As someone in the deep learning space who's not working at one of those companies (deep learning on hadoop with cloudera/red hat model is my game) I'd like to a

As someone in the deep learning space who's not working at one of those companies (deep learning on hadoop with cloudera/red hat model is my game) I'd like to add here that while these labs are doing stuff with ads, it's not necessarily all that they are focused on. I've visited a few of the teams and I'd like to say that many of the things being published out of there (say the recent real time vision stuff with cars published by yann lecun's group) or perhaps the voice stuff coming out of baidu. That being said: I think you're ignoring the structure of the data that are involved with click through rates and the like. Reconstructing sparse data and reccommenders is among the hardest problems in AI. Couple that with the at scale unstructured data(being media like text, audio) being used and we can see some real landmark results. Real value is being created alongside some of the commercial research being done. As for use cases of deep learning outside of ads, my customers are working on many cool problems that don't involve targeting. I've done training as well as deployments with a variety of companies now. Media centric products coupled with users are among some of the coolest datasets out there. Let me say that search and similarity among data is by far the best application of this stuff outside of yet another conv net classifier. There's a practical side to this stuff that doesn't involve ads. I hope that helps a bit.