This is the major transformation now happening in the Artificial Intelligence landscape, not merely the automation of tasks or content generation. The pitch being created by the increasing complexity and demand for intelligent decision-making could well be said to give birth to a new paradigm-the paradigm that challenges the accepted form of AI. On a technological plane, this shift is not merely an evolution but rather a redefinition, a redefinition of how independent intelligence associates with a contemporary enterprise.

Quick Primer: Generative AI in a Nutshell

The Technology Behind the Magic

Foundation Models and Large Language Models

Foundation models provide a generative AI backdrop, they are large systems pre-trained on vast data and adaptable to many tasks. LLMs includ-ing ChatGPT, Bard, and Copilot, and are probably the most spoken-about ones.An LLM is trained with powerful deep neural networks, easily holding millions of tunable parameters, assisted by an attention mechanism, based on a transformer architecture, data sequences are processed in parallel, allowing a much faster training than that of the conventional sequential models, which tend to give better-moth-wrapping outputs.

How Generative AI Learns and Creates?

1. Pattern Recognition

AI models analyze enormous datasets that might contain billions of web pages, documents, and multimedia materials, where they seek out complex relationships and dependencies between different items.

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