4 ms·

You'd need to find some task that is expensive to compute, but trivial to verify to "port" current PoW methods, but the hard part is finding a task that fills t

by zdkl 9y ago

You'd need to find some task that is expensive to compute, but trivial to verify to "port" current PoW methods, but the hard part is finding a task that fills this condition and is useful to actually compute. In any case, you'd still be running a competition between nodes for fastest/best solution, with lots of duplicated effort and consumed resources. That is the key part of crypto consensus afaiu, and regardless of "backend task", there's always going to be waste in this architecture.

bufferoverflow 9y ago
Which should be possible. For instance, your PoW could be "design a Go game bot neural network that's better than the system currently challenges you with". Hard to make, easy to verify.
- vog 9y ago
  > easy to verify This may be a lot harder than you think. How do you decide that one AI plays better than another? By playing lots of games. These games must be random, i.e. not known beforehand, otherwise the new AI could cheat. But then, every cryptocurrency node would use a different set of game settings for validation. Which means that some (unlucky) nodes may reject the new AI while others accept it. The result are regular net splits, making it very hard to establish an keep the global consensus that is absolutely needed for a stable cryptocurrency system. The core issue here is that validation must be 100% deterministic. Each node must run exactly the same calculations for validation, otherwise the consensus network is doomed.
  - setr 9y ago
    N seeds for the prng algorithm shipped with the program to be verified. Each seed provides for one game. N is sufficiently large to effectively remove "luck" for the skill comparison, and sufficiently large to make finding enough seeds allowing wins for a cheating weaker player more difficult than generating a better go player Im assuming all games played against the incumbent; seeds are basically proof of wins. I guess you could still cheat though by copying the incumbent, get a 50-50 ratio of games, and then just find enough winners to make it 49-51.
yorwba 9y ago
The expensive task in machine learning is the training, it can be verified by checking for performance improvement on a validation set. You can make use of the duplicated effort if everyone trains on different examples, then their learned models can be merged/ensembled together for better performance. That's the ideal situation when everyone plays by the rules. To make that scheme work as a "crypto"currency, you'll also have to make sure that breaking the rules doesn't bring a benefit. E.g. the validation examples can't be known beforehand, otherwise someone could just overfit to them to get a higher score with less work. But I think it might be possible to make it work.
- JustFinishedBSG 9y ago
  > it can be verified by checking for performance improvement on a validation set. Not all gradient updates improve performance
  - yorwba 9y ago
    Not all attempts to find a Bitcoin hash of the right difficulty succeed either. Miners would have to continue training until they are fairly certain that it has resulted in an improvement.

by zdkl 9y ago

bufferoverflow 9y ago

Which should be possible. For instance, your PoW could be "design a Go game bot neural network that's better than the system currently challenges you with". Hard to make, easy to verify.

vog 9y ago
> easy to verify This may be a lot harder than you think. How do you decide that one AI plays better than another? By playing lots of games. These games must be random, i.e. not known beforehand, otherwise the new AI could cheat. But then, every cryptocurrency node would use a different set of game settings for validation. Which means that some (unlucky) nodes may reject the new AI while others accept it. The result are regular net splits, making it very hard to establish an keep the global consensus that is absolutely needed for a stable cryptocurrency system. The core issue here is that validation must be 100% deterministic. Each node must run exactly the same calculations for validation, otherwise the consensus network is doomed.
- setr 9y ago
  N seeds for the prng algorithm shipped with the program to be verified. Each seed provides for one game. N is sufficiently large to effectively remove "luck" for the skill comparison, and sufficiently large to make finding enough seeds allowing wins for a cheating weaker player more difficult than generating a better go player Im assuming all games played against the incumbent; seeds are basically proof of wins. I guess you could still cheat though by copying the incumbent, get a 50-50 ratio of games, and then just find enough winners to make it 49-51.

yorwba 9y ago

The expensive task in machine learning is the training, it can be verified by checking for performance improvement on a validation set. You can make use of the duplicated effort if everyone trains on different examples, then their learned models can be merged/ensembled together for better performance. That's the ideal situation when everyone plays by the rules. To make that scheme work as a "crypto"currency, you'll also have to make sure that breaking the rules doesn't bring a benefit. E.g. the validation examples can't be known beforehand, otherwise someone could just overfit to them to get a higher score with less work. But I think it might be possible to make it work.

JustFinishedBSG 9y ago
> it can be verified by checking for performance improvement on a validation set. Not all gradient updates improve performance
- yorwba 9y ago
  Not all attempts to find a Bitcoin hash of the right difficulty succeed either. Miners would have to continue training until they are fairly certain that it has resulted in an improvement.