AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. There are other components involved, such as knowledge bases, data stores, and document repositories.

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Dataconomy

SEPTEMBER 22, 2023

Data archiving is the systematic process of securely storing and preserving electronic data, including documents, images, videos, and other digital content, for long-term retention and easy retrieval. Lastly, data archiving allows organizations to preserve historical records and documents for future reference.

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AWS Machine Learning Blog

DECEMBER 5, 2023

For instance, according to International Data Corporation (IDC), the world’s data volume is expected to increase tenfold by 2025, with unstructured data accounting for a significant portion. Insurance companies are burdened with increasing numbers of claims that they must process.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project typically combines optical character recognition (OCR) and natural language processing (NLP) to automatically read and understand documents. Use the right technology to store data For IDP workflows, most of the data is likely to be documents.

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Towards AI

MAY 3, 2023

When given a query like “classify brain tumor,” the vector database can search for documents or phrases that have similar meanings to the query. It achieves this by comparing the vector representation of the query with the vectors of the stored documents, which encompass past experiences and accumulated knowledge.

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IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. Dev Developers can write, test and document faster using AI tools that generate custom snippets of code. They can also perform self-supervised learning to generalize and apply their knowledge to new tasks.

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