There is no known video game that has NPCs that can fully pass the Turing test as of yet, as it requires a level of artificial intelligence that has not been achieved.
The above text written by ChatGPT, but you probably guessed that already. The prompt was exactly your question.
A more serious reply: Suppose you used one of the current LLMs to drive a videogame NPC. I’m sure game companies must be considering this. I’d be interested to know if any of them have made it work, for the sort of NPC whose role in the game is e.g. to give the player some helpful information in return for the player completing some mini-quest. The problem I anticipate is the pervasive lack of “definiteness” in ChatGPT. You have to fact-check and edit everything it says before it can be useful. Can the game developer be sure that the LLM acting without oversight will reliably perform its part in that PC-NPC interaction?
Something a bit like this has actually been done, with a proper scientific analysis, but without human players so far. (Or at least I am not aware of the latter, but I frankly can no longer keep up with all the applications.)
They (Park et al 2023 https://arxiv.org/abs/2304.03442 ) populated a tiny, Sims-style world with ChatGPT-controlled AIs, enabled them to store a complete record of agent interactions in natural language, synthesise them into conclusions, and draw upon them to generate behaviours—and let them interact with each other. Not only did they not go of the rails—they performed daily routines, and improvised in a a matter consistent with their character backstories when they ran into each other, eerily like in Westworld. It also illustrated another interesting point that Westworld had made—the strong impact of the ability to form memories on emergent, agentic behaviours.
The thing that stood out is that characters within the world managed to coordinate a party—come up with the idea that one should have one, where it would be, when it would be, inform each other that such a decision had been taken, invite each other, invite friends of friends—and that a bunch of them showed up in the correct location on time. The conversations they were having affected their actions appropriately. There is not just a complex map of human language that is self-referential; there are also references to another set of actions, in this case, navigating this tiny world. It does not yet tick the biological and philosophical boxes for characteristics that have us so interested in embodiment, but it definitely adds another layer.
And then we have analysis of and generation of pictures, which, in turn, is also related to the linguistic maps. One thing that floored me was an example from a demo by OpenAI itself where ChatGPT was shown an image of a heavy object, I think a car, that had a bunch of balloons tied to it with string, balloons which were floating—probably filled with helium. It was given the picture and the question “what happens if the strings are cut” and correctly answered “the balloons would fly away”.
It was plausible to me that ChatGPT cannot possibly know what words mean when just trained on words alone. But the fact that we also have training on images, and actions, and they connect these appropriately… They may not have complete understanding (e.g. the distinction between completely hypothetical states, states that are assumed given within a play context, and states that are externally fixed, seems extremely fuzzy—unsurprising, insofar as ChatGPT has never had unfiltered interactions with the physical world, and was trained so extensively on fiction) but I find it increasingly unconvincing to speak of no understanding in light of this.
There is no known video game that has NPCs that can fully pass the Turing test as of yet, as it requires a level of artificial intelligence that has not been achieved.
The above text written by ChatGPT, but you probably guessed that already. The prompt was exactly your question.
A more serious reply: Suppose you used one of the current LLMs to drive a videogame NPC. I’m sure game companies must be considering this. I’d be interested to know if any of them have made it work, for the sort of NPC whose role in the game is e.g. to give the player some helpful information in return for the player completing some mini-quest. The problem I anticipate is the pervasive lack of “definiteness” in ChatGPT. You have to fact-check and edit everything it says before it can be useful. Can the game developer be sure that the LLM acting without oversight will reliably perform its part in that PC-NPC interaction?
Something a bit like this has actually been done, with a proper scientific analysis, but without human players so far. (Or at least I am not aware of the latter, but I frankly can no longer keep up with all the applications.)
They (Park et al 2023 https://arxiv.org/abs/2304.03442 ) populated a tiny, Sims-style world with ChatGPT-controlled AIs, enabled them to store a complete record of agent interactions in natural language, synthesise them into conclusions, and draw upon them to generate behaviours—and let them interact with each other. Not only did they not go of the rails—they performed daily routines, and improvised in a a matter consistent with their character backstories when they ran into each other, eerily like in Westworld. It also illustrated another interesting point that Westworld had made—the strong impact of the ability to form memories on emergent, agentic behaviours.
The thing that stood out is that characters within the world managed to coordinate a party—come up with the idea that one should have one, where it would be, when it would be, inform each other that such a decision had been taken, invite each other, invite friends of friends—and that a bunch of them showed up in the correct location on time. The conversations they were having affected their actions appropriately. There is not just a complex map of human language that is self-referential; there are also references to another set of actions, in this case, navigating this tiny world. It does not yet tick the biological and philosophical boxes for characteristics that have us so interested in embodiment, but it definitely adds another layer.
And then we have analysis of and generation of pictures, which, in turn, is also related to the linguistic maps. One thing that floored me was an example from a demo by OpenAI itself where ChatGPT was shown an image of a heavy object, I think a car, that had a bunch of balloons tied to it with string, balloons which were floating—probably filled with helium. It was given the picture and the question “what happens if the strings are cut” and correctly answered “the balloons would fly away”.
It was plausible to me that ChatGPT cannot possibly know what words mean when just trained on words alone. But the fact that we also have training on images, and actions, and they connect these appropriately… They may not have complete understanding (e.g. the distinction between completely hypothetical states, states that are assumed given within a play context, and states that are externally fixed, seems extremely fuzzy—unsurprising, insofar as ChatGPT has never had unfiltered interactions with the physical world, and was trained so extensively on fiction) but I find it increasingly unconvincing to speak of no understanding in light of this.
Character ai used to have bots good enough to pass. (ChatGPT doesn’t pass, since it was finetuned and prompted to be a robotic assistant.)