3 ms·

And that's exactly the kind of stuff Google is trying to accelerate. One tangential but highly relevant example is their work on predicting molecular properties

And that's exactly the kind of stuff Google is trying to accelerate. One tangential but highly relevant example is their work on predicting molecular properties [0]. They were basically able to use neural networks to speed up the simulator by 300,000x, taking something that would take hours to less than a second. As you mention, this entirely changes the way you work and iterate. Here's a video of Jeff Dean talking more about it [1]. [0] https://ai.googleblog.com/2017/04/predicting-properties-of-molecules-with.html https://ai.googleblog.com/2017/04/predicting-properties-of-m... [1] https://www.youtube.com/watch?v=rP8CGyDbxBY&t=16m43s https://www.youtube.com/watch?v=rP8CGyDbxBY&t=16m43s

coolio2657 7y ago
How can something like this even conceptually make sense? I'm sorry, but can someone explain to me how a neural network could approximate physically-deduced laws of nature (as had-coded into the classical simulator as I imagine) "without any distinguishable error"? This is the biggest bs I have ever heard, since an molecular physics simulator that is beat by a neural network can only just be unoptimized as hell.
- neuronexmachina 7y ago
  I think this is an example of something similar: https://www.sciencedaily.com/releases/2019/05/190517145112.htm https://www.sciencedaily.com/releases/2019/05/190517145112.h... > Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth