It speaks in an accent that is similar to native English speakers learned to speak Cantonese where the tones are messed up. Not only that, but the sentence sounds like a mechanic translation from English to Mandarin, then read character by character in Cantonese.
(long pause switching from English to Cantonese) (speaking indistinctly)… are you asking about other persons, whether they are digital doubles, just like me? (speaking indistinctly again) think they have real directors, writers, (some words I can’t even recognize) (interrupted)
The fact it replicated incomprehensible bad Cantonese as if spoken by an English speaker who sucks at it is the first thing approaching Art that this clunker has ever simulated.
Yeah, I think if AI was good at translations, we’d see a lot more localizations of media, like manga/mangua/manghwa, video games, etc.
But the reality is that when people say LLMs are great at translation, it’s more of a “they can make things understandable (some portion of the time)” not “they make it sound as if a native of the other language is saying the exact same thing in that language that you said in yours”.
LLMs make token associations, there is no understanding. They’ll need bigger neural nets and even better training data to model understanding. They seem to be making progress in programming but that’s because the whole pipeline can be better automated: a program can be compiled to find syntax or grammar errors, and it can be run to check the behaviour. There is no automatic feedback mechanism for things like translation or image generation.
It’s all subjective, too, so someone who wants the models to be perceived as “good” will be more likely to perceive it as good themselves and miss or forgive errors, while skeptics will be looking closely for them. Neutrals won’t be looking for errors but will recognize the uncanny valley (which bad translations can also fall into, though they are usually perceived more as funny than creepy).
It speaks in an accent that is similar to native English speakers learned to speak Cantonese where the tones are messed up. Not only that, but the sentence sounds like a mechanic translation from English to Mandarin, then read character by character in Cantonese.
The fact it replicated incomprehensible bad Cantonese as if spoken by an English speaker who sucks at it is the first thing approaching Art that this clunker has ever simulated.
Thank you for indulging my curiosity!
So it wasn’t even GOOD Cantonese. Figures.
Yeah, I think if AI was good at translations, we’d see a lot more localizations of media, like manga/mangua/manghwa, video games, etc.
But the reality is that when people say LLMs are great at translation, it’s more of a “they can make things understandable (some portion of the time)” not “they make it sound as if a native of the other language is saying the exact same thing in that language that you said in yours”.
LLMs make token associations, there is no understanding. They’ll need bigger neural nets and even better training data to model understanding. They seem to be making progress in programming but that’s because the whole pipeline can be better automated: a program can be compiled to find syntax or grammar errors, and it can be run to check the behaviour. There is no automatic feedback mechanism for things like translation or image generation.
It’s all subjective, too, so someone who wants the models to be perceived as “good” will be more likely to perceive it as good themselves and miss or forgive errors, while skeptics will be looking closely for them. Neutrals won’t be looking for errors but will recognize the uncanny valley (which bad translations can also fall into, though they are usually perceived more as funny than creepy).