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My point is, it's amazing that it's better at decoding phonetic sequences (which are presumably garbled by passing through a human being (me) who doesn't unders

My point is, it's amazing that it's better at decoding phonetic sequences (which are presumably garbled by passing through a human being (me) who doesn't understand them), than a human being who has evolved to use language and has over 30 years of fluent experience.

furyofantares 11y ago
That's really cool, but I don't know if I'd describe that example as being better at decoding phonetic symbols. You might still be better better at mapping phonetic symbols to words and names you know -- which I think is a separate skill than mapping them to new words or names -- it's just that you didn't know that name and the software did. And it might also be better at knowing lots of words, which is yet another skill/property. If you both have learned the word, who is better at recognizing it? And who is better at learning a new word they don't know? These aren't very exacting questions because the comparison between human and machine knowing and learning hasn't been defined. Maybe the machine needs more examples and more contexts to learn a word as well as a human and correctly map to the word as often as a human, but it can also examine more examples and contexts than a human can per unit time and can do a lot of scaling in this regard using increased energy that a human cannot.
oska 11y ago
Any language that uses the latin alphabet has different rules for how pronunciation is derived from or encoded with the letters. e.g. with your example, the letter pair ‘si’ is pronounced differently in Mandarin that it is in English. So it's not surprising that you couldn't write it down properly in pinyin as you don't know how to transcribe Mandarin into pinyin. But Google does. Another example - without knowing how French spelling works there is no way that as an English speaker you could work out how to correctly spell ‘peut’ (can) just from hearing it.
- minot 11y ago
  The last time I checked, it says Minot as minnow whereas the North Dakota town rhymes its name with "why not?" So you'd say it as my knot. Now Google recognizes I'm talking about Minot but it says Minnow back to me.

furyofantares 11y ago

That's really cool, but I don't know if I'd describe that example as being better at decoding phonetic symbols. You might still be better better at mapping phonetic symbols to words and names you know -- which I think is a separate skill than mapping them to new words or names -- it's just that you didn't know that name and the software did. And it might also be better at knowing lots of words, which is yet another skill/property. If you both have learned the word, who is better at recognizing it? And who is better at learning a new word they don't know? These aren't very exacting questions because the comparison between human and machine knowing and learning hasn't been defined. Maybe the machine needs more examples and more contexts to learn a word as well as a human and correctly map to the word as often as a human, but it can also examine more examples and contexts than a human can per unit time and can do a lot of scaling in this regard using increased energy that a human cannot.

oska 11y ago

Any language that uses the latin alphabet has different rules for how pronunciation is derived from or encoded with the letters. e.g. with your example, the letter pair ‘si’ is pronounced differently in Mandarin that it is in English. So it's not surprising that you couldn't write it down properly in pinyin as you don't know how to transcribe Mandarin into pinyin. But Google does. Another example - without knowing how French spelling works there is no way that as an English speaker you could work out how to correctly spell ‘peut’ (can) just from hearing it.

minot 11y ago
The last time I checked, it says Minot as minnow whereas the North Dakota town rhymes its name with "why not?" So you'd say it as my knot. Now Google recognizes I'm talking about Minot but it says Minnow back to me.