15 citations · 26 across the 6 of their papers we have counts for
5 papers · 1 filter
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI
Matija Franklin, Hal Ashton, Rebecca Gorman +1
As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisi…
The dangers in algorithms learning humans' values and irrationalities
Rebecca Gorman, Stuart Armstrong
For an artificial intelligence (AI) to be aligned with human values (or human preferences), it must first learn those values. AI systems that are trained on human behavior, risk mi…
Chess as a Testing Grounds for the Oracle Approach to AI Safety
James D. Miller, Roman Yampolskiy, Olle Haggstrom +1
To reduce the danger of powerful super-intelligent AIs, we might make the first such AIs oracles that can only send and receive messages. This paper proposes a possibly practical m…
Counterfactual equivalence for POMDPs, and underlying deterministic environments
Stuart Armstrong
Partially Observable Markov Decision Processes (POMDPs) are rich environments often used in machine learning. But the issue of information and causal structures in POMDPs has been…
Low Impact Artificial Intelligences
Stuart Armstrong, Benjamin Levinstein
There are many goals for an AI that could become dangerous if the AI becomes superintelligent or otherwise powerful. Much work on the AI control problem has been focused on constru…