Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Alignment Newsletter #48, published by Rohin Shah on the AI Alignment Forum. Find all Alignment Newsletter resources here. In particular, you can sign up, or look through this spreadsheet of all summaries that have ever been in the newsletter. Highlights Quantilizers: A Safer Alternative to Maximizers for Limited Optimization and When to use quantilization (Jessica Taylor and Ryan Carey): A key worry with AI alignment is that if we maximize expected utility for some utility function chosen by hand, we will likely get unintended side effects that score highly by the utility function but are nevertheless not what we intended. We might hope to leverage human feedback to solve this: in particular, an AI system that simply mimics human actions would often be desirable. However, mimicry can only achieve human performance, and cannot improve upon it. The first link is a 2015 paper that introduces quantilization, which interpolates between these two extremes to improve upon human performance while bounding the potential (expected) loss from unintended side effects. In particular, let's suppose that humans have some policy γ (i.e. probability distribution over actions). We evaluate utility or performance using a utility function U, but we do not assume it is well-specified -- U can be any function, including one we would not want to maximize. Our goal is to design a policy π that gets higher expected U than γ (reflecting our hope that U measures utility well) without doing too much worse than γ in the worst case when U was as badly designed as possible. We'll consider a one-shot case: π is used to select an action once, and then the game is over. The core idea behind quantilization is simple: if our policy only does things that the human might have done, any expected loss it incurs corresponds to some loss that the human could incur. So, let's take our human policy γ, keep only the top q-fraction of γ (as evaluated by U), and then sample an action from there. This defines our policy π_q, also called a q-quantilizer. For example, suppose the human would choose A with probability 0.25, B with probability 0.5, and C with probability 0.25, and U(A) > U(B) > U(C). Then a (1/4)-quantilizer would choose A with certainty, a (1/2)-quantilizer would choose randomly between A and B, and a (3/8)-quantilizer would choose A twice as often as B. Note that even if π_q places all of its probability on the worst possible actions (because U was badly designed), by construction the human had probability q of doing the same thing as π_q, and so the expected cost of π_q can be at most (1/q) times larger than the expected cost of the human policy γ. In fact, if we have no other knowledge, a q-quantilizer maximizes expected utility U subject to the constraint of never doing worse than (1/q) times as bad as γ. As soon as you move to the setting with multiple actions, if you choose each action using quantilization, then your worst case bound is exponential in the number of actions. If you assume the cost for each action is independent, you recover the guarantees, but this is not a realistic assumption (as the authors note). Long-term plans are very good or very bad because all of the actions build on each other to achieve some goal, so the costs are not going to be independent. The second link is a recent post that further analyzes quantilization. It points out that the theoretical analysis in the paper assumes that deviating from the human policy can only lead to costs. If the deviation could also forgo benefits, then the theorems no longer apply. Perhaps the bottom (1-q) of actions actually have strong positive benefits, that we failed to model in U. Then by eliminating those actions, we may have lost arbitrary amounts of expected value. In these situations, the only way to bound the expected regret...