Thankfully, I didn’t need to make up a word, or even look too far afield. Simulators have been spoken of before in the context of AI futurism; the ability to simulate with arbitrary fidelity is one of the modalities ascribed to hypothetical superintelligence. I’ve even often spotted the word “simulation” used in colloquial accounts of LLM behavior: GPT-3/LaMDA/etc described as simulating people, scenarios, websites, and so on. But these are the first (indirect) discussions I’ve encountered of simulators as a type creatable by prosaic machine learning, or the notion of a powerful AI which is purely and fundamentally a simulator, as opposed to merely one which can simulate.
(...)A fun way to test whether a name you’ve come up with is effective at evoking its intended signification is to see if GPT, a model of how humans are conditioned by words, infers its correct definition in context.
Types of AI
Agents: An agent takes open-ended actions to optimize for an objective. Reinforcement learning produces agents by default. AlphaGo is an example of an agent.
Oracles: An oracle is optimized to give true answers to questions. The oracle is not expected to interact with its environment.
Genies: A genie is optimized to produce a desired result given a command. A genie is expected to interact with its environment, but unlike an agent, the genie will not act without a command.
Tools: A tool is optimized to perform a specific task. A tool will not act without a command and will not optimize for any objective other than its specific task. Google Maps is an example of a tool.
Simulators:
A simulator is optimized to generate realistic models of a system. The simulator will not optimize for any objective other than realism,although in the course ofdoing so, it might generate instances of agents, oracles, and so on.— Janus, arguing that "Simulator" is something close to an informal True Name for self-supervised predictive models, in Simulators