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AI Research Considerations for Human Existential Safety (ARCHES)

AI Research Considerations for Human Existential Safety (ARCHES)

30 May 2020
Andrew Critch
David M. Krueger
ArXivPDFHTML

Papers citing "AI Research Considerations for Human Existential Safety (ARCHES)"

9 / 9 papers shown
Title
Who's Driving? Game Theoretic Path Risk of AGI Development
Robin Young
LLMSV
34
0
0
28 Jan 2025
Open-Endedness is Essential for Artificial Superhuman Intelligence
Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes
Michael Dennis
Jack Parker-Holder
Feryal M. P. Behbahani
Aditi Mavalankar
Yuge Shi
Tom Schaul
Tim Rocktaschel
LRM
37
21
0
06 Jun 2024
A Review of the Evidence for Existential Risk from AI via Misaligned
  Power-Seeking
A Review of the Evidence for Existential Risk from AI via Misaligned Power-Seeking
Rose Hadshar
26
6
0
27 Oct 2023
Negative Human Rights as a Basis for Long-term AI Safety and Regulation
Negative Human Rights as a Basis for Long-term AI Safety and Regulation
Ondrej Bajgar
Jan Horenovsky
FaML
19
9
0
31 Aug 2022
Open Problems in Cooperative AI
Open Problems in Cooperative AI
Allan Dafoe
Edward Hughes
Yoram Bachrach
Tantum Collins
Kevin R. McKee
Joel Z Leibo
Kate Larson
T. Graepel
34
199
0
15 Dec 2020
Stabilising Experience Replay for Deep Multi-Agent Reinforcement
  Learning
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster
Nantas Nardelli
Gregory Farquhar
Triantafyllos Afouras
Philip Torr
Pushmeet Kohli
Shimon Whiteson
OffRL
117
595
0
28 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
240
1,837
0
03 Feb 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,661
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
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