Prior research often finds that AI creativity is limited: single systems rarely outperform humans, and human-AI collaboration does not exceed human output. We argue these conclusions underestimate AI's potential because most studies do not allow iterative, multi-agent exchanges that mirror the social processes underpin...
Y. Luan, Luning Sun, YeunJoon Kim et al.· 0 citations
This workshop seeks to consolidate efforts by providing an interdisciplinary forum for presenting cutting-edge research, sharing deployment experiences, and showcasing real-world systems in this rapidly evolving field of AI Data Scientist.
Hao Liu, M. Zitnik, Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
As data volumes and analytical demands grow, traditional data science workflows struggle to meet the need for efficiency, scalability, and reliability. The rapid advancement of large language models (LLMs) has opened new possibilities for AI-powered agents to augment or automate end-to-end data science pipelines—from d...
Hao Liu, M. Zitnik, Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
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