15 citations · 27 across the 6 of their papers we have counts for
6 papers
On Avoiding Power-Seeking by Artificial Intelligence
Alexander Matt Turner
We do not know how to align a very intelligent AI agent's behavior with human interests. I investigate whether -- absent a full solution to this AI alignment problem -- we can buil…
Parametrically Retargetable Decision-Makers Tend To Seek Power
Alexander Matt Turner, Prasad Tadepalli
If capable AI agents are generally incentivized to seek power in service of the objectives we specify for them, then these systems will pose enormous risks, in addition to enormous…
Formalizing the Problem of Side Effect Regularization
Alexander Matt Turner, Aseem Saxena, Prasad Tadepalli
AI objectives are often hard to specify properly. Some approaches tackle this problem by regularizing the AI's side effects: Agents must weigh off "how much of a mess they make" wi…
Avoiding Side Effects in Complex Environments
Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli
Reward function specification can be difficult. Rewarding the agent for making a widget may be easy, but penalizing the multitude of possible negative side effects is hard. In toy…
Optimal Policies Tend to Seek Power
Alexander Matt Turner, Logan Smith, Rohin Shah +2
Some researchers speculate that intelligent reinforcement learning (RL) agents would be incentivized to seek resources and power in pursuit of their objectives. Other researchers p…
Conservative Agency via Attainable Utility Preservation
Alexander Matt Turner, Dylan Hadfield-Menell, Prasad Tadepalli
Reward functions are easy to misspecify; although designers can make corrections after observing mistakes, an agent pursuing a misspecified reward function can irreversibly change…