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6 ways generative AI can optimize asset management

IBM Big Data Hub

There is even more help on the horizon with the power of generative artificial intelligence (AI) foundation models, combined with traditional AI, to exert greater control over complex asset environments. These foundation models, built on large language models, are trained on vast amounts of unstructured and external data.

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Four things that matter in the AI hype cycle

CIO Business Intelligence

The capabilities of these new generative AI tools, most of which are powered by large language models (LLM), forced every company and employee to rethink how they work. Vector Databases To make use of a Large Language Model, you’re going to need to vectorize your data. For that, you’ll need an embedding model.

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Rising Tide Rents and Robber Baron Rents

O'Reilly on Data

Perhaps a more direct way to say this in the context of economic value creation is that companies such as Amazon and Google and Facebook had developed a set of remarkable advances in networked and data-enabled market coordination. But over time, something went very wrong. These companies did continue to innovate.

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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

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The case for predictive AI

CIO Business Intelligence

It leverages techniques to learn patterns and distributions from existing data and generate new samples. GenAI models can generate realistic images, compose music, write text, and even design virtual worlds. The critical characteristic of GenAI is its ability to explicitly create something that does not exist in the training data.

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A Data Prediction for 2025

DataKitchen

Central IT Data Teams focus on standards, compliance, and cost reduction. ’ They are data enabling vs. value delivery. Their software purchase behavior will align with enabling standards for line-of-business data teams who use various tools that act on data. Recession: the party is over.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model? Hence, the graph model can be applied productively and effectively in numerous network analysis use cases. Ahh, that’s the topic for another article.

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