3 ms·

I think voice recognition would be a better example. It went from 'toy' to 'everyday use' because the word error rate dropped an order of magnitude.

• yters 6y ago
  Is that due to ML or all the massive crowdsourcing and more keyword/search driven approach? e.g. has dictation apps like dragon improved substantially?
  • morei 6y ago
    Some of it is due to bigger data, but the majority is definitely ML. For constant data, the error rate dropped dramatically due to much improved algorithms. 15 years ago, voice recognition was all hidden markov model based. The data sets were limited, in part due to the cost of collection, but mostly because larger datasets didn't significantly improve accuracy. As algorithms improved, larger datasets became more important as more data did actually improve accuracy.
  • jaimex2 6y ago
    Both funnily enough. ML has been around for years, the main reason it's getting better is that it's easier than ever to collect massive amounts of data to train good models.