We've published an edited transcript for a talk I gave at EA Global 2019. This talk gives an update on the core problem we're trying to solve at Ought and shows what our current experiments look like.

The summary:

In the long run, we want machine learning (ML) to help us resolve open-ended questions like “Should I get this medical procedure?” and “What are the risks in deploying this AI system?” Currently, we only know how to train ML if we have clear metrics for success, or if we can easily provide feedback on the desired outputs. This modified talk (originally given at EA Global 2019) explains some of the mechanism design questions we need to answer to delegate open-ended questions to ML systems. It also discusses how experiments with human participants are making progress on these questions.

Read the transcript

This post was published on August 1, 2019 by Andreas Stuhlmüller.

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