My name is pronounced “YOO-ar SKULL-se” (the “e” is not silent). I’m a PhD student at Oxford University, and I was a member of the Future of Humanity Institute before it shut down. I have worked in several different areas of AI safety research. For a few highlights, see:
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
STARC: A General Framework For Quantifying Differences Between Reward Functions
Risks from Learned Optimization in Advanced Machine Learning Systems
Some of my recent research on the theoretical foundations of reward learning is also described in this sequence.
For a full list of all my research, see my Google Scholar.
Yes, I mostly just mean “low test error”. I’m assuming that real-world problems follow a distribution that is similar to the Solomonoff prior (i.e., that data generating functions are more likely to have low Kolmogorov complexity than high Kolmogorov complexity) -- this is where the link is coming from. This is an assumption about the real world, and not something that can be established mathematically.