Discover how knowledge mining transforms AI workloads by making large datasets more accessible through advanced search capabilities. Learn its benefits and applications to elevate your understanding of AI-driven solutions.
Table of Contents
Question
For which AI workload is the primary objective to make large datasets more accessible through search capabilities?
A. Search engine optimization (SEO)
B. Image segmentation
C. Object detection
D. Knowledge mining
Answer
D. Knowledge mining
Explanation
The primary objective of knowledge mining in AI is to make large datasets more accessible through search capabilities. It involves extracting information from large volumes of unstructured data to create a searchable knowledge store. In Microsoft Azure, knowledge mining is facilitated through solutions such as Azure AI Search. This private, enterprise search solution allows the construction of indexes, enabling internal use or searchable content on public-facing Internet assets. Azure AI Search leverages built-in AI capabilities, including image processing, document intelligence, and natural language processing, to extract and index data. This empowers the system to make previously unsearchable documents accessible and quickly find insights from extensive datasets.
Image segmentation is not used to make large datasets more accessible. It involves dividing an image into segments based on certain characteristics.
Search engine optimization (SEO) is not used to make large datasets more accessible. SEO comprises key techniques that are used for improving the visibility of a website in results from search engines.
Object detection is not used to make large datasets more accessible. It is the task of identifying and locating objects in images.
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