Favorite Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and extract insights directly from the content in digital as well as scanned PDF documents in your enterprise data sources without needing to extract the text first. Customers across industries such as finance, insurance,

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Shared by AWS Machine Learning July 3, 2024

Favorite You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you don’t always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models

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Shared by AWS Machine Learning July 3, 2024

Favorite Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). This feature streamlines the entire RAG workflow, from ingestion to retrieval and prompt augmentation, eliminating the need for custom

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Shared by AWS Machine Learning July 3, 2024

Favorite Generative AI and transformer-based large language models (LLMs) have been in the top headlines recently. These models demonstrate impressive performance in question answering, text summarization, code, and text generation. Today, LLMs are being used in real settings by companies, including the heavily-regulated healthcare and life sciences industry (HCLS). The

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Shared by AWS Machine Learning July 3, 2024

Favorite Foreigners and expats living outside of their home country deal with a large number of emails in various languages daily. They often find themselves struggling with language barriers when it comes to setting up reminders for events like business gatherings and customer meetings. To solve this problem, this post

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Shared by AWS Machine Learning July 3, 2024

Favorite With the rise of generative artificial intelligence (AI), an increasing number of organizations use digital assistants to have their end-users ask domain-specific questions, using Retrieval Augmented Generation (RAG) over their enterprise data sources. As organizations transition from proofs of concept to production workloads, they establish objectives to run and

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Shared by AWS Machine Learning July 2, 2024

Favorite Traditionally, finding relevant information from documents has been a time-consuming and often frustrating process. Manually sifting through pages upon pages of text, searching for specific details, and synthesizing the information into coherent summaries can be a daunting task. This inefficiency not only hinders productivity but also increases the risk

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Shared by AWS Machine Learning July 2, 2024

Favorite In November 2023, we announced Knowledge Bases for Amazon Bedrock as generally available. Knowledge bases allow Amazon Bedrock users to unlock the full potential of Retrieval Augmented Generation (RAG) by seamlessly integrating their company data into the language model’s generation process. This feature allows organizations to harness the power

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Shared by AWS Machine Learning July 2, 2024

Favorite Originally PyTorch used an eager mode where each PyTorch operation that forms the model is run independently as soon as it’s reached. PyTorch 2.0 introduced torch.compile to speed up PyTorch code over the default eager mode. In contrast to eager mode, the torch.compile pre-compiles the entire model into a single graph

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Shared by AWS Machine Learning July 2, 2024

Favorite Media and entertainment companies serve multilingual audiences with a wide range of content catering to diverse audience segments. These enterprises have access to massive amounts of data collected over their many years of operations. Much of this data is unstructured text and images. Conventional approaches to analyzing unstructured data

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Shared by AWS Machine Learning July 1, 2024