Explore the intersection of knowledge management and artificial intelligence (AI) and how AI can solve problems in the field. Learn about the use of machine learning, data science, and neural networks in knowledge and data management. Discover how AI enables knowledge management by pulling information from multiple sources and generating new content. Understand the importance of data cleaning and contextual information in successful AI implementation. See how AI impacts knowledge management by providing targeted and contextually relevant information.
AI in knowledge management involves delivering organizational knowledge and information in an intuitive way to address findability and discoverability.
The use of machine learning and other techniques in AI can aggregate and make sense of large amounts of information, but still requires human intervention and context.
Deep dives
AI in Knowledge Management
AI in the context of knowledge management refers to leveraging machine capabilities to solve traditional and emerging knowledge management and information management problems. It involves delivering organizational knowledge, data, content, and information in an intuitive way that aligns with how people search and interact with their data. This includes addressing findability, discoverability, and efficient use and reuse of information using machine capabilities.
The Intersection of AI and Knowledge Management
AI in knowledge management is not a new concept. Even before the rise of chat GPT and large language models, AI-based solutions were being developed to provide information at the point of need without requiring explicit user queries. The use of machine learning, knowledge graphs, and search technologies allowed for the automatic generation of recommended content and personalized insights. Although chat GPT has gained significant attention recently, the focus is still on solving the problem of delivering information to users without them having to actively search for it.
Machine Capabilities and Contextualization
In the context of AI in knowledge management, machine capabilities refer to using machine learning and other techniques to aggregate and make sense of large amounts of information. This includes leveraging machine learning models, neural networks, ontologies, and graphs to connect data and content, understand shared meaning, and provide context to the information being presented. While AI technologies like large language models can provide answers based on available data, they still require human intervention, validation, and context to accurately understand and meet user needs.
Explainable AI and Black Box AI
A distinction can be made between explainable AI and black box AI. Explainable AI allows human users to understand how the AI system arrived at its answers or recommendations. This transparency enables organizations to make improvements and adjustments as needed. Black box AI, on the other hand, relies heavily on machine learning and can produce accurate results, but lacks transparency and the ability to explain its reasoning. To address this, current solutions are combining explainable AI tools, like knowledge graphs and vector databases, with black box AI systems to improve output quality and control.
In this special episode of Knowledge Cast, CEO Zach Wahl is joined by his colleagues Joe Hilger and Lulit Tesfaye to discuss the intersection of knowledge management and artificial intelligence (AI). They explore what AI means in the context of knowledge management and how it can be leveraged to solve various problems in the field.
Tune in to gain a deeper understanding of the role of AI in knowledge management and its potential impact on organizations.