On Roman Roads: Data Composition For AI & Agents

Abstract: As AI agents propagate into the value chains of the real-world economy, the data supply chains they run on shape the velocity and skew of their impact. What does this have to do with Roman roads? I will cover work on reducing friction in the data flows that agents face and how that plumbing affects the way generated value accrues.

Bio: Luis works on composable systems for measuring, optimizing and exchanging data states across the entire data generating process in machine learning. In regular intervals, he shares his ideas through computer code and longer texts spanning topics such as data optimization [1, 2, 3, 4, 5], ML data formats [1, 2, 3] or measurement tools for ML systems [1, 2, 3, 4, 5]. He also enjoys promoting opportunities for community. He helped initiate machine learning venues such as Data-Centric Machine Learning Research (DMLR) and AI for Good and co-chaired conferences such as ICLR, the DMLR workshop series or ML4H. He is Co-Founder and Chief AI Officer at Brickroadand a final-year PhD research scientist at the Department of Artificial Intelligence of Wojciech Samek at Fraunhofer HHI in Berlin, Germany.