If you think tuning GP parameters are challenging wait until you try tuning hyper parameters for a DL model! It is just a press release, to be fair to DeepMind

If you think tuning GP parameters are challenging wait until you try tuning hyper parameters for a DL model! It is just a press release, to be fair to DeepMind, and I guess they can promote themselves however they wish. My original comment was more from the context of seeing neural network models in practice perform barely any better, if at all, then classic ML models. Just as those comparisons were revealing similarly I was suspecting this use case may be the same to another classic technique. GP is certainly not the shining star of AI right now but it is actively researched and perusing Google scholar on the subject will show you plenty of interesting, but less heralded, results. There are probably several meaningful metrics for this problem that can be examined. If nothing else it is a simple matter of grading the solutions of each, like a university assignment. Also, typically classical techniques are less resource intensive then any neural network methods; the energy savings alone when considered at production scales would be significant.