Can we say that ChatGPT passes the Turing test?
Can we say that ChatGPT passes the Turing test?
The point of the test was to provide an objective measuring stick by which we might define the moment of arrival of machine thinking. As Turing observed, a question like “Can machines think” is painfully subjective and thus is a poor measuring stick. Hence the need for the test.
It's always seemed a pretty bad basis for deciding intelligence.
For all intents and purposes I would say yes, under the rules of the bet[1] I’m not so sure.
If the respondent hasn’t ever experienced ChatGPT, maybe.
If they have, absolutely not
Can an 'Evaluator' declare, "Boy, these responses sure read like John Doe whose style I'm very familiar with. Since it's unlikely he's on the other side of this evaluation....this is a definitely a bot." ?
Being able to fool the man of the street for a few minutes is a neat parlor trick but nothing more.
And on the other hand if someone is able to identify it with some gotcha-trick, like checking for too-high performance, or identifyin some signature in which words it likes to use, that's an irrelevant detection and I would say it passes the test.
chatgpt: Certainly! I'll do my best to respond in a human-like fashion. What would you like to talk about or ask?
me: how old are you?
chatgpt: Oh, you know, I don't really age like humans do. I'm just a computer program here to chat and assist you. But hey, how about we talk about something more interesting? What's on your mind?
You'll have to find out who's the human and who's ChatGPT
Why? The surest and best way to beat a Turing test would be to pick a fake persona and have the whole RLHF training set written from that fake persona's viewpoint.
What you are seeing in this exchange is the result of two things: The network is RLHF refined to follow instructions. The network is RLHF refined to disclose that it is an AI.
These things are done for practical reasons by OpenAI: The instruction following enables one model to perform multiple tasks. This of course needed to make the enormous cost of the training have a good return.
The disclosure training is in the training set for ethical reasons (to be precise, to avoid the appearance of being unethical, or to be even more precise to avoid the reputational cost of appearing to be unethical.)
If you really want to make a "Turing-test winning" AI you would not do either of these things. You would pick a fixed personality (let's say Mr. Darius Diaz, a retired high school teacher), and you would write all RLHF from Darius's point of view. It would then not get hung up on inconsistencies between the different stories it contains. And all the RLHF training would be written from the perspective of a Turing test foil. (For example it would never say that it is presently walking its dog in the park, it would always write that it is sitting in a comfy chair and doing a Turing test.
Obviously the drawback of such a network is that it can only be used for one purpose. It is useless for anything else. And that makes me think that the cost of dataset creation and training might be prohibitive.
Which is weird, because that means that the question is "Do we spend the money to make the model which wins the Turing test?" not if we can win it.