> Actually, I never made the conscious decision to call this class of AI “simulators.” Hours of GPT gameplay and the word fell naturally out of my generative model – I was obviously running simulations.
Simulators - Janus
The way this post is written may give the impression that I wracked my brain for a while over desiderata before settling on this word. Actually, I never made the conscious decision to call this class of AI “simulators.” Hours of GPT gameplay and the word fell naturally out of my generative model – I was obviously running simulations.
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Just saying “this AI is a simulator” naturalizes many of the counterintuitive properties of GPT which don’t usually become apparent to people until they’ve had a lot of hands-on experience with generating text.
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In the simulation ontology, I say that GPT and its output-instances correspond respectively to the simulator and simulacra. GPT is to a piece of text output by GPT as quantum physics is to a person taking a test, or as transition rules of Conway’s Game of Life are to glider. The simulator is a time-invariant law which unconditionally governs the evolution of all simulacra.
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Calling GPT a simulator gets across that in order to do anything, it has to simulate something, necessarily contingent, and that the thing to do with GPT is to simulate! Most published research about large language models has focused on single-step or few-step inference on closed-ended tasks, rather than processes which evolve through time, which is understandable as it’s harder to get quantitative results in the latter mode. But I think GPT’s ability to simulate text automata is the source of its most surprising and pivotal implications for paths to superintelligence: for how AI capabilities are likely to unfold and for the design-space we can conceive.
Also see Scott Alexander's article.