An online retailer has two AI-related initiatives:
Project A uses an AI service to automatically detect product names, competitor brands and overall sentiment in thousands of free-text customer reviews so that marketing dashboards update in near real time.
Project B aims to remove a nightly manual task in which an analyst opens a legacy desktop application, exports a CSV file, renames it and uploads it to a cloud folder.
Which combination of AI concepts best matches Project A and Project B, respectively?
Deep learning classification for Project A and generative AI for Project B
Robotic process automation (RPA) for Project A and large language model (LLM) fine-tuning for Project B
Generative AI for Project A and deep learning for Project B
Natural language processing (NLP) for Project A and robotic process automation (RPA) for Project B
Project A involves extracting structured information (entities and sentiment) from unstructured text. That task is a classic use case for natural language processing (NLP) techniques such as named-entity recognition and sentiment analysis. Project B, in contrast, is a deterministic, rule-based sequence of user-interface actions across existing applications; it does not require the system to "learn" from data, only to repeat prescribed steps, which is exactly what robotic process automation (RPA) is designed to do. The other pairings mis-align the concepts: generative AI focuses on creating new content rather than extracting entities; deep learning is a broad modeling approach rather than a process-automation tool; RPA does not perform text understanding; and large language models are specialized NLP architectures, not UI bots for file exports.
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