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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: Maps and Blueprint; the Two Sides of the Alignment Equation, published by Nora Ammann on October 25, 2022 on The AI Alignment Forum. Tl;dr In this post, I will introduce two conceptual tools I found useful when thinking about (or introducing people to) AI alignment. Namely, I introduce the distinction between Maps and Blueprints (part 1), describe (what I call) the Two Sides of the “Alignment Equation” (part 2), and discuss how all these things relate to each other (part 3). Part 1: Map and Blueprint Most people reading this will already be familiar with the Map–Territory distinction. In this metaphor, the territory represents reality and the map represents your current best understanding of reality. The corollaries: “the map is not the territory”; “maps are never true, but they can be more or less useful”, etc. I want to introduce one more idea into this picture: the blueprint. If the map is what we construct from investigating the word, a blueprint is what allows us to render a (new) bit of territory (e.g., building some artefact). Roughly speaking, map-making is closer to what science is trying to do, while blueprint-making is closer to what engineering is about. While maps are causally posterior to the territory (they are drawn based on what we know about existing territory by looking at it), blueprints are causally prior (they are used to create new bits of territory). In other words, the red arrows in the diagram below depict the authorial relationship between these concepts. Figure 1: The relationship between territory, maps, and blueprints; arrows represent an authorial relationship. (There is an interesting question as to what sort of epistemic object blueprints are (e.g., compared to maps). For those curious, I can recommend Herbert Simon’s "The Sciences of the Artificial" which discusses how (if at all) we can make "the artificial"/design/blueprints our subject of study. Part 2: Two sides of the “Alignment Equation” Here is a simple model that intends to capture the basic structure of the AI alignment problem. I will call it the “Alignment Equation”: Let us assume there are two (intelligent) agents that stand in relationship R to one another. Ri can take different shapes, among which is Raligned, which is a relationship that guarantees alignment between the two agents. Let us name our two agents O and S, referring to the object and subject of this alignment endeavour, respectively. Specifically, we're trying to align O with S, i.e., Raligned, OS. Figure 2: The “alignment equation”, with the object of alignment on one side and the subject of alignment on the other. In other words, when we talk about AI alignment, we talk about how we want one agent—in this case, the AI system(s), i.e., the object of alignment—to stand in a particular relationship to the subject of alignment (e.g., human intents, human values, etc.)—namely, an aligned relationship. Of course, this wee toy model fails to capture a lot of relevant nuance. And yet, it lets us point at and talk about some critical subparts of the problem in fairly simple terms. For example: 1. Finding the right specification for O and S In the toy model, O and S are placeholders. For example, O might represent a single centralised AI system, or multiple centralised AI systems, or a distributed network of AI services, etc. S might represent the intents or preferences of an individual human, or the (~aggregate) values of a collective of humans, or of sentient beings more generally, or something else yet. Finding the right ways to fill in the placeholders matters, and is itself subject to disagreement and study. 2. Finding mechanisms that can shape R Different mechanisms can define or shape the relationship R. We can understand work on AI alignment as exploring what different mechanisms we have at hand and how to a...