Title

AWS re:Invent 2023 - Explore what’s possible with new AWS generative AI services (AIM101)

Summary

Eric Terrell from Atos discusses generative AI and introduces AWS's new service, Amazon Bedrock.
Generative AI is defined as AI that creates new content by mimicking human intelligence, capable of learning languages and complex subject matters.
Amazon Bedrock is a secure environment for running generative AI on private data, integrating foundational models and large language models from Amazon and third parties.
Atos used Bedrock for an internal use case called AI contract insights to assist their legal team in analyzing contracts, saving time and manual effort.
The solution uses OCR for image text, understands text meaning for accurate extraction, and supports multiple languages.
The architecture includes Amazon S3, Postgres, OpenSearch, and a retrieval augmentation generation model.
Lessons learned include the importance of standardization, trial and error in model selection, the significance of data pipelines, and the need for security.
Bedrock is praised for its integration with AWS, security by design, and cost-effective, efficient underlying hardware.
Atos plans to continue improving their contract insights tool with feedback, cost management, AI agents, and further customization.

Generative AI is rapidly evolving, and AWS is at the forefront with services like Amazon Bedrock, which addresses security concerns around using AI with sensitive data.
The integration of third-party models like Claude, Cohere, and Stable Diffusion with AWS services like CodeWhisperer and SageMaker Jumpstart shows AWS's commitment to offering a diverse set of tools for developers.
Atos's use case demonstrates the practical application of generative AI in the legal domain, highlighting the potential for AI to automate complex tasks and provide significant business value.
The emphasis on standardization and simplification in the development process reflects a broader trend in cloud computing towards using established patterns and frameworks to accelerate innovation.
The discussion of cost management and the importance of understanding AWS pricing models indicates a need for transparency and predictability in cloud service costs, especially when dealing with large-scale AI applications.
The mention of purpose-built silicon chips by AWS suggests a trend towards specialized hardware for AI and machine learning tasks, which could lead to more efficient and cost-effective solutions.
The iterative approach to improving the AI contract insights tool, including manual and automated feedback, indicates a continuous development and deployment cycle that is typical in modern software engineering practices.