[KCFforecast]

I don't want to give any hint to the DeepMind, since I an inclined to think that in this field an expert assessment can be better that DeepMind advice. Just to give a simple question or example of the kind of knowledge involved in those predictions: Since energy generation and demand depends a lot of weather conditions, do they have any state of the art machine learning model to forecasting weather conditions?, can they predict the evolution of energy prices?, can they measure the impact of new improvement in reducing the cost in renewable energy, what about the impact of the brexit in the energy market?, are they first class experts in times series or are they simple to apply current technologies like profet or Hyndman's R package for ts. How can they defend that technologies applied at go game like reinforcement learning can be successfully applied to forecasting? Perfect games simulations allow you to get almost infinite data, current world market and weather has only very limited amount of information to be supplied for a model, how can they justify the application of big data techniques to a small available data world. I hope that some of those questions can be addressed, unfortunately while writing this I haven't read the post yet.

[computerex]

Reinforcement learning is used in spam detection and control amongst other things. You can do data augmentation and also do transfer learning.

[KCFforecast]

In this concrete case, how do you do transfer learning? what is the domain you have experience to transfer to the energy of energy? Also, Bayes's naive algorithm can be used in spam detection and usually it gives good results, is RL such a great tool in spam filtering when there is moderate data?