Title

AWS re:Invent 2023 - A new era: The path to generative AI in public sector (WPS206)

Summary

• Generative AI is rapidly advancing and offers a range of capabilities for both professional and personal use.
• The session aimed to guide public sector customers on how to leverage generative AI for production applications.
• The presenters shared their experiences and lessons learned from deploying generative AI solutions.
• The history of generative AI was discussed, including the pivotal paper "Attention Is All You Need" (2017) and the significant impact of ChatGPT's user interface in 2022.
• Generative AI is defined as the creation of new content, such as text, images, videos, or audio, that did not previously exist.
• The generative AI lifecycle was introduced, consisting of four steps: scoping out a use case, selecting a model, enhancing a model, and deploying to production.
• Amazon Bedrock and Amazon SageMaker were presented as tools to deploy and experiment with generative AI models.
• Data security and privacy were emphasized, with assurances that customer data would not be reused or leaked through models.
• The session included demonstrations of generative AI applications, such as summarizing an executive order and evaluating proposals.
• Resources for getting started with generative AI on AWS were provided, including a Coursera course, workshops, and the Generative AI Innovation Center.

Insights

• Generative AI has the potential to transform the public sector by enhancing customer experience, boosting employee productivity, and optimizing business processes.
• The rapid evolution of generative AI models means that what may not be a good use case today could become viable in the near future.
• Large language models, which are a type of foundation model, have significantly increased in complexity, now trained on up to a trillion tokens and hundreds of billions of parameters.
• Amazon Bedrock simplifies the deployment and experimentation with generative AI models, while Amazon SageMaker offers more control and the ability to deploy models in GovCloud regions.
• The session highlighted the importance of prompt construction and the use of techniques like few-shot prompting and retrieval augmented generation (RAG) to improve model performance.
• Fine-tuning and adapting models to specific tasks or domains is possible, allowing organizations to tailor generative AI to their unique needs.
• Deploying generative AI models in production involves considerations around data security, privacy, and the choice between using managed APIs like Bedrock or more hands-on tools like SageMaker.
• The presenters emphasized the low barrier to experimenting with generative AI, encouraging organizations to try out their ideas and learn through practical experience.
• AWS provides a variety of resources and support to help organizations get started with generative AI, including training, proof of concept engagements, and innovation centers.