arXiv Machine Learning By HyunJin Kim, DongHyun Ryu, Xiaoyuan Yi, Jing Yao, Jianxun Lian, Muhua Huang, Shitong Duan, JinYeong Bak, Xing Xie

The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment

Read the original on arXiv Machine Learning →

arXiv:2412. 16468v4 Announce Type: replace Abstract: The emergence of large language models (LLMs) has sparked discussion on Artificial Superintelligence (ASI), a hypothetical AI system that surpasses human intelligence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Aug 26

AI Agents Push Humans Out of the Loop

AI agents are increasingly autonomous, posing significant risks that current designs hinder effective human oversight. The paper argues that oversight is degraded by both design choices and the cognitive decline of users who rely heavily on automation. It calls for prioritizing human cognitive needs in AI agent development, proposing design affordances and protocols to maintain critical judgment and counter skill atrophy.

By Margaret Mitchell, Avijit Ghosh, Samir Passi
arXiv AI
Jun 12

From AGI to ASI

arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.

By Tim Genewein, Matija Franklin, Alexander Lerchner, Laurent Orseau, Samuel Albanie, Adam Bales, Cole Wyeth, Stephanie Chan, Iason Gabriel, Joel Z. Leibo, Allan Dafoe, Marcus Hutter, Thore Graepel, Shane Legg