To be honest Watson didn't use any deep learning (I read the paper, see http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165 http://www.aaai.org/

To be honest Watson didn't use any deep learning (I read the paper, see http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165 http://www.aaai.org/ojs/index.php/aimagazine/article/view/23... ). It uses an ensemble of dozens of question-answering algorithms. Still it's very cool. A name DeepQA was a bit misleading, perhaps "wide QA" would be better. RNN chatbots are the future, but for now they aren't stable enough to fulfill customers' elevated expectations. Also there is a problem with objective function that rewards interesting conversation, there is some new research on this problem that hasn't made it into open RNN chatbots yet. The standard objective function was giving most probable answers, it turned out to be boring. I agree with grandparent that Reinforcement Learning is a right approach for training an intellectual chatbot, but there is a problem of well-designed training curriculum (i.e. some systematic scheme of rewarding and punishing the RL agent). I wouldn't wonder if facebook's M team tries different approaches, including RL, to train on their data.