Journal
AI MAGAZINE
Volume 38, Issue 3, Pages 3-24Publisher
AMER ASSOC ARTIFICIAL INTELL
DOI: 10.1609/aimag.v38i3.2756
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Funding
- Future of Life Institute FLI-RFP-AI1 program [2015-145014]
- NSF [0705765, 0832804]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0832804] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1331932] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0705765] Funding Source: National Science Foundation
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Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system's models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world.
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