Google recently introduced a potentially very interesting open source tool called word2vec. It is software designed to understand the relationship between words without human intervention. How? Through the use of "deep learning" - basically neural network models on steroids. Essentially these models understand the features of an input (in this case words) and how those inputs relate to each other.
You don't have to understand the ins and outs to see how potentially useful software that can do this is. One potential application is better understanding of tweets. Typical sentiment analysis, for example, has a difficult time with tweets due to their short nature and the fact that they are often infused with symbols and sarcasm. With deep learning techniques it may be possible to decipher the sentiment of tweets more accurately. Can you imagine?