For me, the interesting thing about IDA is not capability amplification like self-play, but an attitude towards generation of datasets as a point of intervention into the workings of an AI for all kinds of improvements. So we have some AI that we want to make better in some respect, and the IDA methodology says that to do that, we should employ the AI to generate a dataset for retraining a new version of it that’s better than the original dataset in that respect. Then we retrain the AI using the new dataset. So amplification unpackages the AI into the form of an appropriately influenced dataset, and then learning repackages it for further use.
For me, the interesting thing about IDA is not capability amplification like self-play, but an attitude towards generation of datasets as a point of intervention into the workings of an AI for all kinds of improvements. So we have some AI that we want to make better in some respect, and the IDA methodology says that to do that, we should employ the AI to generate a dataset for retraining a new version of it that’s better than the original dataset in that respect. Then we retrain the AI using the new dataset. So amplification unpackages the AI into the form of an appropriately influenced dataset, and then learning repackages it for further use.