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> So, are reasonable examples now of these models allowing semantic context? This is about where I am stuck. I'll start believing that we truly are on the cusp of a revolution as soon as I see Google Translate reliably knowing when to translate "home" into French as "domicile", "foyer", something those lines, or as "accueil." Right now it seems to very frequently choose "accueil", which is generally wrong, except whe…
Syntax and semantics were developed for human language, yet it's much easier to puzzle the difference in a computer language than in human language. With syntax and semantics so wrapped together, however, it kind of seems like you can go a long way with just capturing syntax, rhythm, word choice and etc. Which is to say the semantic side can be even worse than it seems, ie, nonexistent.
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How does it compare to Google’s BERT and do you have an online demo? Here’s a demo of BERT https://www.pragnakalp.com/demos/BERT-NLP-QnA-Demo/
(Similar to the response for another question.) BERT is a language representation model while Turing-NLG is a language generation model (similar to GPT). They are not directly comparable (they can potentially be massaged to mimic the other, but, not something that we have done yet.)
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Yes.
While Markov chains sound like a schizophrenic, this sounds like a banker on coke. Pretty impressive progress, i guess.
While Markov chains sound like a schizophrenic, this sounds like a spacial disjointed notebook, as if somebody was trying to write in two places at the same time. "Today is Monday, it's Saturday night, I forgot to write to my dad and he only leaves the house for a couple of hours."
Yet, sometimes Markov chains turn out to be the most beautiful art form. His wife Jennifer Neil, whom he met at a barbecue and has been married to since 1998, attributes this creative process to the constant ups and downs in his old job.
"His numbers are just insane," says Jennifer Neil. "I don't know where he keeps them, but they're very mind boggling."
Somewhat disconnected from the actual