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What about synthetic data that is correct? Example: Using the output of a physics simulation to train... The output is synthetic (as in, its not from the real w

What about synthetic data that is correct? Example: Using the output of a physics simulation to train... The output is synthetic (as in, its not from the real world) but it is governed by the equations of motion...

dbish 7y ago
This is the one that seems to be useful today. Running simulations with physics being modeled across objects seems to be useful for reinforcement learning algorithms that interact in the real world. You see some of this with autonomous vehicle training, robot manipulation training, etc. I think there are also emergent interactions that can occur that you wouldn't have modeled directly unless you let the simulation run.
fathead_glacier 7y ago
The example is fundamentally different as physics simulations have closed form solutions or can be numerically approximated for various initial and boundary conditions. Therefore, by definition the generation is not synthetic. Other examples I could think of fall in the category the top comment is referring to.
evjrob 7y ago
The point lalaland1125 is making is that the equations of motion are the existing superior model in this scenario.
- derefr 7y ago
  Right, but presumably we don't want the model to learn those, since they're an impractically low-level model. If you're training an AI for flying a plane, you want it to learn "the simplest model that works"—presumably something like aerodynamics—not a far more computationally-intensive (and data-intensive!) model, like e.g. quantum chromodynamics.
Nition 7y ago
Going from the parent comment's assertion, the answer would be that if you want to train something to predict physics, the original physics simulation that's generating the data is either: 1. Already doing that, in which case it's already the superior model. or 2. Not doing that correctly, in which case the synthetic data output is equally poor.
- mehrdadn 7y ago
  I mean, if (1) is true but you get faster performance, then it's useful, right?
  - aghillo 7y ago
    Yes. A typical example would be the use of statistical emulators, emulating process (physical) based models that are computationally complex in the environmental sciences.
- slavik81 7y ago
  Consider computer graphics and computer vision. If you had a photorealistic renderer, it could be useful for generating training data for a vision algorithm. However, you can't just run your rendering pipeline backwards to create a scene out of an image.

dbish 7y ago

This is the one that seems to be useful today. Running simulations with physics being modeled across objects seems to be useful for reinforcement learning algorithms that interact in the real world. You see some of this with autonomous vehicle training, robot manipulation training, etc. I think there are also emergent interactions that can occur that you wouldn't have modeled directly unless you let the simulation run.

fathead_glacier 7y ago

The example is fundamentally different as physics simulations have closed form solutions or can be numerically approximated for various initial and boundary conditions. Therefore, by definition the generation is not synthetic. Other examples I could think of fall in the category the top comment is referring to.

evjrob 7y ago

The point lalaland1125 is making is that the equations of motion are the existing superior model in this scenario.

derefr 7y ago
Right, but presumably we don't want the model to learn those, since they're an impractically low-level model. If you're training an AI for flying a plane, you want it to learn "the simplest model that works"—presumably something like aerodynamics—not a far more computationally-intensive (and data-intensive!) model, like e.g. quantum chromodynamics.

Nition 7y ago

Going from the parent comment's assertion, the answer would be that if you want to train something to predict physics, the original physics simulation that's generating the data is either: 1. Already doing that, in which case it's already the superior model. or 2. Not doing that correctly, in which case the synthetic data output is equally poor.

mehrdadn 7y ago
I mean, if (1) is true but you get faster performance, then it's useful, right?
- aghillo 7y ago
  Yes. A typical example would be the use of statistical emulators, emulating process (physical) based models that are computationally complex in the environmental sciences.
slavik81 7y ago
Consider computer graphics and computer vision. If you had a photorealistic renderer, it could be useful for generating training data for a vision algorithm. However, you can't just run your rendering pipeline backwards to create a scene out of an image.