Cornell Researcher Proposes “Clearinghouse” Model for Building Trust Between AI Agents
The rise of autonomous AI agents is reshaping how businesses operate — from procurement and contract negotiation to scientific research and customer service. But as these systems take on increasingly consequential tasks, a critical question has gone largely unanswered: what happens when one AI agent makes a commitment to another, and both agents interpret that commitment differently?
It is a question Ming-Chang Chiu has spent years building the tools to answer. A Postdoctoral Associate at Cornell University and a researcher whose work on AI reliability has been published at renowned venues like ICML, ICLR, ICCV, AAAI, and ICASSP, Chiu argues that the AI industry is approaching a trust crisis— one that better models alone cannot solve. Chiu specializes in multimodal large language models, AI reliability, and inter-agent systems. He received his Ph.D. in Computer Science from the University of Southern California and has conducted research at Google, NVIDIA, and...
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