Natural language processing (NLP), one of the most exciting components of AI is all set to rule the way we communicate with the external world. NLP uses computational and mathematical methods to analyze the human language to facilitate interactions with computers using conversational language. Subtopics in this genre include natural language understanding of the inputs created by humans, and natural language generation, to focus on generating natural language narratives.

The most popular approaches to NLP deploy Machine Learning. Natural language processing is improving human-computer conversations at the most advanced levels with applications or systems like Google Duplex, which can act as an agent to perform tasks like making haircut appointments over the phone by conversing with humans. IBM's Debater Project Debater, a groundbreaking AI technology which can argue with the humans on complex topics is another stride attributed to NLP.

The NLP Era

About 50 years ago, mankind began the earliest attempts to analyze the human language through computational methods, though it is in the recent times that these methods have achieved commercial and technical success. Over the years, several factors have led to the growth and popularity of NLP, ranging from the miniaturization of electronics to the rapid increase in the use of digital data available for processing.

These developments, along with the exponential increase in the computational power of computers with the ability to handle an enormous volume of data has led to the development of highly sophisticated mathematical models including deep learning neural networks, the power behind the computers to process conversational language.

The NLP Application Today and the Roadmap for the Future

NLP is the voice behind Siri and Alexa, likewise, customer service chatbots harness the power of NLP to drive customized responses in e-commerce, healthcare and business utilities. Some of the more omnipresent applications of NLP today include virtual assistants, sentiment analysis, customer service, and translation.

As technology continues to grow and evolve, future application of NLP will be more user-oriented.  For example, virtual assistants will be able to answer a lot more complicated questions assessing the implications along with the literal meaning of the question asked. (Q: How is the weather today? A: Rainy, you will need an umbrella). In the future times to come, businesses will be able to offer a plethora of professional customer services, take calls instantly and escalate problems to real people.

The application of NLP is not restricted to solving customer queries or providing customized shopping/ health advice but has evolved into a more technological assistance of sorts. In the current times, NLP can be trained to provide a list of errors, if one uses NLP to ask "what is wrong with my network?"  In the Future, NLP will be able to understand the user's real intent like he wants his network fixed for an access. The future with NLP is exciting as advances in NLP will allow mankind to shift focus from the questions to the results. It will be a giant leap when NLP is able to understand the user's input and provides more complex solutions answering the user's true intent.

NLP Will be Able to Understand Human Emotions

As the NLP technology evolves over generations, computers will extend their present capabilities from processing to understanding human language in a holistic way. Until now, NLP is restricted to infer a limited range of human emotions including the feelings of joy or anger. Eventually, NLP will be programmed to understand more complex elements of the human language such as humour, sarcasm, satire, irony and cynicism.

In the exciting times yet to come, NLP will be integrated with other technologies such as facial and gesture recognition to drive business revenues and make them more agile and efficient.

With Alexa, Siri, Google Duplex the next-gen NLP journey has just begun.