Eric Evans to step down as director of MIT Lincoln Laboratory
During 18 years of leadership, Evans established new R&D mission areas, strengthened ties to the MIT community, and increased inclusion and education efforts.
Techniques for automatic summarization of documents using language models | Amazon Web Services
Summarization is the technique of condensing sizable information into a compact and meaningful form, and stands as a cornerstone of efficient communication in our information-rich age. In a world full…
Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock | Amazon Web Services
Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training…
How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot | Amazon Web Services
This post is co-written with Stanislav Yeshchenko from Q4 Inc. Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging…
Foundational data protection for enterprise LLM acceleration with Protopia AI | Amazon Web Services
The post describes how you can overcome the challenges of retaining data ownership and preserving data privacy while using LLMs by deploying Protopia AI’s Stained Glass Transform to protect your…
Enable faster training with Amazon SageMaker data parallel library | Amazon Web Services
Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now…
Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra | Amazon Web Services
Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images,…
AI accelerates problem-solving in complex scenarios
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch | Amazon Web Services
This is a guest post co-authored by Nafi Ahmet Turgut, Hasan Burak Yel, and Damla Şentürk from Getir. Established in 2015, Getir has positioned itself as the trailblazer in the…
Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning | Amazon Web Services
The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise.…