Speaker
Description
Some of the most exciting fundamental physics discoveries in recent years emerged thanks to large-scale experimental collaborations that radically differed from conventional scientific practices a century ago. The recent success of large-scale AI models trained on highly diverse data sources begs the question: could our scientific conventions yet again be restricting our access to major discoveries? In this talk, I propose that broadening our analyses across datasets, detectors, and even scientific disciplines could be critical to finally answering the grand mysteries of our Universe that have thus far eluded our usual strategies. To achieve this vision, AI methods can help us publish detector-agnostic datasets, construct richer embeddings of our data, and highlight connections across varied domains -- but we also need to take care to ensure that we design these tools to uphold our highest priorities as scientists.
