer actionable insights, answer questions, and make predictions with greater accuracy and speed.
• 😀 AI agents can be connected to various external systems, helping businesses integrate AI into their workflows and automate tasks, such as sending emails or generating production orders based on detected issues.
• 😀 The rise of AI agents is not just for analytics; it also represents a shift towards intelligent, task-oriented systems that can take on operational roles, much like Jarvis in movies.
• 😀 GPT, despite its vast knowledge base, struggles to provide relevant business insights due to its reliance on publicly available information, whereas AI agents can be tailored to a company's specific business knowledge and workflows.
• 😀 Companies are increasingly adopting AI agents to address business challenges, such as managing large-scale projects, detecting anomalies, and optimizing processes, which leads to significant cost savings and operational efficiencies.

Q & A

What is ontology, and how is it applied in AI systems?

Ontology is a philosophical concept that refers to the study of existence and categorization of entities and their relationships. In AI systems, it is used to classify and define structured knowledge so that computers can interpret and use it. By organizing knowledge about entities like products, behaviors, and preferences into clear categories, AI agents can predict outcomes or make recommendations in various business contexts.

How can AI agents predict personal behaviors, such as those of a spouse?

Using ontology, personal behaviors can be categorized and analyzed. For example, by defining attributes like appearance, hobbies, and habits, an AI can use this information to predict responses or behaviors in different situations, such as how a spouse might react in a particular context, or ways to receive affection.

What role does Trinity AI play in the development of AI agents?

Trinity AI helps companies create AI agents by providing tools for knowledge management, ontology design, and integrating structured data sources such as databases, documents, and compliance manuals. This allows businesses to build AI agents that understand and process complex, domain-specific knowledge to make decisions and improve efficiency.

How does Trinity AI improve the efficiency of business tasks like sales analysis?

Trinity AI uses predefined ontologies to quickly analyze sales data, detect anomalies, and identify the causes behind those anomalies. By automating tasks like these, it reduces the time spent on manual analysis and speeds up decision-making, providing businesses with faster insights and reports.

What makes Trinity AI's approach different from traditional AI systems like GPT?

Unlike general AI models like GPT, which are based on open-source data, Trinity AI focuses on enterprise-specific knowledge. It can handle structured data from business databases and documents, such as sales records, compliance regulations, and employee information, allowing it to make informed decisions and automate specific tasks in business operations.

Why is it difficult for AI like GPT to fully replace business-specific knowledge?

GPT and similar models are trained on general knowledge, primarily from publicly available sources like websites, books, and academic papers. They do not have access to proprietary business knowledge such as company-specific data, operational guidelines, or internal documents. This makes them less effective at handling enterprise-specific tasks that require deep knowledge of internal processes and standards.

What is the significance of integrating ontology into business AI systems?

Ontology enables AI systems to understand complex relationships between different business entities and their attributes. By categorizing and defining these entities in a structured way, AI agents can make informed decisions, predict outcomes, and automate tasks like managing inventory, analyzing sales trends, or recommending HR decisions. This integration ensures the AI operates with knowledge that is relevant and specific to the business context.

How can AI agents help improve HR tasks, such as employee recommendations?

AI agents using ontology can streamline HR processes by assessing employee qualifications, performance, and career progression based on predefined criteria. For example, if a company needs to find a team leader for a new project, the AI can analyze internal data, such as experience, skills, and past performance, to recommend the best candidates according to the business's specific requirements.

What challenges do businesses face when implementing AI agents in their operations?

One of the main challenges is ensuring that the AI has access to accurate, well-organized, and relevant data. Many businesses struggle with fragmented knowledge, such as inconsistent documentation or disparate data sources. AI agents need to be trained on this information, and the company must define clear ontologies and knowledge management processes to ensure effective AI deployment.

Can AI agents help in predicting and managing project risks in businesses?

Yes, AI agents can analyze project data and identify potential risks or issues based on historical performance and predefined criteria. By continuously monitoring project metrics, AI can detect anomalies, predict future problems, and provide actionable insights, enabling businesses to take proactive steps to mitigate risks and optimize project outcomes.