I’m more active on Twitter than LW/AF these days: https://twitter.com/DavidSKrueger
Bio from https://www.davidscottkrueger.com/:
I am an Assistant Professor at the University of Cambridge and a member of Cambridge’s Computational and Biological Learning lab (CBL). My research group focuses on Deep Learning, AI Alignment, and AI safety. I’m broadly interested in work (including in areas outside of Machine Learning, e.g. AI governance) that could reduce the risk of human extinction (“x-risk”) resulting from out-of-control AI systems. Particular interests include:
Reward modeling and reward gaming
Aligning foundation models
Understanding learning and generalization in deep learning and foundation models, especially via “empirical theory” approaches
Preventing the development and deployment of socially harmful AI systems
Elaborating and evaluating speculative concerns about more advanced future AI systems
You could try to do tests on data that is far enough from the training distribution that it won’t generalize in a simple immitative way there, and you could do tests to try and confirm that you are far enough off distribution. For instance, perhaps using a carefully chosen invented language would work.