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Not at all. The existence of the Gran Turismo game (a simulator) is not the same thing as an AI that can play Gran Turismo.

by csee 5y ago

stingraycharles 5y ago
But a better analogy would be if an AI generated a computer game that another computer can learn to play. In the end, it’s more like an anonymization layer than anything. If a computer is trained to generate input data for other computers to train with, there’s not a lot special going on.
- manmal 5y ago
  Generating an environment and acting within it are wildly different things. Eg Tesla generates virtual camera footage of traffic situations they want their vehicles to handle correctly. The footage generator is basically a scripted video game director, while the trained AI is one of the most complex software projects ever.
- csee 5y ago
  "But a better analogy would be if an AI generated a computer game that another computer can learn to play." Doesn't the latter AI have a policy which contains novel information that does not exist in the former AI? Even if what you say is true in some abstract information theory sense (and I would question that), there is a world of practical difference in the usefulness of a trained self-driving AI and the game engine within which that AI functions.
taeric 5y ago
But.... if you were to train a model on the simulator of the game, you would expect it to pick up on the rules programmed into the simulator. This is really no different than it picking on the rules embedded in the data gathering of real world data. Any implicit and hidden decisions in that space would be expected to find their ways into the ML.
- redytedy 5y ago
  The tricky part is that the simulator itself may not have easy to understand rules. Waymo has a Neurips talk about training world agent models that are used for car behavior in the simulation itself. Trying to make world agents that are indistinguishable from real-world vehicle behavior (e.g. minimizing jenson-shannon curvature entropy) is a completely different task than training a model to safely transport you somewhere.
  - taeric 5y ago
    Right. That was my angle. Sometimes, they are ready to see after the fact. But, just like bias can enter into data collection, so too can it enter simulation.
- csee 5y ago
  Yes but isn't the optimal policy novel information?
  - taeric 5y ago
    But it's it free of bias and a good execution policy in the real world?
    - csee 5y ago
      That probably depends on how realistic the simulator is. Your trade off is more bias (due to simulation inaccuracy) in exchange for unlimited data (and hence less variance).
      - taeric 5y ago
        Agreed. I could see a good case of training heavily on simulated data, verify/validate on collected data. My question is ultimately how this really helps with making the system ethical. Just moved the bias from collection to simulation. And... I can't see simulation being less impacted with bias.

Not at all. The existence of the Gran Turismo game (a simulator) is not the same thing as an AI that can play Gran Turismo.

by csee 5y ago

Not at all. The existence of the Gran Turismo game (a simulator) is not the same thing as an AI that can play Gran Turismo.

stingraycharles 5y ago

But a better analogy would be if an AI generated a computer game that another computer can learn to play. In the end, it’s more like an anonymization layer than anything. If a computer is trained to generate input data for other computers to train with, there’s not a lot special going on.

manmal 5y ago
Generating an environment and acting within it are wildly different things. Eg Tesla generates virtual camera footage of traffic situations they want their vehicles to handle correctly. The footage generator is basically a scripted video game director, while the trained AI is one of the most complex software projects ever.
csee 5y ago
"But a better analogy would be if an AI generated a computer game that another computer can learn to play." Doesn't the latter AI have a policy which contains novel information that does not exist in the former AI? Even if what you say is true in some abstract information theory sense (and I would question that), there is a world of practical difference in the usefulness of a trained self-driving AI and the game engine within which that AI functions.

taeric 5y ago

But.... if you were to train a model on the simulator of the game, you would expect it to pick up on the rules programmed into the simulator. This is really no different than it picking on the rules embedded in the data gathering of real world data. Any implicit and hidden decisions in that space would be expected to find their ways into the ML.

redytedy 5y ago
The tricky part is that the simulator itself may not have easy to understand rules. Waymo has a Neurips talk about training world agent models that are used for car behavior in the simulation itself. Trying to make world agents that are indistinguishable from real-world vehicle behavior (e.g. minimizing jenson-shannon curvature entropy) is a completely different task than training a model to safely transport you somewhere.
- taeric 5y ago
  Right. That was my angle. Sometimes, they are ready to see after the fact. But, just like bias can enter into data collection, so too can it enter simulation.
csee 5y ago
Yes but isn't the optimal policy novel information?
- taeric 5y ago
  But it's it free of bias and a good execution policy in the real world?
  - csee 5y ago
    That probably depends on how realistic the simulator is. Your trade off is more bias (due to simulation inaccuracy) in exchange for unlimited data (and hence less variance).
    - taeric 5y ago
      Agreed. I could see a good case of training heavily on simulated data, verify/validate on collected data. My question is ultimately how this really helps with making the system ethical. Just moved the bias from collection to simulation. And... I can't see simulation being less impacted with bias.