Description:

Towards the Last Mile of Artificial General Intelligence: Open-World Long-Tailed Learning in Theory and Practice

Dawei Zhou, Assistant Professor, Department of Computer Science, Virginia Tech

Abstract: Artificial General Intelligence (AGI) represents the next generation of AI that can match or exceed human intelligence across a wide spectrum of tasks. Despite remarkable advances, today’s AI systems succeed mainly in data-rich, well-structured settings—identifying common objects, summarizing routine content, or responding to typical queries. They struggle precisely where intelligence matters most—rare, high-stakes, and context-dependent scenarios such as scientific discovery, open-world cybersecurity, and rare disease diagnosis. We argue that this shortfall defines the Last Mile Problem on the path to AGI, which we frame as Open-World Long-Tailed Learning (OpenLT): how can we enable AI systems to reason, adapt, and generalize across the underrepresented, evolving, and open-ended domains? In this talk, I will discuss our group’s recent work on 1) OpenLT Characterization – How can we systematically characterize and uncover novel, complex patterns in open-world data?, 2) OpenLT Adaptation – How can AI models be effectively adapted to open and dynamic environments?, and 3) OpenLT Application and Deployment - hinging on the application of scientific hypothesis generation for 3D metamaterial design to discuss our proposed techniques and theoretical results for open-world long-tailed learning. Finally, I will close with thoughts on how addressing the Last Mile Problem can shape the next decade of AGI research and move us closer to systems that truly understand and operate in the open world.