Semantic Scholar Open Access 2016 314 sitasi

Artificial General Intelligence

James Babcock János Kramár Roman Yampolskiy

Abstrak

There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem – the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.

Topik & Kata Kunci

Computer Science

Penulis (3)

James Babcock

János Kramár

Roman Yampolskiy

Format Sitasi

APA MLA BibTeX

Babcock, J., Kramár, J., Yampolskiy, R. (2016). Artificial General Intelligence. https://doi.org/10.1007/978-3-319-41649-6

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 314×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-319-41649-6
Akses: Open Access ✓