Certainly fantastic results from the colloborators at DeepMind. The next step is going to be a major hurdle though--we'll have to extend it to model-free control. The Test of Time award winner at NIPS, Ali Rahimi, gave a great talk about the ever more essential need for greater scientific rigor and the development of useful theoretical foundations in Machine Learning such that we might make some insights to approach problems in the field that currently seem almost intractable (i.e. high dimensionality, etc.). For example, one thing he mentioned was how we use batch normalization to accelerate gradient descent, and how this is "explained" by saying batch norm "reduces internal covariate shift." The problem is, we really haven't a clue as to *why* reducing internal covariate shift speeds up gradient descent. Essentially, he wants us to move away from the sort of unguided engineering approach, and move towards a culture of and approach to research more similar to Biology (as opposed to just training our models conventionally and trying to reduce error rate as much as possible, and ultimately just a more empirical 'be creative and see what sticks' kind of approach).
Interestingly, this has spawned a debate in the community. Prof. Yann LeCun came out and officially disagreed with Rahimi, stating that the state of the art so far has been developed due to the clever engineering of researchers (he took offense to what Rahimi called it: "Alchemy"), and that practical technology almost always precedes the theory developed to fully explain the technology (i.e. Watt's steam engine came before Carnot Cycle--you can't criticize Watt for not being Carnot, basically). I think the essence of the debate is whether the current culture of research will be beneficial or detrimental for the field going forward--Rahimi seems to think it is becoming detrimental, since it's still a challenge to pedagogically deliver ML to students without any anchoring theoretical foundations (you actually have to "get good" at machine learning by developing a lot of experience and intuition while training models, without being able to anchor back to any real first principles), and the need to have proper explanations for the workings of our ML systems in the context of life or death situations involving humans (i.e. autonomous driving, cancer pathology, etc.). Prof. LeCun thinks that this way of doing things is just fine, and that Rahimi shouldn't needlessly criticize the work done so far, instead go and do the theory work, to which Rahimi replied that the talk he gave was basically a public plea for help.
I see both sides' arguments, and it's been very interesting to follow the discussion so far. Either way, I'm excited to see where we'll be in 5 or 10 years, when we'll have seriously improved and expanded datasets for model training combined with superior hardware like MITs programmable nanophotonic processor or utilizing the phase-change memory work of IBM researchers to allow for massively parallel computing systems, which would be great for ML. Maybe by then we'll have progressed a good bit on developing theoretical foundations in ML.