Remove Deep Learning Remove Metadata Remove Risk Remove Unstructured Data
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The most valuable AI use cases for business

IBM Big Data Hub

The IBM team is even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

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Ontotext’s Semantic Approach Towards LLM, Better Data and Content Management: An Interview with Doug Kimball and Atanas Kiryakov

Ontotext

Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. We use other deep learning techniques for such tasks. Delivering better data mining, NLP and pattern identification – you can make better decisions.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

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The Superpowers of Ontotext’s Relation and Event Detector

Ontotext

The answers to these foundational questions help you uncover opportunities and detect risks. We bundle these events under the collective term “Risk and Opportunity Events” This post is part of Ontotext’s AI-in-Action initiative aimed to empower data, scientists, architects and engineers to leverage LLMs and other AI models.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. BTW, videos for Rev2 are up: [link].

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