Iniciativa Z en español

RAGme-ai is a personalized RAG agent for the web sites you visit and care about.

Use cases include:

1. Interractive personal RAG adding web sites and documents and using an LLM agent to query
2. Collecting web sites or documents on a particular topic and RAGing and searching them
3. Collecting posts from a blog and RAGing and searching them
4. Collecting visited sites from a user's daily activities and RAGing and searching them
5. Collecting documents from a user and RAGing and searching them

RAGing and searching refers to adding documents into a Vector database using some appropriate embedding (process to RAG) and then using an LLM agent to query and use that database as source of knowledge.

About the presenter
Max. Hacker.
IBM Research.
LinkedIn, GitHub

Please join us to learn about the Trust and Safety Evaluations Initiative (TSEI), which is focused on evaluating AI models and applications. Evaluations cover not only safety concerns, like avoiding hate speech, but the emerging challenge of evaluating that the system as a whole is “aligned” with the requirements for the domain and use cases supported. We call this the “last mile” of evaluation, a challenge that most development teams don’t know how to address. You will learn about our efforts to meet all these needs and how you can get involved!

About the presenter
Dean Wampler, IBM

Please join us learn about the Open Trusted Data Initiative (OTDI), which seeks to address a common challenge for organizations that need datasets for AI pre-training, tuning, RAG, and other purposes. While lots of useful datasets are available, it is not always clear which ones are licensed for free and unlimited use, with clear provenance for their history, and suitable governance to be sure the claimed licenses and provenance are in fact valid for the whole dataset. Don't miss this important AI Alliance update and learn how you can get involved!

About the presenter
Joe Olson, IBM