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4 ways generative AI addresses manufacturing challenges

IBM Big Data Hub

Coupled with search and multi-modal interaction, gen AI makes a great assistant. Additionally, these accelerators are pre-integrated with various cloud AI services and recommend the best LLM (large language model) for their domain. Generative AI can create foundation models for assets.

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

Ontotext

Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. standards modeled in a knowledge graph!

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GraphDB and metaphactory Part II: An RDF Database and A Knowledge Graph Platform in Action

Ontotext

However, although some ontologies or domain models are available in RDF/OWL, many of the original datasets that we have integrated into Ontotext’s Life Sciences and Healthcare Data Inventory are not. Visual Ontology Modeling With metaphactory. Although there are already established reference datasets in some domains (e.g.

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

Ontotext

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models.

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

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AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

We translate their documents, presentations, tables, etc. into structured knowledge that can be processed by machines. Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. ” Then this knowledge can be downloaded from the network.

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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.