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The alignment problem from a deep learning perspective
We argue that AGIs trained in similar ways as today’s most capable models could learn to act deceptively to receive higher reward; learn internally-represented goals which generalize beyond their training distributions; and pursue those goals using power-seeking strategies.
Richard Ngo, Lawrence Chan, Sören Mindermann
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Benefits of assistance over reward learning
We illustrate the benefits of agents that try to assist humans, over agents that learn a reward during training and then maximize said reward after deployment.
Rohin Shah, Pedro Freire, Neel Alex, Rachel Freedman, Dmitrii Krasheninnikov, Lawrence Chan, Michael D Dennis, Pieter Abbeel, Anca Dragan, Stuart Russell
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Accounting for Human Learning when Inferring Human Preferences
Harry Giles, Lawrence Chan
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