At a certain point, there really is no difference between "being really good at mimicking the sound of Japanese" and actually knowing it, because in order to mimic it to a high level you will have to actually know it. "Mimic the sound of Japanese" is the equivalent task here to "predict the next token in this text".
For now it’s slightly better than fake crowd noise you’re hearing in movies, but still frequently just gibberish.
The Touring test allows unlimited topics, time, etc. There are competitions that use rules heavily favor bots where bots have “won” in the past but they aren’t actually preforming the test.
ChatGPT seems amazing at first, but that’s because its flaws are so novel to them. People just aren’t used to looking for them so they can overlook how quickly it completely forgets about previous parts of a conversation etc.
Passing the Turing test does not imply general intelligence but saying that what it outputs is "just gibberish" is obviously just another hyperbole from you.
I don’t have some arbitrary rules for what passes the Turing test, but it’s about the worst case not the best.
https://en.wikipedia.org/wiki/Computing_Machinery_and_Intell...
> the test as described
I hope you realize that the original Turing test is where you have a man and a woman trying to convince an interrogator that they are of the opposite sex. The test is to replace one with a machine and see if the interrogator would decide the wrong sex as often as when there's an actual human playing.
So if we're talking about the actual test, as described, the most basic bots have passed it a long time ago. If we're talking about the standard interpretation (convince the interrogator that the bot is human) it's a derived version that has no intrinsic rules and was not described by Turing.
I don’t specifically object to changing the judge from interrogation to observation of a conversation. But, it should be clear his version doesn’t have all the loopholes the modern interpretation does.
> wrong sex
You can read the original paper it’s clear in his version the goal for the computer is trying to convince someone communicating with them it’s human even though the form is to convince someone they are male. “The game may perhaps be criticised on the ground that the odds are weighted too heavily against the machine. If the man were to try and pretend to be the machine he would clearly make a very poor showing. He would be given away at once by slowness and inaccuracy in arithmetic.” https://redirect.cs.umbc.edu/courses/471/papers/turing.pdf
It’s also clear he’s referring to the spirit of the game not the specific details: “It might be urged that when playing the "imitation game" the best strategy for the machine may possibly be something other than imitation of the behaviour of a man. This may be, but I think it is unlikely that there is any great effect of this kind. In any case there is no intention to investigate here the theory of the game, and it will be assumed that the best strategy is to try to provide answers that would naturally be given by a man.”
He does give a benchmark of 70% accurate after five minutes of questioning, but that wasn’t success just a benchmark.
You've clearly never used ChatGPT to help you build or troubleshoot anything before.
Try it before you knock it.
That said, it’s got a lot of code to copy from. So I may try again if when I suspect I am reinventing the wheel.
If you told me "We have made a natural language parser/processor that is at human level" I'd think that was a huge step forward for computing. Nobody can argue with that.
And it is quite possible that it's a question without any meaningful answer. We basically have a "gut feeling" that it is a qualitative rather than a quantitative difference, but is that really based on hard data, or it's just more comfortable for us to think that way?
In the meantime, the practical question is - how useful is it? What can it do?