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Enhancing Knowledge Discovery: Implementing Retrieval Augmented Generation with Ontotext Technologies

Ontotext

This dramatically simplifies the interaction with complex databases and analytics systems. Join us as we demystify the methodologies empowering such implementations, shed light on their range of capabilities, and detail how Ontotext is harnessing these technologies to bring transformative enhancements to our data interaction landscape.

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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

The industry must continually optimize process, improve efficiency, and improve overall equipment effectiveness. Coupled with search and multi-modal interaction, gen AI makes a great assistant. IBM built a workforce advisor that uses summarization and contextual data understanding with intent detection and multi-modal interaction.

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Understanding Social And Collaborative Business Intelligence

datapine

This is done using interactive Business Intelligence and Analytics dashboards along with intuitive tools to improve data clarity. Resources can be optimized through this type of sharing by allowing users to access reports, dashboards, and data that can possibly be just what they require to complete a task or analysis.

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Bridging the Gap Between Industries: The Power of Knowledge Graphs – Part I

Ontotext

Knowledge graphs can also enable the creation of “digital twins”, which make sense of the collected data from various sensors in different systems, spanning the entire vehicle lifecycle. Read our post: Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Here, I will draw upon our own experience from client projects and lessons learned to provide a selection of optimal use cases for knowledge graphs and semantic solutions along with real world examples of their applications. For many organizations, however, the question remains, “Is it the right solution for us?”

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. A comprehensive list of all attributes and symbol codes is given in the document that accompanies the original dataset.

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Understanding Social And Collaborative Business Intelligence

datapine

This is done using interactive Business Intelligence and Analytics dashboards along with intuitive tools to improve data clarity. Resources can be optimized through this type of sharing by allowing users to access reports, dashboards, and data that can possibly be just what they require to complete a task or analysis.