>> To win, you need a reasonably deep understanding of the entities in the game, and their abstract relationships to one another. Ultimately, players need to re

>> To win, you need a reasonably deep understanding of the entities in the game, and their abstract relationships to one another. Ultimately, players need to reason about what they can and cannot do in a complex world. On this, I'm not with Gary Marcus. I think Nethack will probably fall to deep learning at some point, just like other games that everyone thought "require reasoning", like Go, most notably [1]. Perhaps some combination of a classical search with a deep neural net trained with self-play to guide the search will do it. Perhaps some other approach suffering from data and compute gigantism will do it. In any case, what I've learned in the last few years is that there isn't any single problem that deep learning approaches can't solve just by training on tragicomic amounts of data, even if it's so much data that only the likes of Google and Facebook can do the actual training. Assuming that a problem can _only_ be solved by reasoning is setting yourself up for a nasty surprise. After all, PAC-Learning, what we have by ways of a theory in machine learning, does not assume any ability for reasoning. PAC-Learning assumes instead that a concept is a set of instances and that a learner has learned a concept when it can correctly label an instance as a member of a concept, or not, with arbitrarily low probability of arbitrarily low error. In that sense, a system that can only memorise instances can still achieve arbitrarily low error simply by memorising sufficiently many instances. No reasoning needed, whatsoever. Indeed, this is precisely why deep neural nets need to be trained with so much data. Because they are simply trying to memorise enough instances of a concept to minimise their error. So, given enough data, deep neural nets can beat any benchmark. They'll eventually beat Nethack. And we'll still not have learned anything useful, and certainly not beaten a path towards AGI. Machine learning is stuck in a rut where it advances from one little, over-specialised benchmark to the next. We won't make any progress just by coming up with new benchmarks. _______________ [1] Chess had already fallen to symbolic approaches: a book of opening moves and minimax with alpha-beta cutoff; that was Deep Blue, the system that beat Gary Kasparov, and that, despite its name, was not a deep learning system, but a Good, Old-Fashioned AI, symbolic system.