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ABOUT THE TALKS
Winter is Coming… or is it? How to avoid the next AI winter
by Emily Gorcenski
The history of AI is full of boom and bust cycles, the most famous of them being the long AI winter that started in the 1970s following several high-profile failures to deliver. This talk will be a look at historical events in AI development, ranging from the DARPA Speech Understanding Research project to the Lighthill Report and the failure of Japan’s 5th Generation Computer System. We’ll explore what drives AI hype and how mismanaged expectations lead to systemic failures in project management, product management, and technical delivery. But we’ll also look at what does work, and how we can avoid stepping on the same traps that have caught previous generations of AI research: by identifying clear product value, by developing continuous delivery for AI, and by tuning out the noise
Superpower your LLM-RAG Applications with a Knowledge Graph Platform like Neo4j
by Michael Hunger
As you might experienced yourself, LLMs like ChatGPT are powerful but not always trustworthy assistants. Being stochastic models that don’t have access to the relevant data they can fabricate answers that are not grounded in truth. With a combination of a knowledge graph that can store real world information at high fidelity and vector search, you can provide the LLM with the correct, relevant context information it needs to answer your users questions. This is a combination of implementations of a pattern known as retrieval augmented generation (RAG) but with a twist.
In this talk we’ll explain and demonstrate the building blocks of such an approach and show an example in code and in live action. Of course nothing is perfect, that’s why it’s important to walk through challenges with building such GenAI apps and how to address them. And if there is interest, we can dive into some more advanced patterns.
Learn more at
https://development.neo4j.dev/generativeai
