An AI Chatbot for Customer Care

NLP began in the 1950s as the intersection of artificial intelligence and linguistics. Document/Text classification is one of the important and typical task in supervised machine learning (ML). Assigning categories to documents, which can be a web page, library book, media articles, gallery etc. has many applications like e.g. spam filtering, email routing, sentiment analysis etc.

Natural language interfaces permit computers to interact with humans using natural language, for example, to query databases. Coupled with speech recognition and speech synthesis, these capabilities will become more important with the growing popularity of portable computers that lack keyboards and large display screens. Other applications include spell and grammar checking and document summarization. Applications outside of natural language include compilers, which translate source code into lower-level machine code, and computer vision.

A look into the future

Recent advances in artificial intelligence (eg, computer chess) have shown that effective approaches utilize the strengths of electronic circuitry—high speed and large memory/disk capacity, problem-specific data-compression techniques and evaluation functions, highly efficient search—rather than trying to mimic human neural function. Similarly, statistical-NLP methods correspond minimally to human thought processes.

The question is whether NLP has a similar breakthrough application in the wings. One candidate is IBM Watson, which attracted much attention within the biomedical informatics community (eg, the ACMI Discussion newsgroup and the AMIA NLP working group discussion list) after its ‘Jeopardy’ performance. Watson appears to address the admittedly hard problem of question-answering successfully. Although the Watson effort is impressive in many ways, its discernible limitations highlight ongoing NLP challenges.

IBM Watson: a wait-and-see viewpoint Example MOE Chatbot Watson built its lead in the contest with straightforward direct questions whose answers many of the audience.

MOE Chatbot (For Restaurants) is Trained Using IBM Watson which consists of multiple intent and entities feed from real example to understand and response to query accordingly like : Booking an order /table, at a particular time ,Ordering anything from menu using intent, entities and dialogue flow.

For harder questions, Watson's limitations became clearer. Computing the correct response to the question. It can be observed that an obvious and direct opportunity is open to our eyes with the natural language processing for making automated decisions and analysis.

KeyWords: Chatbot, IBM Watson, BOT, Helpdesk, AI, NLP