NLP

NLP is a subset of AI, and primarily focuses on interaction between machines and human languages. It spans over making machines understand, interpret, and generate human languages towards propelling effective human communication along its path-an improvised method of interaction between man and machines. NLP is the most pivotal technology in research and application fields for chatbots, voice assistants, translation, sentiment analysis, and recommendation systems.

NLP also faces dire challenges as human language is much more complex than structured data. Language is vague, context-dependent, and dramatically varies across different regions and cultures. Words can have several meanings, and even the structure of a sentence can highly change the meaning of a message. For example, the "bank of the river" and "bank of a financial institution" seem the same but certainly have different meanings. A few principles such as tokenization, part-of-speech tagging, named entities recognition, and syntactic parsing have stretched beyond assistance in handling challenges in NLP.

The new advance in NLP has primarily been fueled by deep learning during the beginning stages of transformer models. Models such as OpenAI's GPT series or Google's BERT have been important players in the advancement of the way a machine applies the context and how coherent and intelligent responses are produced. One of the most important characteristics of transformers is that they make use of the words of an entire sentence together, not serially, and that they explore long-range text dependencies much further. NLP has great impacts on changing the way we interact with technologies. The use of NLP-enabled chatbots in customer services is efficient because it reduces the query response time and improves user experience. In health care, the promise of NLP is in gaining insight by analyzing patient records and making better decisions.