Yah it is by no means wasteful for AlphaGo to throw away all their training data and then re-train itself!
That kind of ruthless experimentation is how AlphaGo was able to exceed even itself. The willingness to say - all these human games we've fed the computer? All these terabytes of data? It's all meaningless! We're going to throw it all away! We will have AlphaGo determine what is good by playing games against itself!
And I bet you that for the next iteration of AlphaGo, the creators of this system will again, delete their own data and retrain when they have a better approach.
If you don't "waste" your existing datasets (once you reallze the flaws in your data sets), you are being held back by the sunk cost principle. You only have yourself to blame when someone does train for the exact same purposes, but with cleaner data.
The person who has the cleanest source of training data will win in deep learning.
You're sabotaging yourself in my opinion. 30k is nothing when you're just sabotaging the training with faulty data.
That kind of ruthless experimentation is how AlphaGo was able to exceed even itself. The willingness to say - all these human games we've fed the computer? All these terabytes of data? It's all meaningless! We're going to throw it all away! We will have AlphaGo determine what is good by playing games against itself!
And I bet you that for the next iteration of AlphaGo, the creators of this system will again, delete their own data and retrain when they have a better approach.
If you don't "waste" your existing datasets (once you reallze the flaws in your data sets), you are being held back by the sunk cost principle. You only have yourself to blame when someone does train for the exact same purposes, but with cleaner data.
The person who has the cleanest source of training data will win in deep learning.
You're sabotaging yourself in my opinion. 30k is nothing when you're just sabotaging the training with faulty data.