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LLMs are a type of machine learning model designed for natural language processing tasks.

They are trained on massive amounts of text data to predict and generate coherent and contextually relevant text.

LLMs can perform tasks like translating languages, answering questions, summarizing text, and even generating creative writing or code.

Examples of LLMs include OpenAI's GPT models and Google's BERT.

LLMs use deep learning architectures, specifically transformers, to capture and model the relationships between words and concepts in text.

Enables computers to interpret and make decisions based on visual inputs like images and video

Most commonly used for automated detection, facial recognition, quality control, and surveillance, for a cost savings of not needing manual monitoring or review

Enhance operational efficiency and decision-making, while lowering costs.

Enables software bots to mimic human actions to complete repetitive, rule-based tasks

Most commonly used for automating data entry, invoice processing, and report generation, for a cost savings of reducing manual labor and increasing throughput

Improve accuracy, free up human resources, and drive down operational costs.

Enables systems to learn from data and improve performance without explicit programming

Most commonly used for predictions, pattern recognition, and intelligent automation across industries, for a cost savings of faster insights and reduced human intervention

Unlock scalable intelligence and drive continuous improvement through data-driven decision-making.