Vents MagaZine Music and Entertainment Magazine
Related Articles
Earlier we talked about a variety of powerful AI communication technologies, one of which is native language processing (NLP). NLP is no different from Conversational AI rather it is one of the components that makes it possible.
NLP is often exchanged with terms such as natural language understanding (NLU) and native language generation (NLG), but at a higher level, NLP is an umbrella term that combines these two technologies.
Because human speech is very limited, understanding the natural language is what helps the computer determine what the client’s intentions are. Conversational Al looks at the context of what a person has said – not just doing keyword comparisons and looking up the meaning of a word dictionary – to better understand what a person needs. This is important because people can ask for the same thing in many different ways. In fact, Comcast found that there are 1,700 different ways to “I would like to pay off my debt.” Leveraging NLU can help Conversational Al to understand all of these different approaches without explicit training on each variation. A complex NLU can also understand system errors, slang, mispronunciation, short words and term-specific terms – just as one would.
Once the customer’s intention (what the customer wants) has been identified, machine learning is used to find the right answer. Over time, as it processes multiple responses, Conversational Al learns which response works best and improves its accuracy.
Ultimately, the production of a natural language creates a response for the customer. These technologies use their understanding of human speech to construct an easily understandable human-like response as much as possible.
Advanced Conversational AI can also use contextual awareness to remember pieces of information in a long conversation to facilitate natural communication between computer and customer.
AI dialogue engages in contextual dialogue using NLP and other related algorithms. As one develops a larger user input chorus, your AI gets better at detecting patterns and making predictions. Conversational Al works with customers on four broad steps we will explore to get a better feel for this technology:
Step 1: Installation Generation. Here, the user provides input either by voice or text.
Step 2: Input analysis. If the input is based on the text, the original language (NLU) understanding is used to extract the meaning from the given words. If the input is based on speech, the automatic ASR speech recognition is first used to decompose audio into unmodified language tokens.
Step 3: Conversation Management. Here, native language production is used to create an answer to users’ question.
Step 4: Strengthening Learning. Here user inputs are analyzed to refine responses over time to ensure that their responses are correct and accurate.
