As generative AI systems become more powerful and widely deployed, ensuring safety and security is critical. This talk introduces AI red teaming—systematically probing AI systems to uncover potential risks—and demonstrates how to get started using PyRIT (Python Risk Identification Toolkit), an open-source framework for automated and semi-automated red teaming of generative AI systems. Attendees will leave with a practical understanding of how to identify and mitigate risks in AI applications, and how PyRIT can help along the way.

Why can we solve some equations with neat formulas, while others stubbornly resist every trick we know? Equations with squares bow to the quadratic formula. Those with cubes and fourth powers also have solutions. But then the magic stops. And when we, as data scientists, add exponentials, logarithms, or trigonometric terms into models, the resulting equations often cross into territory where no closed-form solutions exist.

This talk is both fun and useful. With Python and SymPy, we’ll “cheat” our way through centuries of mathematics, testing families of equations to see when closed forms appear and when numerical methods are our only option. Attendees will enjoy surprising examples, a bit of mathematical history, and practical insight into when exact solutions exist — and when to stop searching and switch to numerical methods.

Agents need timely and relevant context data to work effectively in an interactive environment. If an agent takes more than a few seconds to react to an action in a client applicatoin, users will not perceive it as intelligent - just laggy.

Real-time context engineering involves building real-time data pipelines to pre-process application data and serve relevant and timely context to agents. This talk will focus on how you can leverage application identifiers (user ID, session ID, article ID, order ID, etc) to identify which real-time context data to provide to agents. We will contrast this approach with the more traditional RAG approach of using vector indexes to retrieve chunks of relevent text using the user query. Our approach will necessitate the introduction of the Agent-to-Agent protocol, an emerging standard for defining APIs for agents.

We will also demonstrate how we provide real-time context data from applications inside Python agents using the Hopsworks feature store. We will walk through an example of an interactive application (TikTok clone).