Read this paper from July of last year and it'll make more sense as to why this research is interesting: http://arxiv.org/pdf/1506.05869.pdf http://arxiv.org/pd

Read this paper from July of last year and it'll make more sense as to why this research is interesting: http://arxiv.org/pdf/1506.05869.pdf http://arxiv.org/pdf/1506.05869.pdf. Seeding a dataset with human labels, then generating new, better data is pretty cool. Similar to deep mind watching go games, then learning to play better than said games. Add to which, humans can't tell the difference between the human generated data and algo generated data. We're about 3 months from where you can bootstrap a system like microsofts with freely available code and data. From there, if "memory modules" start working better so models can "remember specific context" (it can already remember general context), you'll have a bot that's a pretty good model for passing the Turing test. I'm defining a practical Turing test as text-based, human gets 5 questions, other party replies 5 times, then human must predict: human/not human.