AIE Sessions

This talk explores how AI models can leverage structured data, specifically within a lakehouse environment, by understanding context in various shapes rather than relying solely on traditional query-based methods. It emphasizes the shift from asking direct questions to enabling AI to infer and utilize information based on its inherent structure and relationships. The core idea is to unlock more sophisticated AI applications by treating data context as a fundamental input.

This talk discusses the decision to discontinue a multi-agent pipeline, highlighting the challenges encountered in practical implementation. The presenters share insights into why a seemingly promising approach was ultimately abandoned, focusing on the complexities and limitations that arose during development and deployment. The core thesis revolves around the difficulties of scaling and managing sophisticated agentic systems in real-world scenarios.

This talk addresses the critical challenge of ensuring the accuracy and trustworthiness of knowledge graphs generated by Large Language Models (LLMs). It proposes a system for tracking the provenance of information within these graphs, allowing users to trace data back to its original sources. This is essential for building reliable AI applications that depend on accurate knowledge representation.

This talk explores the application of agentic AI principles to the development of mobile games, focusing on how local agents can enhance game mechanics and player experiences. The presenters discuss the potential for AI to create more dynamic and responsive game environments by processing information and making decisions directly on the device. This approach aims to reduce latency and enable richer, more complex gameplay interactions.

This talk addresses the challenge of enabling AI models to retain information across extended video content, effectively giving them memory. The presenter discusses the technical hurdles and the architectural solutions developed to overcome these limitations, allowing for more sophisticated video analysis and interaction.

This talk argues that agentic systems, particularly those involved in coding, require ontologies to manage complexity and ensure reliable operation. Frank Coyle from UC Berkeley posits that as these systems become more sophisticated and capable of independent action, a structured understanding of their environment, tools, and goals becomes crucial for effective development and deployment. Without such a framework, the emergent behaviors of complex agentic systems can become unpredictable and difficult to control.

This talk introduces Learned Execution Graphs (LEGs) as a novel approach to detecting anomalies and drift in API usage. The core thesis is that by modeling API interactions as graphs and learning their execution patterns, systems can proactively identify deviations from normal behavior, which is crucial for maintaining API reliability and security.

This talk explores the evolution of data management from systems of record to systems of context. It argues that shifting focus to contextual data allows for more dynamic and intelligent applications. The core thesis is that by understanding the context surrounding data, systems can become more adaptive and provide richer insights.