Ten Hard Problems in Artificial Intelligence We Must Get Right
Abstract
We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human goals; (4) enabling great applications of AI in real life; (5) addressing economic disruptions; (6) ensuring the participation of all; (7) at the same time ensuring socially responsible deployment; (8) addressing any geopolitical disruptions that AI causes; (9) promoting sound governance of the technology; and (10) managing the philosophical disruptions for humans living in the age of AI. For each problem, we outline the area, identify significant recent work, and suggest ways forward. [Note: this paper reviews literature through January 2023.]
Keywords
Cite
@article{arxiv.2402.04464,
title = {Ten Hard Problems in Artificial Intelligence We Must Get Right},
author = {Gavin Leech and Simson Garfinkel and Misha Yagudin and Alexander Briand and Aleksandr Zhuravlev},
journal= {arXiv preprint arXiv:2402.04464},
year = {2024}
}
Comments
75 + 19 pages