4 ms·

Odd, my impression is that everything moves in the opposite direction. Good synthetic data is expensive. Recording the real world is free. Just a variational a

Odd, my impression is that everything moves in the opposite direction. Good synthetic data is expensive. Recording the real world is free. Just a variational autoencoder with a discrete latent space is good enough to learn a usable phoneme recognition and pronunciation model from raw WAV files with unsupervised learning. And clip shows is just for far you can get without supervision. So what's the point in paying for artificial data if you can solve the problem without it?

zhobbs 5y ago
Recording real world data can be cheap for some use cases, but often labeling it is very expensive.
krapht 5y ago
What. That really depends on your task. In my field, real data is extremely expensive and if we could generate believable synthetic data, we'd save a ton of money.
- hervature 5y ago
  > if we could generate believable synthetic data, we'd save a ton of money. It sounds like you are saying good synthetic data is more expensive?
nomel 5y ago
> Recording the real world is free. Waiting for edge cases to occur, in the real world, is definitely not free.
vhold 5y ago
With synthetic data you can also generate the thing you are trying to solve with the AI in the first place, like generating a depth map, and an object classification map to go with a simulated image. With real world data a human will have to label it.
- fxtentacle 5y ago
  Yes but AIs trained on synthetic depth maps tend to learn the oddities and noise patterns of the rendering, not actually the depth recognition part. That's why so many AIs that work almost perfectly on the Sintel benchmark (synthetic flow and depth data) then fail to transfer over to KITTI (real images with lidar).
variaga 5y ago
Recording real-world data may be cheap (not "free") in some circumstances, but properly labeling real-world data so it will be useful can be incredibly expensive. Even if you're doing completely unsupervised learning, you will still need correctly labeled data at some point to validate your model. And - I tell you as someone who has actually tried to do this - getting a wide range of high-quality voice recordings across multiple speakers is NOT CHEAP, and trying to train a model that will work under real-world conditions using "whatever dataset I downloaded from kaggle.com" is a fool's errand.

zhobbs 5y ago

Recording real world data can be cheap for some use cases, but often labeling it is very expensive.

krapht 5y ago

What. That really depends on your task. In my field, real data is extremely expensive and if we could generate believable synthetic data, we'd save a ton of money.

hervature 5y ago
> if we could generate believable synthetic data, we'd save a ton of money. It sounds like you are saying good synthetic data is more expensive?

nomel 5y ago

> Recording the real world is free. Waiting for edge cases to occur, in the real world, is definitely not free.

vhold 5y ago

With synthetic data you can also generate the thing you are trying to solve with the AI in the first place, like generating a depth map, and an object classification map to go with a simulated image. With real world data a human will have to label it.

fxtentacle 5y ago
Yes but AIs trained on synthetic depth maps tend to learn the oddities and noise patterns of the rendering, not actually the depth recognition part. That's why so many AIs that work almost perfectly on the Sintel benchmark (synthetic flow and depth data) then fail to transfer over to KITTI (real images with lidar).

variaga 5y ago

Recording real-world data may be cheap (not "free") in some circumstances, but properly labeling real-world data so it will be useful can be incredibly expensive. Even if you're doing completely unsupervised learning, you will still need correctly labeled data at some point to validate your model. And - I tell you as someone who has actually tried to do this - getting a wide range of high-quality voice recordings across multiple speakers is NOT CHEAP, and trying to train a model that will work under real-world conditions using "whatever dataset I downloaded from kaggle.com" is a fool's errand.