Artificial intelligence refers to a variety of software and hardware technologies that can be applied in numerous ways for different applications. The terms'machine learning' and'deep learning' are often used interchangeably in the media, but they are not the same thing. In machine learning, the machine builds up the knowledge to complete specific actions based on training data covering multiple datasets. There are many examples of machine learning in our daily lives. The performance of machine learning algorithms is directly related to the available information, which is referred to as'representation'.

When considering what information is important for a certain decision procedure (say, a classification task), there's an interesting gap between what's theoretically --that is, actually-- important on the one hand and what gives good results in practice as input to machine learning (ML) algorithms, on the other. Let's look at sentiment analysis tools as an example. Expression of sentiment is a pragmatic phenomenon. To predict it correctly, we need to know both the meaning of the sentences and the context in which those sentences appeared. How do you get the meaning of a sentence?