https://video.ut0pia.org/videos/watch/66b4f4a0-6637-487f-9681-f46ae4f95007 There has been much work on Autoresearch where the objectives are coding puzzles, toy optimization problems, or static supervised-learning ML tasks. However, for an autonomous agent to assist with a scientific discovery task, the problems must come from real measurement data of the world and they are highly open-ended, requiring a scientific method in the solution loop. In this talk, we show scenarios where the agent has to search over methods, priors, data preprocessing, model classes, and hyperparameters while learning from intermediate failures. Often, a step change in agent performance comes from forming an appropriate scientific hypothesis about how the physical system behaves, implementing that hypothesis correctly in a mathematical model, and executing it on the existing real data. We show how an ontology-based memory system is used in the harness to assist with the hypothesis generation that is key to the agent's success. All demonstrations come from real scientific problems solved in industrial and applied research settings. Speakers: Sina Shahandeh (Radicait): Sina is a cofounder/CTO at Radicait. His background is in scientific computing (PhD) and has lead data/AI function in 3 scale ups (with $770M exit) as well as founding 3 companies. X/Twitter: https://x.com/SinaShahandeh LinkedIn: https://www.linkedin.com/in/sinashahandeh/ Tue, 21 Jul 2026 09:50:30 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/66b4f4a0-6637-487f-9681-f46ae4f95007 All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.