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Generative AI use cases for the enterprise

IBM Big Data Hub

Generative AI ( artificial intelligence ) promises a similar leap in productivity and the emergence of new modes of working and creating. Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While AGI remains theoretical, organizations can take proactive steps to prepare for its arrival by building a robust data infrastructure and fostering a collaborative environment where humans and AI work together seamlessly. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move.

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Part 1: OMG! Not another digital transformation article! Is it about understanding the business drivers?

ARMA International

The nomenclature can extend to include Data as a Service (DaaS), Content as a Service (CaaS), and Managed Content as a Service (MCaaS) to deliver digital interaction experiences to customers. More recent technology trends include Blockchain as a Service (BaaS) and Artificial Intelligence as a Service (AIaaS). Sharma, Rakesh.

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How to choose the best AI platform

IBM Big Data Hub

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.