674 karma · joined March 19, 2015
2019 was a great year for movies for example.
I honestly think people are severely underestimating what a massive impact this is currently having in increasing productivity of python devs who know a little C++.
> Obviously Nuance is more than just speech recognition, but still not sure why people are downplaying how good they were at it.
Because nuance wasn't very good.. at least in all the benchmarks I've seen. It's been a while since I compared numbers it's possible they've improved a lot. They're also known for kinda being dicks with the contracts they offer in B2B.
I have a vague memory of reading that the higher ups just didn't believe in voice-based interfaces.
Then either you and all your coworkers so far have been amazing, or you're the bad one. Either way consider yourself lucky!
Yeah... that's why the paper is a good contribution contrary to what you're saying. Not sure why you're repeating this information.
> What I am complaining about is that the authors are confusing "what can be represented" by a neural network with "what can be easily learned" via SGD. I argued that the peak in test loss in those figures is observed becuse SGD struggles to find a solution that generalizes well, not because those networks are intrinsically less powerful (as the paper seems to imply).
I mean.. those models are by definition less powerful as they have less parameters. The (to me) main point of the paper is to point out a symptom which is interesting. Their explanation for the symptom being (maybe) wrong doesn't detract from the important work of showing the symptom exists.
A legitimate criticism would be that there have been earlier papers showing the same thing.
Sidenote: It seems having a larger model does make it easier for SGD to find good solutions[1]