Blueprint for an AI Bill of Rights

Resources

The discussion highlights valuable resources for developing AI applications, particularly focusing on building LLM systems. Daniel Whitenack shares an article by Eugene Yan that extensively covers practical challenges in creating LLM-based applications, such as evaluations, retrieval, augmented generation, and fine-tuning 1. Chris Benson notes the article's depth, emphasizing its detailed graphs and strategies for improving performance and reducing costs 1.

It's a significant article... incredibly detailed. So I can't. I'm going to dive into this after, after the show today.

--- Chris Benson

This resource is praised for its comprehensive coverage of the LLM ecosystem, making it a must-read for those interested in AI development 1.

   

Risk Management

The conversation shifts to AI risk management and compliance frameworks, which are becoming increasingly relevant in business contexts. Daniel Whitenack discusses the potential for AI frameworks like the NIST AI risk management framework to become standard in enterprise settings, similar to existing compliance structures like GDPR and HIPAA 2. He speculates on the emergence of compliance frameworks that enterprises might impose on vendors to ensure trustworthy AI practices 2.

I suspect we're going to get into some of these scenarios where we'll have some compliance frameworks put into place that certain enterprises start forcing on other providers.

--- Daniel Whitenack

Chris Benson adds that the rapid development of AI models will likely necessitate compliance AI models to keep up with market demands 2.