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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: Quantifying General Intelligence, published by JasonBrown on June 17, 2022 on The AI Alignment Forum. Introduction After having thought about the underlying definitions of agents, goals, and intelligence for quite a while, I realised that these things are often defined quite qualitatively. I decided to try and build up a framework for conceptualising intelligent systems in such a way that it would allow us to quantify things, such as general intelligence. This can then be the starting point for developing tools for quantifying and comparing these entities, giving us a better idea of how close or far systems are to human level general intelligence. The definitions are not meant to be philosophical truths, rather they are meant to be useful tools that will allow us to analyse and gain insight into these systems and how they relate to one another. This is my first long form piece of writing / serious thinking about this sort of thing so any and all feedback is welcome! Desirable Properties We begin by identifying several desired properties that would increase the utility and robustness of our framework, giving us something to aim at. Sufficient: If our definitions relied upon, or referenced, things that are poorly defined themselves, we would just be moving the problem back a step and not actually gaining any insight. Measurable: Intelligence is a broad spectrum, this especially visible in the natural world. A good definition would reflect this and give us a continuous measure of intelligence that allows sensible comparisons. Implementation Independent: It's easy to compare somethings capabilities to humans in order to ascertain their intelligence. We want our definitions to be free from bias towards any particular implementation or version of intelligence, so that it can recognise intelligence which operates in a way unfamiliar to us, or in a way we don't understand. Minimal Grey Areas: Many definitions could leave large grey areas on boundaries between classifications, or not make sense when applied to domains they were not designed with in mind. This should be avoided. Useable: Sometimes a seemingly 'perfect' definition is infeasible to actually apply, and so is of no practical use. A definition which is infeasible to theoretically calculate, could have a method to estimate it. Quantifying how reliable or accurate the estimates are would also be useful. Agents A classic definition of an agent is something that interacts with an environment, choosing an action (or actions), to achieve some desired goal. To make this rigorous, we will define an agent in our framework as something that produces an action based on the state of its environment. The action will be an effect on the environment. In a mathematical sense an agent is a function from the environment state to an effect, the effect is then a function from one environment state to another. Both functions could be stochastic (involving randomness) or deterministic, depending on the environment and the agent. Describing something as an agent is something we do as part of our framework in order to analyse it. There is no physical property that determines if an object is an agent or if it's not - anything can be an agent if we want it to be. Environments The environment includes everything that could affect our agent, and everything our agent can affect. Arguably this is always the entire universe though this is not very helpful and so reasonable simplifications should be made. The environment can also include the agent and its internal state, especially if our agent is able to read or modify its own state. Environments will typically be multi-dimensional. If they represent real-world environments then there will be up to 4 dimensions, 3 for space and 1 for time. When we define an environment, we will also define somet...